Uncertainty Models in Reverse Supply Chain: A Review

Reverse logistic has become an important topic for the organization due to growing environmental concern, government regulation, economic value, and sustainable competitiveness. Uncertainty is one of the key factors in the reverse supply chain that must be controlled; thus, the company could optimize the reverse supply chain function. This paper discusses progress in reverse logistic research. A total of 72 published articles were selected, analyzed, categorized and the research gaps were found among them. The study began by analyzed previous research articles in reverse logistic. In this stage, we also collected and reviewed journals discussing about the reverse supply chain. Meanwhile, the result of this stage shows that uncertainty factor has not been reviewed in detail. The most common theme as the background research in reverse logistic is environmental and economic aspect. Uncertainty in Close Loop Supply Chain is the most widely used approach, followed by the usage on reverse logistics, reverse supply chain and reverse Model. The most used approach and method on uncertainty are Mixed Integer Linear Programing, mixed integer nonlinear Programing, Robust Fuzzy Stochastic Programming, and Improved kriging-assisted robust optimization method. Customer demand, total cost, product returns are the most widely researched aspects. This paper may be useful for academicians, researchers and practitioners in learning on reverse logistic and reverse supply chain; therefore, close loop supply chain can be guidance for upcoming researches. Research opportunity based on this research combines total cost, quality return product, truck capacity, delivery route, remanufacturing capacity, and facility location got optimum function in uncertainty. The research method and approach for MINLP, IK-MRO and RSFP provide many opportunities for research. For theme and area in reverse logistic, close loop supply chain is the theme that provides the most research opportunities

Fuzzy Interactive Approach for a Multi-objective Supplier Selection Problem under Robust Uncertainty

In this paper, the authors proposed a multi-objective Mixed Integer Linear Programming (MILP) model for supplier selection problems. The main aim of the system under the investigation is to plan the companies to supply goods to achieve financial benefit by minimizing the total costs and satisfying the customers with on-time delivery and minimizing rejected items. In this case, some restrictions such as multi-product and multi-period conditions, shortage inventory constraints, and discount circumstances simultaneously are considered. Despite these efforts, due to the uncertainty nature of the problem, some parameters are considering as uncertainty data. For this aim, applying robust counterparts for uncertain parameters plays an essential role in real-world applications of this case. It is concluded that the feasibility and optimality properties of the usual solutions of real-world LPs can be severely affected by small changes of the data and that the robust optimization (RO) methodology can be successfully used to overcome this phenomenon

Sustainable Closed-Loop Mask Supply Chain Network Design Using Mathematical Modeling and a Fuzzy Multi-Objective Approach

The outbreak of the deadly coronavirus, which is increasing the number of victims every day, has created many changes in today�s world. The use of various masks is the most important social tool against this virus. Given the importance of rapid and quality supply of masks in the current situation, it is necessary to study supply chain in particular. In this research, the design of a closed chain supply chain network for different types of masks is assessed. The studied supply chain includes suppliers, manufacturers, distributors, and retailers in the forward flow and collection centers, separate centers, recycling centers, and disposal centers in the backward flow. In this regard, a multi-objective mathematical model with the objectives of increasing the total profit and reducing the total environmental impact, and maximizing social responsibility is presented. The optimization of this mathematical model has been done using a fuzzy optimization approach in GAMS software. The results of this study show that maximizing the total profit and minimizing the environmental effects and maximizing social responsibility are in contrast to each other. In addition, the sensitivity analysis indicated that the customers� demand can affect all aspects of the sustainable supply chain simultaneously

Reverse Logistics Network Design with a 3-Phase Interactive Intuitionistic Fuzzy Goal Programming Approach: A Case Study of Covid-19 in Pathum Thani, Thailand

During outbreaks, a vast quantity of Infected Medical Waste (IMW) can be substantially generated in a short period, which poses a massive risk to medical personnel and surrounding communities. This study proposes an Intuitionistic Fuzzy Multi-Objective Multi-Period Mixed-Integer Linear Programming (IFMOMILP) model for effective IMW management in outbreaks under uncertainty, considering financial and risk factors subject to a priority from Decision Makers (DMs). The primary emphasis is on determining the optimal locations and capacity levels for temporary facilities, including temporary storage and treatment centers, as well as the optimal transportation routes. A 3-phase interactive Intuitionistic Fuzzy Goal Programming (i-IFGP) approach is developed to solve this IFMOMILP model. First, the Jiménez approach is applied to handle the uncertainties. Then, the problem is solved by Intuitionistic Fuzzy Goal Programming (IFGP). An actual case study of the COVID-19 outbreak in Pathum Thani province in Thailand was carried out to demonstrate the effectiveness of the proposed approach. The proposed approach yields solutions with varying feasibility degrees and scaling factors, providing alternatives for DMs. Then, the score function is utilized to imply DMs’ satisfaction with the outcomes, which is a concrete measure since it can reflect the intention of the DMs

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

A distribution network design for fast-moving consumer goods

A distribution network design of fast-moving consumer goods ensures distribution of products in an effective manner by giving  maximum customers’ satisfaction and minimum distribution cost. The study evaluates the distribution through direct shipment and the use of intermediate shipment for distribution of products from plant to depots. A real-life case study in Southwestern Nigeria was defined and solved as a linear programming model to minimise total cost of distribution from plant to the depots with consideration of four routing options. The results show that distribution through intermediaries gives a better solution than routing option with  direct shipment. The best routing option with intermediate points when compared with the routing option with direct shipment gives a savings of 1,819,490.00 Naira which translates to 13.46% cost savings. The study shows that the location of intermediaries is a key decision in distribution network design and that the intermediaries add value to the distribution networks in supply chain. Keywords: Distribution network; Supply chain design; Fast-moving consumer goods; Linear programmin

An Allocation-Routing Optimization Model for Integrated Solid Waste Management

Integrated smart waste management (ISWM) is an innovative and technologically advanced approach to managing and collecting waste. It is based on the Internet of Things (IoT) technology, a network of interconnected devices that communicate and exchange data. The data collected from IoT devices helps municipalities to optimize their waste management operations. They can use the information to schedule waste collections more efficiently and plan their routes accordingly. In this study, we consider an ISWM framework for the collection, recycling, and recovery steps to improve the performance of the waste system. Since ISWM typically involves the collaboration of various stakeholders and is affected by different sources of uncertainty, a novel multi-objective model is proposed to maximize the probabilistic profit of the network while minimizing the total travel time and transportation costs. In the proposed model, the chance-constrained programming approach is applied to deal with the profit uncertainty gained from waste recycling and recovery activities. Furthermore, some of the most proficient multi-objective meta-heuristic algorithms are applied to address the complexity of the problem. For optimal adjustment of parameter values, the Taguchi parameter design method is utilized to improve the performance of the proposed optimization algorithm. Finally, the most reliable algorithm is determined based on the Best Worst Method (BWM)

A FUZZY GOAL PROGRAMMING APPROACH FOR SOLVING MULTI-OBJECTIVE SUPPLY CHAIN NETWORK PROBLEMS WITH PARETO-DISTRIBUTED RANDOM VARIABLES

Uncertainty is unavoidable and addressing the same is inevitable. That everything is available at our doorstep is due to a well-managed modern global supply chain, which takes place despite its efficiency and effectiveness being threatened by various sources of uncertainty originating from the demand side, supply side, manufacturing process, and planning and control systems. This paper addresses the demand- and supply-rooted uncertainty. In order to cope with uncertainty within the constrained multi-objective supply chain network, this paper develops a fuzzy goal programming methodology, with solution procedures. The probabilistic fuzzy goal multi-objective supply chain network (PFG-MOSCN) problem is thus formulated and then solved by three different approaches, namely, simple additive goal programming approach, weighted goal programming approach, and pre-emptive goal programming approach, to obtain the optimal solution. The proposed work links fuzziness in transportation cost and delivery time with randomness in demand and supply parameters. The results may prove to be important for operational managers in manufacturing units, interested in optimizing transportation costs and delivery time, and implicitly, in optimizing profits. A numerical example is provided to illustrate the proposed model