Integrated scheduling of a hybrid manufacturing and recovering system in a multi-product multi-stage environment with carbon emission

Journal of Cleaner Production 222 (2019) 695e709

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Integrated scheduling of a hybrid manufacturing and recovering system in a multi-product multi-stage environment with carbon emission Shan Lu a, *, Lei Xie b, Li Zhu c, Hongye Su b a b c

Institute of Intelligence Science and Engineering, Shenzhen Polytechnic, Shenzhen, 518055, China State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China Department of Control Science and Engineering, Dalian University of Technology, Dalian, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 October 2018 Received in revised form 24 February 2019 Accepted 1 March 2019 Available online 2 March 2019

The closed-loop network design of a hybrid manufacturing and recovering system plays an important role on improving both the production efficiency and environmental quality, especially under a multiproduct multi-stage scenario. Besides, with the growing awareness of environmental concerns by legislators, decision makers have been forced not only to enhance utilization of the returned products or components but also to control carbon emission of the whole hybrid system. To optimize the closed-loop logistics superstructure design, a multi-objective model integrating the manufacturing and recovering process in a multi-product multi-stage environment is proposed in this research. Both operations cost and environmental influence are regarded as two objective categories. In addition, the carbon emission regulation is considered in the model by integrating and characterizing the energy input and greenhouse output throughout the hybrid system. Finally, a numerical case originated from an electronic assembly plant is presented under different scenarios to illustrate the effectiveness of the proposed approach. A sensitivity analysis is given to compare the impacts of carbon emission and return rate on each subobjective of the logistics and environmental issues. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Hybrid system Carbon emission Superstructure optimization Reverse logistics

1. Introduction With the rapid development of manufacturing industry, especially for smart electronic information products, last decades have seen a huge growth in waste or defective products generated from both manufacturing and consumption process, thus increasing the risk of resource waste and environmental pollution. Profitability, flexibility, and environment friendly are highly regarded as the important strategies that have been adopted to deal with these challenges in a dynamic manufacturing environment. Correspondingly, the management of recoverable defective products or wastes has been increasingly paid attention by both industry and academic. Contrary to the traditional manufacturing topology with material starting from raw materials to final products, the recovering

* Corresponding author. E-mail addresses: [email protected] (S. Lu), [email protected] (L. Xie), [email protected] (L. Zhu), [email protected] (H. Su). https://doi.org/10.1016/j.jclepro.2019.03.009 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

system extracts remaining value from the recycled materials in an environmental friendly way. Thus, the materials in the recovering system constitute a reverse logistics which may go through different reprocessing echelons, such as disassembly and remanufacturing (Agrawal et al., 2015). In many cases, the reverse logistics gives rise to material recovering from any production stages. Both the forward and reverse logistics constitute a hybrid manufacturing-recovering system (Fang et al., 2016), contributing to enhance product profits and reduce negative environmental influence. In general, the decisions under this scheme throughout the overall lifecycle of the products should be integrated in a systematic closed-loop scheduling procedure (Shaharudin et al., 2017). Several literature devoted to reverse logistics focus on the supply chain level where the manufacturing topology is not investigated (Hazen et al., 2011)(Heydari et al., 2017). The complex and unstable nature of manufacturing process would result in defective products or semi-products regularly (Jianzhi et al., 2009), and to some extent, complicates the efforts to model the hybrid manufacturingrecovering system. Production scheduling and decision in such a hybrid closed-loop

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network context is very complex, especially in the presence of dynamic multi-product multi-stage topology. Most work in this area is devoted to strategic design or tactical planning control on a supply chain level (Govindan and Soleimani, 2017). Few related literature under the consideration of the hybrid system on a manufacturing operations level are reported. Another significant condition is that many companies encounter the environmental regulations or legislations, such as “European Union Emission Trading Scheme”(Jia et al., 2016), “Waste Electrical and Electronic Equipment Directive”(Ongondo et al., 2011), and “Eco-indicator 99”(Abreu et al., 2017). These principles enforce manufacturers with the responsibility to control waste discharge or emission, including defective product, manufacturing-induced wastes and greenhouse gases (Klein et al., 2018). Therefore, to achieve a sustainable operations goal, modelling on integrated scheduling of the hybrid system that recognizes the interdependence between economic and environmental dimensions is advocated. Although the methodology applied to closed-loop supply chain would provide a reference, the integrated scheduling of the manufacturing and recovering system in this study still remains a critical issue. First, the hybrid system with closed-loop superstructure considers multiple products and multiple processing stages with diversified production and recovering routes, in which material flows may be interconnected or interwoven with each other. This practical superstructure is originated from many assembly industries, such as electronic and automotive manufacturing. Under this scenario, it’s really challenging to coordinate the decisions on all these processing units and materials. Second, green and sustainable regulation promotes to a carbon efficient manufacturing environment. It calls for an emerging approach to manage environmental influence by embedding the energy inputs (e.g. gazes and steam) and greenhouse outputs (e.g. carbon dioxide and other wastes). This work deals with integrated scheduling of the hybrid manufacturing and recovering system along a multi-product multi-stage closed-loop logistics superstructure and introduces a life-cycle assessment design framework devised to allow carbon efficient manufacturing. The remainder of this work is organized as follows: the literature related to reverse logistics and its integration with production is reviewed in Section 2. The problem concerning the integrated scheduling and carbon assessment is analyzed in Section 3. Subsequently, the research methodology including detailed assumptions, model formulation and solution method are presented in Section 4. A case study and the model computational performance are analyzed in Section 5. Finally, conclusions are provided in Section 6. 2. Literature review According to the American Reverse Logistics Executive Council, reverse logistics is defined as ‘The process of planning, implementing, and controlling the efficient, cost effective flow of raw materials, in-process inventory, finished goods and related information from the point of consumption to the point of origin for the purpose of recapturing value or proper disposal’ (Lu and Bostel, 2007). A number of practical researches in various contexts of recovering system have been conducted from the perspective of manufacturing (Lage Junior and Godinho Filho, 2016) and supply chain (De Giovanni et al., 2016). Among them, several works are devoted to identify specific operations activities inherent to remanufacturing and recycling, and proposed quantitative models to integrate the reverse logistics that arises in the system (Sifaleras et al., 2015)(Su and Lin, 2015). Referring to the research by (Govindan et al., 2015), the methodology in terms of modelling the reverse logistics should include four steps: material collection,

descriptive analysis, category selection, and material evaluation. The existing models can be classified into discrete time model and continuous time model (Tako and Robinson, 2012)(Soleimani and Kannan, 2015). As more frequently seen, discrete time model uses the theory of lot-sizing problem with time slot. For example (Zhang et al., 2012), proposed a Lagrangian relaxation-based approach to optimize a closed-loop supply chain as a capacitated lot-sizing problem considering setup costs, product returns, and remanufacturing. Comparably (Turrisi et al., 2013), analyzed the impact of reverse logistics by a continuous time optimization model at an aggregate level under a dynamic scenario. In the published literature, closed-loop supply chain planning has attracted considerable attentions in academia as well as in industry (Diallo et al., 2017), since its close relationship with reverse logistics from the view of business. The closed-loop supply chain consists of forward and reverse logistics in an integrated pattern (Gaur et al., 2017), and aims at maximizing value creation over the entire life cycle of a product with dynamic return and recovery (Shaharudin et al., 2019). Ilgin and Gupta (2010) classified the quantitative studies of the closed-loop supply chain into six groups of production planning, production scheduling, capacity planning, inventory control, forecasting, and uncertainty management. Despite different options for recovery and return, such as reuse, repair, remanufacturing and refurbishing, the system transformed the defective products into as-good-as new conditions (Stindt and Sahamie, 2014). In this regard, it would extend naturally to modelling mechanism in terms of economic, environmental and legislation concerns. A number of related models can be found in ndez, 2016)(Zohal and deterministic conditions (Dondo and Me Soleimani, 2016)(Xu and Wang, 2018) and under uncertain scenarios using stochastic or fuzzy methods (Keyvanshokooh et al., 2016)(Jiao et al., 2018)(Farrokh et al., 2018). Different from the closed-loop supply chain optimization, reverse logistics and its integration with manufacturing and recovering process would cover various significant factors. A typical area attracting most researches is applied in hybrid manufacturingremanufacturing systems (Polotski et al., 2015). For example, the same facility was used for both manufacturing and remanufacturing so as to enhance the system flexibility. Similarly (Kim et al., 2013), analyzed a hybrid manufacturing-remanufacturing system to capture insights into jointly control of production, remanufacturing, and disposal activities, which can be characterized by three monotone switching curves. Due to the fact that the algorithm iteration procedure may increase dramatically when enlarging the size of the problem (Ahiska et al., 2017), developed a heuristic search technique to determine the parameter values efficiently (Assid et al., 2019). further investigated an unreliable facility using setup operations to switch between manufacturing of new items and remanufacturing of returned item, and developed three joint production and setup control policies to model the coordinated decision problem which was solved using a simulationbased optimization technique. An essential part of efficient decision making on the level of manufacturing operations to coordinate the forward and reverse logistics in a real-world closed-loop manufacturing environment, frequently applied in electronic or automobile factories (Hong and Yeh, 2012), may be a complex multi-product multi-stage superstructure. However, among the existing researches, there is lack of considering the production dynamics in such a multi-product multi-stage environment integrated with the recovering system on a manufacturing operations level. Recently, with more concerns on green manufacturing and environmental protection, there has been a growing awareness on simultaneously enhancing production efficiency and reducing greenhouse emission in manufacturing process (Mikul ci c et al.,

S. Lu et al. / Journal of Cleaner Production 222 (2019) 695e709

2016). To evaluate the environmental burdens associated with a product, process or activity, life cycle assessment(LCA) provides a systematic methodology, which covers the entire life cycle including extracting and processing raw materials, manufacturing, transportation and distribution, reuse and maintenance, recycling and final disposal (Pieragostini et al., 2012). (You et al., 2012) developed a multi-objective mixed-integer linear programming(MILP) model for the optimal design and planning of cellulosic ethanol supply chains, and applied LCA method to cope with economic, environmental, and social objectives (Liu et al., 2014). addressed a LCA based biofuel supply chain with multiconversion pathways, and presented a MILP problem that took economic, energy, and environmental criteria into consideration. The LCA was integrated in the model by dividing each pathway into several individual parts (Lim and Lam, 2016). introduced biomass element life cycle analysis to optimize the biomass supply chain and integrate resources with process feedstock via biomass element characteristic using a simple graphical approach (Crenna et al., 2018). studied a framework model for biotic resources assessment inclusion in LCA by adopting biotic resource renewability indicators and building life cycle inventories. Among the existing work, however, limited efforts have been made on the production planning and scheduling in a hybrid manufacturing and recovering system coupled with LCA. Particularly, few attentions have been paid on energy input and greenhouse output of the manufacturing process to quantitatively assess the environmental influence. As mentioned in the introduction, a dynamic decision model is developed that integrates the manufacturing and recovering system with multi-product multi-stage superstructure in a carbonefficient environment. The reverse logistics within the recovering system contributes to recycling the defective products or materials in a more efficient way. Very few decision models investigated on simultaneously optimizing the forward and reverse logistics under multi-product multi-stage scenarios on a manufacturing operations level. Moreover, in view of previous researches on the various specific areas of reverse logistics, the environmental impact for design of such a hybrid system has not been quantitatively analyzed and optimized before. Therefore, an additional novelty of this work is that the proposed model takes into account of carbon-efficient production, and is integrated with energy input and greenhouse Production Unit A

Raw Material Warehouse

Production Unit B

output model. The practical implication is analyzed through numerical simulation from a real-world application. 3. Problem statement To address the problem formally, the multi-product multi-stage closed-loop superstructure that integrates manufacturing and recovering system is described in Fig. 1. The network is derived from a small or medium sized hybrid manufacturing-recovering plant which has a designated forward and reverse logistics topology based on the bill-of-material of each product. In the forward manufacturing system, each type of product is processed stage-by-stage through a designated production route. As the flexible nature of the manufacturing system, the production routes specified for different products may be intersected. The recovering system mainly consists of collecting and recycling, disassembly, retreatment, recycled dispatching, and final disposal units. The recycled materials which could be either the defective products or the defective semi-products are processed through a given route with several specific recovering units. To model the closed-loop superstructure, each node of the network is formulated with its successors and predecessors depending on the forward and reverse logistics. Generally, it is considered that one or several manufacturing modes will be available for process units in the forward manufacturing system. Each production unit using different types of modes requires energy input and generates greenhouse output. The manufacturing modes differ in terms of acquisition and operational cost as well as energy input and greenhouse output (Chaabane et al., 2012). The principle of input and output model with available manufacturing modes is described in Fig. 2. Transportation for shipment of materials between the nodes of the hybrid system is considered. Similarly, each transportation lot also requires energy input and generates greenhouse output. This work considers that the recycled semi-products are proportional to the quantity of the processed at each period, and similar assumption is applied to the recycled products. The recycled items are disassembled and recovered into usable materials which could be raw materials, semi-products or final products, depending on the respective bill-of-material and the attribute of the materials, while the non-usable materials are treated as industrial wastes Production Unit D

Semi-product Warehouse 1

Production Unit C

697

Production Unit F

Semi-product Warehouse 2

Production Unit E

Final product Warehouse

Production Unit G

Recycled Dispatching Unit

Final Disposal Unit

Retreatment Unit

Disassembly Unit

Fig. 1. Multi-product multi-stage closed-loop logistics superstructure.

Collecting & Recycling Unit

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Energy input

Mode 1 Warehouse Mode 2 ...

Material input

Material output

Mode e Warehouse Mode E

Greenhouse output Fig. 2. Input and output model of a production unit.

which will induce a potential environmental influence. In addition, the discussed problem will be modelled under the assumptions as follows: (1) Demand plan for products is known and deterministic. (2) Raw materials, semi-products, and final products are stored in separated warehouses. (3) Lead time between the adjacent units within the hybrid system is ignored. (4) The capacity of transportation and warehouse is not considered. (5) Backorders are not allowed.

4. Model formulation In this section, the addressed problem is formulated by mixed integer linear programming for scheduling of the manufacturing and recovering system simultaneously in a multi-product multistage closed-loop superstructure. Considering the environmental regulation, the energy input and greenhouse output model is integrated to build a carbon-efficient decision framework. Also, to evaluate the economic and environmental impacts on decision making, a multi-objective programming method is investigated to obtain a satisfactory solution. To describe the aforementioned hybrid system, the following notations are defined. Indices: e manufacturing mode iin energy input imc , i’mc semi-product and final product irc recycled material irm raw material jmc production unit jrc , j’rc recovering unit jwh warehouse within the manufacturing system s external supplier of raw materials

t time period Sets: Eðjmc Þ set for manufacturing modes used by production unit IN set for energy input JM set for production units Mðjmc Þ set for materials manufactured by production unit Mðjrc Þ set for materials reprocessed by recovering unit OUT set for greenhouse output P set for final products R set for raw materials RL set for materials within the recovering system S set for external suppliers of raw material T planning horizon W set for semi-products Parameters:

limc ;jmc ;e;t return rate for material imc manufactured by production unit jmc using mode e dcol distribution rate for the output materials of the collecting t and recycling unit ddsa distribution rate for the output materials of the disassembly t unit dret t distribution rate for the output materials of the retreatment unit cciiin ;t cost of one unit of energy input iin ccrirc cost of treating one unit of the recycled material irc at collecting and recycling unit cdsirc cost of treating one unit of the recycled material irc at disassembly unit cipimc cost of holding one unit of final product imc for one time period cirirm cost of holding one unit of raw material irm for one time period

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ciwimc cost of holding one unit of semi-product imc for one time period cimuntre cost of holding one unit of input material of the colimc lecting and recycling unit for one time period cirluntre cost of holding one unit of input material of the recovirc ering units for one time period cirltre irc cost of holding one unit of output material of the recovering units for one time period cmimc ;jmc ;e manufacturing cost of one unit of material imc by production unit jmc using mode e crrirm procurement cost of one unit of raw material irm from the recovering system crsirm s procurement cost of one unit of raw material irm from external suppliers s csjmc ;e setup cost of production unit jmc using manufacturing mode e ctoiout ;t cost of one unit of greenhouse output cvt cost of buying one unit of carbon credit cfdirc cost of treating one unit of the recycled material irc at final disposal unit crdirc cost of treating one unit of the recycled material irc at recycled dispatching unit ctrirc cost of treating one unit of the recycled material irc at retreatment unit iumc iin ;imc ;jmc ;e utilization factor of energy input iin to manufacture imc by production unit jmc using mode e iurc iin ;irc ;jrc utilization factor of energy input iin to treat irc by recovering unit jrc iutr iin utilization factor of energy input iin for transportation oemc iout ;imc ;jmc ;e emission factor of greenhouse output iout to manufacture imc by production unit jmc using mode e oerc iout ;irc ;jrc emission factor of greenhouse output iout to treat irc by recovering unit jrc oetr emission factor of greenhouse output iout for iout transportation tfjwh ;jmc cost of shipping one unit of material from warehouse jwh to production unit jmc tfjmc jwh cost of shipping one unit of material from production unit jmc to warehouse jwh tfjrc ;j’rc cost of shipping one unit of material from recovering unit jrc to j’rc tfjrc ;jwh cost of shipping one unit of material from recovering unit jrc to the corresponding warehouse jwh COin iin normalization factor to convert energy input iin to carbon dioxide equivalent COout iout normalization factor to convert greenhouse output iout to carbon dioxide equivalent LSimc ;jmc ;e;t lower bound of capacity for production unit jmc to manufacture material imc using mode e MTDimc ;t demand for final product RSmax irm st upper bound of capacity for supplying raw material irm by external supplier s2S RSmin irm st lower bound of capacity for supplying raw material irm by external supplier s2S 2e UBCO upper bound of carbon dioxide equivalent emission t UMmc jmc ;e;t upper bound of capacity for production unit jmc using mode e UMcol jrc ;t upper bound of capacity for the collecting and recycling unit UMdsa jrc ;t upper bound of capacity for the disassembly unit UMret jrc ;t upper bound of capacity for the retreatment unit UMfdp upper bound of capacity for the final disposal unit jrc ;t rdp UMjrc ;t upper bound of capacity for the recycled dispatching unit USimc ;jmc ;e;t upper bound of capacity for production unit jmc to manufacture material imc using mode e

699

Variables: CABt purchase quantity of carbon credits Coniin t consumption quantity of energy input Emiiout t emission quantity of greenhouse output IV wh1 irm ;t inventory level for raw material wh3 IV wh2 imc ;t ,IV imc ;t inventory level for semi-product warehouse 1 and 2 respectively IV wh4 inventory level for final product imc ;t untre coltre IV col imc ;t ,IV irc ;t inventory level for input and output materials of the collecting and recycling unit respectively untre tre IV dsa ,IV dsa irc ;t irc ;t inventory level for input and output materials of the disassembly unit respectively untre tre IV ret ,IV ret irc ;t irc ;t inventory level for input and output materials of the retreatment unit respectively untre IV fdp inventory level for input materials of the final disposal irc ;t unit untre tre IV rdp ,IV rdp inventory level for input and output materials of irc ;t irc ;t the recycled dispatching unit respectively MMimc ;jmc ;e;t manufacturing quantity of material imc by production stage jmc using mode e MP rs irm st procurement quantity of raw material from external supplier MP rr irm t procurement quantity of raw material from recovering system fdp rdp dsa ret MRcol irc ;jrc ;t MRirc ;jrc ;t MRirc ;jrc ;t MRirc ;jrc ;t ,MRirc ;jrc ;t treating quantity of the recycled material at collecting and recycling unit, disassembly unit, retreatment unit, final disposal unit, and recycled dispatching unit respectively Qirm ;jwh ;jmc ;t quantity of raw material shipped from warehouse jwh to production unit jmc Qimc ;jmc ;jwh ;t quantity of semi-product or final product shipped from production unit jmc to warehouse jwh Qimc ;jwh ;jmc ;t quantity of semi-product shipped from warehouse jwh to production unit jmc Qimc ;jmc ;jrc ;t quantity of semi-product or final product shipped from production unit jmc to the recovering unit jrc Qirc ;jrc ;j’rc ;t quantity of recycled material shipped from recovering unit jrc to j’rc Qirc ;jrc ;jwh ;t quantity of recycled material shipped from recovering unit jrc to warehouse jwh XSimc ;jmc ;e;t binary variable that indicates whether material imc is manufactured at production unit jmc Y sirm st binary variable that indicates whether raw material irm is supplied by external supplier s2S

4.1. Objective function The proposed model consists of two objective categories. The first objective attempts to minimize the total operations cost results from various manufacturing and recovering activities. The second objective seeks to minimize the environmental impacts that capture greenhouse emission during the transportation and at the end of the product life cycle.

4.1.1. Objectives for operations cost The operations cost mainly includes those of raw materials, manufacturing, setup, inventory, transportation, recovering, energy input, and greenhouse output. Accordingly, the following functions depict these objectives that are expected to be minimized:

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Z1 ¼ CRP þ CM þ CST þ CIMC þ CIRC þ CCR þ CMT þ CRT þ CDA þCRE þ CFD þ CRD þ CEI þ CGO þ CC (1)

T X X imc 2W t¼1

  wh3 ciwimc , IV wh2 imc ;t þ IV imc ;t

(7)

Holding Cost for Final Product Inventory

 Cost of Raw Material Procurement (denoted CRP): which is the cost to procure raw materials and includes two sources:

T X X imc 2P t¼1

cipimc ,IV wh4 imc ;t

(8)

Cost of raw materials procured from external suppliers T X XX irm 2Rs2S t¼1

crsirm s ,MP rs irm st

(2)

Cost of raw materials supplied from the recovering system T XX

 Inventory Cost within the Recovering System (denoted CIRC): which is the cost of inventory within the recovering system, including all the untreated and treated materials by each recovering unit. Holding Cost for Untreated Material Inventory

crrirm ,MP rr irm t

(3)

i2R t¼1

X

T X

imc 2R∪W∪P t¼1

 Cost of Manufacturing (denoted CM): which is the cost to process throughout the whole manufacturing process.

þ

untre IV ret irc ;t

untre

col cimuntre imc ,IV imc ;t

þ

fdpuntre IV irc ;t

þ

þ

rdpuntre IV irc ;t

T X X irc 2RL t¼1

 untre cirluntre , IV dsa irc irc ;t



(9) Holding Cost for Treated Material Inventory

X

X

T X X

imc 2Mðjmc Þjmc 2JM e2Eðjmc Þ t¼1

cmimc ;jmc ;e ,MMimc ;jmc ;e;t

(4)

T X X irc 2RL t¼1

  fdptre rdptre coltre dsatre ret tre cirltre irc , IV irc ;t þ IV irc ;t þ IV irc ;t þ IV irc ;t þ IV irc ;t (10)

 Cost of Setup (denoted CST): which is the cost to setup due to changeover between different products.

X

T X X

jmc 2JM e2Eðjmc Þ t¼1

csjmc ;e ,XSimc ;jmc ;e;t

(5)

 Inventory Cost within the Manufacturing system (denoted CIMC): which is the cost of inventory within the manufacturing system, including raw materials, semi-products and final products. Holding Cost for Raw Material Inventory T X X irm 2R t¼1

cirirm ,IV wh1 irm ;t

(6)

Holding Cost for Semi-product Inventory

3 X T X X X

tfjwh ;jmc ,Qirm ;jwh ;jmc ;t þ

irm 2Rjwh ¼1 jmc ¼1 t¼1 5 X X X

þ

T X

imc 2Mðjmc Þjwh ¼2 jmc ¼4 t¼1 7 X T X X X

þ

imc 2Mðjmc Þjwh ¼3 jmc ¼6 t¼1

X

3 T X X X

tfjwh jmc ,Qimc ;jwh ;jmc ;t þ

X

T XX

irc 2Mðjrc Þjrc ¼1 t¼1

ccrirc ,MRcol irc ;jrc ;t

(11)

 Cost of Transportation within the Manufacturing System (denoted CMT): which is the cost to transport materials within the system. To model the transportation function and the related constraints, jmc ¼ 1:::7 are labeled as production unit A e G, and jwh ¼ 1:::4 are respectively labeled as raw material warehouse, semi-product warehouse 1e2 and final product warehouse.

tfjmc jwh ,Qimc ;jmc ;jwh ;t

imc 2Mðjmc Þ jmc ¼1 jwh ¼2 t¼1 5 X X X

tfjwh jmc ,Qimc ;jwh ;jmc ;t þ

 Cost of Collecting and Recycling (denoted CCR): which is the cost to collect and ship the recycled materials to the recovering system.

T X

imc 2Mðjmc Þ jmc ¼4 jwh ¼3 t¼1 7 T X X X X imc 2Mðjmc Þ jmc ¼6 jwh ¼4 t¼1

tfjmc jwh ,Qimc ;jmc ;jwh ;t tfjmc jwh ,Qimc ;jmc ;jwh ;t

(12)

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 Cost of Transportation within the Recovering System (denoted CRT): which is the cost to transport recycled materials within the recovering system and to the warehouse. Here, jrc ; j’rc ¼ 1:::5 are labeled as recovering units.

X

3 X T X X

tfjrc ;j’rc ,Qirc ;jrc ;j’rc ;t þ

irc 2Mðjrc Þjrc ¼1 j’rc ¼2 t¼1 5 X T X X X

þ

irc 2Mðjrc Þjrc ¼3 j’rc ¼4 t¼1

X

 Cost of Recycled Dispatching (denoted CRD): which is the cost for recycled dispatching process

4 X T X X

tfjrc ;j’rc ,Qirc ;jrc ;j’rc ;t irc 2Mðjrc Þjrc ¼2 j’rc ¼3 t¼1 3 T X X X X

tfjrc ;j’rc ,Qirc ;jrc ;j’rc ;t þ

irc 2Mðjrc Þjrc ¼5 jwh ¼1 t¼1

(13)

tfjrc ;jwh ,Qirc ;jrc ;jwh ;t

X  Cost of Disassembly (denoted CDA): which is the cost for disassembly process

X

T XX

irc 2Mðjrc Þjrc ¼2 t¼1

cdsirc ,MRdsa irc ;jrc ;t

T XX

irc 2Mðjrc Þjrc ¼3 t¼1

Coniin t ¼

X

X

X

imc 2Mðjmc Þjmc 2JM e2Eðjmc Þ 5  X X col þ iurc iin ;irc ;jrc MRirc jrc t irc 2Mðjrc Þ jrc ¼1 0 3 X X X

crdirc ,MRrdp irc ;jrc ;t

(17)

 Cost of Consuming Energy Input (denoted CEI): which is the cost for energy input consumption to drive manufacturing, recovering and transportation

T XX ciin t¼1

ctrirc ,MRret irc ;jrc ;t

T XX

irc 2Mðjrc Þjrc ¼5 t¼1

(14)

 Cost of Retreatment (denoted CRE): which is the cost for retreatment process

X

701

(15)

cciiin ;t Coniin t

(18)

The consumption quantity of energy input is calculated as:

iumc iin ;imc ;jmc ;e MMimc ;jmc ;e;t fdp

rdp

ret þ MRdsa irc jrc t þ MRirc jrc t þ MRirc jrc t þ MRirc jrc t

X



1

3 7 X X

B C Qirm ;jwh ;jmc ;t þ Qimc ;jwh ;jmc ;t B C B irm 2Rjwh ¼1 jmc ¼1 C imc 2Mðjmc Þ jwh ¼2 jmc ¼4 B C 7 4 5 5 B C X X X X X X tr B þ Qi ;j ;j ;t þ Qirc ;jrc ;j’rc ;t C þiuiin B Cciin ; ct B i 2Mðj Þ j ¼1 j ¼2 mc mc wh C irc 2Mðjrc Þ jrc ¼1 j’rc ¼1 mc mc wh B mc C B C 3 X X X B C @þ A Qirc ;jrc ;jwh ;t

(19)

irc 2Mðjrc Þjrc ¼5 jwh ¼1

 Cost of Final Disposal (denoted CFD): which is the cost for final disposal process

 Cost of Treating Greenhouse Output (denoted CGO): which is the cost to decontaminate greenhouse emission induced by manufacturing, recovering and transportation.

T XX

X

T XX

irc 2Mðjrc Þjrc ¼4 t¼1

fdp

cfdirc ,MRirc ;jrc ;t

(16)

ciout t¼1

ctoiout ;t Emiiout t

The quantity of greenhouse emission is calculated as:

(20)

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Emiiout t ¼

X

X

X

oemc iout ;imc ;jmc ;e MMimc ;jmc ;e;t imc 2Mðjmc Þjmc 2JM e2Eðjmc Þ 5  X X col dsa ret þ oerc iout ;irc ;jrc MRirc jrc t þ MRirc jrc t þ MRirc jrc t irc 2Mðjrc Þ jrc ¼1 0 3 3 X X X X X

þ

þ MRfdp þ MRrdp i j t i j t rc rc



rc rc

1

7 X

B C Qirm ;jwh ;jmc ;t þ Qimc ;jwh ;jmc ;t B C B irm 2Rjwh ¼1 jmc ¼1 C imc 2Mðjmc Þ jwh ¼2 jmc ¼4 B C 7 4 5 5 B C X X X X X X Bþ C Q þ Q oetr B imc ;jmc ;jwh ;t irc ;jrc ;j’rc ;t C iout B C irc 2Mðjrc Þ jrc ¼1 j’rc ¼1 B imc 2Mðjmc Þ jmc ¼1 jwh ¼2 C B C 3 X X X B C @þ A Qirc ;jrc ;jwh ;t

(21) ciout ; ct

irc 2Mðjrc Þjrc ¼5 jwh ¼1

 Cost of Buying Carbon Credits (denoted CC): which is the cost to buy carbon credits when carbon emission exceeds the limit. T X

cvt ,CABt

(22)

t¼1

4.2.2. Capacity of production unit Within the manufacturing system, the capacity of each production unit is limited. A set of manufacturing modes could be selected for each production unit to ensure the technical requirements.

X imc 2Mðjmc Þ

4.1.2. Objectives for environmental issues The evaluation of environmental performance may involve various impacts that aggregate the result. To capture the major impacts that significantly represent the environmental issues, the performance is quantified by converting different impacts by carbon emission equivalent. Accordingly, the carbon emission should be minimized to reduce the environmental influence.

0 1 T X X X out in @ Z2 ¼ Emiiout t COiout þ Coniin t CIiin A t¼1

iout 2OUT

(23)

iin 2IN

MMimc ;jmc ;e;t  UMmc jmc ;e;t

cjmc 2JM ; ce2E; ct2T (27)

4.2.3. Logic constraints on setup and production The relationship between setup and production of each production unit could be quantified by an upper and lower boundary. For a production unit that processes a specific material, only one manufacturing mode should be selected within one period.

X XSimc ;jmc ;e;t  1

cimc ; cjmc ; ct

(29)

ce

LSimc ;jmc ;e;t ,XSimc ;jmc ;e;t  MMimc ;jmc ;e;t  USimc ;jmc ;e;t ,XSimc ;jmc ;e;t

cimc 2Mðjmc Þ; cjmc ; ce; ct

(28)

4.2. Constraints 4.2.1. Capacity of external supplier The supply of raw materials by external suppliers is limited by a minimum quantity that ensures economic lot, and by an upper boundary representing suppliers’ maximum capacity. If one kind of raw material is selected by a supplier, it will be used over the planning horizon.

4.2.4. Capacity of recovering units The recovering system consists of several units, including collecting and recycling, disassembly, retreatment, final disposal, and recycled dispatching. The capacity of each recovering unit is limited.

X max rs s Y sirm st RSmin irm st  MP irm st  Y irm st RSirm st

cirm 2R; cs2S; ct2T (24)

Y sirm st



Y sirm ;s;t1

cirm 2R; cs2S; ct2T

(25)

irc 2Mðjrc Þ

X irc 2Mðjrc Þ

X Y sirm st 2f0; 1g

cirm 2R; cs2S; ct2T

(26)

irc 2Mðjrc Þ

col MRcol irc ;jrc ;t  UM jrc ;t

cjrc 2JM ; ct2T

(30)

dsa MRdsa irc ;jrc ;t  UM jrc ;t

cjrc 2JM ; ct2T

(31)

ret MRret irc jrc t  UM jrc ;t

cjrc 2JM ; ct2T

(32)

S. Lu et al. / Journal of Cleaner Production 222 (2019) 695e709

X irc 2Mðjrc Þ

X irc 2Mðjrc Þ

MRfdp  UMfdp irc jrc t jrc ;t

rdp

cjrc 2JM ; ct2T

rdp

MRirc ;jrc ;t  UM jrc ;t

(33)

cjrc 2JM ; ct2T

(34)

703

4.2.8. Disaggregate inventory constraint 4.2.8.1. Raw material warehouse. The raw materials are supplied from both the external suppliers and the recovering system, and are stored in warehouse to be used for initial processing. wh1 IV wh1 irm ;t ¼ IV irm ;t1 þ

X XX MP rs Qirc ;jrc ;jwh ;t irm st þ jrc ¼5jwh ¼1

s2S

 4.2.5. Demand constraint The qualified products are shipped to the final product warehouse for delivery, and the defective products are shipped to the recovering system. Therefore, it is considered that the quantity of qualified products would satisfy the order demand.

MTDimc ;t 

7 X

X

jmc ¼6 jwh ¼4

Qimc ;jmc ;jwh ;t

cimc ¼ 7:::9; ct

(35)

4.2.6. Recycling resource constraint Due to the uncertainty during the manufacturing process, a certain proportion of materials is not able to meet the quality requirements, and thus will be shipped to the recovering system. Here we assume that for a production unit, the quantity of processed materials shipped to the recovering system is proportional to the production quantity.

X jrc ¼1

Qimc ;jmc ;jrc ;t ¼ limc ;jmc ;e;t ,

X MMimc ;jmc ;e;t

cimc ; cjmc

X jrc ¼1

Qimc ;jmc ;jrc ;t

jrc ¼1

X MMimc ;jmc ;e;t ¼ e

X ¼ limc ;jmc ;e;t , MMimc ;jmc ;e;t

Qirm ;jwh ;jmc ;t

X cirm /imc

cirm ; circ /irm ; ct

Qirm ;jwh ;jmc ;t

MP rr irm t ¼ Qirc ;jrc ;jwh ;t

(42)

circ /irm ; cjrc ¼ 5; cjwh ¼ 1; ct

(43)

4.2.8.2. Semi-product warehouse. Since the closed-loop logistics superstructure presented in Fig. 1 involves two different warehouses for semi-products, material balance for each semi-product inventory should be modelled separately. Input materials of the semi-product warehouse come from both the predecessor production units and the recovering system. On the other hand, output materials are shipped to both the successor production units and the recovering system.



cimc ; cjmc

X

3 X X

Qimc ;jmc ;jwh ;t þ

jmc ¼1 jwh ¼2 5 X

jwh ¼2 jmc ¼4

Qimc ;jwh ;jmc ;t

X X jrc ¼5jwh ¼2

Qirc ;jrc ;jwh ;t

cirm ; circ /irm ; ct

ce

Qimc ;jmc ;jrc ;t ¼ limc ;jmc ;e;t ,

(41)

cjwh ¼ 1; cjmc

¼ 1; :::3; ce; ct

(36)

(44)

¼ 4; 5 ; ct X

jwh ¼1 jmc ¼1

wh2 IV wh2 imc ;t ¼ IV imc ;t1 þ

ce

¼ 1:::3; ct

3 X X

(37)

X MMimc ;jmc ;e;t

wh3 IV wh3 imc ;t ¼ IV imc ;t1 þ

cimc ; cjmc

ce

¼ 6; 7 ; ct



(38)

X

5 X X

Qimc ;jmc ;jwh ;t þ

jmc ¼4 jwh ¼3 7 X

jwh ¼3 jmc ¼6

Qimc ;jwh ;jmc ;t

X X jrc ¼5jwh ¼3

Qirc ;jrc ;jwh ;t

cimc ; circ /imc ; ct (45)

4.2.7. Relationship between manufacturing and warehouse For a production unit, the qualified processed materials are shipped to the corresponding warehouse according to the manufacturing system topology. Similarly, for semi-product warehouse, the stored materials are shipped to the successor production units.



 X 1  limc ;jmc ;e;t , MMimc ;jmc ;e;t ¼ Qimc ;jmc ;jwh ;t

cimc ;

4.2.8.3. Final product warehouse. For the final product warehouse, input materials come from both the terminal production units and the recovering system, and output materials are shipped to the customers.

cjmc /jwh ; cjwh ; ct

(39)

ce

X MMi’mc ;jmc ;e;t ¼ ce

X cimc /i’mc

Qimc ;jwh ;jmc ;t

cjwh ¼ 2; 3; cjmc

wh4 IV wh4 imc ;t ¼ IV imc ;t1 þ

¼ 4; :::7; ct (40)

7 X X

Qimc ;jmc ;jwh ;t þ

jmc ¼6 jwh ¼4

MTDimc ;t

X X jrc ¼5jwh ¼4

Qirc ;jrc ;jwh ;t

cimc ¼ 7:::9; circ /imc ; ct (46)

704

S. Lu et al. / Journal of Cleaner Production 222 (2019) 695e709

4.2.8.4. Collecting and recycling unit. For the collecting and recycling unit, the input materials come from the defective semiproducts or final products, and output materials are shipped to the successor recovering units including disassembly and retreatment units. According to the recovering system topology, the output materials of the collecting and recycling unit may have two distribution channels: one leading to disassembly unit, and the other one leading to retreatment unit. Here a parameter dcol t is used to indicate the distribution rate. untre

untre

IV col imc ;t

¼ IV col imc ;t1 þ X

jmc jrc ¼1 t¼1

MRcol irc ;jrc ;t jrc ¼1



tre

tre

col IV col irc ;t ¼ IV irc ;t1 þ



T XX X

T X

X

jrc ¼1

circ ;

(47)

1

tre

tre

T X



irc 2Mðjrc Þjrc ¼1 j’rc ¼2

circ ; ct

(55)

X

X

MRret irc ;jrc ;t 

jrc ¼3

irc 2Mðjrc Þjrc ¼3 j’rc ¼4

circ ;

Qirc ;jrc ;j’rc ;t

5 X X

ct

(56)

t¼1

ct

(48)

ret dret circ 2Mðjrc Þ; cjrc ¼ 3; cj’rc t ,MRirc ;jrc ;t ¼ Qirc ;jrc ;j’rc ;t

¼ 5; ct

¼ 3; ct 

Qirc ;jrc ;j’rc ;t

irc 2Mðjrc Þ jrc ¼1 j’rc ¼3 t¼1

MRret irc ;jrc ;t

ret IV ret irc ;t ¼ IV irc ;t1 þ

3 X X

col dcol circ 2Mðjrc Þ; cjrc ¼ 1; cj’rc t ,MRirc ;jrc ;t ¼ Qirc ;jrc ;j’rc ;t

dcol t

X

2 X X T X

jrc ¼3

cimc ; cimc /irc ; ct

X

X

untre

¼ IV ret irc ;t1 þ 

t¼1



untre

IV ret irc ;t

Qimc ;jmc ;jrc ;t

MRcol irc ;jrc ;t 

Qirc ;jrc ;j’rc ;t

4.2.8.6. Retreatment unit. For the retreatment unit, the input materials are shipped from two distribution channels: one directly from collecting and recycling unit, and the other one from disassembly unit. Similarly, the output materials are shipped by two distribution channels: one leading to the final disposal unit, and the other one leading to the recycled dispatching unit.

(49)

(57)

  ,MRret 1  dret t irc ;jrc ;t ¼ Qirc ;jrc ;j’rc ;t

circ 2Mðjrc Þ; cjrc ¼ 3; cj’rc

¼ 4; ct

,MRcol irc ;jrc ;t ¼ Qi

’ rc ;jrc ;jrc ;t

circ 2Mðjrc Þ; cjrc ¼

1; cj’rc

(58)

¼ 2; ct (50)

4.2.8.5. Disassembly unit. Similarly, the input and output material balance for the disassembly unit are modelled separately. The input materials come from the collecting and recycling unit, and output materials are shipped by two distribution channels: one leading to the retreatment unit, and the other one leading to the final disposal unit. untre

IV dsa irc ;t

X

untre

¼ IV dsa irc ;t1 þ 

X

T XXX

irc 2Mðjrc Þjrc ¼1j’rc ¼2 t¼1

MRdsa irc ;jrc ;t

Qirc ;jrc ;j’rc ;t

circ ; ct

(51)

jrc ¼2

tre

IV dsa irc ;t

tre

¼ IV dsa irc ;t1 þ 

T X

X

MRdsa irc ;jrc ;t 

jrc ¼2

Qirc ;jrc ;j’rc ;t

circ ;

X

4 X X

4.2.8.7. Final disposal unit. Here the final disposal unit deals with useless wastes through proper disposal processes. Only a part of output materials of the disassembly unit and the retreatment unit will be shipped for final disposal. fdpuntre

IV irc ;t



¼ Qirc ;jrc ;j’rc ;t



1  ddsa ,MRdsa t irc ;jrc ;t ¼ Qirc ;jrc ;j’rc ;t

irc 2Mðjrc Þ jrc ¼2 j’rc ¼4 t¼1

MRfdp irc ;jrc ;t

Qirc ;jrc ;j’rc ;t

circ ; ct

(59)

irc 2Mðjrc Þjrc ¼2 j’rc ¼3

ct

untre

(52)

IV rdp i ;t rc

rc

X jrc ¼5

circ 2Mðjrc Þ; cjrc ¼ 2; cj’rc

¼ 3; ct

rdptre

¼ IV irc ;t1 þ 

(54)

T X t¼1

T XXX

irc 2Mðjrc Þjrc ¼3j’ ¼5 t¼1 rc

Qi

’ rc ;jrc ;jrc ;t

MRrdp circ ; ct irc ;jrc ;t

(53) IV irc ;t

X

untre

¼ IV rdp þ i ;t1 

rdptre



3 X X T X

4.2.8.8. Recycled dispatching unit. Here the recycled dispatching unit refers to a recovering unit where the qualified output materials of its predecessor units are packaged and dispatched to the warehouse within the manufacturing system. Since these materials are treated through different recovering units, e.g., disassembly unit or retreatment unit, they are sorted and shipped respectively to different warehouses within the manufacturing system.

circ 2Mðjrc Þ; cjrc ¼ 2; cj’rc

¼ 4; ct

X jrc ¼4

t¼1 dsa ddsa t ,MRirc ;jrc ;t

X

fdpuntre

¼ IV irc ;t1 þ

X

rdp

MRirc ;jrc ;t 

jrc ¼5

Qirc ;jrc ;jwh ;t circ ; ct

(60)

X

4 X X

irc 2Mðjrc Þjrc ¼5 jwh ¼1

(61)

S. Lu et al. / Journal of Cleaner Production 222 (2019) 695e709

4.2.9. Carbon constraint During the planning horizon, the whole hybrid manufacturing and recovering system should be compliance with the limitation of carbon emission, which is stated as follows:

X iout 2OU

Emiiout t COout iout þ

X iin 2IN

CO2 e Coniin t CI in ct iin CABt  UBt

(62)

705

Table 3 Estimated demand for each product. Product imc 2P

Estimated demand MTDimc ;t

P1 P2 P3

t ¼1

t ¼2

t ¼3

t ¼4

t ¼5

55 30 20

60 40 20

40 30 20

50 40 10

45 25 20

Table 4 Distribution rate for the recovering units.

5. Case study

Parameter

5.1. Case description To verify the effectiveness and performance of the proposed optimization model, a case study that originates from an electronic assembly plant is conducted. This study investigates the whole lifecycle of the electronic assembly process, involving not only the forward manufacturing process, but also the reverse logistics to recover the defective products or components. Under this case, a hybrid manufacturing-recovering topology and a closed-loop logistics network are modelled. The manufacturing process consists of main production units of printed circuit board assembly, machine assembly, and aging test. There are typically three sources of raw materials, that is, circuit component recipe which is a set of primary raw materials to support the core function, board which is the base on which the circuit components are printed, and enclosure which is a set of accessories to integrate the whole product. The case mainly investigates three kinds of electronic products. There are totally nice kinds of raw materials which are grouped into three categories, going through seven major production units (A to G) according to the designed process. To depict the logic of each production route and bill-ofmaterial, a matching matrix approach is designed. Table 1 presents the raw material and product matching matrix in which pair ðirm 2R; imc 2PÞ ¼ 1 indicates that the product imc 2P is manufactured by the raw materialirm 2R, and otherwise ðirm 2R; imc 2PÞ ¼ 0. Table 2 depicts the product and production unit matching matrix in which pair ðimc 2P; jmc 2JM Þ ¼ 1 implies that the production of product imc 2P goes through unit jmc 2JM , and otherwise ðimc 2P; jmc 2JM Þ ¼ 0. This case considers a planning horizon of 5 periods. The company complies with a make-to-order strategy to respond to the market requirements. The time-varying product demand for each period is estimated based on short-term forecast and confirmed order data. Due to the uncertainty in return rate, a statistic

Table 1 Raw material and product matching matrix. Product imc 2P

Raw material irm 2R R1

R2

R3

R4

R5

R6

R7

R8

R9

P1 P2 P3

1 0 0

1 0 0

1 0 0

0 1 0

0 1 0

0 1 0

0 0 1

0 0 1

0 0 1

Table 2 Product and production unit matching matrix. Product imc 2P

Production unit jmc 2JM J1

J2

J3

J4

J5

J6

J7

P1 P2 P3

1 0 0

0 1 0

0 0 1

1 1 0

0 0 1

1 1 0

0 0 1

dcol t ddsa t dret t

Distribution rate t ¼1

t ¼2

t ¼3

t ¼4

t ¼5

0.6

0.6

0.6

0.6

0.6

0.7

0.7

0.7

0.7

0.7

0.8

0.8

0.8

0.8

0.8

approach is used to observe the quantity of the returned materials. Consequently, the return rate of each semi-product and product for a long period time is assumed to obey a normal distribution, and the respective mean value is used in the model. The estimated product demand for each planning period is given in Table 3. The distribution rate for each recovering unit is preset based on statistic data, and is assumed to be constant during the planning horizon as given in Table 4. In the above case, estimation on parameter data is required to optimize the scheduling results. However, it is usually difficult to acquire data directly from the industry due to business confidentiality or lack of existing statistic data. To implement the mathematical model with valid data that would comply with the case practice, the related data used for the model are collected from several factories manufacturing the same products with similar operational strategy. Some process or cost data could be tracked from the manufacturing execution system, and the others are analyzed and estimated using interview and statistical methods. Table 5 presents the corresponding upper and lower boundary with respect to the capacities of raw material supply, production unit, and recovering unit. Tables 6 and 7 summarize the range of processing and inventory cost associated with the manufacturing and recovering system respectively. 5.2. Solution approach The nature of the proposed model for optimizing the above case is a MILP problem. Here the model is implemented by LINGO 11.0 optimization software on an Intel i5-4590 3.30 GHz CPU, and is solved using a branch-and-bound algorithm. By configuring the solution procedures, the model characteristics can be observed, as presented in Table 8. A computational time limit 360s is set for the model solution, which means the running procedure stops when either obtaining the optimal solution or triggering the time limit. 5.3. Result evaluation and analysis To analyze the proposed model for the case in terms of material utilization and environmental influence, the computational results should be compared under different scenarios. From the perspective of decision-makers, the model should be investigated to capture the nature of trade-off on: (1) scheduling decisions of each material between the manufacturing and recovering system, when its return rate is varying, and (2) controlling the total operation cost and the carbon emission, when the environmental regulation is given. Therefore, the first attempt is to analyze the impact of return

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S. Lu et al. / Journal of Cleaner Production 222 (2019) 695e709

Table 5 Boundary of the capacity in the case. Parameter

Capacity (unit/period) Lower boundary

Minimum raw material supply capacity

RSmin irm st RSmax irm st UMmc jmc ;e;t UMmc jmc ;e;t UMmc jmc ;e;t UMcol jrc ;t UMdsa jrc ;t UMret jrc ;t fdp UMjrc ;t UMrdp jrc ;t

Maximum raw material supply capacity Production unit capacity (for A-C) Production unit capacity (for D-E) Production unit capacity (for F-G) Collecting and recycling unit capacity Disassembly unit capacity Retreatment unit capacity Final disposal unit capacity Recycled dispatching unit capacity

Upper boundary

10

20

200

300

60

80

60

140

60

140

180

220

180

220

180

120

350

450

350

450

Table 6 Processing and inventory cost associated with the manufacturing system. Parameter

Unit Unit Unit Unit

Capacity (RMB/Unit)

manufacturing cost (for A-G) inventory cost for raw materials inventory cost for work-in-process inventory cost for final products

cmimc ;jmc ;e cirirm ciwimc cipimc

Lower boundary

Upper boundary

400 24 35 55

1300 60 80 90

Table 7 Processing and inventory cost associated with the recovering system. Parameter

Capacity (RMB/period)

Unit processing cost at collecting and recycling unit Unit processing cost at disassembly unit Unit processing cost at retreatment unit Unit processing cost at final disposal unit Unit processing cost at recycled dispatching unit unit inventory cost for untreated materials of each recovering unit

unit inventory cost for untreated materials of each recovering unit

ccrirc cdsirc ctrirc cfdirc crdirc untre

IV dsa irc ;t

untre IV ret irc ;t fdpuntre IV irc ;t untre IV rdp irc ;t coltre IV irc ;t tre IV dsa irc ;t ret tre IV irc ;t rdptre IV irc ;t

Lower boundary

Upper boundary

10 200 60 60 60 20

20 300 80 140 140 25

30

40

40

50

50

60

20

25

30

40

40

50

50

60

Table 8 Model characteristics of the case.

Model for the case

NO. of variables

NO. of continuous variables

NO. of integer variables

NO. of constraints

2448

2248

200

2074

rate fluctuation during different period on the hybrid system by applying 3 scenarios. To quantitatively reflect the return rate for the overall system, an indicator called average return rate (ARR) is introduced, which is defined as the mean value of the return rate for semi-products or products. The ARR value is fixed during the planning horizon in scenario I; however, the rate increases and decreases slightly in scenario II and scenario III respectively. The

ARR value and the solution results for the above 3 scenarios are presented in Fig. 3 and Table 9. The mean run times for optimizing the model with default solver settings are 78s. Compared with scenario I, the total operations cost increases by 32% for scenario II and decreases by 9.3% for scenario III. Hence, it can be found that the total operations cost increases as the ARR value increases. Through observing the individual objectives, the increase of return

S. Lu et al. / Journal of Cleaner Production 222 (2019) 695e709

Fig. 3. The average return rate in the three scenarios.

rate would also result in additional costs which are mainly on raw material procurement from external suppliers, manufacturing, inventory, energy consumption, greenhouse emission, and carbon credits. However, the raw materials shipped from the recovering system decrease. This is because recovering process will induce additional carbon emission where an increase in return rate intensifies this factor. Thus the accumulated recycled materials are mainly stored in warehouse where the inventory cost under scenario II is highest. On the other hand, the quantity of total carbon emission presents the same variation trend as the total operations cost, and increases by 12.7% for scenario II and decreases by 5.3% for scenario III. The results show that improvement on the qualification ratio to process a component helps the plant to reduce the material

707

consumption and also the processing capacity occupied within the hybrid system. Moreover, it means that the improvement would lead to a decrease in carbon emission which contributes to compliance to the environmental regulation. The second attempt is to analyze the impact of carbon credit cost with fixed carbon emission limit and return rate. Similar with the analysis on return rate fluctuation, the scenario I is chosen as a benchmark to compare the results. The original cost for buying a unit carbon credit is 2000 RMB/t in scenario I. For comparison, the original cost is linearly increased and decreased by 2% in each period respectively. Table 10 compares the main optimization objectives under the above cases by using scenario I as a benchmark. It can be observed that the raw material procurement, manufacturing, transportation, energy consumption and greenhouse emission remain unchanged when carbon credit cost fluctuates, thus would not affect the quantity of carbon emission. Fig. 4 shows that the trend on the quantity of carbon credit is with no significant characteristics during the planning horizon. However, the total quantity of carbon credit is the same when its cost fluctuates. This is mainly due to two reasons. Firstly, the carbon emission is calculated by material consumption and transportation, which is decided by the hybrid system topology and production orders. Secondly, considering fulfillment of the production orders, the efforts to reduce carbon emission by configuring the materials and production has been made to optimality. The third attempt is to analyze the impact of carbon emission limit on the whole hybrid system. The energy management department would make a registration on carbon emission plan according to the forecasted production orders. As a benchmark, a carbon emission limit 350t is used for the 5 periods planning horizon, which is labeled as scenario IV. To investigate the effects of the carbon emission limit on decisions of the whole hybrid system, the limit is relaxed to 400t and strengthened to 300t, and both limits are labeled as scenario V and scenario VI respectively. As can

Table 9 Solution results in the three scenarios.

Total operations cost (103 RMB) - Raw material from external suppliers - Raw material from the recovering system - Manufacturing - Inventory of the manufacturing system - Inventory of the recovering system - Transportation within the manufacturing system - Transportation within the recovering system - Energy consumption - Greenhouse emission - Carbon credits Total carbon emission (103 kg)

Scenario I

Scenario II

Scenario III

7709.74 1924.46 109.80 1214.10 5.58 80.94 54.39 5.46 2629.26 782.95 698.11 2009.1

10179.14 3181.35 75.00 1404.16 48.71 181.78 61.71 4.66 2950.04 878.14 1208.57 2264.28

6992.77 1670.03 126.04 1153.15 7.54 72.96 52.45 4.88 2498.15 741.26 485.91 1902.95

Table 10 Solution results with carbon credit cost fluctuation under Scenario I.

Total operations cost (103 RMB) - Raw material from external suppliers - Raw material from the recovering system - Manufacturing - Inventory of the manufacturing system - Inventory of the recovering system - Transportation within the manufacturing system - Transportation within the recovering system - Energy consumption - Greenhouse emission - Carbon credits Total carbon emission (103 kg)

Increase in cost

Decrease in cost

7735.71 1924.46 109.80 1214.1 6.61 81.1 54.39 5.46 2629.26 782.95 722.86 2009.1

7679.4 1924.46 109.80 1214.1 5.21 85.53 54.39 5.46 2629.26 782.95 666.58 2009.1

708

S. Lu et al. / Journal of Cleaner Production 222 (2019) 695e709

Fig. 4. The quantity of carbon credit bought during the planning horizon.

to yield a compromised solution among decisions on manufacturing, recovering and carbon credits. The proposed model presents two characters that are rarely reported in the literature. The first one is optimization of the closed-loop logistics superstructure under multi-product multistage scenarios on a manufacturing operations level. The second one is a compromise decision scheme on trade-off between operations cost and carbon emission regulation by integrating energy input and greenhouse output throughout the manufacturing and recovering process. The model is implemented on a case originated from an electronic assembly plant. The case study demonstrates that the approach is effective to optimally control carbon emission and waste discharge. However, the comparison results also indicate that strengthening the carbon emission limit would reduce material recycling within the recovering system, and in the meanwhile more operations cost rises. In practice, the decision-makers should consider a compromise environmental regulation on both greenhouse emission and recycling strategy in order to drive towards green and sustainable manufacturing. Acknowledgments Youth Innovation Talent Program by Department of Education of Guangdong Province, China (601821K42050)

Table 11 Solution results under different carbon emission limit. Scenario IV Scenario V Scenario VI 3

Total operations cost (10 RMB) - Raw material from external suppliers - Raw material from the recovering system - Manufacturing - Inventory of the manufacturing system - Inventory of the recovering system - Transportation within the manufacturing system - Transportation within the recovering system - Energy consumption - Greenhouse emission - Carbon credits Total carbon emission (103 kg)

7529.74 1924.46 109.80 1214.10 5.34 81.18 54.39

7085.67 1855.39 122.67 1214.10 5.34 71.93 54.39

8029.74 1924.46 109.80 1214.10 5.58 80.94 54.39

5.46

5.44

5.46

2629.26 782.95 518.11 2009.10

2649.11 791.83 98.99 2024.99

2629.26 782.95 1018.113 2009.10

be seen in Table 11, the total operations cost increases as the carbon emission limit is strengthened. Due to the carbon emission restriction, a more stringent regulation would require to buy more carbon credits. It should be noted that, the manufacturing costs for scenario IV and VI are the same, however in scenario V, the raw materials supplied from the recovering system are more than the other two, and those from external suppliers are less. This is because the cost of using the recycled materials as raw materials is smaller if carbon emission restriction is relaxed, which contributes to less cost on buying carbon credits. However, the total quantity of carbon emission under scenario V is 0.79% more than the other two since it takes more capacities to treat the recycled materials on each recovering unit.

6. Conclusion In this paper, an integrated production scheduling problem of a hybrid manufacturing and recovering system with carbon emission has been proposed in a multi-product multi-stage closed-loop superstructure. An optimization model of the addressed problem is developed with two objective categories containing different subobjectives. By integrating energy input and greenhouse output, the carbon emission factors are characterized by life cycle analysis

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