Loop Supply Chain Network Design Research Articles

Bi-objective stochastic closed-loop supply chain network design under uncertain quantity and quality of returns

In this paper, we seek to optimize closed loop supply chain (CLSC) network design by determining the location of distribution and collection facilities and the assignment of forward and reverse flows that maximize the system profit and minimize CO2 emissions. To account for the difficulty of predicting and controlling recoverable items in practice, we incorporate into the proposed bi-objective model three types of uncertainties: return quantity, return quality and remanufacturing costs. We use the ε-constraint method to solve the resulting bi-objective scenario-based two-stage stochastic program and generate a set of Pareto-optimal solutions. Our numerical experiments show that both network configuration and performance levels are sensitive to variations of the quality and quantity of returns, in particular when the penalty for not processing returns is high. They also reveal the importance of stochastic modelling since the value of the stochastic solution can be significant in many cases. However, by increasing the size of the problem, the runtime of the ε-constrained stochastic model can be dramatically increased and memory shortage issues may occur. Therefore, we investigate the computational performance of a metaheuristic method based on a non-dominated sorting genetic algorithm (NSGAII) and a linear programming (LP) relaxation. Our method is capable of approximating the Pareto frontier on instances where the ε-constraint method cannot provide any feasible solution within the same time limit.

Mathematical Modeling of Closed-loop Supply Chain Network based on Environmental and Social Impacts

With the expansion and intensification of competitive environment in today's world, supply chain management has become one of the key issues facing businesses. It has influenced all the activities of organizations to produce products, improve quality, reduce costs and provide the services required by customers. On the other hand, as the volume of pollutants increased, the researchers sought to design networks that, in addition to economic optimization, focused on environmental factors in all sectors. Supply chain network design is a strategic and critical issue that provides an optimal framework for effective and efficient supply chain management. One of the most suitable areas for integration in supply chain networks is the design of closed-loop supply chain networks, which can prevent the overlap caused by the design of separate direct and reverse networks. In this paper, a mixed integer linear programming model for closed loop supply chain network design is presented. The latter model seeks to minimize costs, minimize environmental impact, maximize the amount of worn-out product collected, and maximize supply chain social responsiveness. The proposed model is implemented by Saba Battery Company, which produces various types of batteries. Since the proposed model belongs to the NP-hard category, an exact solution method and two multi-objective genetic algorithms and a multicomponent particle swarm were used to solve the model. .

Green- Closed Loop Supply Chain Network Design by Considering Customer Relationship Management and solving it using Multi-Objective Genetic Algorithm

Background and Objective: Increasing greenhouse gas emissions and pollutants has led organizers and researchers to seek to design and set up networks that focus on environmental factors and reduce pollutants in all sectors. Therefore, the main objective of this paper is to present a fuzzy mathematical programming model for the green closed loop supply chain network, taking into account customer relationship management.Method: This is an applied-development study implemented in Saba Battery Company. In this study, a mixed integer linear programming model is proposed for designing a closed loop supply chain network that seeks to minimize costs and minimize environmental impacts. Also, the concept of customer relationship management in the form of the third objective function has been added to maximize the amount of worn-out product collected to this model.Results: Given that the proposed model belongs to the NP-hard category, a multi-objective genetic algorithm is used to solve the model, and finally, the Pareto's solutions are determined. Based on the results, the two objectives of the economic and environmental objectives are contradictory. That is, moving each one toward the desired one requires movement of the other objective function to the undesirable.Conclusion: In this research, the proposed mathematical programming model has been solved with a multi-objective genetic algorithm, which results indicate the location and capacity of the facility, the amount of production, the amount of inventory and the amount of transportation of the products.

Multi-period multi-product closed loop supply chain network design: A relaxation approach

Growing concern about environmental deterioration, manifested in new laws to protect the environment and business competition, has stimulated the idea of Closed Loop Supply Chains (CLSC). This paper develops strategic decision-making models for two different CLSCs with multiple products over multiple periods. One of the networks has six levels, while the other has four levels, and they can be applied to products with different levels of complexity during inspection and remanufacturing operations. The supply chain networks are modeled as Integer Linear Programs (ILP) designed to assist decision-making in terms of location/allocation, transportation, and inventory. Taking advantage of the properties of the formulations, we propose an integer relaxation approach to obtain the optimal solution to ILP. The proposed models and methodologies are illustrated with case studies from the literature and with hypothetical datasets. This research contributes with design standard configurations for CLSCs that can be used for different types of products. Directly using these configurations is advantageous because the models proposed can be quickly solved using the integer relaxation approach. The associated trials confirm the utility of the models for the case studies and demonstrate encouraging results for the hypothetical datasets.

Green closed-loop supply chain network design: a novel bi-objective chance-constraint approach

In this paper, a novel chance-constrained programming model has been proposed for handling uncertainties in green closed loop supply chain network design. In addition to locating the facilities and establishing a flow between them, the model also determines the transportation mode between facilities. The objective functions are applied to minimize the expected value and variance of the total cost CO2 released is also controlled by providing a novel chance-constraint including a stochastic upper bound of emission capacity. To solve the mathematical model using the General Algebraic Modeling System (GAMS) software, four multi-objective decision-making (MODM) methods were applied. The proposed methodology was subjected to various numerical experiments. The solutions provided by different methods were compared in terms of the expected value of cost, variance of cost, and CPU time using Pareto-based analysis and optimality-based analysis. In Pareto-based analysis, a set of preferable solutions were presented using the Pareto front; then optimality-based optimization was chosen as the best method by using a Simple Additive Weighting (SAW) method. Experimental experiments and sensitivity analysis demonstrated that the performance of the goal attainment method was 13% and 24% better that of global criteria and goal programming methods, respectively.

Application of Exact and Multi-Heuristic Approaches to a Sustainable Closed Loop Supply Chain Network Design

Closed-loop supply chains (CLSC) are gaining popularity due to their efficiency in addressing economic, environmental, and social concerns. An important point to ponder in the distribution of CLSC is that imperfect refrigeration and bad road conditions may result in product non-conformance during the transit and thus such products are to be returned to the supply node. This may hinder the level of customer satisfaction. This paper presents a sustainable closed-loop supply chain framework coupled with cross-docking subject to product non-conformance. A cost model is proposed to investigate the economic and environmental aspects of such systems. The transportation cost is analyzed in terms of total carbon emissions. A set of metaheuristics are administered to solve the model and a novel lower bound is proposed to relax the complexity of the proposed model. The results of different size problems are compared with the branch and bound approach and the proposed lower bound. The results indicate that the proposed research framework, mathematical model, and heuristic schemes can aid the decision-makers in a closed-loop supply chain context.

Multi-product and multi-period closed loop supply chain network design under take-back legislation

This paper addresses the closed loop supply chain (CLSC) network design problem for durable products with consideration of take-back legislation. Each returned end-of-life product can be dismantled into different components. A recovery option is adopted for each component based on its quality level, its economic value, and its environmental impact. First, a multi-product multi-period mixed integer linear programming (MIP) model is proposed while assuming a 100% recovery target. The latter is then extended in order to incorporate other take-back legislations: namely, regulation based on other recovery targets and regulation based on reward-penalty (RP) mechanisms. Sensitivity analysis is then carried out in order to capture how some model parameters influence the take-back decision, the integration of the reverse supply chain, and its structure when there are no regulatory restrictions on the take-back of returns. In addition, the CLSC performance, in terms of environment and economic efficiency, is investigated under different take-back legislations. For the considered case study, a comparative study shows that a higher reverse service level and CLSC profit can be achieved when a regulation based on RP mechanism is implemented.

A closed loop supply chain network design problem with multi-mode demand satisfaction in fuzzy environment

A closed loop supply chain network design problem with multi-mode demand satisfaction in fuzzy environment

Closed loop supply chain network design under uncertain price-sensitive demand and return

In this paper, a closed-loop supply chain network design problem is developed. In the proposed model, expected returned products is estimated as a function of return price and if the amount of returned products is less than the expected amount, decision makers have some choices such as more advertising, incentives (extra cost) for returning more products. Different quality levels are considered for returned products which impacts on the recyclable and remanufacturable fractions of returned products as well as recovery lead time and cost. The model aims to maximize the total profit while making several decisions regarding pricing, the network design, material flow, quantity of manufacturing/remanufacturing, recycling, and inventory in an integrated manner to avoid any sub-optimality. A hybrid genetic algorithm and simulated annealing is proposed to solve the model. Numerical examples and sensitivity analysis are conducted to evaluate the applicability of the proposed model and lead to appropriate managerial decision about profitability of spending extra cost for returning more used products from customers.

An Integrated Hybrid Approach for Circular supplier selection and Closed loop Supply Chain Network Design under Uncertainty

Abstract In recent decades, reverse logistics has garnered considerable attention since it recovers value of returning products, satisfies environmental requirements, and pays attention to customers’ rights. Suppliers, as the first layer of the supply chain network, pose a great impact on environmental pollution. Therefore, in this paper a hybrid approach of fuzzy analysis network process (FANP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL), and multi-objective mixed-integer linear programming (MOMILP) models are developed for circular supplier selection and order allocation in a multi-product circular closed-loop supply chain (C-CLSC) considering multi-depot, capacitated green routing problem using heterogeneous vehicles. In this regard, a mathematical model concerning an inventory-location-routing problem is developed that minimises cost and shortage simultaneously and also deals with imposed uncertainties. A fuzzy solution approach is proposed to simultaneously incorporate uncertainty and to change the multi-objective model into a single-objective model. To motivate the practical aspect of the proposed model in real world applications, we applied the model to an automotive timing belt manufacturer. The obtained results indicate that the proposed model is cost efficient and environmentally friendly for CLSC network designs.

A robust leader-follower approach for closed loop supply chain network design considering returns quality levels

This paper aims to assess the effect of governmental policies on a closed loop supply chain network design to achieve the optimum decision level of the collection policies for the government. For this purpose, a robust closed loop supply chain network design model with an incentive strategy for different return quality levels with a bi-level programming approach is proposed. The government will act as a leader in the outer problem and maximize the total collected returned products with different quality levels. A predefined ratio of customer demand should be satisfied as a constraint for the outer problem. In the inner problem, a closed loop supply chain designer is considered as a follower and tries to maximize the supply chain net profit with respect to government regulations. A heuristic method based on enumeration and a solution methodology consisting of particle swarm optimization for the outer problem and a genetic algorithm for the inner problem are proposed. In addition, we investigate the impact of demand uncertainty on government regulations and the closed loop supply chain configuration by a robust optimization approach. Finally, numerical examples are generated to evaluate the performance of the proposed model. The results show the necessity of using bi-level programming and the superiority of the proposed solution methodology compared with the proposed enumeration method in large-size problems.

An accelerated benders decomposition algorithm for a bi-objective green closed loop supply chain network design problem

Recently, social awareness, governmental legislations and competitive business environment have spurred researchers to pay much attention to closed-loop supply chain network design. In order to support the arising trend, this paper presents a comprehensive mathematical model for a multi-period, multi-product, multi-modal and bi-objective green closed-loop supply chain. The objective of the model is to minimize the total cost and environmental emissions through making the best decisions on facility location, transportation amounts and inventory balances. According to the inherent complexity of the problem and considering multi-product, multi-period and multi- modality assumptions makes it hard to handle, and as for the solution approach, an effective accelerated benders decomposition algorithm is implemented. Then, computational results for a set of numerical example are discussed. Besides, the model and solution approach are applied on a wire-and-cable industry. Then, a sensitivity analysis is implemented in an effort to validate the model. Results reveal applicability of the proposed mathematical model and presented solution approach. Following the obtained results, it can be validly concluded that the suggested solution approach leads to more than 13 percent reduction in total cost for the studied case, and can be even employed for larger and more complex real-world industrial applications.

Remanufacturing Network Design for Dual-Channel Closed-Loop Supply Chain

Closed-loop supply chain (CLSC) is a prominent concept emphasizing on both economic and environmental aspects. A traditional view on these CLSCs for remanufacturing consists of two stages: forward logistics and reverse logistics. There is very little literature considering the impact of dual-channel business on closed loop supply chain network design for remanufacturing. In this research, we consider a CLSC where the remanufacturer needs to satisfy the demands from both online and office channels, A mixed integer nonlinear program is developed to determine the optimal network, and prices of both online and office return, with the objective of maximizing the manufacturer’s profit.

Carbon market sensitive robust optimization model for closed loop supply chain network design under uncertainty

The adoption of closed-loop supply chain (CLSC) network is one of the effective approaches to reduce carbon emissions. In current globalization, inherent uncertainty exists in business environment so there is a need to be design robust supply chains. This paper proposes a deterministic mixed integer linear programming (MILP) model integrating economics and carbon emission considerations including selection of production technologies and transportation mode as a part of CLSC network strategic and tactical decisions. The robust counterpart of the proposed deterministic model is developed based on three alternative uncertainty sets to represent the imprecise input parameters. The robust counterpart is used to study the supply chain performance by considering the two most globally practiced carbon regulatory policies; carbon tax policy and carbon trading policy. Numerical results show that total cost of the proposed robust optimization model under each uncertainty set is greater than the total cost of deterministic model. The additional cost is due to solution space of each uncertainty set to accommodate any uncertainty level. As uncertainty level increases the overall supply chain cost worsen. Moreover, the results suggest that carbon tax rate has direct relation with overall supply chain cost whereas having carbon market trading flexibility in carbon trading policy, this policy is more efficient policy as compared to carbon tax policy. Furthermore, the proposed robust optimization model is useful for mangers to achieve not only a robust supply chain network design which can withstand any possible uncertainty level but also significant reduction in carbon emissions by choosing suitable carbon-efficient policy.

A robust bi-level programming model to design a closed loop supply chain considering government collection’s policy

This study aims in providing a new approach regarding design of a closed loop supply chain network through emphasizing on the impact of the environmental government policies based on a bi-level mixed integer linear programming model. Government is considered as a leader in the first level and tends to set a collection rate policy which leads to collect more used products in order to ensure a minimum distribution ratio to satisfy a minimum demands. In the second level, private sector is considered as a follower and tries to maximize its profit by designing its own closed loop supply chain network according to the government used products collection policy. A heuristic algorithm and an adaptive genetic algorithm based on enumeration method are proposed and their performances are evaluated through computational experiences. The comparison among numerical examples reveals that there is an obvious conflict between the government and CLSC goals. Moreover, it shows that this conflict should be considered and elaborated in uncertain environment by applying Min-Max regret scenario based robust optimization approach. The results show the necessity of using robust bi-level programming in closed loop supply chain network design under the governmental legislative decisions as a leader-follower configuration.

Optimization of a sustainable closed loop supply chain network design under uncertainty using multi-objective evolutionary algorithms

Optimization of a sustainable closed loop supply chain network design under uncertainty using multi-objective evolutionary algorithms

A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount

Abstract With the increasing importance of the efficient purchasing function, supplier selection decisions have become more strategic in supply chain management. In the past few years, the closed-loop green supply chain network has faced increasing attention with regard to the environmental regulations, social awareness and customer pressure. In order to cope with these two issues, this paper proposes an integrated mathematical programming model for multi-period, multi-product and capacitated closed loop green supply chain in which suppliers offer quantity discounts in order to motivate buyers to purchase more. The model objective functions are the minimization of economic cost and environmental emissions and maximization of customer satisfaction with determining best suppliers, purchasing amount, location-allocation facilities, transportation mode, technology type, carbon dioxide emissions, inventory levels and flows between facilities. Sensitivity analysis is conducted to validate the model for some test problems. Computational results show the effectiveness and applicability of the proposed model. Also, results have revealed that supply chain total costs are significantly reduced by considering quantity discount.

Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach

Closed loop supply chain network design (CL-SCND) is a critical economic and environmental activity. The closing of the loop to handle return, uncertainty in business environment, various supply chain risks, impact network design processes and performance of the firm in the long term. Thus, it is important to design robust and reliable supply chain structures and obtain network configurations which can always outperform the other configurations under the worst cases of risks and uncertainty. A generic closed-loop supply chain network based on mixed integer programming formulation is proposed with direct shipping to the customer from manufacturing plants as well as shipping through distribution centers under supply risks, transportation risk and uncertain demand using a robust optimization (RO) approach. A large number of numerical tests are carried out to test the performance of the model by considering a total of four levels of uncertainty for four different network structures types. The results of the tests confirm that the risk and uncertainty based integrated supply chain network models are more efficient (cost effective) than the other set of network configurations which treats the supply chain risks and uncertainty post-ante. To demonstrate the applicability of the proposed model, the case of an Indian e-commerce firm which wants to redesign its supply chain structure is presented. The results of case study show that the topology obtained from integrated treatment of risk and uncertainty called as RORU model, outperform other supply chain networks on various network performance indicators such as supply chain costs, the number of facilities open or close and the amount of products flowing through supply chain echelon. Thus, RO based mathematical modeling to address risks and its applicability for SCND for close loop supply chain is proposed, demonstrated and applied in practical cases.

A Robust Reliable Closed Loop Supply Chain Network Design under Uncertainty: A Case Study in Equipment Training Centers

The aim of this paper is to propose a robust reliable bi-objective supply chain network design (SCND) model that is capable of controlling different kinds of uncertainties, concurrently. In this regard, stochastic bi-level scenario based programming approach which is used to model various scenarios related to strike of disruptions. The well-known method helps to overcome adverse effects of disruptions and extend a network that is less vulnerable regarding disruptions strike. Also, scenario-based modeling approach enables decision makers (DMs) to the model uncertainty of model parameters regarding different scenarios that are disregarded in reliable SCND research scope. An effective robust programming method is employed to control the risk-aversion level of output decisions that helps company managers to adjust long-term effects of their decisions via determining uncertainty level of model parameters. Notably, extended bi-objective programming model minimizes total costs of network design aside with maximization of responsiveness of supply chain network. Agile and fast performing networks could be regarded as a long-term competitive advantage for companies that are modeled in the extended form as a different objective besides cost minimization. Finally, the extended robust reliable network model is implemented and evaluated based on real case study of a national project and output results demonstrates efficiency and applicability of proposed reliable network.

An Integrated Closed-loop Supply Chain Configuration Model and Supplier Selection based on Offered Discount Policies

In this paper, a mathematical programming model is presented for integrated problem of closed loop supply chain network design and supplier selection. The suppliers propose discount policy based on purchase amount and loyalty of buyers which are both taken from the elements of RFM (Recency, Frequency and Monetary) technique. The main objective of this paper which is formulated by a mixed-integer programming model is to minimize the total cost through determining location of facilities, production plan, inventory levels, flows between facilities, transportation type, purchasing amount and selecting best supplier based on a beneficial relationship. Sensitivity analysis is carried out to validate the model and examine the effects of considering discount according to purchase amount and loyalty on the supply chains costs and decisions. Computational results show the effectiveness and usefulness of the model.