Reverse Logistics Network Design Problem Research Articles

Multi-echelon sustainable reverse logistics network design with incentive mechanism for eco-packages

With the pollution caused by express packaging waste, eco-packages are being commonly used to promote a green supply chain. However, extant research has yet to address the efficacious reverse logistics network design problem while considering both economic and environmental consumption in the recycling process. In this study, a multi-echelon reverse logistics network is designed to collect eco-packages from customers, instead of door-to-door recycling by couriers. This study aims to solve the capacity location-routing problem in a reverse logistics network integrated with the incentive mechanism for customers, which is regarded as NP-hard. To avoid the combinatorial explosion, a multi-objective hybrid genetic algorithm-tabu search (MOHGATS) is developed to reduce search space and improve search speed by changing search strategies. Computational experiments are conducted to verify the proposed model and algorithm based on several test instances and a real-world case study. The impacts of the quantity of eco-packages, the incentive costs for customers, and the quantity and capacity of recycling stations on the reverse logistics network are explored from both economic and environmental perspectives, providing a reference for practical operation and promotion of eco-packages on a large scale.

Benders decomposition for a reverse logistics network design problem in the dairy industry

Benders decomposition for a reverse logistics network design problem in the dairy industry

Benders decomposition for the distributionally robust optimization of pricing and reverse logistics network design in remanufacturing systems

The pricing and reverse logistics network design problem in remanufacturing has attracted considerable attention in recent years due to increasingly serious environmental problems. In this study, we consider a pricing and reverse logistics network design problem with price-dependent return quality uncertainty. To handle the high uncertainty in return quality, we propose a distributionally robust risk-averse model to safeguard the profits of investors in extreme situations. We propose a Benders decomposition approach to solve the proposed model. It is enhanced through valid inequalities, local branching, in-out variant methods and scenario-based aggregated cuts. Computational experiments demonstrate that the distributionally robust model can effectively hedge against high uncertainty and that the enhanced Benders decomposition methods significantly outperform their classical counterparts and the off-the-shelf solver Gurobi. Lastly, managerial insights are explored, and future research directions are outlined.

A Novel Reverse Logistics Network Design Considering Multi-Level Investments for Facility Reconstruction with Environmental Considerations

Reverse logistics is convincingly one of the most efficient solutions to reduce environmental pollution and waste of resources by capturing and recovering the values of the used products. Many studies have been developed for decision-making at tactical, practical, and operational levels of the reverse supply chain. However, many enterprises face a challenge that is how to design the reverse logistics networks into their existing forward logistics networks to account for both economic and environmental sustainability. In this case, it is necessary to design a novel reverse logistics network by reconstructing the facilities based on the existing forward logistics network. Multi-level investments are considered for facility reconstruction because more investment and more advanced remanufacturing technologies need to be applied to reduce the carbon emissions and improve facility capacities. Besides, uncertain elements include the demand for new products and return quantity of used products, making this problem challenging. To handle those uncertain elements, a bi-objective stochastic integer nonlinear programming model is proposed to facilitate this novel reverse logistics network design problem with economic and environmental objectives, where tactical decisions of facility locations, investment level choices, item flows, and vehicle assignments are involved. To show the applicability and computational efficiency of the proposed model, several numerical experiments with sensitivity analysis are provided. Finally, the trade-off between the profit and carbon emissions is presented and the sensitive analysis of changing several key input parameters is also discussed.

A Bi-Objective Reverse Logistics Network Design Under the Emission Trading Scheme

In general, reverse logistics network design has been driven by a need to reduce costs and to improve customer service without considering its environmental impact. In this paper, we address a reverse logistics network design problem regarding carbon emission. The problem is formulated as a bi-objective, mixed-integer, and nonlinear programming model under various operation technologies and transport modes in the truck tire remanufacturing industry. An improved non-dominated sorting genetic algorithm II (NSGA-II) solves this NP-hard problem with bi-objectives. The numerical cases demonstrate the validation of the proposed model and the advantage of improved NSGA-II over the basic NSGA-II. Furthermore, we conducted extensive sensitivity analysis, and several managerial insights are derived.

Reverse logistics network design: a water flow-like algorithm approach

This study investigates the reverse logistics network design problem, including collection/inspection, recovery and disposal centers that a mixed integer linear programming model is considered. In this network, returned products from customer zones are collected in collection/inspection centers and after quality inspection, and also after separation, recoverable products are shipped to recovery centers and scrapped ones are transported to disposal centers. NP-hardness of this problem is proved in many papers, so a novel meta-heuristic solution method aiming minimization of total costs comprised fixed opening cost of collection/inspection, recovery and disposal centers and transportation cost of products between opened centers using priority based encoding presentation is proposed. Comparison of outputs from this proposed algorithm and a modified genetic algorithm shows the excellence of this new solution method. Finally, some directions for future research are proposed.

An analysis of fully fuzzy linear programming with fuzzy decision variables through logistics network design problem

Recently, there is a growing attention by the researchers to solve and interpret the analysis of fully fuzzy linear programming problems in which all of the parameters as well as the decision variables are considered as fuzzy numbers. Under a fully uncertain environment where all of the data are stated as fuzzy, presenting the reasonable range of values for the decision variables may be comparatively better than the currently available crisp solutions so as to provide ranges of flexibility to decision makers. However, there is still a scarcity of solution methodologies on fuzzy mathematical programs with fuzzy decision variables. Based on this motivation, a new parametric method which is mainly based on α-cut representation of fuzzy intervals is proposed in this paper by incorporating the decision maker's attitude toward risk. In order to illustrate validity and practicality of the proposed method, it is applied to a generic reverse logistics network design model including fuzzy decision variables. To the best of our knowledge, this is the first study in the literature which presents fuzzy efficient solutions and analysis for a fully fuzzy reverse logistics network design problem with fuzzy decision variables. The provided solutions by the proposed method are also compared to the available solution methodologies from the literature in terms of computational efficiency, solution quality and ease of use. By using the proposed method, the decision makers can be supported by yielding fuzzy efficient solutions under different uncertainty levels and risk attitudes. The computational results have also shown that more reliable and necessarily precise solutions can be generated by the proposed method for a risk-averse decision maker.

Developing scenario-based robust optimisation approaches for the reverse logistics network design problem under uncertain environments

In the last decade, economic benefits and environmental legislation have imposed reverse logistics activities, induced by various forms of return, to organisations. Reverse logistics network design is a major strategic issue. This paper discusses scenario–based stochastic programming method and robust optimisation approaches including minimisation conditional value–at–risk (CVaR), p–robust regret and min–max regret models to find the most appropriate method dealing with uncertain environment in designing reverse logistics network. Firstly, the reverse logistics network design model is developed by using two–stage stochastic programming approach integrating CVaR in its objective function as a robustness criterion ensuring that the amount of objective function is not worse than the CVaR value with specified probability (confidence level) under all realisations. Also, the advantages of stochastic programming method are investigated respect to deterministic model under all defined scenarios. Then, the scenario–based robust optimisation methods are compared with the stochastic and deterministic ones to disclose their advantages and disadvantages.

Multiperiod and stochastic formulations for a closed loop supply chain with incentives

Reverse logistics network design problem we focus on is about locating distribution centers, inspection centers and remanufacturing facilities, and determining the acquisition price as well as the amount of returned goods to be collected depending on the unit cost savings and competitor’s acquisition price. We introduce the multiple periods setting and stochastic demand formulated by scenarios. We develop two mathematical programming models to determine the pricing strategy of the recovered products together with the optimal network that must be designed to be the most profitable closed cycle. Our methodology is based on a Golden Section Search with some flexibility that enables us to fix the used product acquisition price and then solve the model as an integer linear programming. Moreover, we establish dependent size fixed costs of opening a distribution, an inspection, and a remanufacturing centers, and show that they have a strong impact on the Golden Section search behavior.

An improved decoding procedure and seeker optimization algorithm for reverse logistics network design problem

Recently, Reverse Logistics (RL) and product recovery options such as recycling, remanufacturing and reusing have become important issues due to the environmental, economical issues and legal regulations. Due to this fact, companies should take into account the utilized recovery option while preparing their strategic planning activities (like network design) instead of using traditional production planning models. However, since RL network design problems are in the class of NP-hard, solving large scaled problems by exact algorithms is very difficult. Therefore, many meta-heuristics optimization algorithms have been proposed to provide near optimal solutions for supply chain, RL and closed-loop supply chain network design problems in the literature. In this paper, available decoding algorithms for solving generic RL design problems are revised so as to balance the problem without introducing any dummy node on the chromosome. Moreover, the proposed decoding procedure takes into account “equal transportation cost” situation. Then, a priority-based seeker optimization algorithm (SOA) which utilizes fuzzy reasoning procedure is developed for solution of the problem. In order to test performance of the algorithm, a numerical examined is provided and obtained results are compared with particle swarm optimization (PSO) algorithm which is another swarm intelligence technique. Computational results show that SOA is superior to PSO in terms of both solution quality and computational time for the example RL network design problem.

Multi-level reverse logistics network design under uncertainty

Localising facilities and assigning product flows in a reverse logistics environment is a crucial but difficult strategic management decision, certainly when value decay plays an important part. Despite numerous publications regarding closed-loop supply chain design, very few addressed the impact of lead times and the high level of uncertainty in reverse processes. In this article, a single product reverse logistics network design problem with multiple layers and multiple routings is considered. To this end, a new advanced strategic planning model with integrated queueing relationships is built that explicitly takes into account stochastic delays due to various processes like collection, production and transportation, as well as disturbances due to various sources of variability like uncertain supply, uncertain process times, unknown quality, breakdowns, etc. Their impact is measured by transforming these delays into work-in-process, which affects profit through inventory costs. This innovative modeling approach is difficult to solve because of both combinatorial and nonlinear continuous relationships. The differential evolution algorithm with an enhanced constraint handling method is proposed as an appropriate heuristic to solve this model close to optimality. A number of scenarios for a realistic case illustrate the power of this optimisation tool.