Problem In Closed-loop Supply Chain Research Articles

Global sustainable closed-loop supply chain considering Incoterms rules and advertisement impacts

Industrial information integration plays a crucial role in modern supply chains by ensuring the smooth flow of data across all stages, including recovery, recycling, and disposal, which is essential for the successful implementation of a closed-loop supply chain (CLSC) model. Building on this, our paper addresses a global CLSC problem by incorporating International Commercial Terms (Incoterms) and international transportation modes, bridging global supply chain operations with sustainability criteria. This innovative approach advances the development of a globally sustainable CLSC by focusing on the integration of economic, environmental, and social factors, i.e., the triple bottom line of sustainability. Specifically, we address environmental concerns through the introduction of carbon taxation and enhance social sustainability by exploring the impact of advertising on customer satisfaction. To further refine this model, we classify customers based on their sustainability engagement and apply a fuzzy programming approach to account for uncertainty in customer demand influenced by advertising. To solve this complex global CLSC model, we conduct a thorough analysis of constraints and develop a robust Lagrangian relaxation reformulation. While the initial solution may result in infeasibility, we propose a heuristic algorithm that ensures feasible solutions. Our efficient Lagrangian-based heuristic, incorporating an adaptive strategy, is capable of solving large-scale networks with an approximate 10% optimality gap. Ultimately, this research provides both a comprehensive framework for practitioners to improve the environmental performance and global operations of their supply chains, as well as significant theoretical contributions to the field of industrial information systems.

Simultaneous Pickup-and-Delivery Production-Routing Problem in closed-loop supply chain with remanufacturing and disassembly consideration

In a context of Closed-Loop Supply Chain, Production-Routing Problem with Pickup and Delivery is presented. In its basic form, Production-Routing Problem (PRP) attempts to solve jointly Dynamical Lot-Sizing and Inventory-Routing Problems. In this paper, a novel PRP model seeking to minimize the total cost of manufacturing, remanufacturing, disassembly, inventory, and routing by taking into account the remanufacturing and disassembly processes of End-of-Life returned products is provided. Novel hybrid heuristics based on Two-Phase Iterative and Relax-and-Fix heuristics were developed and outperform Branch-and-Cut algorithm for large size instances with a small vehicle capacity. Main insights derived from sensitivity analyses are (i) managers can use the model to trade off the cost saving resulting from the integration of forward and reverse flows, on the one hand, and any additional cost incurred by the organizational changes related to the integration, on the other hand, (ii) a high level of remanufacturing rate for returned products is not necessarily profitable, and managers should develop a return policy that leads to the appropriate level of remanufacturing rate, and (iii) investing in expanding the remanufacturing or the disassembly capacities can significantly reduce the total cost until a threshold level, beyond which any further investment is not beneficial.

A multi-objective closed-loop supply chain under uncertainty: An efficient Lagrangian relaxation reformulation using a neighborhood-based algorithm

Population growth and unprecedented industrialization have contributed to a significant surge in global waste generation, underscoring the need for efficient waste management practices such as recycling, disassembling, restoration, and disposal. Closed-loop supply chain (CLSC) networks offer a solution for effective waste management by enabling the recycling, reassembly, and reuse of waste products. However, designing an optimal CLSC network presents challenges due to uncertainties associated with various parameters, including customer demand, sustainable development goals, and the complexity of network design optimization. This paper addresses these challenges by proposing a comprehensive CLSC network that optimizes environmental, economic, and social footprints through a multi-objective optimization approach. To account for parameter uncertainties, we employ scenario generation using a scenario-based stochastic programming procedure. To solve the proposed model, we transform the multi-objective formulation into a single-objective model using a weighted sum method considering a set of problem-specific heuristics. Furthermore, we leverage Lagrangian relaxation theory to formulate various problem reformulations, aimed at attaining an optimal lower bound for the CLSC problem. We complement this with a neighborhood-based algorithm that helps identify a feasible upper bound. By utilizing a sub-gradient framework, we iteratively reoptimize both the selected reformulation and the neighborhood-based algorithm, culminating in the achievement of an optimal solution. To exemplify the practical applicability of our research, we present a case study focusing on the light engineering industry in Bangladesh. Through rigorous analyses, we showcase that our proposed algorithm can deliver an optimal solution with an optimality gap of 0.06. By offering insights into the successful implementation of our CLSC network in a real-world context, we aim to provide valuable guidance for practitioners seeking to establish efficient and sustainable waste management systems.

A Fuzzy Programming Based Approach to a Multi-Objective Multi-Echelon Green Closed-Loop Supply Chain Problem

Abstract It has become a social and legal obligation to take into account environmental factors as well as economic factors when designing the supply chain network. Reducing the emission of harmful gases in supply chain operations, recycling and efficient use of resources must be taken into consideration for future generations. The supply chain created in this study, in addition to the abovementioned features, includes supplier selection, warehouse and distribution center setup, transportation amounts between facilities, and transportation modes of products to be transported. This model, which is a multi-objective multi-echelon green closed-loop supply chain, is a mixed integer linear mathematical model and tries to maximize the joint satisfaction of the objectives with the help of a fuzzy approach using Zimmermann’s minimum operator.

Designing a multi-objective closed-loop supply chain for multi-period multi-generational products with social impacts considerations

This paper assesses a multi-period closed-loop supply chain (CLSC) for making strategic and operational decisions in multi-generation applications. For this purpose, joint decisions on network design, inventory control, production planning, pricing, supplier selection, and service level requirements are determined along with the forward flows of the supply chain. Besides, a segmentation policy is proposed for disassembling and recovering the different quality grades of returned products. All the raised issues have become more challengeable when an inner competition on coexistence goes up between the incumbent and new generations. Accordingly, this paper attempts to bridge the gaps in the literature by proposing a bi-objective mathematical model that involves a demand function with the integration of price drop effects, marketing efforts, and technology desirability to tackle trade-offs decisions between multiple generations. This research aims to simultaneously optimize the total profit and social benefits. To solve the problem, first, the ε-constraint method has been applied to transform the bi-objective problem into a single objective. Then, as a great deal to solve the complex mixed-integer non-linear program model, an outer approximation algorithm is applied based on approximation, relaxation, and decomposition. Finally, a case example in a mobile phone CLSC problem has been investigated to validate the proposed model and provide managerial insights.

A Neutrosophic Fuzzy Optimisation Model for Optimal Sustainable Closed-Loop Supply Chain Network during COVID-19

In this paper, a sustainable closed-loop supply chain problem is modelled in conditions of uncertainty. Due to the COVID-19 pandemic situation, the designed supply chain network seeks to deliver medical equipment to hospitals on time within a defined time window to prevent overcrowding and virus transmission. In order to achieve a suitable model for designing a sustainable closed-loop supply chain network, important decisions such as locating potential facilities, optimal flow allocation, and vehicle routing have been made to prevent the congestion of vehicles and transmission of the COVID-19 virus. Since the amount of demand in hospitals for medical equipment is unknown, the fuzzy programming method is used to control uncertain demand, and to achieve an efficient solution to the decision-making problem, the neutrosophic fuzzy method is used. The results show that the designed model and the selected solution method (the neutrosophic fuzzy method) have led to a reduction in vehicle traffic by meeting the uncertain demand of hospitals in different time windows. In this way, both the chain network costs have been reduced and medical equipment has been transferred to hospitals with social distancing.

Closed-loop supply chain design for the transition towards a circular economy: A systematic literature review of methods, applications and current gaps

Over the last decade, significant attention has been devoted to Closed-Loop Supply Chain (CLSC) design problems. As such, this review aims at assessing whether the current modelling approaches for CLSC problems can support the transition towards a Circular Economy at a supply chain level. The paper comprehensively assesses the extent to which existing modelling approaches evaluate the performance of supply chains across the complete spectrum of sustainability dimensions. Also, the capability of the current approaches of incorporating strategic, tactical, and operational decisions is considered, along with adopted solution methodologies. As a result, a comprehensive analysis was performed on 254 selected articles. This paper emphasises how most of the current literature in the field is affected by a disconnection between supply chain design and the founding principles of Circular Economy. Specifically, the CLSC literature exhibits a reductionist interpretation of the Circular Economy. CLSC studies focusing on all three dimensions of sustainability are relatively rare, and performance measurement approaches appear to be very much focused on monetary issues. While methodological contributions appear adequate to focus on the non-deterministic nature of CLSC design problems, there is paucity of empirically-grounded research. Coherently, a research agenda is proposed, in order to address the mentioned gaps and increase the relevance of this research field to practice.

A multi-objective optimization of closed-loop supply chain problem with vehicle routing

A multi-objective optimization of closed-loop supply chain problem with vehicle routing

A multi-objective optimization of closed-loop supply chain problem with vehicle routing

Product recovery has become significant business strategies to increase a competitive edge in business and also in the society. Parts from discarded products due to rapid advancement and post-consumer products before & after end-of-life (EOL) are recovered to reduce landfill waste and to have become a part of circular economy. Product recovery is made possible with the help of Closed-loop supply chain (CLSC). This paper concentrates on multi-period, multi-product, and multi-echelon Closed Loop Green Supply Chain (CLGSC) network. A bi-objective (cost and emission) Mixed Integer Linear Programming (MILP) model has been formulated for the network and has been optimized using Goal Programming approach and Genetic Algorithm. Results are discussed for providing some managerial insights of the model.

Optimizing a two-level closed-loop supply chain under the vendor managed inventory contract and learning: Fibonacci, GA, IWO, MFO algorithms

This paper addresses a new multi-level serial closed-loop supply chain (CLSC) model with an integrated formulation of batch deliveries, quality-dependent return rate, random defective rate, rework process, and learning effects. The vendor managed inventory contract has motivated companies to cope with the bullwhip effect and its associated shortages. However, getting precise information on manufacturing/remanufacturing time is a challenge owing to human learning. Accordingly, this paper tries to incorporate the learning effect on the inventory control problem. This is a bridge to fill the gaps in the literature by considering how the learning process affects the production time and system costs in a CLSC problem. Moreover, the mean-standard deviation utility function is used in order to take the random defective rate into account, to minimize mean system costs with respect to the standard deviation. Due to the complexity of the proposed mixed-integer nonlinear programming model, three metaheuristic algorithms named, genetic algorithm, invasive weed optimization algorithm, and moth flame optimization algorithm are used to solve the problem, and Fibonacci algorithm will examine the validity of the applied methods. Two sets of numerical examples in small, medium, and large sizes are presented to illustrate the performance of the algorithms in terms of objective function values and required CPU-time. Afterward, a statistical analysis is utilized in order to evaluate the proposed algorithms. Finally, the results show how significantly the learning effect in manufacturing/remanufacturing time affects the costs of CLSC problem.

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”

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.

Designing a data-driven leagile sustainable closed-loop supply chain network

ABSTRACT Nowadays, there is a great deal of interest in applying sustainability concepts for logistics and supply chain management. This paper proposes a new multi objective model in the area of closed loop supply chain problem integrated with lot sizing by considering lean, agility and sustainability factors simultaneously. In this regard, responsiveness, environmental, social and economic aspects are regarded in the model in addition to the capacity and service-level constraints. Most importantly, strategic and operational backup decisions are developed to increase the resiliency of the system against disruption of the facilities and routes simultaneously. In the following, a new hybrid metaheuristic algorithm comprised a parallel Multi-Objective Particle Swarm Optimization (PMOPSO) algorithm and a multi objective social engineering optimizer (MOSEO) is developed to deal with large size problems efficiency. To ensure about the effectiveness of the proposed hybrid algorithm, the results of this algorithm are compared with a Non-dominated Sorting Genetic Algorithm (NSGA-II).

A novel approach integrating FANP and MOMILP for the collection centre location problem in closed-loop supply chain

ABSTRACT Supply chain network design plays a crucial role in supply chain management. With government support as well as restriction and legislation, the enhancement of environmental awareness, and the drive of economic interest, reverse logistics have been incorporated into the supply chain, forming a closed-loop supply chain. The collection centre, which helps recycle and classify returned products, is a vital hub in closed-loop networks. Regarding the location problem, this study proposes a novel approach that integrates a fuzzy analysis network process and multi-objective mixed-integer linear programming to analyse qualitative and quantitative factors: social, political, and environmental factors, including costs, emissions, and responsiveness. Hence, an ε-constraint method is applied to examine the proposed framework. Finally, uncertain scenarios and different scale problems are discussed. Orthogonal experiments provide a sufficient basis for decision-making.

Modeling and solving a sustainable closed loop supply chain problem with pricing decisions and discounts on returned products

Increasing rates of environmental and social concerns involved in various communities have recently attracted researchers' attention to consider them in proper designs of sustainable supply chains (SSCs). In this paper, the planning problem of a multi-period multi-echelon sustainable closed loop supply chain (SCLSC) is investigated. A comprehensive model that seriously takes into account the social and environmental aspects of the supply chain (SC) decisions and measures their social and environmental effects using the GRI (Global Reporting Initiative) guideline indicators is proposed. The proposed planning model includes tactical decisions comprising the price of the product and logistic decisions. A discount offer is used on the returned product as an incentive policy to increase customers' willingness to return back the used products. In this offer, the discount depends on the quality of the returned products. Besides, as the discount for upcoming purchases stimulates customers to buy new products more, the product demand depends on the discount offer in addition to the product price. Moreover, two recovery approaches including remanufacturing and recycling alongside disposing of very low quality returned products are considered. Categorizing returned products according to their quality, quality-dependent recovery decisions for returned products, and the quality-dependent discount offer is the other novel points of this paper. The proposed model is implemented in a case study involving a CFL light bulbs supply chain. The TH (Torabi-Hassini) procedure is employed to solve the newly-developed three-objective optimization problem. The results of sensitivity analyses on the case-study demonstrate that the discount offer significantly improve the collecting rate of used products, which in turn helps improving sustainability. Finally, the Pareto optimal solutions show that the proposed approach provides a comprehensive planning model for SSCs which enables the decision makers to use a proper solution that considers legislation, social preference, cost, and environmental objectives.

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods.

ROBUST PRODUCTION OPTIMIZATION WITH CLOSED LOOP SUPPLY CHAIN SUPPORT

We present a robust optimisation model of a production process supported by the products recovery processes. We introduce a Closed Loop Supply Chain (CLSC) model that supports a production planning (PP). The CLSC model is based on a CLSC framework that consists of some logistic centers. According to our knowledge, one can hardly find PP models supported by CLSC. One of the most important problems with those models is the computational complexity involved in preparing production plans for a long planning horizon. This increases a number of additional numerical variables of the CLSC problems. These models are usually integer programming models solved with branch and bound algorithms, in which the search space grows exponentially. Another problem of the CLSC process is uncertain parameters within this process. In addition, the robust models are considered in the paper based upon robust optimisation theory.

Fuzzy Mathematical Model For A Lot-Sizing Problem In Closed-Loop Supply Chain

The aim of lot sizing problems is to determine the periods where production takes place and the quantities to be produced in order to satisfy the customer demand while minimizing the total cost. Due to its importance on the efficiency of the production and inventory systems, Lot sizing problems are one of the most challenging production planning problems and have been studied for many years with different modeling features. In this paper, we propose a fuzzy mathematical model for the single-item capacitated lot-sizing problem in closed-loop supply chain. The possibility approach is chosen to convert the fuzzy mathematical model to crisp mathematical model. The obtained crisp model is in the form of mixed integer linear programming (MILP), which can be solved by existing solver in crisp environment to find optimal solution. Due to the complexity of the problems harmony search (HS) algorithm and genetic algorithm (GA) have been used to solve the model for fifteen problem. To verify the performance of the algorithm, we computationally compared the results obtained by the algorithms with the results of the branch-and-bound method. Additionally, Taguchi method was used to calibrate the parameters of the meta-heuristic algorithms. The computational results show that, the objective values obtained by HS are better from GA results for large dimensions test problems, also CPU time obtained by HS are better than GA for Large dimensions.

Competitive Pricing and Remanufacturing Problem in an Uncertain Closed-Loop Supply Chain with Risk-Sensitive Retailers

Nowadays, pricing and remanufacturing problems under uncertain markets have gained increasing attention from both industrial and academic fields. In the literature, it is generally assumed that all the channel members are risk-neutral, ignoring the influences of channel members’ risk attitudes in the face of dynamic market. This paper focuses on a pricing problem in a closed-loop supply chain (CLSC) with two competitive risk-sensitive retailers under uncertain environment. The uncertainty is associated with the recycling costs, consumer demands and remanufacturing costs. Due to the dynamic market, supply chain managers may be unable to collect enough historical data to estimate these demands and costs when making pricing and remanufacturing decisions. In such cases, experts’ estimations are usually employed to describe these uncertain parameters. To deal with these human estimations, an uncertainty theory-based model is proposed. Based on the equilibrium results, how the retailers’ risk sensitivity and human estimations (uncertain degrees) affect the prices and profits is analyzed. It is found that both the retailers will get lower profits while the manufacturer will gain more profit when either of the two retailers becomes more risk-averse. We also find that a higher level of uncertainty in the supply chain will induce a higher collecting rate.

A new bi-objective model for a closed-loop supply chain problem with inventory and transportation times

This paper considers a closed-loop supply chain design problem including several producers, distributors, customers, collecting centers, recycle centers, revival centers, raw materials customers considering several periods, existing inventory and shortage in distribution centers, and transportation cost and time. This problem is formulated as a bi-objective integer nonlinear programming model. The aim of this model is to determine numbers and locations of supply chain elements, their capacity levels, allocation structure, mode of transportation between them, amount of transported products between them, amount of existing inventory, and shortage in distribution centers in each period to minimize the sum of system costs and transportation time in the network. To validate this model and show the applicability of it for small-sized problems, GAMS software is used. Because this given problem is NP-hard, a Bee Colony Optimization (BCO) algorithm is proposed to solve medium and large-sized problems. Furthermore, to examine the efficiency of the proposed BCO algorithm, the associated results are compared with the results obtained by the Genetic Algorithm (GA). Finally, the conclusion is provided. (C) 2016 Sharif University of Technology. All rights reserved.

A Hybrid Territory Defined evolutionary algorithm approach for closed loop green supply chain network design

The Closed loop Supply chain network distribution is one of the most important problems with much real world application in supply chain management area. Presently climate change problem is one of the major concerns for Researchers. Closed loop green supply chain (GCLSC) problem is the extension of closed loop supply chain problem. Semiconductor industries are one of the major industries and a number of waste products in semiconductor industries are quite high. We have considered reducing the waste in semiconductor by recycling the useful waste electronic equipment. In GCLSC, we consider to maximize the profit in forward supply chain whereas we attempt to minimize the Carbon footprints at the same time. In this paper we used a hybrid of Estimation of distribution algorithm (EDA) and Territory Defined multi-objective algorithm to select the optimum number of facilities in the closed loop supply chain network. To examine the effectiveness of our Hybrid Territory Defined algorithm (EDATDEA), we compare the results with those obtained by NSGA II on a same GCLSC problem with different problem sizes and the same data sets.