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

In this paper, a two-stage stochastic programming modelling is proposed to design a multi-period, multistage, and single-commodity integrated forward/reverse logistics network design problem under uncertainty. The problem involves both strategic and tactical decision levels. The first stage deals with strategic decisions, which are the number, capacity, and location of forward and reverse facilities. At the second stage tactical decisions such as base stock level as an inventory policy is determined. The generic introduced model consists of suppliers, manufactures, and distribution centers in forward logistic and collection centers, remanufactures, redistribution, and disposal centers in reverse logistic. The strength of proposed model is its applicability to various industries. The problem is formulated as a mixed-integer linear programming model and is solved by using Benders’ Decomposition (BD) approach. In order to accelerate the Benders’ decomposition, a number of valid inequalities are added to the master problem. The proposed accelerated BD is evaluated through small-, medium-, and large-sized test problems. Numerical results reveal that proposed solution algorithm increases convergence of lower bound and upper bound of BD and is able to reach an acceptable optimality gap in a convenient CPU time.

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

Sustainability ◽

10.3390/su11092710 ◽

2019 ◽

Vol 11 (9) ◽

pp. 2710 ◽

Cited By ~ 3

Author(s):

Xuehong Gao

Keyword(s):

Network Design ◽

Carbon Emissions ◽

Reverse Logistics ◽

Network Design Problem ◽

Logistics Network ◽

Logistics Networks ◽

Environmental Objectives ◽

Reverse Logistics Network ◽

Forward Logistics ◽

Multi Level

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.

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An Integrated Bi-Objective Reverse Logistics Network Design for Remanufacturing

Exploring Innovative and Successful Applications of Soft Computing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-4785-5.ch015 ◽

2014 ◽

pp. 281-316

Author(s):

Ali Zolghadr Shojai ◽

Jamal Shahrabi ◽

Masoud Jenabi

Keyword(s):

Network Design ◽

Reverse Logistics ◽

Network Design Problem ◽

Evolutionary Strategy ◽

Test Problems ◽

Logistics Network ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Multi Stage ◽

Reverse Logistics Network

Growing environmental and economical concern has led to increasing attention towards management of product return flows. An effective and efficient reverse logistics network enables companies to gain more profit and customer satisfaction. Consequently, the reverse logistics network design problem has become a critical issue. After a brief introduction to the basic concepts of reverse logistics, the authors formulate a new integrated multi-stage, multi-period, multi-product reverse logistics model for a remanufacturing system where the inventory is considered. Two objectives, minimization of the costs and maximization of coverage, are addressed. Since such network design problems belong to a class of NP-hard problems, a multi-objective genetic algorithm and a multi-objective evolutionary strategy algorithm are developed in order to find the set of non-dominated solutions. Finally, the model is tested on test problems with different sizes, and the proposed algorithms are compared based on the number, quality, and distribution of non-dominated solutions that belong to the Pareto front.

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