- Email: [email protected]
POLCA: Centralised vs. Decentralised Job Release
9th IFAC Conference on Manufacturing Modelling, Management and 9th Control 9th IFAC IFAC Conference Conference on on Manufacturing Manufacturing Modelling, Modelling, Management Management and and Control 9th IFAC Conference on Manufacturing Modelling, Management and Berlin, Germany, August 28-30, 2019 Available Control 9th IFAC Conference on Manufacturing Modelling, Management and online at www.sciencedirect.com Berlin, Germany, August 28-30, Control 9th IFAC Conference on Manufacturing Modelling, Management and Berlin, Germany, August 28-30, 2019 2019 Control Berlin, Control Berlin, Germany, Germany, August August 28-30, 28-30, 2019 2019 Berlin, Germany, August 28-30, 2019
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IFAC PapersOnLine 52-13 (2019) 1427–1431 POLCA: Centralised vs. Decentralised Job Release POLCA: Centralised vs. Decentralised Job Release POLCA: Centralised vs. Decentralised Job POLCA: Centralised Centralised vs. vs. Decentralised Decentralised Job Job Release Release POLCA: Release Nuno O. Fernandes* Matthias Thürer** Luís Pinto Ferreira*** POLCA: Centralised vs. Decentralised Job Release Nuno O. Fernandes* Matthias Thürer** Luís Pinto Ferreira*** Nuno O. Fernandes* Matthias Thürer** Luís Pinto Ferreira***
S. Carmo-Silva**** Nuno Thürer** Luís Luís Pinto Pinto Ferreira*** Ferreira*** S. Nuno O. O. Fernandes* Fernandes* Matthias Matthias Thürer** S. Carmo-Silva**** Carmo-Silva**** Nuno O. Fernandes* Matthias Thürer** Luís Pinto Ferreira*** S. Carmo-Silva**** S. Carmo-Silva**** S. Carmo-Silva**** *School of Technology, Instituto Politécnico de Castelo Branco, *School of Technology, Instituto Politécnico Politécnico de de Castelo Castelo Branco, Branco, *School Technology, Instituto Casteloof Branco, 6000-76, Portugal (e-mail:denogf@ ipcb.pt). *School Technology, Instituto Politécnico Castelo Branco, Casteloof Branco, 6000-76, Portugal (e-mail: nogf@ ipcb.pt). *School of Technology, Instituto Politécnico de Castelo Branco, Castelo Branco, 6000-76, Portugal (e-mail: nogf@ ipcb.pt). ** School of Electrical andofInformation Engineering, Institute of Physical Internet, Jinan University *School Technology, Instituto Politécnico denogf@ Castelo Branco, Castelo Branco, 6000-76, Portugal (e-mail: ipcb.pt). ** Electrical and Engineering, Institute Physical Internet, Castelo Branco, 6000-76, Portugal (e-mail:of ipcb.pt). ** School School of ofZhuhai, Electrical and Information Information Engineering, Institute ofnogf@ Physical Internet, Jinan Jinan University University 519070, PR China, (email: [email protected]). Castelo Branco, 6000-76, Portugal (e-mail: nogf@ ipcb.pt). ** School of Electrical and Information Engineering, Institute of Physical Internet, Jinan University Zhuhai, 519070, PR China, (email: [email protected]). ** School of*** Electrical and Information Engineering, Institute of Physical Internet, Jinan University Zhuhai, 519070, PR China, (email: [email protected]). Luis 519070, Pinto Ferreira, ISEP – School of Institute Engineering, Polytechnic of Porto, ** School of*** Electrical and Information Engineering, of Physical Internet, Jinan University Zhuhai, PR China, (email: [email protected]). Luis Pinto Ferreira, ISEP – School of Engineering, Polytechnic of Porto, Zhuhai, 519070, PR China, (email: [email protected]). *** Luis Pinto Ferreira, ISEP – School [email protected]). Engineering, Polytechnic of Porto, Porto 4200-072, Portugal (e-mail: Zhuhai, 519070, PR China, (email: [email protected]). *** Luis Pinto Ferreira, ISEP – School of Engineering, Polytechnic of Porto, Porto 4200-072, Portugal (e-mail: [email protected]). *** Luis Pinto Ferreira, ISEP – School [email protected]). Engineering, Polytechnic of Porto, Porto 4200-072, Portugal (e-mail: ****ALGORITMI research unit, Dept. of Production and Systems, University of Minho, *** Luis Pinto Ferreira, ISEP – School [email protected]). Engineering, Polytechnic of Porto, Porto 4200-072, Portugal (e-mail: [email protected]). ****ALGORITMI research unit, Dept. of Production and Porto 4200-072, Portugal ****ALGORITMI research unit, Dept.(e-mail: of(e-mail: Production and Systems, Systems, University University of of Minho, Minho, Braga, 4710-05, Portugal [email protected]). Porto 4200-072, Portugal (e-mail: [email protected]). ****ALGORITMI research unit, Dept. of Production and Systems, University of Braga, 4710-05, Portugal (e-mail: [email protected]). ****ALGORITMI research unit, Dept. of Production and Systems, University of Minho, Minho, Braga, 4710-05, Portugal (e-mail: [email protected]). ****ALGORITMI research unit, Dept. of(e-mail: Production and Systems, University of Minho, Braga, 4710-05, Portugal [email protected]). Braga, 4710-05, Portugal (e-mail: [email protected]). Braga, 4710-05, Portugal (e-mail: [email protected]). Abstract: POLCA (Paired-cell Overlapping Loops of Cards with Authorization) is a production control Abstract: POLCA (Paired-cell Overlapping Loops of with Authorization) is control Abstract:specifically POLCA (Paired-cell Overlapping Loops of Cards Cards Authorization) products. is aa production production system designed for low-volume, high-mix and with custom-engineered It is acontrol visual Abstract: POLCA (Paired-cell Overlapping Loops of Cards with Authorization) is aa production control system specifically designed for low-volume, high-mix and custom-engineered products. It is a visual Abstract: POLCA (Paired-cell Overlapping Loops of Cards with Authorization) is production system specifically designedthe forflow low-volume, high-mix and floor custom-engineered products. It is loops acontrol visual control system that manages of jobs though the shop by making use of overlapping of Abstract: POLCA (Paired-cell Overlapping Loops of Cards with Authorization) is aoverlapping production system specifically designed for low-volume, high-mix and custom-engineered products. It is aacontrol visual control system that manages the flow of jobs though the shop floor by making use of loops of system specifically designed for low-volume, high-mix and custom-engineered products. It is visual control system that manages the flow of jobs though the shop floor by making use of overlapping loops ofa cards between pairs of successive manufacturing cells. These overlapping loops of cards realize system specifically designed forflow low-volume, high-mix and custom-engineered products. It isrealize a visual control system that manages the of jobs though the shop floor by making use of overlapping loops ofa cards between pairs of successive manufacturing cells. These overlapping loops of cards control system that manages the flow of jobs though the shop floor by making use of overlapping loops cards between pairs of successive manufacturing cells. These overlapping loops of cards realize a decentralized decision-making structure for job release. This means that jobsuse do not await release inof control system that manages the flow of jobs though the shop floor by making of overlapping loops ofa cards between pairs of successive manufacturing cells. These overlapping loops of cards realize decentralized decision-making structure for job release. This means that jobs do not await release in cards between pairs of successive manufacturing cells. These overlapping loops of cards realize decentralized decision-making structure for job release. This means that jobs do not await release in a centralized pre-shop Rather jobsmanufacturing arefor immediately forwarded tooverlapping a gateway manufacturing cells at their cards between pairspool. of successive cells. These loops ofawait cards realize decentralized decision-making structure job release. This means that jobs do not release in centralized pre-shop pool. Rather jobs immediately forwarded to aa gateway cells at decentralized decision-making structure for jobavailable release. This means thatHowever, jobsmanufacturing do not await release in aa centralized pre-shop pool.wait Rather jobs are arelists immediately forwarded tocards. gateway manufacturing cells at their their shop arrival, where they in release for POLCA the Workload Control decentralized decision-making structure for job release. This means that jobs do not await release in a centralized pre-shop pool. Rather jobs are immediately forwarded to a gateway manufacturing cells at their shop arrival, where they wait in release lists for available POLCA cards. However, the Workload Control centralized pre-shop pool. Rather jobs arelists immediately forwarded tocards. a gateway manufacturing cellsControl at their shop arrival, where they wait in release for available POLCA However, the Workload literature has shown that using a centralized pre-shop pool provides important benefits. Therefore, this centralized pre-shop pool. Rather jobs are immediately forwarded to a gateway manufacturing cells at their shop arrival, arrival, where they wait in release release lists for for available POLCA cards. However, the Workload Workload Control literature has shown that using aa centralized pre-shop pool provides important benefits. Therefore, this shop they wait in lists available POLCA cards. However, the Control literature haswhere shown that using centralized pre-shop pool provides important benefits. Therefore, this paper investigates the impact of centralized job release on POLCA performance. Using simulation, we shop arrival, where they wait inofrelease lists for available POLCA cards. However, the Workload Control literature has shown that using a centralized centralized pre-shop pool provides important benefits. Therefore, this paper investigates the impact job release on performance. Using simulation, we literature hasthat shown using centralized pre-shop pool provides important benefits. Therefore, this paper investigates thethat impact ofarelease centralized job release on POLCA POLCA performance. Using simulation,rule we demonstrate centralized job outperforms decentralised release if the right card acquisition literature hasthat shown using centralized pre-shop pool provides important benefits. Therefore, this paper investigates the impact centralized job release on POLCA performance. Using simulation, we demonstrate jobof outperforms decentralised release if the card acquisition rule paper thethat impact ofarelease centralized job release on performance. Using simulation, we demonstrate thatiscentralized centralized release outperforms decentralised release if and the right right cardcalling acquisition rule for jobinvestigates release used. Thisjob has important implications for POLCA both research practice for more paper investigates the impact of centralized job release on POLCA performance. Using simulation, we demonstrate that centralized job release outperforms decentralised release if the right card acquisition rule for job release is used. This has important implications for both research and practice calling for more demonstrate that centralized job release outperforms decentralised release if the right card acquisition rule for job release is used. This has important implications for both research and practice calling for more research on POLCA systems. demonstrate that centralized job release outperforms decentralised release if the right card acquisition rule for job release is used. This has important implications for both research and practice calling for more research on POLCA systems. for job release is used. This has important implications for both research and practice calling for more research on POLCA systems. for job release is used. ThisFederation hasPOLCA; important implications for Hosting bothSimulation. research and Ltd. practice calling for more research on POLCA systems. © 2019, IFAC (International of Automatic Control) by Elsevier All rights reserved. Keywords: Control; Centralised job release; research onProduction POLCA systems. Keywords: Control; research onProduction POLCA systems. Keywords: Production Control; POLCA; POLCA; Centralised Centralised job job release; release; Simulation. Simulation. Keywords: Production Control; Control; POLCA; POLCA; Centralised Centralised job job release; release; Simulation. Simulation. Keywords: Production Keywords: Production Control; POLCA; Centralised job release; cells, whereSimulation. they wait in cell-specific release lists. In contrast, 1. INTRODUCTION cells, where where they they wait wait in in cell-specific cell-specific release release lists. lists. In In contrast, contrast, cells, the Workload Control literature has shown that using a pre1. INTRODUCTION 1. INTRODUCTION cells, where they wait in cell-specific release lists. In contrast, the Workload Control literature has shown that using a preprecells, where they wait in cell-specific release lists. In contrast, the Workload Control literature has shown that using a 1. INTRODUCTION shop pool provides important benefits, such as a global POLCA (i.e. Paired-cell Overlapping Loops of Cards with 1. INTRODUCTION cells, whereprovides they wait inliterature cell-specific release lists. In contrast, the Workload Control has shown that using aa view preshop pool important benefits, such as a global view POLCA (i.e. Paired-cell Overlapping Loops of Cards with the Workload Control literature has shown that using pre1. INTRODUCTION shop pool provides important benefits, such as a global view POLCA (i.e. Paired-cell Overlapping Loops control of Cards with of the shop floor, a better workload balance between Authorization) is a card-based production system the Workload Control literature has shown that using a preshop pool provides important benefits, such as a global view POLCA (i.e. Paired-cell Overlapping Loops of Cards with of the shop floor, a better workload balance between Authorization) is aa card-based card-based production control system shop pool provides important benefits, such as a global view POLCA (i.e. Paired-cell Overlapping Loops of Cards with of the shop floor, a better workload balance between Authorization) is production control system (or cells), reduced throughput times, and specifically designed for low-volume, high-mix (LVHM) and workstations shop pool provides important benefits, such as a global view POLCA (i.e. Paired-cell Overlapping Loops of Cards with of the shop floor, a better workload balance between Authorization) is a card-based production control system workstations (or cells), reduced throughput times, and specifically designed for low-volume, high-mix (LVHM) and of the shop floor,cells), adue better workload balance between Authorization) is a products card-based production control system (or reduced throughput times, and specifically designed for low-volume, high-mix (LVHM) and workstations increased reliability of dates, among others (Melnyk custom-engineered (Suri, 1998, 2018). It was of the shop floor, a better workload balance between Authorization) is a card-based production control system workstations (or cells), reduced throughput times, and specifically designed for low-volume, high-mix (LVHM) and increased reliability of due dates, among others (Melnyk custom-engineered products (Suri, 1998, 2018). It was workstations (orHendry cells), reduced throughput times, specifically for low-volume, high-mix (LVHM) and increased reliability of dueand dates, among others custom-engineered products 1998,to the 2018). It was Ragatz, 1989, Kingsman, 1991,(Melnyk Land and and proposed bydesigned Suri (1998) as an (Suri, alternative well-known workstations (or cells), reduced throughput times, and specifically designed for low-volume, high-mix (LVHM) and increased reliability of due dates, among others (Melnyk custom-engineered products (Suri, 1998, 2018). It was Ragatz, 1989, Hendry and Kingsman, 1991, Land and proposed by Suri (1998) as an alternative to the well-known increased reliability of dueand dates, among others (Melnyk custom-engineered products 1998, 2018). Itdo was Ragatz, 1989, Hendry Kingsman, 1991, Land and proposed by SuriSystem (1998) as an (Suri, alternative to the well-known Gaalman 1998). An approach to accommodate centralised job Toyota Kanban (Sugimori et al., 1977), which not increased reliability of due dates, among others (Melnyk and custom-engineered products (Suri, 1998, 2018). It was Ragatz, 1989, Hendry and Kingsman, 1991, Land proposed by Suri (1998) as an alternative to the well-known Gaalman 1998). An approach to accommodate centralised job Toyota Kanban System (Sugimori et al., 1977), which do not Ragatz, 1989, Hendry and Kingsman, 1991, Land and proposed by SuriSystem (1998) as anproduction, alternative to thewhich well-known 1998).theAnPOLCA approach to accommodate centralised job Toyota Kanban (Sugimori et al., 1977), do not Gaalman release within framework is the addition of card typically apply to LVHM and to ConWIP Ragatz, 1989, Hendry and Kingsman, 1991, Land and proposed by SuriSystem (1998) as anproduction, alternative to thewhich well-known Gaalman 1998). An approach to accommodate centralised job Toyota Kanban Kanban System (Sugimori et al., al., 1977), 1977), which do not not release release within the POLCA framework is the addition of card typically apply to LVHM and to ConWIP Gaalman 1998). An approach to accommodate centralised job Toyota (Sugimori et do within the POLCA framework is the addition of each card typically apply to LVHM production, and to balancing ConWIP control loops between a central release function and (Spearman et al.System 1990) that lacks from workload Gaalman 1998). An approach to accommodate centralised job Toyota Kanban (Sugimori et al., 1977), which do not release within the POLCA framework is the addition of card typically apply to LVHM production, and to ConWIP control loops between a central release function and each (Spearman et al. 1990) that lacks from workload balancing release within the POLCA framework is the addition of card typically apply to LVHM production, and to ConWIP loops between a this central release function and each (Spearman et al. 1990) that lacks from workload balancing control gateway cell. However, changes thethe POLCA structure capability (Germs andLVHM Riezebos, 2010). release within the POLCA framework is addition of card typically apply to production, and to ConWIP control loops between a central release function and each (Spearman et al. 1990) that lacks from workload balancing gatewayloops cell. However, However, this changes the function POLCA and structure capability (Germs (Germs and Riezebos, Riezebos, 2010). control between a this central release each (Spearman et al. 1990) that lacks from workload balancing gateway cell. changes the POLCA structure capability and 2010). with unforeseen consequences on its performance. Therefore, control loops between a this central release function and each (Spearman et al. 1990) that that lacks from workload gateway cell. However, changes the POLCA structure capability and Riezebos, 2010). with unforeseen consequences on its its performance. Therefore, POLCA is(Germs a visual system manages the flowbalancing of jobs with gateway cell. However, this changes the POLCA structure capability (Germs and Riezebos, 2010). unforeseen consequences on performance. Therefore, study usesHowever, simulation to changes assess, for the firstTherefore, time, the POLCA is(Germs visual system that2010). manages the the flow flow of of jobs jobs this gateway cell. this the POLCA structure capability andsystem Riezebos, with unforeseen consequences on its performance. POLCA is aa visual that manages this study uses simulation to assess, for the first time, the though the shop floor by making use of overlapping loops of with unforeseen consequences itsthe performance. Therefore, this study uses simulation to on assess, for the first time, the POLCAthe is shop a visual visual system that use manages the flow flow loops of jobs jobs impact of centralised job release on POLCA system. though floor by making of overlapping of POLCA is a system that manages the of with unforeseen consequences on its performance. Therefore, this study uses simulation to assess, for the first time, though the shoppairs floorofby making use of overlapping loops of impact of centralised job release on the POLCA system. cards between successive manufacturing cells. Cards this study uses simulation to assess, the first time, the the of centralised job release on the for POLCA system. POLCA is shop a visual that use manages the flow ofCards jobs though the floor by making of overlapping loops of cards between between pairs ofsystem successive manufacturing cells. though the shop floor by making use ofpair overlapping loops of impact this study uses simulation to assess, for the first time, the impact of centralised job release on the POLCA system. cards pairs of successive manufacturing cells. Cards The remainder of this paper is structured as follows. In Section provide capacity signals between each of cells, ensuring impact of centralised job release on the POLCA system. though the shop floor by making use of overlapping loops of cards between pairs of successive manufacturing cells. Cards The remainder of this paper is structured as follows. In Section provide capacity signals between each pair of cells, ensuring cards between pairs of successive manufacturing cells. Cards impact of centralised job release on the POLCA system. The remainder of this paper is structured as follows. In Section provide capacity signals between eacheffectively pair of cells, weremainder review the POLCA system. The simulation model used that upstream cells use their capacity by ensuring working cards between pairs of successive manufacturing cells. Cards 2, The of this paper is as In provide capacity signals between each pair of 2, weremainder review the the POLCA system. The simulation simulation model used that upstream upstream cells use their capacity effectively by ensuring working The of thisdescribed papersystem. is structured structured as3,follows. follows. In Section Section provide capacity signals between each pair(Suri of cells, cells, ensuring 2, we review POLCA The model used that cells use their capacity effectively by working in our study is then in Section before results are only on jobs that are needed downstream 2018). In this The remainder of this paper is structured as follows. In Section provide capacity signals between each pair of cells, ensuring 2, we review the POLCA system. The simulation model used that upstream cells use their capacity effectively by working in our study is then described in Section 3, before results are only on jobs that are needed downstream (Suri 2018). In this 2, we POLCA system. Theinsimulation used that upstream cells use their downstream capacity effectively by between working our review studydiscussed isthe then described in Section 3, beforemodel results are only on POLCA jobs that are needed (Suri 2018). In this in presented, and analysed Section 4. Finally, way, synchronises the processes 2, we review the POLCA system. The simulation model used that upstream cells use their capacity effectively by working in our study is then described in Section 3, before results are only on jobs that are needed downstream (Suri 2018). In this presented, discussed and analysed in Section 4. Finally, way, POLCA synchronises the processes between in our studydiscussed isarethen described in Section 3,where before4. results are only on POLCA jobs that are needed (Suri In this andinanalysed in5, Section Finally, way, synchronises processes between conclusions drawn Section managerial manufacturing cells, reducingdownstream the the waiting time2018). between the presented, in our study is then described in Section 3, before results are only on jobs that are needed downstream (Suri 2018). In this presented, discussed and analysed in Section 4. Finally, way, POLCA synchronises the processes between conclusions are drawn in Section 5, where managerial manufacturing cells, reducing the waiting time between the presented, discussed and in5, Section 4. Finally, way, POLCA synchronises the processes between are future drawn inanalysed Section where managerial manufacturing cells, reducing the waiting time between the conclusions implications and research directions are also outlined. completion of a job in one cell and its start in the next cell presented, discussed and analysed in Section 4. Finally, way, POLCA synchronises the processes between conclusions and are future drawnresearch in Section Section 5, where where managerial manufacturing reducing between the implications directions are also alsomanagerial outlined. completion of aacells, job in in one cell cellthe andwaiting its start starttime in the the next cell cell conclusions are drawn in 5, manufacturing cells, reducing the waiting time between the implications and future research directions are outlined. completion of job one and its in next (Riezebos, are future drawnresearch in Section 5, where managerial manufacturing reducing between the conclusions implications and directions are also outlined. completion2010). of aacells, job in in one cell cellthe andwaiting its start starttime in the the next cell cell (Riezebos, 2010). implications and future research directions are also completion of job one and its in next (Riezebos, 2010). 2. THE POLCAdirections SYSTEMare also outlined. implications and future research outlined. completion of a job in one cell and its start in the next cell (Riezebos, 2010). POLCA not2010). only operates at the shop floor level, but also 2. THE THE POLCA POLCA SYSTEM SYSTEM (Riezebos, 2. POLCA not2010). only operates operates at at the the shop shop floor floor level, level, but but also also (Riezebos, POLCA not only 2. THE POLCA SYSTEM decides on the release of jobs to shop the shop floor. However, We first describe2. the production control THEoriginal POLCAPOLCA SYSTEM POLCA not only operates at floor level, but also decides on the release of jobs jobs to shop the shop shop floor. However, We first first describe describe2. the the original POLCA production control control POLCA not only operates at the the floorfloor. level, but This also We THE POLCA SYSTEM decides on the release of to the However, original POLCA production POLCA does not have a central job release function. system in Section 2.1 and then, in Section 2.2, we extend the POLCA not only operates at the shop floor level, but also decides on the release of jobs to the shop floor. However, We first describe the original POLCA production control POLCA does not have a central job release function. This system in Section 2.1 and then, in Section 2.2, we extend the decides on thenot release of jobs to job the shop floor. However, We firstinframework describe the original production control POLCA does haveawait a central release function. This system Section 2.1 and then,centralised inPOLCA Sectionjob 2.2,release. we extend the means that jobs do not release in a pre-shop pool. Rather POLCA to include decides on thenot release of jobs to job the shop floor. However, firstin describe the original production control POLCA does not haveawait a central central job release function. This We system inframework Section 2.1 2.1 and then,centralised inPOLCA Sectionjob 2.2,release. we extend extend the means that jobs do not release in a pre-shop pool. Rather POLCA to include POLCA does have a release function. This system Section and then, in Section 2.2, we the means thatimmediately jobs do not await releasetoingateway a pre-shop pool. Rather POLCA framework to include centralised job release. jobs are forwarded manufacturing POLCA does not haveawait a central job function. This system inframework Section 2.1 and then,centralised in Sectionjob 2.2,release. we extend the means that jobs do not release in aarelease pre-shop pool. Rather POLCA to include jobs are immediately forwarded to gateway manufacturing means that jobs do not await release in pre-shop pool. Rather POLCA framework to include centralised job release. jobs are immediately forwarded to gateway manufacturing means thatimmediately jobs do not await releaseto a pre-shop pool. Rather POLCA framework to include centralised job release. jobs forwarded manufacturing jobs are are immediately forwarded toingateway gateway manufacturing Copyright © 2019 IFAC 1445Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © 2019, IFAC (International Federation of Automatic Control) jobs are immediately forwarded to gateway manufacturing Copyright © 2019 IFAC 1445 Copyright 2019 responsibility IFAC 1445Control. Peer review©under of International Federation of Automatic Copyright © 1445 Copyright © 2019 2019 IFAC IFAC 1445 10.1016/j.ifacol.2019.11.399 Copyright © 2019 IFAC 1445
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2.1 The original POLCA System
3. SIMULATION STUDY
POLCA (see e.g. Suri 1998, 2010 and 2018) makes use of overlapping loops of cards between pairs of successive cells in the routing of a job. Since loops overlap, every cell, except the first and the last, belongs to two POLCA loops. This is illustrated in Fig.1. A fixed amount of POLCA cards is allocated to each loop, imposing a WIP cap in the loop, i.e. between work cells. POLCA loops ensure that cells will only process jobs for which capacity has been reserved at the downstream cell of the loop. Cards are not part-number specific, i.e., they can be acquired by any job entering the loop. Cards are attached to a job when it enters the first (or upstream) cell and detached after a job has finished processing at the second (or downstream) cell of the loop. The number of POLCA cards attached to each job depends on the workload of the job and on the quantum of the cards. Detached cards are then sent back to the first cell, where they can be attached to new arriving jobs. In the original POLCA system only jobs that have been authorized by a high-level MRP system can start processing at a cell whenever a card becomes available. Pre-shop Pool of Jobs
POLCA Card
POLCA Card
Cell C
Cell B
Cell A
Fig. 1. Illustration of the original POLCA system. 2.2 Extending POLCA with Centralised Job Release A main problem of planning in cellular manufacturing systems is related to insufficient synchronisation of the processes between manufacturing cells, resulting in waiting times between cells, while at the same time, other cells might face a lack of work to be done. Riezebos (2010) denote this as unbalance. The original POLCA system uses decentralized job release and decision-making, pushing job release decisions to the cells and work teams (Suri, 2018). However, centralised job release provides important benefits, such as better workload balance between manufacturing cells. Pre-shop Pool of Jobs
Release Card
The simulation models considered in the study are outlined in Section 3.1. Section 3.2 details the card acquisition and the dispatching rules considered in the study. The experimental design and the measures used to evaluate performance are then presented in section 3.3. 3.1 Overview of the Simulation Models Simulation models of two shops, a pure flow shop (PFS) and a general flow shop (GFS) (Oosterman et al. 2000), have been implemented using ARENA software. These simulation models are stochastic, whereby jobs’ inter-arrival times, routings, operation times and due dates are stochastic variables. Each shop contains six manufacturing cells, where each cell is modelled as a single constant capacity resource. Job routings in the GFS are determined by drawing from a discrete uniform distribution [2,6]. This means that the average number of cells in the routing of the job is four. In the PFS number of cells in the routing of each job is six. Operation processing times follow a truncated 2-Erlang distribution with a maximum of 4 time-units and a mean of 1 time-unit before truncation. Setup times are assumed sequence independent and hence enclosed in the operation processing times in order to avoid interactions due to workload fluctuations caused by sequence dependent setup times. The inter-arrival time of jobs to the production system follows an exponential distribution with a mean that based on the number of operations (one per cell) in the routing of a job deliberately result in 90% utilisation level. Due dates are set exogenously by adding to the job entry time a random allowance factor, uniformly distributed between 35 and 55 time-units for the GFS and between 49 and 69 timeunits for the PFS. As in previous simulation studies on POLCA (Fernandes and Carmo-Silva 2006, Lödding et al. 2003, and Thürer et al. 2018a, 2018b), it is assumed that all required materials are available and all necessary information regarding shop floor routings and processing times is known upon the arrival of a job to the production system.
POLCA Card
POLCA Card
3.2 Card Acquisition and Dispatching Rules Cell A
Cell B
Cell C
Fig. 2. Illustration of POLCA with centralised job release. An approach to accommodate centralised job release within the POLCA framework is the addition of loops of cards between a central release function and each gateway cell. These loops allow to control job release, while balancing the workload between the cells, as illustrated in Fig.2. Note that in case illustrated there is a single gateway cell and therefore a single release loop is added to the POLCA structure. In the resulting production control system, a job to be released to a manufacturing cell for processing must first acquire a release card. Like POLCA cards, release cards are not part number specific and, therefore, can be acquired by any job requiring processing at a specific gateway cell.
In our study every card represents a quantum of one job. An identical number of cards is used per POLCA loop in each experiment. The balanced shop considered in our study justifies this assumption. To determine the best performing number of cards per loop it is common practice in simulation studies to define it as an experimental variable. Six levels for number of cards per POLCA loop are considered in this study, namely 12, 14, 16, 18, 20 and 22 cards. These values have been chosen based on preliminary simulation runs. Release loops reflect every gateway cell in the routing of the job. The same six levels for the number of cards per loop used at POLCA loops, but divided by 2, were used at release loops. This is because in POLCA loops, cards account for the aggregate load at both, the upstream and the downstream cells
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of the loop, while in release loops, cards account only for the load at a single gateway cell. POLCA uses authorisation dates to define when a job should start at each cell. However, Thürer et al. (2018a) recently showed that authorisation dates might have a direct detrimental effect on the percentage of tardy jobs and on the mean tardiness performance. Authors suggested that authorization dates may be used to signal the priority of jobs, and not when a job should start. Therefore, in our study both priority dispatching and POLCA cards acquisition is based on the Earliest Authorisation Date (EAD). This is determined as follows: 𝜏𝜏𝑗𝑗 = 𝑑𝑑𝑗𝑗 -∑𝑘𝑘𝑘𝑘𝑅𝑅𝑗𝑗 𝑏𝑏𝑘𝑘
(1)
Where: 𝜏𝜏𝑗𝑗 is the authorization date of job j; 𝑑𝑑𝑗𝑗 is the due date of job j; 𝑏𝑏𝑘𝑘 is the lead time at cell k; 𝑅𝑅𝑗𝑗 is the set of remaining cells in the routing of job j.
MODCS rule was proposed Thürer et al. (2016), which proved to be effective. The rule divides the set of jobs waiting release in the pre-shop pool into two classes: urgent, i.e., jobs with an earliest authorisation date on the first cell in it routing that has already passed and non-urgent jobs. Urgent jobs receive priority over non-urgent jobs and are sequenced according to a capacity slack index using Eq. (2). Non-urgent jobs are sequenced according to the EAD, using Eq. (1). 𝑠𝑠𝑗𝑗
=
𝐿𝐿𝑗𝑗𝑗𝑗 × 𝜔𝜔𝑘𝑘 𝑗𝑗 (𝑁𝑁 −𝑊𝑊 ) 𝑘𝑘 𝑘𝑘
𝑛𝑛𝑗𝑗
(2)
Where: 𝑠𝑠𝑗𝑗 is the capacity slack ratio that results from releasing job j; 𝐿𝐿𝑗𝑗𝑗𝑗 is the direct workload of job j to cell k; 𝑁𝑁𝑘𝑘 is the workload norm at cell k; 𝑊𝑊𝑘𝑘 is direct workload at cell k; 𝜔𝜔𝑘𝑘 is a factor that takes the values of 1 or 0, depending if the cell k is in the routing of job j or not; 𝑛𝑛𝑗𝑗 is the number of cells in the routing of job j; Above indexes concerning card acquisition at the pre-shop pool are calculated in our study every time a new job enters the pre-shop pool, or an operation is completed at a cell. 4
scenario was replicated 100 times. All results were collected over 13,000 time-units following a warm-up period of 3,000 time-units. Three main performance measures are considered in this study, as follows: mean total throughput time – the mean of the completion date minus the arrival time date of jobs; percentage tardy – the percentage of jobs completed after the due date; and the standard deviation of lateness. In addition to these performance indicators, we also measure the mean shop throughput time - the mean of the completion date minus the release time of jobs; While the total throughput time includes the time that a job waits before being released into production, the shop throughput time only measures the time after the job is released to the shop floor. 4. RESULTS
Concerning card acquisition at the pre-shop pool for job release, three card acquisition rules have been tested, namely: EAD at the first station in the routing of the job; Modified Capacity Slack (MODCS) and First-Come-First-Served (FCFS), which is used as a benchmark.
∑𝑘𝑘𝑘𝑘𝑅𝑅
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Experimental Design
The experimental factors are: (i) the job release strategy (centralised and decentralised); (ii) the card acquisition rule at the pre-shop pool for job release (FCFS, EAD and MODD); (iii) The shop configuration (GFS and PFS) and (iv) six levels for the number of cards per control loop. Each experimental
To assess performance differences between production control strategies, detailed performance results will be presented next in Section 4.1 where we focus on the impact of centralised job release for the general flow shop configuration. Results for the pure flow shop configuration are then assessed in Section 4.2. 4.1 Performance Assessment: GFS Results are here presented in the form of performance curves. The left-hand starting point of the curves represents the tightest number of cards allowed in control loops. The number of cards per loop increases step-wise (according to the levels referred in section 3.2) by moving from left to right in each graph, with each data point representing one card number level. The lefthand point in each figure represents the lowest number of cards per control loop. Incresing the number of cards per control loop (i.e., from left to right along the curve) increases the level of work-in-process and, thus increases the shop floor throughput times. Fig.3 shows the impact of the job release strategy on the POLCA performance for the GFS. The percentage of tardy jobs, total throughput time and standard deviation of lateness are here plotted against the shop throughput time for both, centralised and decentralised job release. The following can be concluded from the results: Influence of centralised job release: Centralised job release allows for shorter shop throughput times, compared with decentralised job release – that is, curves are pushed to the left. It also allows improving POLCA performance concerning total throughput time, percentage of tardy jobs and the standard deviation of lateness, if the right card acquisition rule for job release is used. Influence of the card acquisition rule: The card acquisition rule for job release has a strong and mixed impact on the POLCA performance. MODCS outperforms EAD for the percentage of tardy jobs, while performing slightly better than FCFS. Concerning the total throughput time, the performance of the three card acquisition rules for centralised job release is identical.
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(a)
(a)
10
15 14
13
Tardy Jobs (%)
Tardy Jobs (%)
9 8 7
6
12 11
10 9 8
7 6
5
5
21
22 23 24 Shop Throughput Time (time units)
25
28
29
27
26
25
24 21
22 23 24 Shop Throughput Time (time units)
40 39 38
37 36 35 34 33
25
28
29
18 16 14
12 10 22 23 24 Shop Throughput Time (time units)
30 31 32 Shop Throughput Time (time units)
33
(c) Standard Dev. of Lateness (time units)
Standard Dev. of Lateness (time units)
(c) 20
21
33
(b)
28
Total Throughput Time (time units)
Total Throughput Time (time units)
(b)
30 31 32 Shop Throughput Time (time units)
25
50 45 40 35 30 25 20 15 10 28
29
30 31 32 Shop Throughput Time (time units)
33
Fig. 3. Performance results for the GFS.
Fig. 4. Performance results for the PFS.
Finally, concerning the standard deviation of lateness, FCFS outperform the other two rules. So, the choice of the card acquisition rule depends on performance objectives to achieve. It should be notice that a higher standard deviation, namely under a tight WIP-cap, means that some (large) jobs are being much delayed at the pool.
the same direct flow, which is maximum in the PFS, leads to a relative deterioration of centralised job release, concerning the shop throughput time. That is, performance curves of centralised and decentralised job release become closer. Increasing the jobs’ routings directness also leads to a significant improvement in the relative performance of the FCFS cards acquisition rule for job release, concerning the percentage of tardy jobs. However, this is obtained at the cost of a higher standard deviation of lateness.
4.2 Performance Assessment: PFS To assess the performance in the PFS configuration Fig.4, (a)(c), presents the total throughput time, percentage tardy and standard deviation of lateness results, respectively, over the shop floor throughput time.
These results indicate that the relative performance of centralised vs. decentralised job release is influenced by the directness of the jobs’ routings, i.e., the system configuration. 6. CONCLUSIONS
Analysing results, the following can be concluded: Influence of the shop configuration. Increasing the jobs’ routings directness, i.e., the degree to which job routings share
POLCA is card-based production control system that uses decentralised decision-making, pushing job release decisions
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to the cells and work teams. However, centralised job release can provide important benefits, as pointed out in the Workload Control literature. Therefore, in this paper we changed the POLCA framework to accommodate centralised job release by adding cards loops between the central release function and the gateways manufacturing cells. Results indicate that centralised job release may outperform decentralised release for both, the general flow shop and the pure flow shop configurations. A higher impact was observed for the general flow shop configuration. Centralised job release however may lead to some large jobs being much delayed at the pre-shop pool, if the EAD card acquisition rule or a capacity slack rule, such as MODCS, are used at the pool. A major limitation of our study is that our findings are based on restricted set of experimental factors. Therefore, we suggest extending the study to a broader range of manufacturing environments and production conditions. ACKNOWLEDGMENTS This work had the financial support of FCT - Fundação para a Ciência e Tecnologia of Portugal under the project PEst20152020: UID/ CEC/ 00319/ 2013.
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Suri, R. (1998). Quick Response Manufacturing: A companywide approach to reducing leadtimes. Productivity Press. Suri, R. (2010). It’s about time: The competitive advantage of quick response manufacturing, Productivity Press. Suri, R. (2018). The Practitioner's Guide to POLCA: The Production Control System for High-Mix, Low-Volume and Custom Products. Productivity Press. Thürer, M. Fernandes, N.O., Stevenson, M., Silva, C., and Carmo-Silva, C. (2018a). POLC-A: an assessment of POLCA’s authorization element, Journal of Intelligent Manufacturing, Online First. Thürer, M., Fernandes, N.O., Stevenson, M., Qu, T. and Li, C.D. (2018b). Centralized vs. Decentralized Control Decision in Card-Based Control Systems: Comparing kanban Systems and COBACABANA; International Journal of Production Research; (in print). Thürer, M., Stevenson, M., and Qu, T. (2016). Job sequencing and selection within workload control order release: an assessment by simulation, International Journal of Production Research, 54 (4), 1061-1075.
REFERENCES Fernandes, N.O. and Carmo-Silva, S. (2016). Generic POLCA - A production and materials flow control mechanism for quick response manufacturing, International Journal of Production Economics, 104 (1), 74-84. Germs, R. and Riezebos, J. (2010). Workload balancing capability of pull systems in MTO production, International Journal of Production Research, 48(8), 2345–2360. Hendry, L. and Kingsman, B. (1991). A Decision Support System for Job release in Make-to-order Companies, International Journal of Production Research, 11 (6), 616. Land, M. and Gaalman, G.J.C. (1998). The performance of workload control concepts in jobs shops: improving the release method, International Journal of Production Economics, 56-57 (1), 347-364. Lödding, H., Yu, K.-W., and Wiendahl, H.-P. (2003). Decentralized WIP-oriented manufacturing control (DEWIP), Production Planning & Control, 14 (1), 42-54. Melnyk S. A., and Ragatz, G. L. (1989) Order review/ release: research issues and perspectives, International Journal of Production Research, 27 (7), 1081-1096. Oosterman, B., Land, M.J., and Gaalman, G. (2000). The influence of shop characteristics on workload control, International Journal of Production Economics, 68 (1), 107-119. Riezebos, J. (2010). Design of POLCA material control systems, International Journal of Production Research, 48 (5), 1455-1477. Sugimori, Y., Kusunoki, K., Cho, F. and Uchikawa, S. (1977). Toyota production system and kanban system: materialization of just-in-time and respect-for-human system. International Journal of Production Research, 15 (6), 553–564. 1449
ScienceDirect
IFAC PapersOnLine 52-13 (2019) 1427–1431 POLCA: Centralised vs. Decentralised Job Release POLCA: Centralised vs. Decentralised Job Release POLCA: Centralised vs. Decentralised Job POLCA: Centralised Centralised vs. vs. Decentralised Decentralised Job Job Release Release POLCA: Release Nuno O. Fernandes* Matthias Thürer** Luís Pinto Ferreira*** POLCA: Centralised vs. Decentralised Job Release Nuno O. Fernandes* Matthias Thürer** Luís Pinto Ferreira*** Nuno O. Fernandes* Matthias Thürer** Luís Pinto Ferreira***
S. Carmo-Silva**** Nuno Thürer** Luís Luís Pinto Pinto Ferreira*** Ferreira*** S. Nuno O. O. Fernandes* Fernandes* Matthias Matthias Thürer** S. Carmo-Silva**** Carmo-Silva**** Nuno O. Fernandes* Matthias Thürer** Luís Pinto Ferreira*** S. Carmo-Silva**** S. Carmo-Silva**** S. Carmo-Silva**** *School of Technology, Instituto Politécnico de Castelo Branco, *School of Technology, Instituto Politécnico Politécnico de de Castelo Castelo Branco, Branco, *School Technology, Instituto Casteloof Branco, 6000-76, Portugal (e-mail:denogf@ ipcb.pt). *School Technology, Instituto Politécnico Castelo Branco, Casteloof Branco, 6000-76, Portugal (e-mail: nogf@ ipcb.pt). *School of Technology, Instituto Politécnico de Castelo Branco, Castelo Branco, 6000-76, Portugal (e-mail: nogf@ ipcb.pt). ** School of Electrical andofInformation Engineering, Institute of Physical Internet, Jinan University *School Technology, Instituto Politécnico denogf@ Castelo Branco, Castelo Branco, 6000-76, Portugal (e-mail: ipcb.pt). ** Electrical and Engineering, Institute Physical Internet, Castelo Branco, 6000-76, Portugal (e-mail:of ipcb.pt). ** School School of ofZhuhai, Electrical and Information Information Engineering, Institute ofnogf@ Physical Internet, Jinan Jinan University University 519070, PR China, (email: [email protected]). Castelo Branco, 6000-76, Portugal (e-mail: nogf@ ipcb.pt). ** School of Electrical and Information Engineering, Institute of Physical Internet, Jinan University Zhuhai, 519070, PR China, (email: [email protected]). ** School of*** Electrical and Information Engineering, Institute of Physical Internet, Jinan University Zhuhai, 519070, PR China, (email: [email protected]). Luis 519070, Pinto Ferreira, ISEP – School of Institute Engineering, Polytechnic of Porto, ** School of*** Electrical and Information Engineering, of Physical Internet, Jinan University Zhuhai, PR China, (email: [email protected]). Luis Pinto Ferreira, ISEP – School of Engineering, Polytechnic of Porto, Zhuhai, 519070, PR China, (email: [email protected]). *** Luis Pinto Ferreira, ISEP – School [email protected]). Engineering, Polytechnic of Porto, Porto 4200-072, Portugal (e-mail: Zhuhai, 519070, PR China, (email: [email protected]). *** Luis Pinto Ferreira, ISEP – School of Engineering, Polytechnic of Porto, Porto 4200-072, Portugal (e-mail: [email protected]). *** Luis Pinto Ferreira, ISEP – School [email protected]). Engineering, Polytechnic of Porto, Porto 4200-072, Portugal (e-mail: ****ALGORITMI research unit, Dept. of Production and Systems, University of Minho, *** Luis Pinto Ferreira, ISEP – School [email protected]). Engineering, Polytechnic of Porto, Porto 4200-072, Portugal (e-mail: [email protected]). ****ALGORITMI research unit, Dept. of Production and Porto 4200-072, Portugal ****ALGORITMI research unit, Dept.(e-mail: of(e-mail: Production and Systems, Systems, University University of of Minho, Minho, Braga, 4710-05, Portugal [email protected]). Porto 4200-072, Portugal (e-mail: [email protected]). ****ALGORITMI research unit, Dept. of Production and Systems, University of Braga, 4710-05, Portugal (e-mail: [email protected]). ****ALGORITMI research unit, Dept. of Production and Systems, University of Minho, Minho, Braga, 4710-05, Portugal (e-mail: [email protected]). ****ALGORITMI research unit, Dept. of(e-mail: Production and Systems, University of Minho, Braga, 4710-05, Portugal [email protected]). Braga, 4710-05, Portugal (e-mail: [email protected]). Braga, 4710-05, Portugal (e-mail: [email protected]). Abstract: POLCA (Paired-cell Overlapping Loops of Cards with Authorization) is a production control Abstract: POLCA (Paired-cell Overlapping Loops of with Authorization) is control Abstract:specifically POLCA (Paired-cell Overlapping Loops of Cards Cards Authorization) products. is aa production production system designed for low-volume, high-mix and with custom-engineered It is acontrol visual Abstract: POLCA (Paired-cell Overlapping Loops of Cards with Authorization) is aa production control system specifically designed for low-volume, high-mix and custom-engineered products. It is a visual Abstract: POLCA (Paired-cell Overlapping Loops of Cards with Authorization) is production system specifically designedthe forflow low-volume, high-mix and floor custom-engineered products. It is loops acontrol visual control system that manages of jobs though the shop by making use of overlapping of Abstract: POLCA (Paired-cell Overlapping Loops of Cards with Authorization) is aoverlapping production system specifically designed for low-volume, high-mix and custom-engineered products. It is aacontrol visual control system that manages the flow of jobs though the shop floor by making use of loops of system specifically designed for low-volume, high-mix and custom-engineered products. It is visual control system that manages the flow of jobs though the shop floor by making use of overlapping loops ofa cards between pairs of successive manufacturing cells. These overlapping loops of cards realize system specifically designed forflow low-volume, high-mix and custom-engineered products. It isrealize a visual control system that manages the of jobs though the shop floor by making use of overlapping loops ofa cards between pairs of successive manufacturing cells. These overlapping loops of cards control system that manages the flow of jobs though the shop floor by making use of overlapping loops cards between pairs of successive manufacturing cells. These overlapping loops of cards realize a decentralized decision-making structure for job release. This means that jobsuse do not await release inof control system that manages the flow of jobs though the shop floor by making of overlapping loops ofa cards between pairs of successive manufacturing cells. These overlapping loops of cards realize decentralized decision-making structure for job release. This means that jobs do not await release in cards between pairs of successive manufacturing cells. These overlapping loops of cards realize decentralized decision-making structure for job release. This means that jobs do not await release in a centralized pre-shop Rather jobsmanufacturing arefor immediately forwarded tooverlapping a gateway manufacturing cells at their cards between pairspool. of successive cells. These loops ofawait cards realize decentralized decision-making structure job release. This means that jobs do not release in centralized pre-shop pool. Rather jobs immediately forwarded to aa gateway cells at decentralized decision-making structure for jobavailable release. This means thatHowever, jobsmanufacturing do not await release in aa centralized pre-shop pool.wait Rather jobs are arelists immediately forwarded tocards. gateway manufacturing cells at their their shop arrival, where they in release for POLCA the Workload Control decentralized decision-making structure for job release. This means that jobs do not await release in a centralized pre-shop pool. Rather jobs are immediately forwarded to a gateway manufacturing cells at their shop arrival, where they wait in release lists for available POLCA cards. However, the Workload Control centralized pre-shop pool. Rather jobs arelists immediately forwarded tocards. a gateway manufacturing cellsControl at their shop arrival, where they wait in release for available POLCA However, the Workload literature has shown that using a centralized pre-shop pool provides important benefits. Therefore, this centralized pre-shop pool. Rather jobs are immediately forwarded to a gateway manufacturing cells at their shop arrival, arrival, where they wait in release release lists for for available POLCA cards. However, the Workload Workload Control literature has shown that using aa centralized pre-shop pool provides important benefits. Therefore, this shop they wait in lists available POLCA cards. However, the Control literature haswhere shown that using centralized pre-shop pool provides important benefits. Therefore, this paper investigates the impact of centralized job release on POLCA performance. Using simulation, we shop arrival, where they wait inofrelease lists for available POLCA cards. However, the Workload Control literature has shown that using a centralized centralized pre-shop pool provides important benefits. Therefore, this paper investigates the impact job release on performance. Using simulation, we literature hasthat shown using centralized pre-shop pool provides important benefits. Therefore, this paper investigates thethat impact ofarelease centralized job release on POLCA POLCA performance. Using simulation,rule we demonstrate centralized job outperforms decentralised release if the right card acquisition literature hasthat shown using centralized pre-shop pool provides important benefits. Therefore, this paper investigates the impact centralized job release on POLCA performance. Using simulation, we demonstrate jobof outperforms decentralised release if the card acquisition rule paper thethat impact ofarelease centralized job release on performance. Using simulation, we demonstrate thatiscentralized centralized release outperforms decentralised release if and the right right cardcalling acquisition rule for jobinvestigates release used. Thisjob has important implications for POLCA both research practice for more paper investigates the impact of centralized job release on POLCA performance. Using simulation, we demonstrate that centralized job release outperforms decentralised release if the right card acquisition rule for job release is used. This has important implications for both research and practice calling for more demonstrate that centralized job release outperforms decentralised release if the right card acquisition rule for job release is used. This has important implications for both research and practice calling for more research on POLCA systems. demonstrate that centralized job release outperforms decentralised release if the right card acquisition rule for job release is used. This has important implications for both research and practice calling for more research on POLCA systems. for job release is used. This has important implications for both research and practice calling for more research on POLCA systems. for job release is used. ThisFederation hasPOLCA; important implications for Hosting bothSimulation. research and Ltd. practice calling for more research on POLCA systems. © 2019, IFAC (International of Automatic Control) by Elsevier All rights reserved. Keywords: Control; Centralised job release; research onProduction POLCA systems. Keywords: Control; research onProduction POLCA systems. Keywords: Production Control; POLCA; POLCA; Centralised Centralised job job release; release; Simulation. Simulation. Keywords: Production Control; Control; POLCA; POLCA; Centralised Centralised job job release; release; Simulation. Simulation. Keywords: Production Keywords: Production Control; POLCA; Centralised job release; cells, whereSimulation. they wait in cell-specific release lists. In contrast, 1. INTRODUCTION cells, where where they they wait wait in in cell-specific cell-specific release release lists. lists. In In contrast, contrast, cells, the Workload Control literature has shown that using a pre1. INTRODUCTION 1. INTRODUCTION cells, where they wait in cell-specific release lists. In contrast, the Workload Control literature has shown that using a preprecells, where they wait in cell-specific release lists. In contrast, the Workload Control literature has shown that using a 1. INTRODUCTION shop pool provides important benefits, such as a global POLCA (i.e. Paired-cell Overlapping Loops of Cards with 1. INTRODUCTION cells, whereprovides they wait inliterature cell-specific release lists. In contrast, the Workload Control has shown that using aa view preshop pool important benefits, such as a global view POLCA (i.e. Paired-cell Overlapping Loops of Cards with the Workload Control literature has shown that using pre1. INTRODUCTION shop pool provides important benefits, such as a global view POLCA (i.e. Paired-cell Overlapping Loops control of Cards with of the shop floor, a better workload balance between Authorization) is a card-based production system the Workload Control literature has shown that using a preshop pool provides important benefits, such as a global view POLCA (i.e. Paired-cell Overlapping Loops of Cards with of the shop floor, a better workload balance between Authorization) is aa card-based card-based production control system shop pool provides important benefits, such as a global view POLCA (i.e. Paired-cell Overlapping Loops of Cards with of the shop floor, a better workload balance between Authorization) is production control system (or cells), reduced throughput times, and specifically designed for low-volume, high-mix (LVHM) and workstations shop pool provides important benefits, such as a global view POLCA (i.e. Paired-cell Overlapping Loops of Cards with of the shop floor, a better workload balance between Authorization) is a card-based production control system workstations (or cells), reduced throughput times, and specifically designed for low-volume, high-mix (LVHM) and of the shop floor,cells), adue better workload balance between Authorization) is a products card-based production control system (or reduced throughput times, and specifically designed for low-volume, high-mix (LVHM) and workstations increased reliability of dates, among others (Melnyk custom-engineered (Suri, 1998, 2018). It was of the shop floor, a better workload balance between Authorization) is a card-based production control system workstations (or cells), reduced throughput times, and specifically designed for low-volume, high-mix (LVHM) and increased reliability of due dates, among others (Melnyk custom-engineered products (Suri, 1998, 2018). It was workstations (orHendry cells), reduced throughput times, specifically for low-volume, high-mix (LVHM) and increased reliability of dueand dates, among others custom-engineered products 1998,to the 2018). It was Ragatz, 1989, Kingsman, 1991,(Melnyk Land and and proposed bydesigned Suri (1998) as an (Suri, alternative well-known workstations (or cells), reduced throughput times, and specifically designed for low-volume, high-mix (LVHM) and increased reliability of due dates, among others (Melnyk custom-engineered products (Suri, 1998, 2018). It was Ragatz, 1989, Hendry and Kingsman, 1991, Land and proposed by Suri (1998) as an alternative to the well-known increased reliability of dueand dates, among others (Melnyk custom-engineered products 1998, 2018). Itdo was Ragatz, 1989, Hendry Kingsman, 1991, Land and proposed by SuriSystem (1998) as an (Suri, alternative to the well-known Gaalman 1998). An approach to accommodate centralised job Toyota Kanban (Sugimori et al., 1977), which not increased reliability of due dates, among others (Melnyk and custom-engineered products (Suri, 1998, 2018). It was Ragatz, 1989, Hendry and Kingsman, 1991, Land proposed by Suri (1998) as an alternative to the well-known Gaalman 1998). An approach to accommodate centralised job Toyota Kanban System (Sugimori et al., 1977), which do not Ragatz, 1989, Hendry and Kingsman, 1991, Land and proposed by SuriSystem (1998) as anproduction, alternative to thewhich well-known 1998).theAnPOLCA approach to accommodate centralised job Toyota Kanban (Sugimori et al., 1977), do not Gaalman release within framework is the addition of card typically apply to LVHM and to ConWIP Ragatz, 1989, Hendry and Kingsman, 1991, Land and proposed by SuriSystem (1998) as anproduction, alternative to thewhich well-known Gaalman 1998). An approach to accommodate centralised job Toyota Kanban Kanban System (Sugimori et al., al., 1977), 1977), which do not not release release within the POLCA framework is the addition of card typically apply to LVHM and to ConWIP Gaalman 1998). An approach to accommodate centralised job Toyota (Sugimori et do within the POLCA framework is the addition of each card typically apply to LVHM production, and to balancing ConWIP control loops between a central release function and (Spearman et al.System 1990) that lacks from workload Gaalman 1998). An approach to accommodate centralised job Toyota Kanban (Sugimori et al., 1977), which do not release within the POLCA framework is the addition of card typically apply to LVHM production, and to ConWIP control loops between a central release function and each (Spearman et al. 1990) that lacks from workload balancing release within the POLCA framework is the addition of card typically apply to LVHM production, and to ConWIP loops between a this central release function and each (Spearman et al. 1990) that lacks from workload balancing control gateway cell. However, changes thethe POLCA structure capability (Germs andLVHM Riezebos, 2010). release within the POLCA framework is addition of card typically apply to production, and to ConWIP control loops between a central release function and each (Spearman et al. 1990) that lacks from workload balancing gatewayloops cell. However, However, this changes the function POLCA and structure capability (Germs (Germs and Riezebos, Riezebos, 2010). control between a this central release each (Spearman et al. 1990) that lacks from workload balancing gateway cell. changes the POLCA structure capability and 2010). with unforeseen consequences on its performance. Therefore, control loops between a this central release function and each (Spearman et al. 1990) that that lacks from workload gateway cell. However, changes the POLCA structure capability and Riezebos, 2010). with unforeseen consequences on its its performance. Therefore, POLCA is(Germs a visual system manages the flowbalancing of jobs with gateway cell. However, this changes the POLCA structure capability (Germs and Riezebos, 2010). unforeseen consequences on performance. Therefore, study usesHowever, simulation to changes assess, for the firstTherefore, time, the POLCA is(Germs visual system that2010). manages the the flow flow of of jobs jobs this gateway cell. this the POLCA structure capability andsystem Riezebos, with unforeseen consequences on its performance. POLCA is aa visual that manages this study uses simulation to assess, for the first time, the though the shop floor by making use of overlapping loops of with unforeseen consequences itsthe performance. Therefore, this study uses simulation to on assess, for the first time, the POLCAthe is shop a visual visual system that use manages the flow flow loops of jobs jobs impact of centralised job release on POLCA system. though floor by making of overlapping of POLCA is a system that manages the of with unforeseen consequences on its performance. Therefore, this study uses simulation to assess, for the first time, though the shoppairs floorofby making use of overlapping loops of impact of centralised job release on the POLCA system. cards between successive manufacturing cells. Cards this study uses simulation to assess, the first time, the the of centralised job release on the for POLCA system. POLCA is shop a visual that use manages the flow ofCards jobs though the floor by making of overlapping loops of cards between between pairs ofsystem successive manufacturing cells. though the shop floor by making use ofpair overlapping loops of impact this study uses simulation to assess, for the first time, the impact of centralised job release on the POLCA system. cards pairs of successive manufacturing cells. Cards The remainder of this paper is structured as follows. In Section provide capacity signals between each of cells, ensuring impact of centralised job release on the POLCA system. though the shop floor by making use of overlapping loops of cards between pairs of successive manufacturing cells. Cards The remainder of this paper is structured as follows. In Section provide capacity signals between each pair of cells, ensuring cards between pairs of successive manufacturing cells. Cards impact of centralised job release on the POLCA system. The remainder of this paper is structured as follows. In Section provide capacity signals between eacheffectively pair of cells, weremainder review the POLCA system. The simulation model used that upstream cells use their capacity by ensuring working cards between pairs of successive manufacturing cells. Cards 2, The of this paper is as In provide capacity signals between each pair of 2, weremainder review the the POLCA system. The simulation simulation model used that upstream upstream cells use their capacity effectively by ensuring working The of thisdescribed papersystem. is structured structured as3,follows. follows. In Section Section provide capacity signals between each pair(Suri of cells, cells, ensuring 2, we review POLCA The model used that cells use their capacity effectively by working in our study is then in Section before results are only on jobs that are needed downstream 2018). In this The remainder of this paper is structured as follows. In Section provide capacity signals between each pair of cells, ensuring 2, we review the POLCA system. The simulation model used that upstream cells use their capacity effectively by working in our study is then described in Section 3, before results are only on jobs that are needed downstream (Suri 2018). In this 2, we POLCA system. Theinsimulation used that upstream cells use their downstream capacity effectively by between working our review studydiscussed isthe then described in Section 3, beforemodel results are only on POLCA jobs that are needed (Suri 2018). In this in presented, and analysed Section 4. Finally, way, synchronises the processes 2, we review the POLCA system. The simulation model used that upstream cells use their capacity effectively by working in our study is then described in Section 3, before results are only on jobs that are needed downstream (Suri 2018). In this presented, discussed and analysed in Section 4. Finally, way, POLCA synchronises the processes between in our studydiscussed isarethen described in Section 3,where before4. results are only on POLCA jobs that are needed (Suri In this andinanalysed in5, Section Finally, way, synchronises processes between conclusions drawn Section managerial manufacturing cells, reducingdownstream the the waiting time2018). between the presented, in our study is then described in Section 3, before results are only on jobs that are needed downstream (Suri 2018). In this presented, discussed and analysed in Section 4. Finally, way, POLCA synchronises the processes between conclusions are drawn in Section 5, where managerial manufacturing cells, reducing the waiting time between the presented, discussed and in5, Section 4. Finally, way, POLCA synchronises the processes between are future drawn inanalysed Section where managerial manufacturing cells, reducing the waiting time between the conclusions implications and research directions are also outlined. completion of a job in one cell and its start in the next cell presented, discussed and analysed in Section 4. Finally, way, POLCA synchronises the processes between conclusions and are future drawnresearch in Section Section 5, where where managerial manufacturing reducing between the implications directions are also alsomanagerial outlined. completion of aacells, job in in one cell cellthe andwaiting its start starttime in the the next cell cell conclusions are drawn in 5, manufacturing cells, reducing the waiting time between the implications and future research directions are outlined. completion of job one and its in next (Riezebos, are future drawnresearch in Section 5, where managerial manufacturing reducing between the conclusions implications and directions are also outlined. completion2010). of aacells, job in in one cell cellthe andwaiting its start starttime in the the next cell cell (Riezebos, 2010). implications and future research directions are also completion of job one and its in next (Riezebos, 2010). 2. THE POLCAdirections SYSTEMare also outlined. implications and future research outlined. completion of a job in one cell and its start in the next cell (Riezebos, 2010). POLCA not2010). only operates at the shop floor level, but also 2. THE THE POLCA POLCA SYSTEM SYSTEM (Riezebos, 2. POLCA not2010). only operates operates at at the the shop shop floor floor level, level, but but also also (Riezebos, POLCA not only 2. THE POLCA SYSTEM decides on the release of jobs to shop the shop floor. However, We first describe2. the production control THEoriginal POLCAPOLCA SYSTEM POLCA not only operates at floor level, but also decides on the release of jobs jobs to shop the shop shop floor. However, We first first describe describe2. the the original POLCA production control control POLCA not only operates at the the floorfloor. level, but This also We THE POLCA SYSTEM decides on the release of to the However, original POLCA production POLCA does not have a central job release function. system in Section 2.1 and then, in Section 2.2, we extend the POLCA not only operates at the shop floor level, but also decides on the release of jobs to the shop floor. However, We first describe the original POLCA production control POLCA does not have a central job release function. This system in Section 2.1 and then, in Section 2.2, we extend the decides on thenot release of jobs to job the shop floor. However, We firstinframework describe the original production control POLCA does haveawait a central release function. This system Section 2.1 and then,centralised inPOLCA Sectionjob 2.2,release. we extend the means that jobs do not release in a pre-shop pool. Rather POLCA to include decides on thenot release of jobs to job the shop floor. However, firstin describe the original production control POLCA does not haveawait a central central job release function. This We system inframework Section 2.1 2.1 and then,centralised inPOLCA Sectionjob 2.2,release. we extend extend the means that jobs do not release in a pre-shop pool. Rather POLCA to include POLCA does have a release function. This system Section and then, in Section 2.2, we the means thatimmediately jobs do not await releasetoingateway a pre-shop pool. Rather POLCA framework to include centralised job release. jobs are forwarded manufacturing POLCA does not haveawait a central job function. This system inframework Section 2.1 and then,centralised in Sectionjob 2.2,release. we extend the means that jobs do not release in aarelease pre-shop pool. Rather POLCA to include jobs are immediately forwarded to gateway manufacturing means that jobs do not await release in pre-shop pool. Rather POLCA framework to include centralised job release. jobs are immediately forwarded to gateway manufacturing means thatimmediately jobs do not await releaseto a pre-shop pool. Rather POLCA framework to include centralised job release. jobs forwarded manufacturing jobs are are immediately forwarded toingateway gateway manufacturing Copyright © 2019 IFAC 1445Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © 2019, IFAC (International Federation of Automatic Control) jobs are immediately forwarded to gateway manufacturing Copyright © 2019 IFAC 1445 Copyright 2019 responsibility IFAC 1445Control. Peer review©under of International Federation of Automatic Copyright © 1445 Copyright © 2019 2019 IFAC IFAC 1445 10.1016/j.ifacol.2019.11.399 Copyright © 2019 IFAC 1445
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2.1 The original POLCA System
3. SIMULATION STUDY
POLCA (see e.g. Suri 1998, 2010 and 2018) makes use of overlapping loops of cards between pairs of successive cells in the routing of a job. Since loops overlap, every cell, except the first and the last, belongs to two POLCA loops. This is illustrated in Fig.1. A fixed amount of POLCA cards is allocated to each loop, imposing a WIP cap in the loop, i.e. between work cells. POLCA loops ensure that cells will only process jobs for which capacity has been reserved at the downstream cell of the loop. Cards are not part-number specific, i.e., they can be acquired by any job entering the loop. Cards are attached to a job when it enters the first (or upstream) cell and detached after a job has finished processing at the second (or downstream) cell of the loop. The number of POLCA cards attached to each job depends on the workload of the job and on the quantum of the cards. Detached cards are then sent back to the first cell, where they can be attached to new arriving jobs. In the original POLCA system only jobs that have been authorized by a high-level MRP system can start processing at a cell whenever a card becomes available. Pre-shop Pool of Jobs
POLCA Card
POLCA Card
Cell C
Cell B
Cell A
Fig. 1. Illustration of the original POLCA system. 2.2 Extending POLCA with Centralised Job Release A main problem of planning in cellular manufacturing systems is related to insufficient synchronisation of the processes between manufacturing cells, resulting in waiting times between cells, while at the same time, other cells might face a lack of work to be done. Riezebos (2010) denote this as unbalance. The original POLCA system uses decentralized job release and decision-making, pushing job release decisions to the cells and work teams (Suri, 2018). However, centralised job release provides important benefits, such as better workload balance between manufacturing cells. Pre-shop Pool of Jobs
Release Card
The simulation models considered in the study are outlined in Section 3.1. Section 3.2 details the card acquisition and the dispatching rules considered in the study. The experimental design and the measures used to evaluate performance are then presented in section 3.3. 3.1 Overview of the Simulation Models Simulation models of two shops, a pure flow shop (PFS) and a general flow shop (GFS) (Oosterman et al. 2000), have been implemented using ARENA software. These simulation models are stochastic, whereby jobs’ inter-arrival times, routings, operation times and due dates are stochastic variables. Each shop contains six manufacturing cells, where each cell is modelled as a single constant capacity resource. Job routings in the GFS are determined by drawing from a discrete uniform distribution [2,6]. This means that the average number of cells in the routing of the job is four. In the PFS number of cells in the routing of each job is six. Operation processing times follow a truncated 2-Erlang distribution with a maximum of 4 time-units and a mean of 1 time-unit before truncation. Setup times are assumed sequence independent and hence enclosed in the operation processing times in order to avoid interactions due to workload fluctuations caused by sequence dependent setup times. The inter-arrival time of jobs to the production system follows an exponential distribution with a mean that based on the number of operations (one per cell) in the routing of a job deliberately result in 90% utilisation level. Due dates are set exogenously by adding to the job entry time a random allowance factor, uniformly distributed between 35 and 55 time-units for the GFS and between 49 and 69 timeunits for the PFS. As in previous simulation studies on POLCA (Fernandes and Carmo-Silva 2006, Lödding et al. 2003, and Thürer et al. 2018a, 2018b), it is assumed that all required materials are available and all necessary information regarding shop floor routings and processing times is known upon the arrival of a job to the production system.
POLCA Card
POLCA Card
3.2 Card Acquisition and Dispatching Rules Cell A
Cell B
Cell C
Fig. 2. Illustration of POLCA with centralised job release. An approach to accommodate centralised job release within the POLCA framework is the addition of loops of cards between a central release function and each gateway cell. These loops allow to control job release, while balancing the workload between the cells, as illustrated in Fig.2. Note that in case illustrated there is a single gateway cell and therefore a single release loop is added to the POLCA structure. In the resulting production control system, a job to be released to a manufacturing cell for processing must first acquire a release card. Like POLCA cards, release cards are not part number specific and, therefore, can be acquired by any job requiring processing at a specific gateway cell.
In our study every card represents a quantum of one job. An identical number of cards is used per POLCA loop in each experiment. The balanced shop considered in our study justifies this assumption. To determine the best performing number of cards per loop it is common practice in simulation studies to define it as an experimental variable. Six levels for number of cards per POLCA loop are considered in this study, namely 12, 14, 16, 18, 20 and 22 cards. These values have been chosen based on preliminary simulation runs. Release loops reflect every gateway cell in the routing of the job. The same six levels for the number of cards per loop used at POLCA loops, but divided by 2, were used at release loops. This is because in POLCA loops, cards account for the aggregate load at both, the upstream and the downstream cells
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of the loop, while in release loops, cards account only for the load at a single gateway cell. POLCA uses authorisation dates to define when a job should start at each cell. However, Thürer et al. (2018a) recently showed that authorisation dates might have a direct detrimental effect on the percentage of tardy jobs and on the mean tardiness performance. Authors suggested that authorization dates may be used to signal the priority of jobs, and not when a job should start. Therefore, in our study both priority dispatching and POLCA cards acquisition is based on the Earliest Authorisation Date (EAD). This is determined as follows: 𝜏𝜏𝑗𝑗 = 𝑑𝑑𝑗𝑗 -∑𝑘𝑘𝑘𝑘𝑅𝑅𝑗𝑗 𝑏𝑏𝑘𝑘
(1)
Where: 𝜏𝜏𝑗𝑗 is the authorization date of job j; 𝑑𝑑𝑗𝑗 is the due date of job j; 𝑏𝑏𝑘𝑘 is the lead time at cell k; 𝑅𝑅𝑗𝑗 is the set of remaining cells in the routing of job j.
MODCS rule was proposed Thürer et al. (2016), which proved to be effective. The rule divides the set of jobs waiting release in the pre-shop pool into two classes: urgent, i.e., jobs with an earliest authorisation date on the first cell in it routing that has already passed and non-urgent jobs. Urgent jobs receive priority over non-urgent jobs and are sequenced according to a capacity slack index using Eq. (2). Non-urgent jobs are sequenced according to the EAD, using Eq. (1). 𝑠𝑠𝑗𝑗
=
𝐿𝐿𝑗𝑗𝑗𝑗 × 𝜔𝜔𝑘𝑘 𝑗𝑗 (𝑁𝑁 −𝑊𝑊 ) 𝑘𝑘 𝑘𝑘
𝑛𝑛𝑗𝑗
(2)
Where: 𝑠𝑠𝑗𝑗 is the capacity slack ratio that results from releasing job j; 𝐿𝐿𝑗𝑗𝑗𝑗 is the direct workload of job j to cell k; 𝑁𝑁𝑘𝑘 is the workload norm at cell k; 𝑊𝑊𝑘𝑘 is direct workload at cell k; 𝜔𝜔𝑘𝑘 is a factor that takes the values of 1 or 0, depending if the cell k is in the routing of job j or not; 𝑛𝑛𝑗𝑗 is the number of cells in the routing of job j; Above indexes concerning card acquisition at the pre-shop pool are calculated in our study every time a new job enters the pre-shop pool, or an operation is completed at a cell. 4
scenario was replicated 100 times. All results were collected over 13,000 time-units following a warm-up period of 3,000 time-units. Three main performance measures are considered in this study, as follows: mean total throughput time – the mean of the completion date minus the arrival time date of jobs; percentage tardy – the percentage of jobs completed after the due date; and the standard deviation of lateness. In addition to these performance indicators, we also measure the mean shop throughput time - the mean of the completion date minus the release time of jobs; While the total throughput time includes the time that a job waits before being released into production, the shop throughput time only measures the time after the job is released to the shop floor. 4. RESULTS
Concerning card acquisition at the pre-shop pool for job release, three card acquisition rules have been tested, namely: EAD at the first station in the routing of the job; Modified Capacity Slack (MODCS) and First-Come-First-Served (FCFS), which is used as a benchmark.
∑𝑘𝑘𝑘𝑘𝑅𝑅
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Experimental Design
The experimental factors are: (i) the job release strategy (centralised and decentralised); (ii) the card acquisition rule at the pre-shop pool for job release (FCFS, EAD and MODD); (iii) The shop configuration (GFS and PFS) and (iv) six levels for the number of cards per control loop. Each experimental
To assess performance differences between production control strategies, detailed performance results will be presented next in Section 4.1 where we focus on the impact of centralised job release for the general flow shop configuration. Results for the pure flow shop configuration are then assessed in Section 4.2. 4.1 Performance Assessment: GFS Results are here presented in the form of performance curves. The left-hand starting point of the curves represents the tightest number of cards allowed in control loops. The number of cards per loop increases step-wise (according to the levels referred in section 3.2) by moving from left to right in each graph, with each data point representing one card number level. The lefthand point in each figure represents the lowest number of cards per control loop. Incresing the number of cards per control loop (i.e., from left to right along the curve) increases the level of work-in-process and, thus increases the shop floor throughput times. Fig.3 shows the impact of the job release strategy on the POLCA performance for the GFS. The percentage of tardy jobs, total throughput time and standard deviation of lateness are here plotted against the shop throughput time for both, centralised and decentralised job release. The following can be concluded from the results: Influence of centralised job release: Centralised job release allows for shorter shop throughput times, compared with decentralised job release – that is, curves are pushed to the left. It also allows improving POLCA performance concerning total throughput time, percentage of tardy jobs and the standard deviation of lateness, if the right card acquisition rule for job release is used. Influence of the card acquisition rule: The card acquisition rule for job release has a strong and mixed impact on the POLCA performance. MODCS outperforms EAD for the percentage of tardy jobs, while performing slightly better than FCFS. Concerning the total throughput time, the performance of the three card acquisition rules for centralised job release is identical.
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(a)
(a)
10
15 14
13
Tardy Jobs (%)
Tardy Jobs (%)
9 8 7
6
12 11
10 9 8
7 6
5
5
21
22 23 24 Shop Throughput Time (time units)
25
28
29
27
26
25
24 21
22 23 24 Shop Throughput Time (time units)
40 39 38
37 36 35 34 33
25
28
29
18 16 14
12 10 22 23 24 Shop Throughput Time (time units)
30 31 32 Shop Throughput Time (time units)
33
(c) Standard Dev. of Lateness (time units)
Standard Dev. of Lateness (time units)
(c) 20
21
33
(b)
28
Total Throughput Time (time units)
Total Throughput Time (time units)
(b)
30 31 32 Shop Throughput Time (time units)
25
50 45 40 35 30 25 20 15 10 28
29
30 31 32 Shop Throughput Time (time units)
33
Fig. 3. Performance results for the GFS.
Fig. 4. Performance results for the PFS.
Finally, concerning the standard deviation of lateness, FCFS outperform the other two rules. So, the choice of the card acquisition rule depends on performance objectives to achieve. It should be notice that a higher standard deviation, namely under a tight WIP-cap, means that some (large) jobs are being much delayed at the pool.
the same direct flow, which is maximum in the PFS, leads to a relative deterioration of centralised job release, concerning the shop throughput time. That is, performance curves of centralised and decentralised job release become closer. Increasing the jobs’ routings directness also leads to a significant improvement in the relative performance of the FCFS cards acquisition rule for job release, concerning the percentage of tardy jobs. However, this is obtained at the cost of a higher standard deviation of lateness.
4.2 Performance Assessment: PFS To assess the performance in the PFS configuration Fig.4, (a)(c), presents the total throughput time, percentage tardy and standard deviation of lateness results, respectively, over the shop floor throughput time.
These results indicate that the relative performance of centralised vs. decentralised job release is influenced by the directness of the jobs’ routings, i.e., the system configuration. 6. CONCLUSIONS
Analysing results, the following can be concluded: Influence of the shop configuration. Increasing the jobs’ routings directness, i.e., the degree to which job routings share
POLCA is card-based production control system that uses decentralised decision-making, pushing job release decisions
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to the cells and work teams. However, centralised job release can provide important benefits, as pointed out in the Workload Control literature. Therefore, in this paper we changed the POLCA framework to accommodate centralised job release by adding cards loops between the central release function and the gateways manufacturing cells. Results indicate that centralised job release may outperform decentralised release for both, the general flow shop and the pure flow shop configurations. A higher impact was observed for the general flow shop configuration. Centralised job release however may lead to some large jobs being much delayed at the pre-shop pool, if the EAD card acquisition rule or a capacity slack rule, such as MODCS, are used at the pool. A major limitation of our study is that our findings are based on restricted set of experimental factors. Therefore, we suggest extending the study to a broader range of manufacturing environments and production conditions. ACKNOWLEDGMENTS This work had the financial support of FCT - Fundação para a Ciência e Tecnologia of Portugal under the project PEst20152020: UID/ CEC/ 00319/ 2013.
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Suri, R. (1998). Quick Response Manufacturing: A companywide approach to reducing leadtimes. Productivity Press. Suri, R. (2010). It’s about time: The competitive advantage of quick response manufacturing, Productivity Press. Suri, R. (2018). The Practitioner's Guide to POLCA: The Production Control System for High-Mix, Low-Volume and Custom Products. Productivity Press. Thürer, M. Fernandes, N.O., Stevenson, M., Silva, C., and Carmo-Silva, C. (2018a). POLC-A: an assessment of POLCA’s authorization element, Journal of Intelligent Manufacturing, Online First. Thürer, M., Fernandes, N.O., Stevenson, M., Qu, T. and Li, C.D. (2018b). Centralized vs. Decentralized Control Decision in Card-Based Control Systems: Comparing kanban Systems and COBACABANA; International Journal of Production Research; (in print). Thürer, M., Stevenson, M., and Qu, T. (2016). Job sequencing and selection within workload control order release: an assessment by simulation, International Journal of Production Research, 54 (4), 1061-1075.
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