Moving forward in reverse : a review into strategic decision making in reverse logistics

Reverse Logistics, the process of managing the backward flows of materials from a point of use to a point of recovery or proper disposal, has gained increased industry acceptance as a strategy for both competitive advantage and sustainable development. This has stimulated a growing number of researchers to investigate Strategic management issues relating to the set up and control of effective and efficient Reverse Logistics systems. This paper systematically reviews the most important works in this field, with a focus on papers that concentrate on the strategic decision making process involved in the design and operation of a Reverse Logistics process with remanufacturing. The review found that: the majority of work is primarily focused on OEM specific issues; the sectors receiving the most attention are the ones under the greatest pressure from environmental legislation; and previous research findings from Rubio et al. (2009) and Fleischmann et al. (2000) are reaffirmed that the Reverse Logistics field is growing, but characterised by mainly quantitative, mathematical models. Future research efforts should be focused on the empirical investigation of the Reverse Logistics design process for all types of remanufacturers

Developing a meat supply chain network design using a multi-objective possibilistic programming approach

Purpose The purpose of this paper is to present a study in developing a cost-effective meat supply chain network design aiming to minimizing the total cost of transportation, the number of transportation vehicles and the delivery time of meat products. The developed model was also used for determining the optimum numbers and allocations of farms and abattoirs that need to be established and the optimal quantity flow of livestock from farms to abattoirs and meat products from abattoirs to retailers. Design/methodology/approach A multi-objective possibilistic programming model was formulated with a focus on minimizing the total cost of transportation, the number of transportation vehicles and the delivery time of meat products. Three sets of Pareto solutions were obtained using the three different solution methods. These methods are the LP-metrics method, the ɛ-constraint method and the weighted Tchebycheff method, respectively. The TOPSIS method was used for seeking a best Pareto solution as a trade-off decision when optimizing the three conflicting objectives. Findings A case study was also applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. The research concludes that the ɛ-constraint method has the superiority over the other two proposed methods as it offers a better solution outcome. Research limitations/implications This work addresses as interesting avenues for further research on exploring the delivery planner under different types of uncertainties and transportation means. Also, environmentalism has been increasingly becoming a significant global problem in the present century. Thus, the presented model could be extended to include the environmental aspects as an objective function. Practical implications The developed design methodology can be utilized for food supply chain designers. Also, it could be applied to realistic problems in the field of supply chain management. Originality/value The paper presents a methodology that can be used for tackling a multi-objective optimization problem of a meat supply chain network design. The proposed optimization method has the potential in solving the similar issue providing a compromising solution due to conflicting objectives in which each needs to be achieved toward an optimum outcome to survive in the competitive sector of food supply chains network. </jats:sec

A proposed mathematical model for closed-loop network configuration based on product life cycle

Products may be returned over their life cycle. Industrial experiences show that there are three main return–recovery pairs. Commercial returns are repaired. End-of-use returns often are remanufactured. In addition, end-of-life returns are recycled. However, up to now, no optimization model is proposed for closed-loop configuration based on three return–recovery pairs. The repaired and remanufactured products can be sold in the same or secondary market. In this paper, we design and configure a general closed-loop supply chain network based on product life cycle. The network includes a manufacturer, collection, repair, disassembly, recycling, and disposal sites. The returned products are collected in a collection site. Commercial returns go to a repair site. End-of-use and end-of-life returns are disassembled. Then, end-of-life returns are recycled. The manufacturer uses recycled and end-of-use parts and new parts to manufacture new products. The new parts are purchased from external suppliers. A mixed-integer linear programming model is proposed to configure the network. The objective is to maximize profit by determining quantity of parts and products in the network. We also extend the model for the condition that the remanufactured products are sent to the secondary market. The mathematical models are validated through computational testing and sensitivity analysis

Innovative Logistics Management under Uncertainty using Markov Model

This paper proposes an innovative uncertainty management using a stochastic model to formulate logistics network starting from order processing, purchasing, inventory management, transportation, and reverse logistics activities. As this activity chain fits well with Markov process, we exploit the very principle to represent not only the transition among various activities, but also the inherent uncertainty that has plagued logistics activities across the board. The logistics network model is thus designed to support logistics management by retrieving and analyzing logistics performance in a timely and cost effective manner. The application of information technology entails this network to become a Markovian information model that is stochastically predictable and flexibly manageable. A case study is presented to highlight the significance of the model. Keywords: Logistics network; Markov process; Risk management; Uncertainty management

Design of demand driven return supply chain for high-tech products

Purpose: The purpose of this study is to design a responsive network for after-sale services of high-tech products. Design/methodology/approach: Analytic Hierarchy Process (AHP) and weighted max-min approach are integrated to solve a fuzzy goal programming model. Findings: Uncertainty is an important characteristic of reverse logistics networks, and the level of uncertainty increases with the decrease of the products’ life-cycle. Research limitations/implications: Some of the objective functions of our model are simplified to deal with non-linearities. Practical implications: Designing after-sale services networks for high-tech products is an overwhelming task, especially when the external environment is characterized by high levels of uncertainty and dynamism. This study presents a comprehensive modeling approach to simplify this task. Originality/value: Consideration of multiple objectives is rare in reverse logistics network design literature. Although the number of multi-objective reverse logistics network design studies has been increasing in recent years, the last two objective of our model is unique to this research area.Peer Reviewe

Optimal production planning for a multi-product closed loop system with uncertain demand and return

We study the production planning problem for a multi-product closed loop system, in which the manufacturer has two channels for supplying products: producing brand-new products and remanufacturing returns into as-new ones. In the remanufacturing process, used products are bought back and remanufactured into as-new products which are sold together with the brand-new ones. The demands for all the products are uncertain, and their returns are uncertain and price-sensitive. The problem is to maximize the manufacturer\u27s expected profit by jointly determining the production quantities of brand-new products, the quantities of remanufactured products and the acquisition prices of the used products, subject to a capacity constraint. A mathematical model is presented to formulate the problem and a Lagrangian relaxation based approach is developed to solve the problem. Numerical examples are presented to illustrate the model and test the solution approach. Computational results show that the proposed approach is highly promising for solving the problems. The sensitivity analysis is also conducted to generate managerial insights

A three-stage model for closed-loop supply chain configuration under uncertainty

In this paper, a general closed-loop supply chain (CLSC) network is configured which consists of multiple customers, parts, products, suppliers, remanufacturing subcontractors, and refurbishing sites. We propose a three-stage model including evaluation, network configuration, and selection and order allocation. In the first stage, suppliers, remanufacturing subcontractors, and refurbishing sites are evaluated based on a new quality function deployment (QFD) model. The proposed QFD model determines the relationship between customer requirements, part requirements, and process requirements. In addition, the fuzzy sets theory is utilised to overcome the uncertainty in the decision-making process. In the second stage, the closed-loop supply chain network is configured by a stochastic mixed-integer nonlinear programming model. It is supposed that demand is an uncertain parameter. Finally in the third stage, suppliers, remanufacturing subcontractors, and refurbishing sites are selected and order allocation is determined. To this end, a multi-objective mixed-integer linear programming model is presented. An illustrative example is conducted to show the process. The main novel innovation of the proposed model is to consider the CLSC network configuration and selection process simultaneously, under uncertain demand and in an uncertain decision-making environment

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

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. 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