Multi-objective multi-facility green manufacturing closed-loop supply chain under uncertain environment

2019 ◽  
Vol 39 (1) ◽  
pp. 58-76 ◽  
Author(s):  
Behzad Karimi ◽  
Amir Hossein Niknamfar ◽  
Babak Hassan Gavyar ◽  
Majid Barzegar ◽  
Ali Mohtashami
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Closed Loop ◽  
Distribution Centers ◽  
Nsga Ii ◽  
Closed Loop Supply Chain ◽  
Content Type ◽  
Uncertain Environment ◽  
Multi Objective ◽  
Hierarchy Process

Purpose Today’s, supply chain production and distribution of products to improve the customer satisfaction in the shortest possible time by paying the minimum cost, has become the most important challenge in global market. On the other hand, minimizing the total cost of the transportation and distribution is one of the critical items for companies. To handle this challenge, this paper aims to present a multi-objective multi-facility model of green closed-loop supply chain (GCLSC) under uncertain environment. In this model, the proposed GCLSC considers three classes in case of the leading chain and three classes in terms of the recursive chain. The objectives are to maximize the total profit of the GCLSC, satisfaction of demand, the satisfactions of the customers and getting to the proper cost of the consumers, distribution centers and recursive centers. Design/methodology/approach Then, this model is designed by considering several products under several periods regarding the recovery possibility of products. Finally, to evaluate the proposed model, several numerical examples are randomly designed and then solved using non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm. Then, they are ranked by TOPSIS along with analytical hierarchy process so-called analytic hierarchy process-technique for order of preference by similarity to ideal solution (AHP-TOPSIS). Findings The results indicated that non-dominated ranked genetic algorithm (NRGA) algorithm outperforms non-dominated sorting genetic algorithm (NSGA-II) algorithm in terms of computation times. However, in other metrics, any significant difference was not seen. At the end, to rank the algorithms, a multi-criterion decision technique was used. The obtained results of this method indicated that NSGA-II had better performance than ones obtained by NRGA. Originality/value This study is motivated by the need of integrating the leading supply chain and retrogressive supply chain. In short, the highlights of the differences of this research with the mentioned studies are as follows: developing multi-objective multi-facility model of fuzzy GCLSC under uncertain environment and integrating the leading supply chain and retrogressive supply chain.

A multi-objective mixed-integer linear model for sustainable fruit closed-loop supply chain network

2020 ◽  
Vol 31 (5) ◽  
pp. 1351-1373
Author(s):  
Younis Jabarzadeh ◽  
Hossein Reyhani Yamchi ◽  
Vikas Kumar ◽  
Nader Ghaffarinasab
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Environmental Indicators ◽  
Mixed Integer ◽  
Computational Time ◽  
Distribution Centers ◽  
Closed Loop Supply Chain ◽  
Content Type ◽  
Multi Objective ◽  
Lp Metric

PurposeThis paper aims to present a closed-loop supply chain (CLSC) optimization problem for a perishable agricultural product to achieve three pillars of sustainability, including minimizing total network costs and carbon dioxide emissions from different network activities and maximizing responsiveness to demands simultaneously.Design/methodology/approachThe research problem is formulated as a multi-objective mixed-integer linear programming model, and classical approaches, including the LP-Metric and weighted Tchebycheff method, have been applied to solve the optimization model. A set of test problems has been proposed to validate the model, and the results are presented.FindingsComputational time to find Pareto optimal solutions by using the weighted Tchebycheff method was twice as much as that of the LP-Metric method. Also, the result of the study is a mathematical model that can be applied to other products that are close to the fruit, such as vegetables.Research limitations/implicationsThe present study is limited to fruits supply chains and the inventory is considered at the distribution centers only. The study also considers only one type of transport.Practical implicationsThe paper can assist supply chain managers to define strategies to achieve a sustainable CLSC network configuration for the fruits.Originality/valueThis study is one of the early studies to consider environmental indicators in fruits supply chain design along with two other indicators of sustainability, namely, economic and social indicators. Therefore, this can help supply chain managers to achieve sustainability by optimizing location decisions, inventory quantities and flow between facilities.

Designing a Bi-Objective Closed-Loop Supply Chain Problem with Shortage and All Unit Discount: “Nondominated Sorting Genetic Algorithm II” and “Multi-Objective Particle Swarm Optimization”

2020 ◽  
Vol 19 (04) ◽  
pp. 701-736
Author(s):  
Masoomeh Vazifeh Pirnagh ◽  
Hamed Davari-Ardakani ◽  
Seyed Hamid Reza Pasandideh
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Closed Loop ◽  
Nsga Ii ◽  
Swarm Optimization ◽  
Closed Loop Supply Chain ◽  
The Real ◽  
Multi Objective

Nowadays, due to environmental issues, government rules and economic interests have increased attention to the collection and recovery of products, which has led to the formation of new concepts such as reverse and closed-loop supply chains. The implementation of the closed-loop supply chain as a solution to sustainable development is expanding from one hand and increasing the profitability of companies on the other. For this purpose, a mathematical model was developed to design an integrated closed-loop supply chain network, which is a combination of two-problem localization problems and flow optimization. The proposed model was designed to minimize network costs and to maximize the level of responsiveness to customers. The cost parameters of establishing centers in this model are uncertain; to overcome the model’s uncertainties, stochastic programming is used. In the mathematical model, supplier, manufacturer, distributor and customer in the direct supply chain and collection/rehabilitation, destruction, recycling centers and, second-type distribution center for sale of second-hand products as well as second-hand products customers in the reverse flow are considered, to be closer to the real today world. This model is multi-periodic mix integer nonlinear programming where the shortage has allowed. To motivate and encourage customers to buy more, in addition to getting closer to the real world and it happens more in practice, is considered all units of discount for transportation cost in the forward flow. To solve this model Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) is using. The parameter tuning was done using the Taguchi method. Then, the important criteria for measurement and comparison of performance algorithms have used, including the Mean Ideal Distance, Diversification Metric, Number of Pareto-optimal Solutions, and the Quality Metric. Results of the Comparative metrics show that NSGA-II outperforms MOPSO in almost all cases in achieving the best trade-off solutions.

scholarly journals Mathematical modeling for optimizing the blood supply chain network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amir Rahimzadeh Dehaghani ◽  
Muhammad Nawaz ◽  
Rohullah Sultanie ◽  
Tawiah Kwatekwei Quartey-Papafio
Keyword(s):  
Supply Chain ◽  
Blood Supply ◽  
Supply Chain Network ◽  
Distribution Centers ◽  
Nsga Ii ◽  
Content Type ◽  
Supply Chain Networks ◽  
Multi Objective ◽  
Blood Supply Chain ◽  
The Cost

PurposeThis research studies a location-allocation problem considering the m/m/m/k queue model in the blood supply chain network. This supply chain includes three levels of suppliers or donors, main blood centers (laboratories for separation, storage and distribution centers) and demand centers (hospitals and private clinics). Moreover, the proposed model is a multi-objective model including minimizing the total cost of the blood supply chain (the cost of unmet demand and inventory spoilage, the cost of transport between collection centers and the main centers of blood), minimizing the waiting time of donors in blood donating mobile centers, and minimizing the establishment of mobile centers in potential places.Design/methodology/approachSince the problem is multi-objective and NP-Hard, the heuristic algorithm NSGA-II is proposed for Pareto solutions and then the estimation of the parameters of the algorithm is described using the design of experiments. According to the review of the previous research, there are a few pieces of research in the blood supply chain in the field of design queue models and there were few works that tried to use these concepts for designing the blood supply chain networks. Also, in former research, the uncertainty in the number of donors, and also the importance of blood donors has not been considered.FindingsA novel mathematical model guided by the theory of linear programming has been proposed that can help health-care administrators in optimizing the blood supply chain networks.Originality/valueBy building upon solid literature and theory, the current study proposes a novel model for improving the supply chain of blood.

Robust optimization model for sustainable supply chain for production and distribution of polyethylene pipe

2020 ◽  
Vol 15 (4) ◽  
pp. 1613-1653
Author(s):  
Jaber Valizadeh ◽  
Ehsan Sadeh ◽  
Zainolabedin Amini Sabegh ◽  
Ashkan Hafezalkotob
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Environmental Impacts ◽  
Closed Loop ◽  
Optimal Location ◽  
Computational Time ◽  
Closed Loop Supply Chain ◽  
Content Type ◽  
Proposed Model ◽  
Production And Distribution

Purpose In this study, the authors consider the key decisions in the design of the green closed-loop supply chain (CSLC) network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, in this paper is the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered. Design/methodology/approach In this study, the author consider the key decisions in the design of the green CLSC network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered. Findings The results indicate that the results obtained from the colonial competition algorithm have higher quality than the genetic algorithm. This quality of results includes relative percentage deviation and computational time of the algorithm and it is shown that the computational time of the colonial competition algorithm is significantly lower than the computational time of the genetic algorithm. Furthermore, the limit test and sensitivity analysis results show that the proposed model has sufficient accuracy. Originality/value Solid modeling of the green supply chain of the closed loop using the solid optimized method by Bertsimas and Sim. Development of models that considered environmental impacts to the closed loop supply chain. Considering the impact of the technology type in the manufacture of products and the recycling of waste that will reduce emissions of environmental pollutants. Another innovation of the model is the multi-cycle modeling of the closed loop of supply chain by considering the uncertainty and the fixed and variable cost of transport.

Multi-objective robust optimization for multi-stage-multi-product agile closed-loop supply chain under uncertainty in the context of circular economy

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saeid Jafarzadeh Ghoushchi ◽  
Iman Hushyar ◽  
Kamyar Sabri-Laghaie
Keyword(s):  
Supply Chain ◽  
Robust Optimization ◽  
Circular Economy ◽  
Closed Loop ◽  
Mixed Integer ◽  
Closed Loop Supply Chain ◽  
Content Type ◽  
Multi Objective ◽  
Multi Stage ◽  
Proposed Model

PurposeA circular economy (CE) is an economic system that tries to eliminate waste and continually use resources. Due to growing environmental concerns, supply chain (SC) design should be based on the CE considerations. In addition, responding and satisfying customers are the challenges managers constantly encounter. This study aims to improve the design of an agile closed-loop supply chain (CLSC) from the CE point of view.Design/methodology/approachIn this research, a new multi-stage, multi-product and multi-period design of a CLSC network under uncertainty is proposed that aligns with the goals of CE and SC participants. Recycling of goods is an important part of the CLSC. Therefore, a multi-objective mixed-integer linear programming model (MILP) is proposed to formulate the problem. Besides, a robust counterpart of multi-objective MILP is offered based on robust optimization to cope with the uncertainty of parameters. Finally, the proposed model is solved using the e-constraint method.FindingsThe proposed model aims to provide the strategic choice of economic order to the suppliers and third-party logistic companies. The present study, which is carried out using a numerical example and sensitivity analysis, provides a robust model and solution methodology that are effective and applicable in CE-related problems.Practical implicationsThis study shows how all upstream and downstream units of the SC network must work integrated to meet customer needs considering the CE context.Originality/valueThe main goal of the CE is to optimize resources, reduce the use of raw materials, and revitalize waste by recycling. In this study, a comprehensive model that can consider both SC design and CE necessities is developed that considers all SC participants.

scholarly journals Designing a New Multi-Echelon Multi-Period Closed-Loop Supply Chain Network by Forecasting Demand Using Time Series Model: A Genetic Algorithm

2022 ◽  
Author(s):  
Shahab Safaei ◽  
Peiman Ghasemi ◽  
Fariba Goodarzian ◽  
Mohsen Momenitabar
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Supply Chain ◽  
Closed Loop ◽  
Critical Role ◽  
Time Series Model ◽  
Supply Chain Network ◽  
Distribution Centers ◽  
Closed Loop Supply Chain ◽  
Proposed Model

Abstract In the closed-loop supply chain, demand plays a critical role. The flow of materials and commodities in the opposite direction of the normal chain is inevitable too. So, in this paper, a new multi-echelon multi-period closed-loop supply chain network is addressed to minimize the total costs of the network. The considered echelons include suppliers, manufacturers, distribution centers, customers, and recycling and recovery units of components in the proposed network. Also, a linear programming model considering factories' vehicles and rental cars of transportation companies is formulated for the proposed problem. Moreover, the products demand is predicted by Auto-Regressive Integrated Moving Average (ARIMA) time series model to decrease the amount of shortage may happens in the network. To solve the proposed model, GAMS software is used in small-sized problems and a genetic algorithm in large-sized problems is employed. Numerical results show that the proposed model is closer to the real situation and the proposed solution method is efficient. Accordingly, sensitivity analysis is performed on important parameters to show the performance of the proposed model.

Robust Hazardous Materials Closed-Loop Supply Chain Network Design with Emergency Response Teams Location

2021 ◽  
pp. 036119812199207
Author(s):  
Nasrin Mohabbati-Kalejahi ◽  
Alexander Vinel
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Emergency Response ◽  
Closed Loop ◽  
Hazardous Materials ◽  
Risk Exposure ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Distribution Centers ◽  
Closed Loop Supply Chain

Hazardous materials (hazmat) storage and transportation pose threats to people’s safety and the environment, which creates a need for governments and local authorities to regulate such shipments. This paper proposes a novel mathematical model for what is termed the hazmat closed-loop supply chain network design problem. The model, which can be viewed as a way to combine several directions previously considered in the literature, includes two echelons in the forward direction (production and distribution centers), three echelons in the backward direction (collection, recovery, and disposal centers), and emergency response team positioning. The two objectives of minimizing the strategic, tactical, and operational costs as well as the risk exposure on road networks are considered in this model. Since the forward flow of hazmat is directly related to the reverse flow, and since hazmat accidents can occur at all stages of the lifecycle (storage, shipment, loading, and unloading, etc.), it is argued that such a unified framework is essential. A robust framework is also presented to hedge the optimization model in case of demand and return uncertainty. The performance of both models is evaluated based on a standard dataset from Albany, NY. Considering the trade-offs between cost and risk, the results demonstrate the design of efficient hazmat closed-loop supply chain networks where the risk exposure can be reduced significantly by employing the proposed models.

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