Reverse Logistics Network Problem Research Articles

A novel sustainable multi-objective optimization model for forward and reverse logistics system under demand uncertainty

The paper aims to present a multi-product, multi-stage, multi-period, and multi-objective, probabilistic mixed-integer linear programming model for a sustainable forward and reverse logistics network problem. It looks at original and return products to determine both flows in the supply chain—forward and reverse—simultaneously. Besides, to establish centres of forward and reverse logistics activities and make a decision for transportation strategy in a more close-to-real manner, the demand is considered uncertain. We attempt to represent all major dimensions in the objective functions: First objective function is minimizing the processing, transportation, fixed establishing cost and costs of CO2 emission as environmental impacts. Furthermore, the processing time of reverse logistics activities is developed as the second objective function. Finally, in the third objective function, it is tried to maximize social responsibility. Indeed, a complete sustainable approach is developed in this paper. In addition, this model provides novel environmental constraint and social matters in the objective functions as its innovation and contribution. Another contribution of this paper is using probabilistic programming to manage uncertain parameters. Moreover, a non-dominated sorting genetic algorithm (NSGA-II) is configured to achieve Pareto front solutions. The performance of the NSGA-II is compared with a multi-objective particle swarm optimization (MOPSO) by proposing 10 appropriate test problems according to five comparison metrics using analysis of variance (ANOVA) to validate the modeling approach. Overall, according to the results of ANOVA and the comparison metrics, the performance of NSGA-II algorithm is more satisfying compared with that of MOPSO algorithm.

Sustainable reverse logistics optimization with triple bottom line approach: An integration of disassembly line balancing

The objective of this study is to develop the sustainable reverse logistics network with integration of disassembly line balancing to examine decisions in view of Triple Bottom Line. The Triple Bottom Line approach is a framework that considers three strategies which are economic, environmental, and social. Disassembly has great importance in reverse logistics operations. Therefore, disassembly line balancing approach has been integrated into sustainable reverse logistics network problem for the first time. A Multi period Multi-Objective Mixed Integer Nonlinear Programming model is proposed and solved with Improved Augmented Epsilon Constraint (AUGMECON-2) technique. Pareto optimal solution set is obtained. In order to demonstrate the applicability of the suggested model, developed solution procedure has been applied on end of life mobile phone recycling network. Objectives are indicated as social impact maximization, economic and environmental effect minimization. This study is applied for a three-year time period. Disassembly processes are also taken into account. The model respectively determines: (i) optimal quantity of WEEE between the facilities and inventory amounts, (ii) the number of workstations in disassembly centers (iii) the number of facilities that will be opened (iv) optimal task assignments to workstations in disassembly centers. Sensitivity analysis is proposed to examine the impact of incremental changes in WEEE amounts on the sustainable reverse logistics network. According to each scenario the number of changes in facilities, objective function values and tasks assigned to workstations are specified. This developed model could offer managers to take strategic decisions in recycling processes with regard to environmental and social impact and help to gain managerial insights as well.

Reverse Logistics Network Problem using simulated annealing with and without Priority-algorithm

In recent years, Reverse Logistics (RL) has become a field of importance for all organizations due to growing environmental concerns, legislation, corporate social responsibility and sustainable competitiveness. In Reverse logistics, the used or returned products are collected after their acquisition and inspected for sorting into the different categories. The next step is to disposition them for repair, remanufacturing, recycling, reuse or final disposal. Manufacturers may adopt reverse logistics by choice or by force, but they have to decide whether performing the activities themselves or outsourcing to a third party (Martin et al., 2010). Lourenço et al., (2003) described three main areas of improvement within the RL process. Firstly, companies can reduce the level of returns through the analysis of their causes. Secondly, they can work on the improvement of the return’s process and, thirdly, they can create value from the returns. This paper considers the multistage reverse Logistics Network Problem (mrLNP) proposed by Lee et al., (2008). With minimizing the total of costs to reverse logistics shipping cost. We will demonstrate the mrLNP model will be formulated as a three-stage logistics network model. Since such network design problems belong to the class of NP-hard problems we propose a Simulated Annealing (SA) and simulated annealing with priority (priSA) with special neighborhood search mechanisms to find the near optimal solution consisting of two stages. Computer simulations show the several numerical examples by using, SA, priSA and priGA(Genetic algorithm with priority-based encoding method) and effectiveness of the proposed method.

Reverse logistics network for municipal solid waste management: The inclusion of waste pickers as a Brazilian legal requirement

This study proposes a reverse logistics network involved in the management of municipal solid waste (MSW) to solve the challenge of economically managing these wastes considering the recent legal requirements of the Brazilian Waste Management Policy. The feasibility of the allocation of MSW material recovery facilities (MRF) as intermediate points between the generators of these wastes and the options for reuse and disposal was evaluated, as well as the participation of associations and cooperatives of waste pickers. This network was mathematically modelled and validated through a scenario analysis of the municipality of São Mateus, which makes the location model more complete and applicable in practice. The mathematical model allows the determination of the number of facilities required for the reverse logistics network, their location, capacities, and product flows between these facilities. The fixed costs of installation and operation of the proposed MRF were balanced with the reduction of transport costs, allowing the inclusion of waste pickers to the reverse logistics network. The main contribution of this study lies in the proposition of a reverse logistics network for MSW simultaneously involving legal, environmental, economic and social criteria, which is a very complex goal. This study can guide practices in other countries that have realities similar to those in Brazil of accelerated urbanisation without adequate planning for solid waste management, added to the strong presence of waste pickers that, through the characteristic of social vulnerability, must be included in the system. In addition to the theoretical contribution to the reverse logistics network problem, this study aids in decision-making for public managers who have limited technical and administrative capacities for the management of solid wastes.

An optimization model for reverse logistics network under stochastic environment by using genetic algorithm

Recovery of used products has become increasingly important recently due to economic reasons and growing environmental or legislative concern. Product recovery, which comprises reuse, remanufacturing and materials recycling, requires an efficient reverse logistic network. One of the main characteristics of reverse logistics network problem is uncertainty that further amplifies the complexity of the problem. The degree of uncertainty in terms of the capacities, demands and quantity of products exists in reverse logistics parameters. With consideration of the factors noted above, this paper proposes a probabilistic mixed integer linear programming model for the design of a reverse logistics network. This probabilistic model is first converted into an equivalent deterministic model. In this paper we proposed multi-product, multi-stage reverse logistics network problem for the return products to determine not only the subsets of disassembly centers and processing centers to be opened, but also the transportation strategy that will satisfy demand imposed by manufacturing centers and recycling centers with minimum fixed opening cost and total shipping cost. Then, we propose priority based genetic algorithm to find reverse logistics network to satisfy the demand imposed by manufacturing centers and recycling centers with minimum total cost under uncertainty condition. Finally, we apply the proposed model to a numerical example.

Network model and optimization of reverse logistics by hybrid genetic algorithm

The interest about recovery of used products and materials have been increased. Therefore, reverse logistics network problem (rLNP) will be powerful and get a great potential for winning consumers in a more competitive context in the future. We formulate a mathematical model of remanufacturing system as three-stage logistics network model for minimizing the total of costs to reverse logistics shipping cost and fixed opening cost of the disassembly centers and processing centers. And we consider a multi-stage, multi-product and some attach condition for disassembly centers and processing centers, respectively. For solving this problem, we propose a genetic algorithm (GA) with priority-based encoding method consisting of 1st and 2nd stages combined a new crossover operator called as weight mapping crossover (WMX). A heuristic approach is applied in the 3rd stage to transportation of parts from processing center to manufacturer. Numerical experiments with various scales of rLNP models show the effectiveness and efficiency of our approach by comparing the recent researches.

A Multi-Stage Reverse Logistics Network Problem by Using Hybrid Priority-Based Genetic Algorithm

Today remanufacturing problem is one of the most important problems regarding to the environmental aspects of the recovery of used products and materials. Therefore, the reverse logistics is gaining become power and great potential for winning consumers in a more competitive context in the future. This paper considers the multi-stage reverse Logistics Network Problem (m-rLNP) while minimizing the total cost, which involves reverse logistics shipping cost and fixed cost of opening the disassembly centers and processing centers.In this study, we first formulate the m-rLNP model as a three-stage logistics network model. Following for solving this problem, we propose a Genetic Algorithm pri (GA) with priority-based encoding method consisting of two stages, and introduce a new crossover operator called Weight Mapping Crossover (WMX). Additionally also a heuristic approach is applied in the 3rd stage to ship of materials from processing center to manufacturer. Finally numerical experiments with various scales of the m-rLNP models demonstrate the effectiveness and efficiency of our approach by comparing with the recent researches.