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Reconfigurable express logistics center: a simulation study
Proceedings of the 20th World Congress Proceedings of the 20th World The International Federation of Congress Automatic Control Proceedings Proceedings of of the the 20th 20th World World Congress Congress Available online at www.sciencedirect.com The of Control Toulouse, France,Federation July 9-14, 2017 The International International Federation of Automatic Automatic Control The International Federation of Automatic Control Toulouse, France, July 9-14, 2017 Toulouse, France, July 9-14, 2017 Toulouse, France, July 9-14, 2017
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IFAC PapersOnLine 50-1 (2017) 11770–11775
Reconfigurable express logistics center: a simulation study Reconfigurable Reconfigurable express express logistics logistics 1,center: center: aa2 simulation simulation study study
Xiang T.R. Kong1,*, Hao Luo2 Xiang T.R. T.R. Kong Kong1,1,*, *, Hao Hao Luo Luo22 Xiang T.R. Kong *, Hao Luo Xiang 1 HKU-ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of 1 1HKU-ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of 1 HKU-ZIRI Lab Physical Internet, of Industrial Hong Kong, Pokfulam Road, Hong Department Kong (e-mail: HKU-ZIRI Lab for for Physical Internet, Department of [email protected]) Industrial and and Manufacturing Manufacturing Systems Systems Engineering, Engineering, The The University University of of Hong Kong, Pokfulam Road, Hong Kong (e-mail: [email protected]) Hong Kong, Pokfulam Road, Hong Kong (e-mail: [email protected]) 2 Hong Kong, Pokfulam Road, Hong Kong (e-mail: [email protected]) Department of Transportation Economy and Logistics Management, College of Economics, Shenzhen University, Shenzhen, 2 2 2Department of Transportation Economy Department of Transportation Economy and Logistics Management, College of Economics, Shenzhen University, Shenzhen, P.R. China [email protected]) Department of (e-mail: Transportation Economy and and Logistics Logistics Management, Management, College College of of Economics, Economics, Shenzhen Shenzhen University, University, Shenzhen, Shenzhen, P.R. China (e-mail: [email protected]) P.R. China (e-mail: [email protected]) P.R. China (e-mail: [email protected]) Abstract: This paper is motivated from a rapidly expanding express delivery service provider that has Abstract: This from expanding express service provider that Abstract: This paper is motivated from rapidly expanding express delivery service provider that has recently itsmotivated express logistics center in one region with delivery high demand is planning to Abstract:reengineered This paper paper is is motivated from aaa rapidly rapidly expanding express delivery serviceand provider that has has recently reengineered its express logistics center in one region with high demand and is planning to recently reengineered its express logistics center in one region with high demand and is planning to reproduce such operations in other regions with seasonal and volatile demands. A simulation model is recently reengineered its express logistics center in one region with high demand and is planning to reproduce such operations in other regions with seasonal and volatile demands. A simulation model is reproduce such operations in other regions with seasonal and volatile demands. A simulation model is built to enable a reconfigurable express logistics center. Three main control policies: sequencing, order reproduce such operations in other regions with seasonal and volatile demands. A simulation model is built aaa reconfigurable logistics center. Three main control policies: sequencing, order built to to enable enable reconfigurable express logistics To center. Three the main controlfindings policies:from sequencing, order releasing and machine flexibilityexpress are considered. generalize research the simulation built to enable reconfigurable express logistics center. Three main control policies: sequencing, order releasing and machine flexibility are considered. To generalize the research findings from the simulation releasing and machine machine flexibility are considered. considered. To generalize generalize theout research findings from the the simulation study, experimental design technique has been applied to carry sensitivity analyses by simulation using full releasing and flexibility are To the research findings from study, experimental design technique been carry study, experimental design technique has been applied to carry out out sensitivity sensitivity analyses analyses by by using using full full combination of control factors, demandhas orders andapplied systemto parameters. study, experimental design technique has been applied to carry out sensitivity analyses by using full combination of control factors, demand orders and system parameters. combination of of control control factors, factors, demand demand orders orders and and system system parameters. parameters. combination © 2017, IFAC (International of Automaticand Control) Hosting by Elsevier Ltd.ofAll rights reserved. Keywords: Express logisticsFederation center, Configuration control, Simulation, Design experiments Keywords: Express logistics center, Configuration and control, Simulation, Design of experiments Keywords: Express logistics center, Configuration and control, Simulation, Design of experiments Keywords: Express logistics center, Configuration and control, Simulation, Design of experiments
1. INTRODUCTION 1. INTRODUCTION 1. INTRODUCTION 1. INTRODUCTION In express delivery industry, express centres play the In delivery industry, express play In express delivery industry, centres play the significant in an organization’s businesscentres decision-making In express expressrole delivery industry, express express centres play the the significant role in an organization’s business decision-making significant role in an organization’s business decision-making processes and have a long-term impact on the support for significant role in an organization’s business decision-making processes have the support for processes and have aaa long-term long-term impact on the 2009). support The for enterprise and achievements (Kathy impact Roper on et al. processes and have long-term impact on the support for enterprise achievements (Kathy Roper et al. 2009). The enterprise achievements (Kathycentres Roperiset etnotal. al. only 2009). The challenge for today’s express howThe to enterprise achievements (Kathy Roper 2009). challenge for express is challenge for today’s today’s express centres centres is not not only only how to to handle colossal order fulfilment commitments, but how challenge for today’s express centres is not only how to handle order fulfilment commitments, how to handlea colossal colossal order fulfilment commitments, but howthey to draw competitive advantage from the way in but which handle colossal order fulfilment commitments, but how to draw a competitive advantage from the way in which they draw a competitive advantage from the way in which they flexibly respond to dynamic variations (Apte and draw a competitive advantage from the way in which they flexibly respond and flexibly respond to dynamic variations (Apte and Viswanathan 2000).to the core variations component (Apte of express flexibly respond toAs dynamic dynamic variations (Apte and Viswanathan 2000). As the core component of express Viswanathan 2000). As the core component of express logistics center, the sorting system is of crucial importance. It Viswanathan 2000). As the core component of express logistics center, the sorting system is of crucial importance. logistics center, the sorting system is of crucial importance. It determines whether a physical commodity that traded via the logistics center, the sorting system is of crucial importance. It It determines aaa physical commodity that traded via determines whether whether physicalsmoothly commodity thatbuyer traded via the the e-commerce can be delivered to the or not. determines whether physical commodity that traded via the e-commerce e-commerce can can be be delivered delivered smoothly smoothly to to the the buyer buyer or or not. not. e-commerce can be delivered smoothly to the buyer or not. However, there are several decision variables concerned with However, there are However, theresystem are several several decision variables concerned with the sorting in decision express variables centres concerned in termswith of However, there are several decision variables concerned with the sorting system in express centres in terms the sorting sorting and system in policies expresssuch centres in number terms of of configuration control as optimal the system in express centres in terms of configuration control such of configuration and control policies such as optimal number of sortation slots,and placement of the induction stations,number and order configuration and control policies policies such as as optimal optimal number of sortation slots, placement of the induction stations, and order sortation slots, placement of the induction stations, and order release mechanism. Moreover, these design decisions involve sortation slots, placement of the induction stations, and order release design decisions involve release mechanism. Moreover, these design decisions involve complex trade-offs Moreover, where the these parameters making these release mechanism. mechanism. Moreover, these design for decisions involve complex trade-offs where the parameters for making complex trade-offs where the parameters for making these trade-offs are often not well understood. Practical complex trade-offs where the parameters for making these these trade-offs often not trade-offs are are often not well wellmoreunderstood. understood. Practical uncertainties make the problem complicatedPractical (Kong, trade-offs are often not well understood. Practical uncertainties make the problem uncertainties make The the uncertainties problem more more complicated (Kong, Chen et al., 2016). cancomplicated be produced (Kong, by the uncertainties make the problem more complicated (Kong, Chen et al., 2016). The uncertainties can be by Chen et al., al., 2016). 2016). The uncertainties uncertainties candisruptive be produced produced by the the unexpected booming demands orcan machine Chen et The be produced by the unexpected booming demands or disruptive machine unexpected which booming demands or performance disruptive and machine breakdowns all affect the actual incur unexpected booming demands or disruptive machine breakdowns which affect the actual and breakdowns which all affect the actual performance and incur costs. The decision support for facility managers breakdowns which all all affect is therequired actual performance performance and incur incur costs. The decision support is required for facility managers costs. The decision support is required for facility managers who need to prioritize what is important rather than simply costs. The decision support is required for facility managers who prioritize what is than simply who need to prioritize what is important rather than simply urgent to to gain maximum Hence, proactive who need need to prioritize what effectiveness. is important important rather rather than simply urgent to gain maximum effectiveness. Hence, proactive urgent to gain maximum effectiveness. Hence, proactive decision makers are more concerned with identifying specific urgent to gain maximum effectiveness. Hence, proactive decision are concerned identifying specific decision makers makers are more more concerned with identifying specific performance trends or levels and with analysing trade-offs in decision makers are more concerned with identifying specific performance trends or levels and analysing trade-offs in performance trends or ormanagement levels and and and analysing trade-offs in operations to quantify service trade-offs (Kong, Fang performance trends levels analysing in operations to quantify management and service (Kong, Fang operations to quantify quantify management management and and service service (Kong, (Kong, Fang Fang et al., 2015). operations to et et al., al., 2015). 2015). et al., 2015). Many researchers have paid great attention on designing and Many have designing Many researchers have paid great attention on designing and implementing cost-effective orderattention sortationon for Many researchers researchers have paid paid great great attention onapproaches designing and and implementing cost-effective order sortation approaches implementing cost-effective order sortation approaches for manufacturing factories and logistics warehouse (Bozer and implementing cost-effective order sortation approaches for for manufacturing factories and logistics warehouse (Bozer and manufacturing factories and logistics warehouse (Bozer and Sharp 1985, Bard et al. 1993, Johnson and Lofgren 1994, manufacturing factories and logistics warehouse (Bozer and Sharp Sharp 1985, Bard et al. 1993, Johnson and Lofgren 1994, Sharp 1985, 1985, Bard Bard et et al. al. 1993, 1993, Johnson Johnson and and Lofgren Lofgren 1994, 1994,
Galbreth and Blackburn 2006). However, the sortation Galbreth and Blackburn 2006). sortation Galbreth that and integrates Blackburndesign 2006). However, the sortation operation and However, operational the decisions for Galbreth and Blackburn 2006). However, the sortation operation that integrates design and operational decisions operation that integrates integrates design and operational operational decisions decisions for for express logistics is rarelydesign discussed. operation that and for express express logistics logistics is is rarely rarely discussed. discussed. express logistics is rarely discussed. Reconfiguration express center (REC) is thus put forward for Reconfiguration express center (REC) thus forward for Reconfiguration express center (REC) is thus put forward for sortation operations, which involves theis design Reconfiguration express center (REC) issystem thus put putlayout forward for sortation operations, which involves the system layout design sortation operations, which involves the system layout design and control policies evaluation to provide flexible responses sortation operations, which involves the system layout design and control policies to flexible and control policies evaluation to provide flexible responses to The significances of REC forresponses sortation anddynamic control changes. policies evaluation evaluation to provide provide flexible responses to dynamic changes. The significances of REC for to dynamic changes. The significances of REC for sortation operations can be summarized as follows. Firstly, both to dynamic changes. The significances of REC for sortation sortation operations can summarized follows. operations set-up can be be and summarized asapproaches follows. Firstly, Firstly, both machine control as should both be operations can be summarized as follows. Firstly, both machine control should be machine set-up set-up and control approaches should be reconfigured and and scalable underapproaches uncertainties through machine set-up and control approaches should be reconfigured and scalable under uncertainties through reconfigured and scalable scalable trade-offs. under uncertainties uncertainties through investigating performance REC for sortation reconfigured and under through investigating performance trade-offs. REC for sortation investigating performance trade-offs. RECmore for proactive sortation operations helps facility managers become investigating performance trade-offs. REC for sortation operations helps facility managers become more proactive operations helps facility managers become more proactive when they are faced with increasingly changing customer operations helps facility managers become more proactive when faced increasingly changing customer when they are faced with increasingly changing customer requests, dueare dates and with regulations. Secondly, express centres when they they are faced with increasingly changing customer requests, due dates and regulations. Secondly, express requests, due dates and regulations. Secondly, express centres located in dispersed geographical markets have different requests, due dates and regulations. Secondly, express centres centres located in dispersed geographical markets have different located in dispersed geographical markets have different demand variations and order patterns. These facilities are located in dispersed geographical markets have different demand variations and order patterns. These facilities are demandequipped variations andvarious order system patterns. These facilities are usually with layout, order sequence, demand variations and order patterns. These facilities are usually equipped with various system layout, order sequence, usually size equipped with variousflexibility. system layout, layout, order sequence, batch and with machine RECorder for sequence, sortation usually equipped various system batch size and machine flexibility. REC sortation batch size can and offer machine flexibility. REConfor fordesign sortation operations important guidance and batch size and machine flexibility. REC for sortation operations can offer important guidance on design and operations of can express offer important important guidance on and designacross and operation centres guidance over time operations can offer on design and operation of express centres over time and across operation of express centres over time and across geographical regions. operation of express centres over time and across geographical regions. geographical regions. geographical regions. This paper aims to explore a systematic study of express This paper aims explore aaa systematic study of This paper aims to to with explore systematic studyquestions: of express express centerpaper configuration the following research This aims to explore systematic study of express center configuration with the following research questions: center configuration withanthe the followingcontrol research questions: ⑴ How can we define integrated policy for REC center configuration with following research questions: ⑴ we define policy for ⑴ How How can can wesequencing, define an an integrated integrated control policyflexibility for REC REC including order releasing control and machine ⑴ How can we define an integrated control policy for REC including order sequencing, releasing and machine flexibility including order order sequencing, sequencing, releasing releasing and and machine machine flexibility flexibility strategies? including strategies? strategies? ⑵ How to assess the impact of combined control schemes on strategies? ⑵ assess of control schemes ⑵ How How to toperformance assess the the impact impact of combined combined controlgeographical schemes on on sortation of REC in different ⑵ How to assess the impact of combined control schemes on sortation performance of REC in different geographical sortation performance of REC in different different geographical geographical regions (e.g. South, East of andREC West China)? sortation performance in regions South, East China)? regions (e.g. South, East and and West West China)? ⑶ What(e.g. kinds of managerial insights could be gained from regions (e.g. South, East and West China)? ⑶ What kinds of managerial insights ⑶ What What kinds of of managerial managerial insights insights could could be be gained gained from from results analyses? ⑶ kinds could be gained from results analyses? results analyses? analyses? results We use discrete-event simulation to model REC for sortation We use discrete-event discrete-event simulation to model REC for sortation We to operations. Two main simulation reasons include: (1)REC nonefor related We use use discrete-event simulation to model model REC forofsortation sortation operations.models Two main main reasons include: (1)fornone none of related operations. Two reasons include: (1) of analytical considered were suitable this study due operations. Two main reasons include: (1) none of related related analytical models considered weremathematical suitable for for this this study due analytical models considered were suitable study to REC is intractable in a single model, not to analytical models considered were suitable for this study due due to REC REC is is intractable intractable in in aa single single mathematical mathematical model, model, not not to to to to REC is intractable in a single mathematical model, not to
2405-8963 © IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Copyright © 2017, 2017 IFAC 12272 Peer review© of International Federation of Automatic Control. Copyright 2017 IFAC 12272 Copyright ©under 2017 responsibility IFAC 12272 Copyright © 2017 IFAC 12272 10.1016/j.ifacol.2017.08.1986
Proceedings of the 20th IFAC World Congress Toulouse, France, July 9-14, 2017 Xiang T.R. Kong et al. / IFAC PapersOnLine 50-1 (2017) 11770–11775
mention to obtain the optimal results within feasible solution time and (2) simulation provides a user-friendly interface for logistics professionals while it is a powerful analysis tool to capture the complexities of the real world to a reasonable level, which allows implementation of what-if, sensitivity scenarios and alternatives comparison on a computer (Marinov and Viegas 2009). Next section, the related works will be reviewed. The description of motivating case and associated challenges are illustrated in section 3. In section 4, simulation experiments are conducted for analysing sortation operations. Results analysis and managerial insights are obtained in section 5. Finally, observations and contributions are synthesized and suggestions are made for future developments. 2. LITERATURE REVIEW The problem of sorting system design and control has been discussed in past decades in manufacturing, logistics and service industries. Cost and efficiency analysis of order sorting system have been evaluated (Galbreth and Blackburn 2006). The impacts of number and length of sorting slots, and optimal order-to-lane assignment as well as sorting strategies on order sorting performance have examined (Bozer and Sharp 1985, Bard et al. 1993, Johnson and Lofgren 1994). Johnson and Meller (2002) comprehensively investigated the induction processes of split-case sorting system. Jarrah and Bard (1994) presented three interrelated models to find a minimum cost configuration for a typical general mail facility associated with daily machine schedules. Adjusting a single control parameter may not change the recommended system configuration while varying several parameters simultaneously has a critical influence on the system. The impacts of alternative policies and configurations are simulated and quantified in the design and control of a logistics order picking system (Manzini, Gamberi et al. 2005). These authors also argued that an improved customer service can be achieved through effective order releasing. Moreover, it exists the trade-offs between order releasing mechanisms, work-in-process inventory and ontime completion (Chan, Humphreys et al. 2001). It is also worth noting that a balance must be struck between the resulting uncertainty a firm faces and the use of flexibility that can accommodate uncertainty. There is the potential to improve sorting productivity by utilizing some flexibility strategies such as machine flexibility. Design of experiments is a useful technique with a series of tests for improving process design (Ekren, Heragu et al. 2010, Chackelson, Errasti et al. 2013), which provides the observation and analysis through inputs, processes and outputs variations. Computer simulation is often employed during the experimentation phase for assisting managers and engineers in the design of a new system. The relative better design alternative can be obtained through the comparison of simulation results before the system is actually implemented. According to Bard (1997), discrete event simulation is able to test alternative solutions for express delivery service efficiently, especially when process uncertainties and interdependencies occur. Meanwhile, simulation is one of the
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effective tools to evaluate control mechanisms under demand variations (Chan and Chan 2005). The facility design and sequencing/dispatching controls of manufacturing shop-floor or logistics warehouse have been examined. However, the systematic study integrating both design and operational issues in the express center is still scarcely. Problem also remains to be solved on determining the optimal quantity and position of sorting slots in the express sorting system. 3. MOTIVATING SCENARIO The motivating case comes from one of the largest express delivery enterprises in China; it operates around 200 express centres and employs 90,000 people. The company currently employs manual sorting system (MSS) and all express freights are sorted while picked manually from a line-type conveyor. Recently, the firm has realized the importance of express center configuration for order sortation. Its senior managers want to know if changing its facilities configuration and control would enable it to achieve significant cost savings and service enhancement. Therefore, it commences the design and pilot test of a new semi-automated sorting system (ASS) to fulfil the increasingly complicated commitments in the existing express centres. The new system is equipped with advanced Internet-of-things (IoT) sensors such as automated bar-code sorters and it can accurately identify and dispatch express freights. In addition, the information control system of ASS is linked with enterprise high-level management system. Thus, the real-time freight information can be traced and tracked. Figure 1 illustrates the reengineered express logistics center layout and workflow with ASS. It can be considered as a queuing system along with inbound dock, receiving zone, sorting zone, dispatching zone and outbound dock. Particularly, sorting zone consists of both manual sorting area and automated sorting area. The detailed processes and management issues have been defined as follows. A few parts of outgoing express freights are cross-docked directly to the destination and other parts of freights have to be processed through the ASS in fixed sorting waves commonly ranging from 30 minutes to 2 hours. The orders are mainly sorted using a high-speed conveyor network, a control system and barcodes on the individual package. The next available sorting strategy is used in current system (Johnson 1997). It is a rule which assigns the next available order to a sorting lane with little blocking. Based on this sorting strategy, the control system decides whether to divert the orders into the proper sorting slots based on the congestion in the sorting lane or push the orders for a round trip on the dog-track until it can be sorted. However, the existing field management totally depends upon the experience and skills of managers and the operations fulfil the daily commitments in the front-line with little regard to what was planned.
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The accuracy of models plays an essential role in developing a successful simulation study. A simulation study consists of a series of steps and tasks that need to be followed in a specific order, which are often performed in an interactive and iterative manner. We choose FlexSim® 6.0 to evaluate the configuration comparison and combined effect of various control policies on REC for sortation operations. This commercial software is particularly accurate in simulating real logistics terminals and analysing a mass of data with intelligent object-based development architecture.
and can be ignored. Therefore, we can use the simulation model to represent the practical sorting process in an express logistics center. 4.1 Control Policies in REC 4.1.1 Order Releasing Immediate order release (IOR) to the sorting machines as soon as it arrives at express center may sometimes give it a leading edge on its due dates and reduce the level of inventory, but it may make operational site more congested. A superior mechanism therefore may be one which results in short order lead time for a given level of WIP (Chan, Humphreys et al. 2001). It can be argued that other order release mechanisms are also applicable to a typical order sorting system. Constant work in process (CWIP) is a mechanism that a new order will not be launched unless a place in the system has been vacated for it. In another words, the number of orders in the system is maintained constant with equal total input and output rate under this releasing rule. 4.1.2 Order Sequencing A large number of awaiting orders should be batched and sequenced before entering the sorting system. Related policies discussed in this paper are first-come-first-served (FCFS) and nearest due date first (DD). At many times, FCFS is made necessary by the nature of the process. According to service standards of same-day-delivery, nextday-delivery and 2-day-delivery, DD is used for scheduling within the fixed due dates.
Fig. 1. Reengineered express centre configuration with ASS 4. SIMULATION EXPERIMENTS The simulation tasks are divided into three phases: simulation preparation, model building/implementation, and experimental design and results analysis. In this paper, we focus on investigating the impact of independent variables on dependent variables. We adopt the triangular distribution due to it is a good approximation to use in the absence of data, especially if a minimum, maximum, and most likely value can be estimated (Masmoudi, Chtourou et al. 2007). The assumptions for the simulation model are as follows: Batch sizes/types of express freight in each sorting time window are already known. Setup time for freight is negligible and equal to zero. Arrival interval of express freight conforms to triangular distribution. There is sufficient buffer space for freight before and after sorting. The efficiency of machine and personnel adopts averaged default values. All the input data used for the simulation are collected from the real operations at the case company and have received the feedback from managers. The average demands in peak period in South China, East China and West China are 71,000 pieces, 62,000 pieces and 42,000 pieces respectively; and 55,000 pieces, 47,000 pieces and 40,000 pieces in non-peak period. The developed models and simulation runs are iteratively piloted, which have also been validated by the firm. To further validate our model, we compare the statistics of the throughput time for scenario MSS to the real time performance in September 2013. The relative error is small
4.1.3 Machine Flexibility A combined assessment of flexibility along with order release mechanisms and sequencing rules can be examined. In this paper, we focus on examining a measure of flexibility defined as machine flexibility. It intends to provide alternative options for decision-makers to decide which sorting slot in which position should be open to replace the one that has been disrupted. Related policies examined are no machine flexibility (NULL) and eliminate/ reallocate a random slot (OR). 4.2 Design of Experiment The simulation is conducted to evaluate the circle-type ASS sortation system design with combined control factors and various demand patterns in South China, West China and East China. Estimates for the daily input profiles are based on measurements of a medium-sized express center on consecutive seven days in September 2013, which were selected by the firm to be representative examples of the future average daily throughput of 2015 and have been modified slightly to protect company interests. Data related to machine efficiency and costs are based on supplier information. The simulation model is assumed to be a nonterminating system and the steady state analysis could be conducted. The length of the simulation run (10080 min) was selected and first 2880-min is used as warm-up period. In order to collect statistically reliable data and reduce the random effects, each simulation run has 10 independent replications.
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Specifically, we look at four basic factors in the simulation and each factor has two levels. Full factorial design with 2n factor-level for each performance measure are selected and conducted in this paper since it is very efficient forms of experimentation (Chackelson, Errasti et al. 2013). A total of 160 (24×10) response values are employed to determine the main and interaction effects of the factors on performance measurements. Table 1 presents four factors and associated factor levels. Table 1 Factors and levels of factors used in the experiments Factors Order releasing Machine flexibility Local sequencing Demand Pattern
Factor Levels Immediate order release (IOR) Constant work in process (CWIP) No machine flexibility (NULL) Eliminate a random slot and reallocate the workload with other random slot (OR) First-come-first-served (FCFS) Nearest due date first (DD) High seasonal demand with high demand variance (HDHV) Medium/Low seasonal demand with medium/low demand variance (MLDMLV)
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5. RESULTS AND DISCUSSION 5.1 Demand Patterns As pointed out by Lau, Xie et al. (2008), the factor of demand pattern (Dt) of express freight at period t is generated by the following formula:
2 t Dt base season sin noise snormal () SCycle where base is the approximated value close to average demand; SCycle is the seasonal trend of the demand, and is 365 days in simulations; noise represents the demand variance; snormal() is a standard normal random number generator. The other parameters are characteristic parameters for the demand generators. Two demand patterns (HDHV, MLDMLV) are used in simulations representing different combinations of demand generators (A1, A2, A3, A4, A5, A6). Table 2 (a) Demand generators and their characteristics Demand
Base
Season
Noise
A1
71,000
457,000
7500
A2
55,000
422,000
4200
A3
62,000
412,000
2500
A4
47,000
334,000
1800
A5
42,000
82,000
4900
A6
40,000
29,000
563
Generator
(b) Demand pattern of a typical express logistics center in South China, East China and West China
Fig. 2. The simulation model of circle-type ASS In this study, the measurement variables considered are WIP inventory, cycle time, and capacity utilization with costs assumed to be constants. Therefore, the value in business cycle (VBC) is regulated by the function: VBC = f (P1, P2, P3); Where, P1 = WIP Inventory = (Input order – total order processed) / (Input order) * 100%; P2 = Cycle time = ∑ (Queue time, Sorting time, Move time); P3 = Capacity utilization = (Actual capacity / Normal capacity) * 100%. Regarding to P1, it shows the number of entities remaining in the system (WIP inventory in sorting system) and the higher the WIP, the worse the customer service. We calculate the averaged cycle time of a random express order to stand for P2. P3 indicates the averaged utilization of the sorting conveyor and slots.
Demand
EC in South
EC in East
EC in West
Pattern
China
China
China
HDHV
A1
A3
A5
MLDMLV
A2
A4
A6
The characteristics of demand generators are shown in Table 2. Demand generator A1 represents a high seasonality and extreme variability while A2 signifies a slightly smaller seasonality and very big variability in South china. A3 and A4 represent a high seasonality and big variability, and a relatively smaller seasonality with medium variability in East China respectively. In West China, a high seasonality and boomed variability (A5) as well as a low seasonality and small variability (A6) are illustrated. Two demand patterns (HDHV, MLDMLV) representing the different combinations of demand generators were used in this study. 5.2 The Impact of Combined Control Policies on Sorting Performance of REC The main focus of this section is on studying the combined control schemes on sorting performance in different regional express logistics centres.
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5.2.1 REC in South China During the peak-hour period, Figure 3(a) shows the control combinations are fluctuated with demands change in four fixed processing time window. In the first time window, the lowest WIP inventory is achieved by using CWIP_FCFS_NULL. CWIP_FCFS_OR mechanism achieves the relative second lowest WIP inventory in the following time window. The opposite performance is measured during employing IOR_DD_NULL. From the analysis of overall trend of performance curve in Figure 4(a), the CWIP scheme is more effective to reduce WIP inventory compared with IOR while the control combinations of sequencing. The overall utilization is in the shape of smile curve with the bottom point from 79% to 87%. Figure 3(b) signifies the group of CWIP_FCFS_OR are the preferable management policy in South China under nonpeak period, considering the indicator of utilization ratio. Another interesting result is the codomain of utilization in peak-period is around 90% while reduces to 70% during nonpeak cycle. The insights can be obtained that system utilization is directly changed with the demands fluctuation while the control combination is not varied too much.
WIP inventory of ASS during non-peak period is illustrated in Figure 4(b), where an inverse V-shape can be identified with maximized inventory point. CWIP_FCFS_NULL can reduce WIP inventory most effectively among all policy combinations. It justifies that the effect of machine flexibility NULL on this operational performance is upper than the OR strategy. Due to the demands decreasing, the utilization falls again within the range between 40% and 50%. 5.2.3 REC in West China Although several control combinations have impact on utilization, the best option is still CWIP_FCFS_OR scheme in peak-hour period. However, the utilization of ASS slumps into the lowest utilization due to the decreasing demands, less than thirty percent, as seen Figure 5. Another interesting result is that control combination of IOR_FCFS_NULL outperforms CWIP_FCFS_OR under demands at a relatively low level.
(a) ASS utilization ratio during peak-hour period
(a) WIP inventory and utilization ratio of ASS during peak-hour period
(b) WIP inventory and utilization ratio of ASS during non-peak period Fig. 4. Performance evaluation of REC in East China under combined control policies (b) ASS utilization during non-peak period Fig. 3. Performance evaluation of REC in South China under combined control policies 5.2.2 REC in East China Figure 4(a) shows the interaction between control combinations and ASS utilization ratio during peak-hour period. The trend of utilization is almost consistent, fluctuating between 60% and 70%. CWIP_FCFS_OR is still on the top compared with other control groups. 12276
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(a) ASS utilization ratio during peak-hour period
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REFERENCES
(b) ASS utilization ratio during non-peak period Fig. 5. Performance evaluation of REC in West China under combined control policies 5.3 Managerial Implications The experimental design also provides several generalizable findings as follows: The estimated increasing and decreasing trend of WIP inventory is greatly affected by control policies in either busy or slack season. Facility managers could significantly reduce the WIP inventory through adjusting the control combinations. The system utilization is directly varied with the demand fluctuations while the according control combination is not varied too much. The performance results of cycle time during the peak period and non-period almost conform to the utilization measurement. The CWIP scheme is more effective to reduce WIP inventory compared with IOR while the control combinations of sequencing and machine flexibility impose different effects under volatile demands. The control combination of CWIP_FCFS_OR is almost the best policy to preserve the lowest inventory and relative high utilization in the majority of regional express centres. 6. CONCLUSIONS Considering the real challenges in existing express center, this study analyses configuration and control alternatives for sorting in different regional express centres by evaluating three key performance indicators (WIP inventory, cycle time and system utilization). This systematic decision support is required especially for express center managers who need to prioritize what is important rather than simply urgent in order to gain maximum effectiveness. Nowadays, Chinese express delivery industry seeks to improve logistics service standards with right quantity, right time and right products for end customers. Thus it is more important to guarantee a minimum WIP inventory in express centres than the highest system utilization. For this particular case, the main recommendations suggested to the company are to release orders following CWIP scheme rather than general IOR and to implement DD in South China and FCFS in East, West China. The machine flexibility rules (OR, NULL) should be executed along with other two policies dynamically to maintain higher system utilization and shorter cycle time.
Apte, U. M., & Viswanathan, S, 2000. Effective cross docking for improving distribution efficiencies. International Journal of Logistics Research and Applications, 3(3), 291-302. Bard, J. F, 1997. Benchmarking simulation software for use in modeling postal operations. Computers & Industrial Engineering, 32(3), 607-625. Bard, J. F., et al, 1993. Design of semi-automated mail processing facilities. IIE Transactions, 25(4), 88-101. Bozer, Y. A. and G. P. Sharp, 1985. An empirical evaluation of a general purpose automated order accumulation and sortation system used in batch picking. Material Flow, 2(2-3), 111-131. Chackelson, C., et al, 2013. Evaluating order picking performance trade-offs by configuring main operating strategies in a retail distributor: A Design of Experiments approach. International Journal of Production Research, 51(20). Chan, F., et al., 2001. Order release mechanisms in supply chain management: a simulation approach. International Journal of Pysical Dstribution & Lgistics Mnagement, 31(2), 124-139. Chan, F. T. and H. K. Chan, 2005. Design of a PCB plant with expert system and simulation approach. Expert Systems with Applications, 28(3), 409-423. Ekren, B. Y., et al., 2010. Simulation based experimental design to identify factors affecting performance of AVS/RS. Computers & Industrial Engineering, 58(1), 175-185. Gunasekaran, A., Patel, C., & McGaughey, R. E, 2004. A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333-347. Galbreth, M. R., & Blackburn, J. D, 2006. Optimal acquisition and sorting policies for remanufacturing. Production and Operations Management, 15(3), 384-392. Jarrah, A. I. and J. F. Bard, 1994. Equipment selection and machine scheduling in general mail facilities. Management Science 40(8), 1049-1068. Johnson, M. E, 1997. The impact of sorting strategies on automated sortation system performance. IIE Transactions, 30(1), 67-77. Johnson, M. E. and R. D. Meller, 2002. Performance analysis of split-case sorting systems. Manufacturing & Service Operations Management, 4(4), 258-274. Kathy O. Roper, Jun Ha Kim and Sang-hoon Lee, 2009. Strategic Facility Planning: A White Paper on Strategic, International Facility Management Association. Kong, X. T., Fang, J., Luo, H., & Huang, G. Q. 2015. Cloud-enabled real-time platform for adaptive planning and control in auction logistics center. Computers & Industrial Engineering, 84, 79-90. Kong, X. T., Chen, J., Luo, H., & Huang, G. Q. 2016. Scheduling at an auction logistics centre with physical internet. International Journal of Production Research, 54(9), 2670-2690. Lau, R., et al., 2008. Effects of inventory policy on supply chain performance: A simulation study of critical decision parameters. Computers & Industrial Engineering, 55(3), 620-633. Manzini, R., et al., 2005. Design and control of a flexible orderpicking system (FOPS): A new integrated approach to the implementation of an expert system. Journal of Manufacturing Technology Management, 16(1), 18-35. Marinov, M. and J. Viegas, 2009. A simulation modelling methodology for evaluating flat-shunted yard operations. Simulation Modelling Practice and Theory, 17(6), 1106-1129. Masmoudi, W., et al., 2007. Labor and machine sizing through a Simulation-Expert-System-based Approach. Simulation Modelling Practice and Theory 15(1), 98-110.
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IFAC PapersOnLine 50-1 (2017) 11770–11775
Reconfigurable express logistics center: a simulation study Reconfigurable Reconfigurable express express logistics logistics 1,center: center: aa2 simulation simulation study study
Xiang T.R. Kong1,*, Hao Luo2 Xiang T.R. T.R. Kong Kong1,1,*, *, Hao Hao Luo Luo22 Xiang T.R. Kong *, Hao Luo Xiang 1 HKU-ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of 1 1HKU-ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of 1 HKU-ZIRI Lab Physical Internet, of Industrial Hong Kong, Pokfulam Road, Hong Department Kong (e-mail: HKU-ZIRI Lab for for Physical Internet, Department of [email protected]) Industrial and and Manufacturing Manufacturing Systems Systems Engineering, Engineering, The The University University of of Hong Kong, Pokfulam Road, Hong Kong (e-mail: [email protected]) Hong Kong, Pokfulam Road, Hong Kong (e-mail: [email protected]) 2 Hong Kong, Pokfulam Road, Hong Kong (e-mail: [email protected]) Department of Transportation Economy and Logistics Management, College of Economics, Shenzhen University, Shenzhen, 2 2 2Department of Transportation Economy Department of Transportation Economy and Logistics Management, College of Economics, Shenzhen University, Shenzhen, P.R. China [email protected]) Department of (e-mail: Transportation Economy and and Logistics Logistics Management, Management, College College of of Economics, Economics, Shenzhen Shenzhen University, University, Shenzhen, Shenzhen, P.R. China (e-mail: [email protected]) P.R. China (e-mail: [email protected]) P.R. China (e-mail: [email protected]) Abstract: This paper is motivated from a rapidly expanding express delivery service provider that has Abstract: This from expanding express service provider that Abstract: This paper is motivated from rapidly expanding express delivery service provider that has recently itsmotivated express logistics center in one region with delivery high demand is planning to Abstract:reengineered This paper paper is is motivated from aaa rapidly rapidly expanding express delivery serviceand provider that has has recently reengineered its express logistics center in one region with high demand and is planning to recently reengineered its express logistics center in one region with high demand and is planning to reproduce such operations in other regions with seasonal and volatile demands. A simulation model is recently reengineered its express logistics center in one region with high demand and is planning to reproduce such operations in other regions with seasonal and volatile demands. A simulation model is reproduce such operations in other regions with seasonal and volatile demands. A simulation model is built to enable a reconfigurable express logistics center. Three main control policies: sequencing, order reproduce such operations in other regions with seasonal and volatile demands. A simulation model is built aaa reconfigurable logistics center. Three main control policies: sequencing, order built to to enable enable reconfigurable express logistics To center. Three the main controlfindings policies:from sequencing, order releasing and machine flexibilityexpress are considered. generalize research the simulation built to enable reconfigurable express logistics center. Three main control policies: sequencing, order releasing and machine flexibility are considered. To generalize the research findings from the simulation releasing and machine machine flexibility are considered. considered. To generalize generalize theout research findings from the the simulation study, experimental design technique has been applied to carry sensitivity analyses by simulation using full releasing and flexibility are To the research findings from study, experimental design technique been carry study, experimental design technique has been applied to carry out out sensitivity sensitivity analyses analyses by by using using full full combination of control factors, demandhas orders andapplied systemto parameters. study, experimental design technique has been applied to carry out sensitivity analyses by using full combination of control factors, demand orders and system parameters. combination of of control control factors, factors, demand demand orders orders and and system system parameters. parameters. combination © 2017, IFAC (International of Automaticand Control) Hosting by Elsevier Ltd.ofAll rights reserved. Keywords: Express logisticsFederation center, Configuration control, Simulation, Design experiments Keywords: Express logistics center, Configuration and control, Simulation, Design of experiments Keywords: Express logistics center, Configuration and control, Simulation, Design of experiments Keywords: Express logistics center, Configuration and control, Simulation, Design of experiments
1. INTRODUCTION 1. INTRODUCTION 1. INTRODUCTION 1. INTRODUCTION In express delivery industry, express centres play the In delivery industry, express play In express delivery industry, centres play the significant in an organization’s businesscentres decision-making In express expressrole delivery industry, express express centres play the the significant role in an organization’s business decision-making significant role in an organization’s business decision-making processes and have a long-term impact on the support for significant role in an organization’s business decision-making processes have the support for processes and have aaa long-term long-term impact on the 2009). support The for enterprise and achievements (Kathy impact Roper on et al. processes and have long-term impact on the support for enterprise achievements (Kathy Roper et al. 2009). The enterprise achievements (Kathycentres Roperiset etnotal. al. only 2009). The challenge for today’s express howThe to enterprise achievements (Kathy Roper 2009). challenge for express is challenge for today’s today’s express centres centres is not not only only how to to handle colossal order fulfilment commitments, but how challenge for today’s express centres is not only how to handle order fulfilment commitments, how to handlea colossal colossal order fulfilment commitments, but howthey to draw competitive advantage from the way in but which handle colossal order fulfilment commitments, but how to draw a competitive advantage from the way in which they draw a competitive advantage from the way in which they flexibly respond to dynamic variations (Apte and draw a competitive advantage from the way in which they flexibly respond and flexibly respond to dynamic variations (Apte and Viswanathan 2000).to the core variations component (Apte of express flexibly respond toAs dynamic dynamic variations (Apte and Viswanathan 2000). As the core component of express Viswanathan 2000). As the core component of express logistics center, the sorting system is of crucial importance. It Viswanathan 2000). As the core component of express logistics center, the sorting system is of crucial importance. logistics center, the sorting system is of crucial importance. It determines whether a physical commodity that traded via the logistics center, the sorting system is of crucial importance. It It determines aaa physical commodity that traded via determines whether whether physicalsmoothly commodity thatbuyer traded via the the e-commerce can be delivered to the or not. determines whether physical commodity that traded via the e-commerce e-commerce can can be be delivered delivered smoothly smoothly to to the the buyer buyer or or not. not. e-commerce can be delivered smoothly to the buyer or not. However, there are several decision variables concerned with However, there are However, theresystem are several several decision variables concerned with the sorting in decision express variables centres concerned in termswith of However, there are several decision variables concerned with the sorting system in express centres in terms the sorting sorting and system in policies expresssuch centres in number terms of of configuration control as optimal the system in express centres in terms of configuration control such of configuration and control policies such as optimal number of sortation slots,and placement of the induction stations,number and order configuration and control policies policies such as as optimal optimal number of sortation slots, placement of the induction stations, and order sortation slots, placement of the induction stations, and order release mechanism. Moreover, these design decisions involve sortation slots, placement of the induction stations, and order release design decisions involve release mechanism. Moreover, these design decisions involve complex trade-offs Moreover, where the these parameters making these release mechanism. mechanism. Moreover, these design for decisions involve complex trade-offs where the parameters for making complex trade-offs where the parameters for making these trade-offs are often not well understood. Practical complex trade-offs where the parameters for making these these trade-offs often not trade-offs are are often not well wellmoreunderstood. understood. Practical uncertainties make the problem complicatedPractical (Kong, trade-offs are often not well understood. Practical uncertainties make the problem uncertainties make The the uncertainties problem more more complicated (Kong, Chen et al., 2016). cancomplicated be produced (Kong, by the uncertainties make the problem more complicated (Kong, Chen et al., 2016). The uncertainties can be by Chen et al., al., 2016). 2016). The uncertainties uncertainties candisruptive be produced produced by the the unexpected booming demands orcan machine Chen et The be produced by the unexpected booming demands or disruptive machine unexpected which booming demands or performance disruptive and machine breakdowns all affect the actual incur unexpected booming demands or disruptive machine breakdowns which affect the actual and breakdowns which all affect the actual performance and incur costs. The decision support for facility managers breakdowns which all all affect is therequired actual performance performance and incur incur costs. The decision support is required for facility managers costs. The decision support is required for facility managers who need to prioritize what is important rather than simply costs. The decision support is required for facility managers who prioritize what is than simply who need to prioritize what is important rather than simply urgent to to gain maximum Hence, proactive who need need to prioritize what effectiveness. is important important rather rather than simply urgent to gain maximum effectiveness. Hence, proactive urgent to gain maximum effectiveness. Hence, proactive decision makers are more concerned with identifying specific urgent to gain maximum effectiveness. Hence, proactive decision are concerned identifying specific decision makers makers are more more concerned with identifying specific performance trends or levels and with analysing trade-offs in decision makers are more concerned with identifying specific performance trends or levels and analysing trade-offs in performance trends or ormanagement levels and and and analysing trade-offs in operations to quantify service trade-offs (Kong, Fang performance trends levels analysing in operations to quantify management and service (Kong, Fang operations to quantify quantify management management and and service service (Kong, (Kong, Fang Fang et al., 2015). operations to et et al., al., 2015). 2015). et al., 2015). Many researchers have paid great attention on designing and Many have designing Many researchers have paid great attention on designing and implementing cost-effective orderattention sortationon for Many researchers researchers have paid paid great great attention onapproaches designing and and implementing cost-effective order sortation approaches implementing cost-effective order sortation approaches for manufacturing factories and logistics warehouse (Bozer and implementing cost-effective order sortation approaches for for manufacturing factories and logistics warehouse (Bozer and manufacturing factories and logistics warehouse (Bozer and Sharp 1985, Bard et al. 1993, Johnson and Lofgren 1994, manufacturing factories and logistics warehouse (Bozer and Sharp Sharp 1985, Bard et al. 1993, Johnson and Lofgren 1994, Sharp 1985, 1985, Bard Bard et et al. al. 1993, 1993, Johnson Johnson and and Lofgren Lofgren 1994, 1994,
Galbreth and Blackburn 2006). However, the sortation Galbreth and Blackburn 2006). sortation Galbreth that and integrates Blackburndesign 2006). However, the sortation operation and However, operational the decisions for Galbreth and Blackburn 2006). However, the sortation operation that integrates design and operational decisions operation that integrates integrates design and operational operational decisions decisions for for express logistics is rarelydesign discussed. operation that and for express express logistics logistics is is rarely rarely discussed. discussed. express logistics is rarely discussed. Reconfiguration express center (REC) is thus put forward for Reconfiguration express center (REC) thus forward for Reconfiguration express center (REC) is thus put forward for sortation operations, which involves theis design Reconfiguration express center (REC) issystem thus put putlayout forward for sortation operations, which involves the system layout design sortation operations, which involves the system layout design and control policies evaluation to provide flexible responses sortation operations, which involves the system layout design and control policies to flexible and control policies evaluation to provide flexible responses to The significances of REC forresponses sortation anddynamic control changes. policies evaluation evaluation to provide provide flexible responses to dynamic changes. The significances of REC for to dynamic changes. The significances of REC for sortation operations can be summarized as follows. Firstly, both to dynamic changes. The significances of REC for sortation sortation operations can summarized follows. operations set-up can be be and summarized asapproaches follows. Firstly, Firstly, both machine control as should both be operations can be summarized as follows. Firstly, both machine control should be machine set-up set-up and control approaches should be reconfigured and and scalable underapproaches uncertainties through machine set-up and control approaches should be reconfigured and scalable under uncertainties through reconfigured and scalable scalable trade-offs. under uncertainties uncertainties through investigating performance REC for sortation reconfigured and under through investigating performance trade-offs. REC for sortation investigating performance trade-offs. RECmore for proactive sortation operations helps facility managers become investigating performance trade-offs. REC for sortation operations helps facility managers become more proactive operations helps facility managers become more proactive when they are faced with increasingly changing customer operations helps facility managers become more proactive when faced increasingly changing customer when they are faced with increasingly changing customer requests, dueare dates and with regulations. Secondly, express centres when they they are faced with increasingly changing customer requests, due dates and regulations. Secondly, express requests, due dates and regulations. Secondly, express centres located in dispersed geographical markets have different requests, due dates and regulations. Secondly, express centres centres located in dispersed geographical markets have different located in dispersed geographical markets have different demand variations and order patterns. These facilities are located in dispersed geographical markets have different demand variations and order patterns. These facilities are demandequipped variations andvarious order system patterns. These facilities are usually with layout, order sequence, demand variations and order patterns. These facilities are usually equipped with various system layout, order sequence, usually size equipped with variousflexibility. system layout, layout, order sequence, batch and with machine RECorder for sequence, sortation usually equipped various system batch size and machine flexibility. REC sortation batch size can and offer machine flexibility. REConfor fordesign sortation operations important guidance and batch size and machine flexibility. REC for sortation operations can offer important guidance on design and operations of can express offer important important guidance on and designacross and operation centres guidance over time operations can offer on design and operation of express centres over time and across operation of express centres over time and across geographical regions. operation of express centres over time and across geographical regions. geographical regions. geographical regions. This paper aims to explore a systematic study of express This paper aims explore aaa systematic study of This paper aims to to with explore systematic studyquestions: of express express centerpaper configuration the following research This aims to explore systematic study of express center configuration with the following research questions: center configuration withanthe the followingcontrol research questions: ⑴ How can we define integrated policy for REC center configuration with following research questions: ⑴ we define policy for ⑴ How How can can wesequencing, define an an integrated integrated control policyflexibility for REC REC including order releasing control and machine ⑴ How can we define an integrated control policy for REC including order sequencing, releasing and machine flexibility including order order sequencing, sequencing, releasing releasing and and machine machine flexibility flexibility strategies? including strategies? strategies? ⑵ How to assess the impact of combined control schemes on strategies? ⑵ assess of control schemes ⑵ How How to toperformance assess the the impact impact of combined combined controlgeographical schemes on on sortation of REC in different ⑵ How to assess the impact of combined control schemes on sortation performance of REC in different geographical sortation performance of REC in different different geographical geographical regions (e.g. South, East of andREC West China)? sortation performance in regions South, East China)? regions (e.g. South, East and and West West China)? ⑶ What(e.g. kinds of managerial insights could be gained from regions (e.g. South, East and West China)? ⑶ What kinds of managerial insights ⑶ What What kinds of of managerial managerial insights insights could could be be gained gained from from results analyses? ⑶ kinds could be gained from results analyses? results analyses? analyses? results We use discrete-event simulation to model REC for sortation We use discrete-event discrete-event simulation to model REC for sortation We to operations. Two main simulation reasons include: (1)REC nonefor related We use use discrete-event simulation to model model REC forofsortation sortation operations.models Two main main reasons include: (1)fornone none of related operations. Two reasons include: (1) of analytical considered were suitable this study due operations. Two main reasons include: (1) none of related related analytical models considered weremathematical suitable for for this this study due analytical models considered were suitable study to REC is intractable in a single model, not to analytical models considered were suitable for this study due due to REC REC is is intractable intractable in in aa single single mathematical mathematical model, model, not not to to to to REC is intractable in a single mathematical model, not to
2405-8963 © IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Copyright © 2017, 2017 IFAC 12272 Peer review© of International Federation of Automatic Control. Copyright 2017 IFAC 12272 Copyright ©under 2017 responsibility IFAC 12272 Copyright © 2017 IFAC 12272 10.1016/j.ifacol.2017.08.1986
Proceedings of the 20th IFAC World Congress Toulouse, France, July 9-14, 2017 Xiang T.R. Kong et al. / IFAC PapersOnLine 50-1 (2017) 11770–11775
mention to obtain the optimal results within feasible solution time and (2) simulation provides a user-friendly interface for logistics professionals while it is a powerful analysis tool to capture the complexities of the real world to a reasonable level, which allows implementation of what-if, sensitivity scenarios and alternatives comparison on a computer (Marinov and Viegas 2009). Next section, the related works will be reviewed. The description of motivating case and associated challenges are illustrated in section 3. In section 4, simulation experiments are conducted for analysing sortation operations. Results analysis and managerial insights are obtained in section 5. Finally, observations and contributions are synthesized and suggestions are made for future developments. 2. LITERATURE REVIEW The problem of sorting system design and control has been discussed in past decades in manufacturing, logistics and service industries. Cost and efficiency analysis of order sorting system have been evaluated (Galbreth and Blackburn 2006). The impacts of number and length of sorting slots, and optimal order-to-lane assignment as well as sorting strategies on order sorting performance have examined (Bozer and Sharp 1985, Bard et al. 1993, Johnson and Lofgren 1994). Johnson and Meller (2002) comprehensively investigated the induction processes of split-case sorting system. Jarrah and Bard (1994) presented three interrelated models to find a minimum cost configuration for a typical general mail facility associated with daily machine schedules. Adjusting a single control parameter may not change the recommended system configuration while varying several parameters simultaneously has a critical influence on the system. The impacts of alternative policies and configurations are simulated and quantified in the design and control of a logistics order picking system (Manzini, Gamberi et al. 2005). These authors also argued that an improved customer service can be achieved through effective order releasing. Moreover, it exists the trade-offs between order releasing mechanisms, work-in-process inventory and ontime completion (Chan, Humphreys et al. 2001). It is also worth noting that a balance must be struck between the resulting uncertainty a firm faces and the use of flexibility that can accommodate uncertainty. There is the potential to improve sorting productivity by utilizing some flexibility strategies such as machine flexibility. Design of experiments is a useful technique with a series of tests for improving process design (Ekren, Heragu et al. 2010, Chackelson, Errasti et al. 2013), which provides the observation and analysis through inputs, processes and outputs variations. Computer simulation is often employed during the experimentation phase for assisting managers and engineers in the design of a new system. The relative better design alternative can be obtained through the comparison of simulation results before the system is actually implemented. According to Bard (1997), discrete event simulation is able to test alternative solutions for express delivery service efficiently, especially when process uncertainties and interdependencies occur. Meanwhile, simulation is one of the
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effective tools to evaluate control mechanisms under demand variations (Chan and Chan 2005). The facility design and sequencing/dispatching controls of manufacturing shop-floor or logistics warehouse have been examined. However, the systematic study integrating both design and operational issues in the express center is still scarcely. Problem also remains to be solved on determining the optimal quantity and position of sorting slots in the express sorting system. 3. MOTIVATING SCENARIO The motivating case comes from one of the largest express delivery enterprises in China; it operates around 200 express centres and employs 90,000 people. The company currently employs manual sorting system (MSS) and all express freights are sorted while picked manually from a line-type conveyor. Recently, the firm has realized the importance of express center configuration for order sortation. Its senior managers want to know if changing its facilities configuration and control would enable it to achieve significant cost savings and service enhancement. Therefore, it commences the design and pilot test of a new semi-automated sorting system (ASS) to fulfil the increasingly complicated commitments in the existing express centres. The new system is equipped with advanced Internet-of-things (IoT) sensors such as automated bar-code sorters and it can accurately identify and dispatch express freights. In addition, the information control system of ASS is linked with enterprise high-level management system. Thus, the real-time freight information can be traced and tracked. Figure 1 illustrates the reengineered express logistics center layout and workflow with ASS. It can be considered as a queuing system along with inbound dock, receiving zone, sorting zone, dispatching zone and outbound dock. Particularly, sorting zone consists of both manual sorting area and automated sorting area. The detailed processes and management issues have been defined as follows. A few parts of outgoing express freights are cross-docked directly to the destination and other parts of freights have to be processed through the ASS in fixed sorting waves commonly ranging from 30 minutes to 2 hours. The orders are mainly sorted using a high-speed conveyor network, a control system and barcodes on the individual package. The next available sorting strategy is used in current system (Johnson 1997). It is a rule which assigns the next available order to a sorting lane with little blocking. Based on this sorting strategy, the control system decides whether to divert the orders into the proper sorting slots based on the congestion in the sorting lane or push the orders for a round trip on the dog-track until it can be sorted. However, the existing field management totally depends upon the experience and skills of managers and the operations fulfil the daily commitments in the front-line with little regard to what was planned.
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The accuracy of models plays an essential role in developing a successful simulation study. A simulation study consists of a series of steps and tasks that need to be followed in a specific order, which are often performed in an interactive and iterative manner. We choose FlexSim® 6.0 to evaluate the configuration comparison and combined effect of various control policies on REC for sortation operations. This commercial software is particularly accurate in simulating real logistics terminals and analysing a mass of data with intelligent object-based development architecture.
and can be ignored. Therefore, we can use the simulation model to represent the practical sorting process in an express logistics center. 4.1 Control Policies in REC 4.1.1 Order Releasing Immediate order release (IOR) to the sorting machines as soon as it arrives at express center may sometimes give it a leading edge on its due dates and reduce the level of inventory, but it may make operational site more congested. A superior mechanism therefore may be one which results in short order lead time for a given level of WIP (Chan, Humphreys et al. 2001). It can be argued that other order release mechanisms are also applicable to a typical order sorting system. Constant work in process (CWIP) is a mechanism that a new order will not be launched unless a place in the system has been vacated for it. In another words, the number of orders in the system is maintained constant with equal total input and output rate under this releasing rule. 4.1.2 Order Sequencing A large number of awaiting orders should be batched and sequenced before entering the sorting system. Related policies discussed in this paper are first-come-first-served (FCFS) and nearest due date first (DD). At many times, FCFS is made necessary by the nature of the process. According to service standards of same-day-delivery, nextday-delivery and 2-day-delivery, DD is used for scheduling within the fixed due dates.
Fig. 1. Reengineered express centre configuration with ASS 4. SIMULATION EXPERIMENTS The simulation tasks are divided into three phases: simulation preparation, model building/implementation, and experimental design and results analysis. In this paper, we focus on investigating the impact of independent variables on dependent variables. We adopt the triangular distribution due to it is a good approximation to use in the absence of data, especially if a minimum, maximum, and most likely value can be estimated (Masmoudi, Chtourou et al. 2007). The assumptions for the simulation model are as follows: Batch sizes/types of express freight in each sorting time window are already known. Setup time for freight is negligible and equal to zero. Arrival interval of express freight conforms to triangular distribution. There is sufficient buffer space for freight before and after sorting. The efficiency of machine and personnel adopts averaged default values. All the input data used for the simulation are collected from the real operations at the case company and have received the feedback from managers. The average demands in peak period in South China, East China and West China are 71,000 pieces, 62,000 pieces and 42,000 pieces respectively; and 55,000 pieces, 47,000 pieces and 40,000 pieces in non-peak period. The developed models and simulation runs are iteratively piloted, which have also been validated by the firm. To further validate our model, we compare the statistics of the throughput time for scenario MSS to the real time performance in September 2013. The relative error is small
4.1.3 Machine Flexibility A combined assessment of flexibility along with order release mechanisms and sequencing rules can be examined. In this paper, we focus on examining a measure of flexibility defined as machine flexibility. It intends to provide alternative options for decision-makers to decide which sorting slot in which position should be open to replace the one that has been disrupted. Related policies examined are no machine flexibility (NULL) and eliminate/ reallocate a random slot (OR). 4.2 Design of Experiment The simulation is conducted to evaluate the circle-type ASS sortation system design with combined control factors and various demand patterns in South China, West China and East China. Estimates for the daily input profiles are based on measurements of a medium-sized express center on consecutive seven days in September 2013, which were selected by the firm to be representative examples of the future average daily throughput of 2015 and have been modified slightly to protect company interests. Data related to machine efficiency and costs are based on supplier information. The simulation model is assumed to be a nonterminating system and the steady state analysis could be conducted. The length of the simulation run (10080 min) was selected and first 2880-min is used as warm-up period. In order to collect statistically reliable data and reduce the random effects, each simulation run has 10 independent replications.
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Specifically, we look at four basic factors in the simulation and each factor has two levels. Full factorial design with 2n factor-level for each performance measure are selected and conducted in this paper since it is very efficient forms of experimentation (Chackelson, Errasti et al. 2013). A total of 160 (24×10) response values are employed to determine the main and interaction effects of the factors on performance measurements. Table 1 presents four factors and associated factor levels. Table 1 Factors and levels of factors used in the experiments Factors Order releasing Machine flexibility Local sequencing Demand Pattern
Factor Levels Immediate order release (IOR) Constant work in process (CWIP) No machine flexibility (NULL) Eliminate a random slot and reallocate the workload with other random slot (OR) First-come-first-served (FCFS) Nearest due date first (DD) High seasonal demand with high demand variance (HDHV) Medium/Low seasonal demand with medium/low demand variance (MLDMLV)
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5. RESULTS AND DISCUSSION 5.1 Demand Patterns As pointed out by Lau, Xie et al. (2008), the factor of demand pattern (Dt) of express freight at period t is generated by the following formula:
2 t Dt base season sin noise snormal () SCycle where base is the approximated value close to average demand; SCycle is the seasonal trend of the demand, and is 365 days in simulations; noise represents the demand variance; snormal() is a standard normal random number generator. The other parameters are characteristic parameters for the demand generators. Two demand patterns (HDHV, MLDMLV) are used in simulations representing different combinations of demand generators (A1, A2, A3, A4, A5, A6). Table 2 (a) Demand generators and their characteristics Demand
Base
Season
Noise
A1
71,000
457,000
7500
A2
55,000
422,000
4200
A3
62,000
412,000
2500
A4
47,000
334,000
1800
A5
42,000
82,000
4900
A6
40,000
29,000
563
Generator
(b) Demand pattern of a typical express logistics center in South China, East China and West China
Fig. 2. The simulation model of circle-type ASS In this study, the measurement variables considered are WIP inventory, cycle time, and capacity utilization with costs assumed to be constants. Therefore, the value in business cycle (VBC) is regulated by the function: VBC = f (P1, P2, P3); Where, P1 = WIP Inventory = (Input order – total order processed) / (Input order) * 100%; P2 = Cycle time = ∑ (Queue time, Sorting time, Move time); P3 = Capacity utilization = (Actual capacity / Normal capacity) * 100%. Regarding to P1, it shows the number of entities remaining in the system (WIP inventory in sorting system) and the higher the WIP, the worse the customer service. We calculate the averaged cycle time of a random express order to stand for P2. P3 indicates the averaged utilization of the sorting conveyor and slots.
Demand
EC in South
EC in East
EC in West
Pattern
China
China
China
HDHV
A1
A3
A5
MLDMLV
A2
A4
A6
The characteristics of demand generators are shown in Table 2. Demand generator A1 represents a high seasonality and extreme variability while A2 signifies a slightly smaller seasonality and very big variability in South china. A3 and A4 represent a high seasonality and big variability, and a relatively smaller seasonality with medium variability in East China respectively. In West China, a high seasonality and boomed variability (A5) as well as a low seasonality and small variability (A6) are illustrated. Two demand patterns (HDHV, MLDMLV) representing the different combinations of demand generators were used in this study. 5.2 The Impact of Combined Control Policies on Sorting Performance of REC The main focus of this section is on studying the combined control schemes on sorting performance in different regional express logistics centres.
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5.2.1 REC in South China During the peak-hour period, Figure 3(a) shows the control combinations are fluctuated with demands change in four fixed processing time window. In the first time window, the lowest WIP inventory is achieved by using CWIP_FCFS_NULL. CWIP_FCFS_OR mechanism achieves the relative second lowest WIP inventory in the following time window. The opposite performance is measured during employing IOR_DD_NULL. From the analysis of overall trend of performance curve in Figure 4(a), the CWIP scheme is more effective to reduce WIP inventory compared with IOR while the control combinations of sequencing. The overall utilization is in the shape of smile curve with the bottom point from 79% to 87%. Figure 3(b) signifies the group of CWIP_FCFS_OR are the preferable management policy in South China under nonpeak period, considering the indicator of utilization ratio. Another interesting result is the codomain of utilization in peak-period is around 90% while reduces to 70% during nonpeak cycle. The insights can be obtained that system utilization is directly changed with the demands fluctuation while the control combination is not varied too much.
WIP inventory of ASS during non-peak period is illustrated in Figure 4(b), where an inverse V-shape can be identified with maximized inventory point. CWIP_FCFS_NULL can reduce WIP inventory most effectively among all policy combinations. It justifies that the effect of machine flexibility NULL on this operational performance is upper than the OR strategy. Due to the demands decreasing, the utilization falls again within the range between 40% and 50%. 5.2.3 REC in West China Although several control combinations have impact on utilization, the best option is still CWIP_FCFS_OR scheme in peak-hour period. However, the utilization of ASS slumps into the lowest utilization due to the decreasing demands, less than thirty percent, as seen Figure 5. Another interesting result is that control combination of IOR_FCFS_NULL outperforms CWIP_FCFS_OR under demands at a relatively low level.
(a) ASS utilization ratio during peak-hour period
(a) WIP inventory and utilization ratio of ASS during peak-hour period
(b) WIP inventory and utilization ratio of ASS during non-peak period Fig. 4. Performance evaluation of REC in East China under combined control policies (b) ASS utilization during non-peak period Fig. 3. Performance evaluation of REC in South China under combined control policies 5.2.2 REC in East China Figure 4(a) shows the interaction between control combinations and ASS utilization ratio during peak-hour period. The trend of utilization is almost consistent, fluctuating between 60% and 70%. CWIP_FCFS_OR is still on the top compared with other control groups. 12276
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(a) ASS utilization ratio during peak-hour period
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REFERENCES
(b) ASS utilization ratio during non-peak period Fig. 5. Performance evaluation of REC in West China under combined control policies 5.3 Managerial Implications The experimental design also provides several generalizable findings as follows: The estimated increasing and decreasing trend of WIP inventory is greatly affected by control policies in either busy or slack season. Facility managers could significantly reduce the WIP inventory through adjusting the control combinations. The system utilization is directly varied with the demand fluctuations while the according control combination is not varied too much. The performance results of cycle time during the peak period and non-period almost conform to the utilization measurement. The CWIP scheme is more effective to reduce WIP inventory compared with IOR while the control combinations of sequencing and machine flexibility impose different effects under volatile demands. The control combination of CWIP_FCFS_OR is almost the best policy to preserve the lowest inventory and relative high utilization in the majority of regional express centres. 6. CONCLUSIONS Considering the real challenges in existing express center, this study analyses configuration and control alternatives for sorting in different regional express centres by evaluating three key performance indicators (WIP inventory, cycle time and system utilization). This systematic decision support is required especially for express center managers who need to prioritize what is important rather than simply urgent in order to gain maximum effectiveness. Nowadays, Chinese express delivery industry seeks to improve logistics service standards with right quantity, right time and right products for end customers. Thus it is more important to guarantee a minimum WIP inventory in express centres than the highest system utilization. For this particular case, the main recommendations suggested to the company are to release orders following CWIP scheme rather than general IOR and to implement DD in South China and FCFS in East, West China. The machine flexibility rules (OR, NULL) should be executed along with other two policies dynamically to maintain higher system utilization and shorter cycle time.
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