Multi-objective model for end-of-life tires reverse logistics: Enhancing sustainability through a techno-political framework

A multi-objective model to configure an electronic reverse logistics network and third party selection

Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty

Reverse logistics—that is, the distribution activities involved in product returns—is receiving growing attention. Because of the difference and interaction between the forward and reverse distributions, how to integrate the forward and reverse channels has become an emerging issue. Few studies have attempted to integrate the forward and reverse distributions. Furthermore, the increasing opportunities for cost savings and customer satisfaction in such integrated distribution have prompted third-party logistics providers (3PLs) to get involved in forward and reverse logistics operations. This paper first develops a multiobjective model considering activities of the 3PLs for forward and reverse distributions simultaneously. Two objectives are included in the proposed model: (a) maximization of the returned products shipped from customers back to the collection facilities and (b) minimization of the total costs associated with the forward and reverse logistics operations. A fuzzy goal programming approach is applied to determine the compromise solution for the multiobjective model. A genetic algorithm with two subalgorithms is then developed to solve the problem. Numerical experiments are presented to demonstrate the applicability of the formulated model and the proposed solution method.

A hybrid fuzzy multi-objective model for carpet production planning with reverse logistics under uncertainty

A robust bi-level optimization modelling approach for municipal solid waste management; a real case study of Iran

Literature review: Strategic network optimization models in waste reverse supply chains

Purpose In recent years, governmental regulations and the pressure of non-governmental organizations have convinced corporations to consider sustainable issues in their decisions. A simultaneous design of forward and reverse logistics can keep us away from sub-optimality caused by tackling these two phases (forward and reverse logistics) separately. Design/methodology/approach Hence, this paper presents a new multi-objective mathematical model for integrated forward and reverse logistics regarding economic, environmental and social issues. A new hybrid multi-objective metaheuristic algorithm is developed to obtain a set of efficient solutions (Pareto solutions). The proposed algorithm hybridizes a well-known, non-dominated genetic algorithm (NSGA-II) with a simulated annealing algorithm. Findings To validate the algorithm, its results are compared to the obtained solutions from simple NSGA-II with respect to some comparison metrics. The numerical results show the efficiency of the proposed algorithm. Finally, concluding remarks and future research directions are provided. Originality/value By applying a model presented in this paper, one can reach to sustainable and integrated logistics network which considers forward and reverse flow of commodities simultaneously.

A proposed method for third-party reverse logistics partner selection and order allocation in the cellphone industry

The objective of this study is to present a state-of-the-art review on applications and trends of meta-heuristics for remanufacturing problems. This literature review mainly encompass the most popular and frequently employed meta-heuristics like genetic algorithm, artificial bee colony, particle swarm optimization, ant colony optimization, simulated annealing, tabu search, variable neighborhood search and the hybrid of these approaches. By adopting a systematic procedure consisting of article collection and article selection, this research work selected 123 articles for literature analysis. The selected literature is categorized based on the application area of the remanufacturing problem; meta-heuristics techniques i.e. individual or hybrid meta-heuristics used; and mathematical models objective type (single or multi-objective models); objective function types based on the sustainable dimension (economic, social or environmental objective); objective functions such as minimize cost/time, maximize profit, etc. considered in a single and multi-objective models. Based on the analysis it is found that production planning and scheduling is the most focused application area of remanufacturing followed with reverse logistics. Further, GA is the most popular individual meta-heuristics used for optimization of the problem. The analysis also finds that hybrid meta-heuristics have gained increased attention in the past few years. The majority of the remanufacturing optimization models considered single objective. The present study also suggests several research avenues for further investigation.

A multi-objective decision-making model to select waste electrical and electronic equipment transportation media

Reverse logistics has been one of the complex and popular topics drawing attentions of the researchers and practitioners. Recovery of products has many advantages to companies in reducing costs and protecting the environment. In this study, one of the recovery options, namely reuse, is analysed. For this pursuit, a mathematical model is developed to plan production and distribution of the reusable products as well as the new products by considering the forward and reverse flows of the products. The model is a multi-echelon supply chain model composed of multiple customers, multiple distributors, multiple transshipment points, and a factory. Due to vagueness, ambiguity and lack of information in the reverse logistics, the problem is constructed and solved under the fuzzy environment. The model is implemented on a hypothetical supply chain network based on an industrial case, and the results of the model are compared with the results of the other methods.

Making decisions about the design and implementation of a logistics network iscrucial as it has long-term impacts. However, it is important to consider thatdemand factors and the number of returned items by customers may change overtime. Therefore, it is necessary to design a logistics network that can adapt tovarious demand fluctuations. The main goal of this study is to calculate thequantity of products that should be sent at different times in a supply chainnetwork to minimize the overall cost of reverse logistics and tardiness time.Accordingly, a multi-objective mathematical model is proposed that aims tooptimize the total cost and the amount of delay in sending customer orders in athree-level logistics network, assuming that some parameters are uncertain.Additionally, the minimization of waiting time, considering the level of delayin sending, is applied as the second objective function. To handle theuncertainty in the reverse logistics network, a fuzzy approach is implemented,and the proposed model is solved using GAMS software. Furthermore, to solve themathematical model in large dimensions, the Cuckoo Optimization Algorithm (COA)is applied in MATLAB software, and the results are compared to the globaloptimal solution. The outcomes show that the proposed algorithm has a desirableperformance, as the total values sent to the manufacturer are equal to thoseobtained from the exact solution, and the objective function value decreases asthe number of repetitions increases.

During natural and abnormal accidents, many people are injured, and a large number of wastes and rubbish are produced, so it is necessary to collect the injured and take them to treatment centers, which must be done in the reaction phase. Also, in the recovery and reconstruction phase, since a large amount of hazardous and non-hazardous waste is produced during accidents, effective measures should be taken to collect and recycle them if necessary. Both of these cases can be considered as a reverse logistics problem. This paper investigates reverse logistics planning in the response, improvement, and reconstruction phases in earthquake conditions. Due to the nature of the problem, it is expected that we will face a multi-objective problem, and the problem condition causes the issue of uncertainty. By increasing the dimensions of the problem, the NSGA-II meta-heuristic algorithm has been used to solve the two-objective model of the problem and the result indicates that the proposed solution algorithm works well and the quality of the answer and its solution time are appropriate. The results indicate that as capacity increases, the number of distribution centers built to meet demand decreases and the distribution center constructed may be far from some shelters, leading to increased transportation costs. According to the mentioned issues, this research uses reverse logistics in the response and recovery phases. Also, information about Tehran city will be used as data for the case study.

Post-disaster debris reverse logistics management under psychological cost minimization

Some electronic devices have a short lifetime, and variety-seeking and consumerism are increasingly growing in today’s societies. Moreover, electronic wastes contain precious substances such as gold, silver, copper, and aluminum. The proper disposal and processing of them by recycling offer considerable advantages to the environment, given the hazardous natures of such devices’ substances. The proposed reverse logistics with waste electrical and electronic equipment (WEEE) is an important task considered by researchers, the use of which offers economic benefits and reduces the environmental impacts of wastes. The present study models the electrical and electronic equipment (EEE) reverse logistics process as a bi-objective mixed-integer programming model under uncertainties. The mathematical model investigates two objectives: an economic objective and an environmental objective. The first is minimizing cost, while the second is maximizing the environmental score by reverse logistics processes in recovering and recycling. The parameters of demand and WEEE return rate which is obtained from the customer were considered as two uncertain parameters. A scenario-based stochastic programming (SSP) approach is applied to deal with the uncertainties. A case study of an electronic equipment manufacturer in Esfahan, Iran was included. The model was solved by a nominal approach and an SSP approach via the epsilon-constraint (EC) and augmented epsilon-constraint (AEC) methods to obtain optimal Pareto solutions and compare the methods. Finally, the optimal results of the two approaches were evaluated. The results indicated that the SSP approach using the AEC method had better outcomes.