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The distribution network composed of location and route is an important part of e-commerce logistics. With the continuous improvement of e-commerce requirements for logistics level, the practice of planning logistics network only from the perspective of the network location or the vehicle route can no longer meet the actual demand. In addition to the comprehensive consideration of the location-routing problem, the reverse logistics caused by customers’ returning goods should be taken into account. In this paper, the destruction and reorganization strategy of adaptive large-scale neighborhood search algorithm was introduced into the traditional genetic algorithm, so as to conduct research on the logistics location-routing problem under the background of integration of collection and distribution. Finally, the effectiveness of the optimized genetic algorithm was verified by Matlab tools and the existing bench-marking data set of the location-routing problem, which provided reference for the planning and decision-making of logistics enterprises.

Multi-criterion decision-making models are widely used in supplier selection problems. This study contributes to a green supplier selection problem considering the green manufacturing, green transportation, and green procurement. This study contributes to reverse logistics, eco-design, reusing, recycling, and remanufacturing with their high impact on the industries. In addition to the logistics costs and transportation costs, the carbon emissions are considered. With regard to the game theory, this paper uses a cooperative green supplier selection model. If transportation requirements of two or more companies are combined, it will help manufacturers to have less \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${CO}_{2}$$\end{document}CO2 emissions with lower cost. After creating the optimization model to consider the uncertainty, this cooperative game theory model is established in a fuzzy environment. In this regard, a fuzzy rule-based (FRB) system is deployed and the set of fuzzy IF–THEN rules is considered. The proposed FRB model is contributed for the first time in the area of green supplier selection problem. Finally, some sensitivity analyses are conducted in a numerical example to evaluate the proposed model. With regard to the findings, although the cost of CO2 emission of horizontal cooperation is increased, the cost saving of companies is increased. It means our total cost is optimal in a logistic network using the cooperative game theory. The results also indicate that horizontal cooperation in logistic network causes less cost and benefits for each company. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-17015-2.

Made available in DSpace on 2022-04-29T08:31:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-10-01 Organization and planning of reverse logistics networks make sustainable processes more efficient. Thus, an important sector for connecting the collection and waste paper recycling echelons is the intermediate center. In this study, a Mixed Integer Linear Programming model, which is multi-objective, multi-product, multi-level and multi-period, was developed to optimize the waste paper logistics processes of intermediate centers. The formulation includes the following echelons: collection, considering vehicle routing with different capacities; inventory of non-baling materials; baling sorted waste and inventory of bales; selling bales; disposal of non-recyclable waste. The aims of the model include: maximizing the collected waste; minimizing distances; maximizing both the production and sale of bales, and minimizing costs. According to research carried out in the literature, the developed model is a new proposal and to implement it, the Weighted Goal Programming and Revised Multi-Choice Goal Programming approaches were used to deal with multiple objectives and incorporate uncertainty into the quantity of waste available for collection. To analyze the proposed model, computational tests were executed with instances based on real data from a Brazilian company in the sector. For all performed tests, General Algebraic Modeling System 23.6.5 modeling language and CPLEX 12.2.0.2 solver were used for modeling and optimization. The results show that this study presents formulation and technological approaches that represent real situations and provide competitive solutions to the problem. Sáo Paulo State University “Júlio de Mesquita Filho” [Universidade Estadual Paulista Júlio de Mesquita Filho] School of Engineering of Guaratinguetá [Faculdade de Engenharia de Guaratinguetá] Production Department, Av. Ariberto Pereira da Cunha, 333, Portal das Colinas Agulhas Negras Military Academy [Academia Militar das Agulhas Negras] Rodovia Presidente Dutra, km 306, s/n Escola Preparatória de Cadetes do Exército Avenida Papa Pio XII 350 Jardim Chapadão Campinas - SP Brazil, Avenida Papa Pio XII, 350, Jardim Chapadão, Campinas - SP Sáo Paulo State University “Júlio de Mesquita Filho” [Universidade Estadual Paulista Júlio de Mesquita Filho] School of Engineering of Guaratinguetá [Faculdade de Engenharia de Guaratinguetá] Production Department, Av. Ariberto Pereira da Cunha, 333, Portal das Colinas

Problem definition : We examine a dynamic assignment problem faced by a large wireless service provider (WSP) that is a Fortune 100 company. This company manages two warranties: (i) a customer warranty that the WSP offers its customers and (ii) an original equipment manufacturer (OEM) warranty that OEMs offer the WSP. The WSP uses devices refurbished by the OEM as replacement devices, and hence their warranty operation is a closed-loop supply chain. Depending on the assignment the WSP uses, the customer and OEM warranties might become misaligned for customer-device pairs, potentially incurring a cost for the WSP. Academic/practical relevance : We identify, model, and analyze a new dynamic assignment problem that emerges in this setting called the warranty matching problem. We introduce a new class of policies, called farsighted policies, which can perform better than myopic policies. We also propose a new heuristic assignment policy, the sampling policy, which leads to a near-optimal assignment. Our model and results are motivated by a real-world problem, and our theory-guided assignment policies can be used in practice; we validate our results using data from our industrial partner. Methodology : We formulate the problem of dynamically assigning devices to customers as a discrete-time stochastic dynamic programming problem. Because this problem suffers from the curse of dimensionality, we propose and analyze a set of reasonable classes of assignment policies. Results : The performance metric that we use for a given assignment policy is the average time that a replacement device under a customer warranty is uncovered by an OEM warranty. We show that our assignment policies reduce the average uncovered time and the expected number of out-of-OEM-warranty returns by more than 75% in comparison with our industrial partner’s current assignment policy. We also provide distribution-free bounds for the performance of a myopic assignment policy and of random assignment, which is a proxy for the WSP’s current policy. Managerial implications : Our results indicate that, in closed-loop supply chains, being completely farsighted might be better than being completely myopic. Also, policies that are effective in balancing short-term and long-term costs can be simple and effective, as illustrated by our sampling policy. We describe how the performance of myopic and farsighted policies depend on the size and length of inventory buildup.

Product returns have been introduced to the supply chain because of the need to protect the environment and some circumstances beyond a manufacturer’s control (such as product recalls). Introducing product returns has led to the formation of closed loop supply chains, that is, situations in which organisations both manufacture and remanufacture their own products. Closed loop supply chains (CLSCs) have been known to be uncertain in the timing, quality and quantity of the returns. In addition to these uncertainties, most organisations lack the necessary infrastructure and technology to perform reverse logistics operations on their own. For this reason, they seek assistance from external parties such as third party reverse logistics providers. Third parties often have the expertise and technology required to run reverse logistics operations. At the same time, the rapid pace of e-commerce has led to manufacturers adopting more than one channels for selling their products. However, in a closed loop supply chain setting, the dual channel has been defined by more than just selling products both offline and online. The aim of this paper was to review literature on the use of multiple and dual channels in reverse logistics and closed loop supply chains and identifies research gaps as well as future research directions on the topic. The paper investigated how multiple and dual channels have been utilised to address issues in reverse logistics and closed loop supply chains. A content-based analysis was carried out for 57 papers. The identified characteristics of these papers were classified into 9 categories. By reviewing literature on dual channel supply chains, the paper was able to identify the ways in which different channels are formed in closed loop supply chains. The purpose of the paper was threefold. Firstly, the literature review described the general characteristics of dual channel closed loop supply chains based on what was revealed by existing literature. The review also classified literature based on the definition of dual channel CLSC, research methodology and topic of focus. Finally, the research proposed some meaningful gaps and directions for future research. The results of the review showed that there is need to expand literature on the competition of channels, greening strategies and carbon emissions and supply chain dynamics and stability in dual channel closed loop supply chains. Future research should also consider relaxing some assumptions and incorporating real case studies.

PurposeThis paper designs an optimal closed-loop supply chain network with an integrated forward and reverse logistics to examine the possibility of remanufacturing end-of-life (EoL) ships.Design/methodology/approachExplanatory variables are used to estimate the number of EoL ships available in a closed-loop supply chain network. The estimated number of EoL ships is used as an input in the model and then it is solved by a mixed-integer linear programming (MILP) model of the closed-loop supply chain network to minimise the total logistic costs. A discounted payback period formula is developed to calculate the length of time to recoup an investment based on the investment's discounted cash flows. Existing ship wrecking industry clusters in the Western region of India are used as the case study to apply the proposed model.FindingsThe MILP model has optimised the total logistics costs of the closed-loop supply network and ascertained the optimal number and location of remanufacturing for building EoL ships. The capital and variable costs required for establishing and operating remanufacturing centres are computed. To remanufacture 30 ships a year, the discounted payback period of this project is estimated to be less than two years.Practical implicationsShip manufacturing businesses are yet to re-manufacture EoL ships, given high upfront capital expenditure and operational challenges. This study provides management insights into the costs and benefits of EoL ship remanufacturing; thus, informing the decision-makers to make strategic operational decisions.Originality/valueThe design of an optimal close loop supply chain network coupled with a Bayesian network approach and discounted payback period formula for the collection and remanufacturing of EoL ships provides a new integrated perspective to ship manufacturing.

Purpose This study aims to design a reliable multi-level, multi-product and multi-period location-inventory-routing three-echelon supply chain network, which considers disruption risks and uncertainty in the inventory system. Design/methodology/approach A robust optimization approach is used to deal with the effects of uncertainty, and a mixed-integer nonlinear programming multi-objective model is proposed. The first objective function seeks to minimize inventory costs, such as ordering costs, holding costs and carrying costs. It also helps to choose one of the two modes of bearing the expenses of shortage or using the excess capacity to produce at the expense of each. The second objective function seeks to minimize the risk of disruption in distribution centers and suppliers, thereby increasing supply chain reliability. As the proposed model is an non-deterministic polynomial-time-hard model, the Lagrangian relaxation algorithm is used to solve it. Findings The proposed model is applied to a real supply chain in the aftermarket automotive service industry. The results of the model and the current status of the company under study are compared, and suggestions are made to improve the supply chain performance. Using the proposed model, companies are expected to manage the risk of supply chain disruptions and pay the lowest possible costs in the event of a shortage. They can also use reverse logistics to minimize environmental damage and use recycled goods. Originality/value In this paper, the problem definition is based on a real case; it is about the deficiencies in the after-sale services in the automobile industry. It considers the disruption risk at the first level of the supply chain, selects the supplier considering the parameters of price and disruption risk and examines surplus capacity over distributors’ nominal capacity.

Running the business smoothly for protecting the environment is a significant challenge, on which industries are trying something to do at their level best. Reverse logistics play an important role in system design by reducing environmental consequences and increasing economic and social impacts. Given the recent fluctuations of the market, the production cost and ordering cost are considered triangular fuzzy numbers in this study. Customers’ demand is met at the right time, and there is no shortage of items; thus, attention can be paid to two warehouses of a retailer. The setup costs Purchasing costs and deterioration costs of this system are affected by the learning effects, which lead to a decrease in the total cost. Inflation is a significant problem in the market because manufacturing, remanufacturing, and retailers are all affected. This study proposes a reverse logistics system model so that customers can resolve their complaints about defective items and carbon emissions under two warehouses. Numerical results show that the fuzzy model is more economically beneficial than the crisp model, finds that the crisp and fuzzy model saw a difference of 0.34% in total cost. Two numerical examples illustrate this study, and a sensitivity analysis is performed using tables and graph.

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.

Every organisation has an upper limit to the number of orders or products that it can manufacture or remanufacture per unit time. In reverse logistics operations, capacity limits can lead to inefficiencies in the remanufacturing process. In this paper, comparisons were made of the Bullwhip effect (BWE) in closed-loop systems that have collection and remanufacturing capacity limits and those that do not. Collection and remanufacturing capacity limits were introduced for a system where a company had to collect ‘enough’ products before remanufacturing can begin. This introduced collection backlogs, remanufacturing backlogs and remanufacturing downtimes to the closed loop supply chain (CLSC). By adopting a systems dynamics approach, the research performed ‘what-if’ analyses of the closed-loop system under different levels of the factors under investigation. Two case studies were investigated: one remanufacturing electric vehicle batteries (low demand, slow moving item) and the other remanufacturing kitchen appliances (high demand, fast moving item). Firstly, introducing collection and remanufacturing capacity limits in the reverse chain increased the BWE to a level higher than the reverse chain without any capacity limits, but not to the level of the forward chain without any product returns. Secondly, introducing collection and remanufacturing capacity limits for a closed-loop system where a company had to collect ‘enough’ products before remanufacturing begins had different impacts depending on the product demand size and speed. The presence of external returns by other parties not regulated by an organisation had an impact of lowering the BWE in the closed-loop system and it also impacted how the other factors under investigation affected the Bullwhip effect. These findings were used to provide managerial insights for organisations venturing into reverse logistics.

Current national and international regulations, along with growing environmental concerns, have deeply influenced the design of supply chain networks. These decisions stem from the fact that decision-makers try to design the supply chain network to align with their economic and environmental objectives. In this paper, a new closed-loop supply chain network with sales agency and customers is formulated. The proposed model has four echelons in the forward direction and five echelons in the backwards direction. The model not only considers several constraints from previous studies, but also addresses new constraints in order to better explore real-life problems that employ different transportation modes and that rely on sale agency centers. The objective function is to maximize the total profit. In addition, this study firstly considers distinct cluster of customers based on the product life cycle. These customers are utilized in different levels of the proposed network in order to purchase the final products, returned products, and recycled products. The structure of the model is based on linear mixed-integer programming, and the proposed model has been investigated through a case study regarding the manufacturing industry. To verify the model efficiency, a set of metaheuristics and hybrid algorithm are applied in various test problems along with a data from a real-world case study in a building construction industry. The findings of the proposed network illustrated that using the attributes of sale agency centers and clusters of customers both increase the problem total revenue and the number of the collected returned products.

The growing concerns for achieving sustainability has gained much attention from waste management experts with particular focus on reverse logistics, as it supports concepts like circular economy, material recovery, and improved environmental performances. This paper describes a multi-stage network-based model to minimize the overall cost for the reverse logistics management of inert construction waste across its entire life cycle. This model takes a unique two-type costing approach to overcome the ambiguities and deficiencies existing in previous models. Type-I cost refers to the facility-based costing (FBC) and Type-II cost refers to non-facility based costing (NFBC). The mixed-integer linear programming technique is applied using the LINGO software. A case study of construction waste management in Hong Kong is conducted to validate the developed model, which includes waste generation point as the starting node, public fill reception facility and recycling facility as an intermediate node, and landfill as an ending node. The result shows about 24% reduction in the total cost compared to the base case. Furthermore, to evaluate the impact of uncertainties on the cost parameters, a detailed scenario based sensitivity analysis is conducted. The optimal result shows that the larger portion of total cost come from the NFBC component. Therefore, NFBC is critical in defining the overall reverse logistics network and thus, should be given more emphasis in the design of an effective construction waste management system.

Due to the increased interests in environmental issues along with stringent environmental legislation and regulations, companies start taking a fresh look at the impact on their reverse logistic activties on the environment. This paper is an example of the recovery of valuable material that can be recycled/recovered or remanufactured at the end of product useful life by designing an effective reverse logistics network. In this study, a mixed integer linear programming (MILP) model is proposed to determine a long-term strategy for end-of-life (EOL). The mathematical model not only takes into account the minimization of system operating costs, but also considered minimization of carbon emissions related to the transportation and processing of used products. Therefore, the objective in this model was to minimize the transportation and operating cost as well as minimizing environmental effects these activities. The results of this study show the trade-off between the costs and carbon emissions, and cost effectiveness for improving environmental performance, all of which have great practical implication on decision-making of network configurations a reverse logistics system. The proposed model is validated by examining a case study from wind turbine (WT) sector.

The research aims to measure the effectiveness of the strategies of modern retailers which implementing reverse logistics activities, and to be believe that it could increase not only business competition, but also consumer satisfaction. The object of research is 20 modern retailers ranging from convenience stores to hypermarket stores. The methodology implemented in the form of setting parameters and assessment criteria which are arranged in the form of priority scale and analyzed using the Analytical Hierarchy Process or AHP method. The respondents are the decision makers in terms of product delivery, logistics, distribution and warehouse including making purchases both directly and indirectly. Sub parameters and sub criteria that are arranged then reevaluated using the ExpertChoice instrument to see the resulting scale consistency. Broadly speaking, the highest priority scale was 0.441 for the criteria for agility, 0.428 for the efficiency criteria, 0.318 for the stakeholder criteria, 0.271 for the process criteria and 0.146 for the environment criteria. The result of simulation cost is less than 8 percent of the profit margin per product returned.

Our research in progress is aimed at discovering how sustainable development and reverse logistics appear in the practice of companies in Hungary. Our goal is to develop an analysis framework and test it on a Hungarian sample that examines the relationship between the elements of the reverse logistics system and logistics practices. This was tested on the available sample. In the course of observing the supply chains, we intend to focus on trends of sustainable development, with an outlook on the international practice. We pay special attention to the reverse logistics aspects of company practice, presenting the logistical processes according to the environmental challenges. The background of our analyses consists of processing the literature and conducting primer assessment.

In this paper game theory is applied to maximise profit by identifying the optimal channel to collect and remanufacture used products in an electronic equipment retail network context. Without a detailed method to compute reverse logistics cost, the most profitable channel may not be chosen. Therefore, the main aim of the paper is to realistically model manufacturer, retailer and third-party as three different collection channel options to compete over return rates. A more realistic and detailed method to compute the reverse logistics cost is introduced because it is crucial in determining the most profitable channel. In the non-cooperative and cooperative version of the game the manufacturer gains the highest and the retailer the second highest payoff, since both players benefit from selling new products. Furthermore, the third-party can turn the collection of used products into a successful business by using synergy effects between different clients within the logistics network. There is consistency between the results of the non-cooperative and the cooperative version of the game. Moreover, if fines are imposed externally (for example by legislation), the best option is to cooperate in the grand coalition. Subcontracting the third-party yields the highest payoffs due to their lowest reverse logistics cost.

Most cities, notably major and agricultural ones, are faced with environmental and waste problems. Distribution and collection of agricultural crops can be challenging duties as world demand and production are substantially increased. Accordingly, resource depletion, environmental concern, and the importance of the circular economy have convinced this research group to focus on a Closed-Loop Supply Chain (CLSC) network design. In this study, a new mixed linear mathematical model for a CLSC was developed which minimizes the CLSC’s total costs and which tackles and controls air pollution. Contrary to previous works about supply chain network design, we firstly consider citrus fruits’ crates in our model. To solve the model, two leading algorithms, Genetic Algorithm and Simulated Annealing, are employed and a third recently successful method, Keshtel Algorithm, is utilized. Further, two hybridization algorithms stemmed from mentioned ones are applied. Finally, the results are assessed by different criteria and compared, and then the two best algorithms are chosen in this case. Consequently, in order to achieve the most effective result, a real case study of crates was conducted. The results obviously presented applicability and efficiency of the proposed model. Thus, the most suitable network for CLSC of citrus fruits’ crates was designed in which the costs and emissions were reduced.

In this paper, a mixed-integer linear programming model is discussed to provide joint decision making for facility location and production–distribution across countries for both forward and reverse logistics. A hybrid facility network is considered for cost-cutting and equipment sharing where the facilities of forward logistics are also equipped to provide reverse logistics services. The model considers the dynamic production and storage capacity of the facilities which can be expanded if required. Furthermore, the effectiveness of the model is tested to deal with disruptions due to man-made or natural disasters. The dynamic facility allocation enables the model to withstand the demand/supply disruptions in a disaster-affected zone. Besides this, the model considers carbon emissions caused due to manufacturing, remanufacturing, repair, storage and transportation. These emissions are regulated using cap and trade policy Thus, the proposed model balances resilience and sustainability under uncertain market demand and product returns. The chance-constrained approach is used to obtain the deterministic equivalence of the stochastic demand and returns. The paper also investigates the changes in emission and production level in each country under demand and supply disruptions. The parameters of the model are mapped with the various dimensions of big data such as volume, velocity and variety. The proposed model is solved using randomly generated data sets having realistic parameters with essential big data characteristics.

PurposeEnvironmental issues have become an important concern in modern supply chain management. The structure of closed-loop supply chain (CLSC) networks, which considers both forward and reverse logistics, can greatly improve the utilization of materials and enhance the performance of the supply chain in coping with environmental impacts and cost control.Design/methodology/approachA biobjective mixed-integer programming model is developed to achieve the balance between environmental impact control and operational cost reduction. Various factors regarding the capacity level and the environmental level of facilities are incorporated in this study. The scenario-based method and the Epsilon method are employed to solve the stochastic programming model under uncertain demand.FindingsThe proposed stochastic mixed-integer programming (MIP) model is an effective way of formulating and solving the CLSC network design problem. The reliability and precision of the Epsilon method are verified based on the numerical experiments. Conversion efficiency calculation can achieve the trade-off between cost control and CO2 emissions. Managers should pay more attention to activities about facility operation. These nodes might be the main factors of costs and environmental impacts in the CLSC network. Both costs and CO2 emissions are influenced by return rate especially costs. Managers should be discreet in coping with cost control for CO2 emissions barely affected by return rate. It is advisable to convert the double target into a single target by the idea of “Efficiency of CO2 Emissions Control Reduction.” It can provide managers with a way to double-target conversion.Originality/valueWe proposed a biobjective optimization problem in the CLSC network considering environmental impact control and operational cost reduction. The scenario-based method and the Epsilon method are employed to solve the mixed-integer programming model under uncertain demand.

International audience; In less than truckload transportation, shippers collaborate to reduce their logistics costs by consolidating their transportation requests in the procurement of transportation services from a carrier for serving the requests. In this paper, we study shipper collaboration in forward and reverse logistics, in which multiple shippers with forward or/and reverse logistics operations consolidate their transportation requests. In the forward and reverse logistics, manufacturers deliver new products to their customers and used products are collected from customers and transported to remanufacturers for repair or reproduction. This gives rise to a new vehicle routing problem with pickup and delivery requests and three different types of depots (product depots, vehicle depots and recycle depots). A hybrid approach combining greedy randomized adaptive search procedure (GRASP) and iterated local search (ILS) is proposed to find a near optimal solution of the problem. Numerical experiments on a large set of randomly generated instances with different problem sizes demonstrate that shipper collaboration in forward and reverse logistics can realize significant cost savings compared with the isolated operation of each shipper without cooperation, and the proposed approach is effective in the sense that it can find a high quality solution in a reasonable computation time.

This work was undertaken to develop a strategy to enhance the profit in the reverse logistics of end-of-life products. In this paper, a novel strategy based on hybrid bee colony and bat optimization technique is presented to perform reverse logistics. The aim of the present paper is to maximize the profit in reverse logistic based manufacturing. The proposed optimization technique was used to schedule the disassembly of end-of-life products so that the time spent in reverse logistics was reduced. Moreover, the proposed technique increases the amount of product required during disassembly, so that the loss of component is reduced. Thus, the proposed technique can enhance the manufacturer’s profit by reducing the time and cost required for disassembly. Ultimately, the proposed technique can provide a suitable technique for multi-period disassembly in manufacturing industries.

Reverse logistics (RL) plays a significant role in circular economic development, especially in natural resource- and energy-intensive industries (NREI) as a long-term strategy to make a breakthrough in their development. In this study, we construct an evolutionary game theoretical model to investigate the cooperation between NREI companies that adopt one of two reverse logistics operating strategies: self-operation or joint venture. We derive the evolutionary stable strategy of the game through a replicator dynamic system. Simulation experiments are also conducted to validate the theoretical results, to show the evolution, and to analyze the sensitivities to parameters, based on the data of the steel industry that we collected from China. Among many other outcomes, our results show that self-operation is more likely to succeed in operating the current reverse logistics system. The methodologies and results are useful in supporting NREI's reverse logistics decisions. The practical applications of the study can be extended to other NREI sectors when the way of RL implementation is to be decided.

In recent years, mobile payments have gradually replaced cash payments, resulting in a gradual decline in the number of automatic teller machines (ATMs) demanded by banks. Through investigation and analysis, we determine four means to deal with decommissioned ATMs, and construct thereafter an ATM reverse logistics (RL_ATMs) network model, which includes suppliers, producers, warehouses, operators, maintenance centers, collection and inspection centers, disposal centers, remanufacturing centers, and recycling centers. This model is further expressed as a mixed integer linear programming (MILP) model. Given that an ATM recycling network has planned and batched characteristics, a percentage diversion method is proposed to transform a real multi-cycle problem to a single-cycle problem. The RL_ATMs network constructed in this study presents the two forms of ATMs, functional modules and the entire machine. We used the actual situations of the related companies and enterprises in Anhui Province and its surrounding areas, as well as major banks’ ATMs, as bases in using the LINGO software to solve the proposed MILP model with the objective function of minimizing costs and environmental emissions, and obtain the relevant companies’ launch operations. Lastly, we analyzed the relationship between coefficients in the percentage diversion method and calculation results, cost, and carbon emissions. Accordingly, we find that the number of remanufacturing and maintenance centers has no evident impact on the objective function, transportation costs account for a large proportion of the total cost, and emissions tax is small.

The closed-loop supply chain considers conceptually the possibility of reverse logistics with the use of recycling, remanufacturing and disposal centers. This study contributes for the first time a green closed-loop supply chain framework for the ventilators, which are highly important in the case of the COVID-19 pandemic. The proposed model simulates a case study of Iranian medical ventilator production. The proposed model includes environmental sustainability to limit the carbon emissions as a constraint. A novel stochastic optimization model with strategic and tactical decision making is presented for this closed-loop supply chain network design problem. To make the proposed ventilator logistics network design more realistic, most of the parameters are considered to be uncertain, along with the normal probability distribution. Finally, to show the managerial dimensions under the COVID-19 pandemic for our proposed model, some sensitivity analyses are performed. Results confirm the high impact of carbon emissions and demand variations on the optimal solution in the case of COVID-19.

Customers’ pressure, social responsibility and government regulations have motivated the enterprises to consider the Reverse Logistics (RL) in their operations. Recently, companies frequently outsource RL to Third-Party Reverse Logistics Providers (3PRLPs) to concentrate on their prime concern and diminish costs. In the present study, relevant sustainability criteria are employed for a decision-making example of Sustainable 3PRLP (S3PRLP) selection problem of an Indian Electronics Company. To choose the most suitable S3PRLP, a novel integrated approach by combining Combined Compromise Solution (CoCoSo) and CRiteria Importance Through Intercriteria Correlation (CRITIC) approaches under the context of Single-Valued Neutrosophic Sets (SVNSs). This approach offers a new procedure for determining the criteria weights with the use of CRITIC technique. The efficiency and practicability of the present approach is numerically demonstrated by solving an illustrative case study of S3PRLPs selection within SVNSs context. Moreover, sensitivity analysis and comparison with previously developed techniques are presented to highlight the robustness and strength of the introduced method. The result of this work concludes that the introduced methodology can recommend more feasible performance when facing with indeterminate and inconsistent knowledge, and qualitative data.

Efficient urban planning requires managers’ experience and knowledge of reverse logistics in solid urban waste processes. Forecasting tools are needed to control, select and manage municipal solid waste. This paper presents the application of dynamic modeling approaches, namely, a linear autoregressive seasonal model, a model based on a FeedForward Artificial Neural Network and a Recurrent Neural Networks model, in order to forecast the unknown flows of end-of-life tires 12 months ahead. The models were identified using a database comprising four years of historical series related to the unknown flows of end-of-life tires. These were obtained through an exploratory analysis based on the annual sales reports of new tires issued by the Brazilian Institute of Geography and Statistics and reports related to the number of vehicles in circulation issued by Brazil’s National Traffic Department. The results show that the models are able to carry out consistent forecasts over the horizon of a year ahead and the predictions are capable of identifying seasonalities and supporting decision making in urban waste management.

The vehicle routing problem with backhauls (VRPB) allows to integrate inbound and outbound routes, which is an efficient strategy to reduce routing costs and also to reduce the environmental and social impacts of transportation. In this paper, we analyze the VRPB literature with a sustainability perspective, which covers environmental and social objectives, collaborative networks and reverse logistics. First, to better understand and analyze the VRPB literature, all related works are characterized according to a common taxonomy provided for routing problems. This taxonomy is extended to differentiate between economic, environmental and social objectives. After identification of all VRPB papers that include sustainability issues, these are analyzed and discussed in more detail. The analysis reveals that research on VRPBs with sustainability concerns is recent and relatively scarce and the most popular aspects investigated are the minimization of fuel consumption and $$\hbox {CO}_{2}$$ emissions. Future research lines driven by sustainability concerns are suggested for the VRPB as a promoter of green logistics.

Increased concern for the environment has led to urgent need to design the closed-loop supply chain network of hazardous products economically and ecologically. In this paper, we focus on the optimal design problem of multi-period closed-loop supply chain network of hazardous products considering uncertain demands and returns, expandable facility capacities and social acceptable risk simultaneously. In each period, the built facilities have the ability to expand within a certain scope. The problem is formulated as a mixed-integer nonlinear programming model, which can determine the number, location and expansion scale of the facilities and the forward and reverse logistics quantities between the facilities in each period simultaneously. The goal is to minimize the expected cost over the multi-period planning horizon, including the facilities building, operating and expansion costs and the costs related to manufacturing, collection, processing, recovery and transportation. In order to solve the proposed model, two classes of dummy variables are introduced to equivalently transform it into a mixed-integer linear programming, which can be optimally solved by LINGO. A case study is presented to illustrate the validity of the proposed model. The dynamic design with expansion strategy addressed in this paper is compared with two different strategies of static design and dynamic design without expansion. The results highlight that the dynamic design with expansion strategy has the advantages in saving expenses and raising the average expected collection rate of hazardous wastes.

Optimal queuing system design includes deciding about some variables such as location, capacity, price, and delivery time. In order to develop an optimized system, it is essential to solve the optimization problem for all the decision variables simultaneously. In this paper, a make-to-order system is considered. In this system, some facilities are developed in different points. According to the make-to-order system definition, these facilities keep no inventory from final product. Orders are received from customers and the requested products are assembled. Due to existence of some differences among customers, they are divided into two distinct categories including express and regular customers. Each category has purchasing and upgrading demand. Upgrading demand is related to the used products that are referred to the facilities by reverse logistics for upgrade. Profit is maximized through attracting these two types of demand from different demand points. Utilizing market segmentation, different prices can be offered to each category. Difference in price is due to difference in delivery time. It means maximum guaranteed time to deliver the product. This obligation raises customer satisfaction. But lower delivery time causes increase in price and lower power in competition. Increase is price is due to designing a queue with higher capacity. In this problem, location of facilities should be selected among some potential points. There are several competitors in each potential location. Each facility is modeled as a queuing system. In these systems, decision variables are location, capacity, price, and delivery time. Finally, the optimization problem is solved by the genetic algorithm and the optimized value for each variable is gained. Facilities with these variables will have the maximum profit.

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.

The use of pallets is crucial in supply chain and transportation processes, with wood pallets representing the most common packaging type in the US and in Europe. This work focuses on the environmental and economic impact of wood pallet reverse logistics, quantifying the context in which preemptive remanufacturing schedules are useful. Preemptive schedules allow the service provider to reduce transportation emissions and costs by combining multiple pallet components remanufacturing activities. However, this advantage has to be weighed against the losses associated with repairing or replacing a still functional component earlier than required. An integer linear optimization model analyzes this trade-off, first from an environmental then from an economic perspective. This model is applied using the two most common pallet types as case studies: 48- by 40-inch stringer and block pallets. Different handling and loading scenarios are considered. The impact of transportation distance on the efficiency of preemptive policies is explored through a sensitivity analysis. Results show the positive impact of preemptive remanufacturing on costs and CO2-equivalent emissions in all scenarios, with larger benefits in good handling or light loading scenarios.

Considering the influence of carbon emissions trading, the fuzzy stochastic programming model was established to cut back the total cost of carbon trading balance. Modeling this chain is carried out by accounting for carbon cap-and-trade considerations and total cost optimization. In this paper, we analyze the low-carbon integrated forward/reverse logistics network and made relevant simulation tests. The results show that the changes of the confidence level and carbon emission limits have obvious influences on logistics costs. If the emission limit is large, carbon trading mechanism has little effect on the total logistics cost in the same scenario. Therefore, the government needs to use the appropriate emission limits to guide enterprises to reduce carbon emissions, and enterprises can make coping strategies according to the different limit at the same time. Therefore, the fuzzy random programming model proposed in this paper is practical. Its decision making applying the proposed algorithm is reasonable and applicable and could provide decision basis for enterprise managers.

This paper presents a two-stage stochastic programming model for reverse logistics network design (RLND) under uncertainty with dynamic supply sources' locations. The primary objective of the optimization model is to maximize the expected profit generated by selling recycled materials to the secondary markets and to make the landfilling option less attractive. In comparison with the state-of-the-art stochastic optimization models in this area, which mainly focus on the expected optimal value, this paper emphasizes the role of source separation of recyclable materials to increase the productivity level at the collection centers (CC). Indeed, the quantity of materials collected from the supply sources and the recycling rates at the CC are the primary sources of uncertainty considered in this study. A Sample Average Approximation (SAA) procedure is developed to solve the stochastic model and perform sensitivity analyses on the number of supply sources, the sample size and the level of uncertainty targeting the random parameters. Managerial implications are discussed through a case study from the demolition industry in the province of Quebec, Canada. Although the use of source separation centers (SSC) improves the network performance in both rural and urban zones, the additional flexibility provided by these platforms reaches its best efficiency in the case of high-density urban areas. Finally, the results suggest significant RLND adjustments that lead to an increase in the average profit by 17.6% and recycle around 29% of additional building materials waste from demolition.

Rapid and revolutionary changes in technology and rising demand for consumer electronics have led to staggering rates of accumulation of electrical and electronic equipment waste, viz., WEEE or e-waste. Consequently, e-waste has become one of the fastest growing municipal solid waste streams in the United States making its efficient management crucial in supporting the efforts to create and sustain green cities. Accurate estimations on the amount of e-waste might help in increasing the efficiency of waste collection, recycling and disposal operations that have become more complicated and unpredictable. Early work focusing on prediction of e-waste generation includes a wide range of methodologies. Among these, grey forecasting models have drawn attention due to their capability to provide meaningful results with relatively small-sized or limited data. The performance of grey models heavily rely on their parameters. The purpose of this study is to present a novel forecasting technique for e-waste predictions with multiple inputs in presence of limited historical data. The proposed nonlinear grey Bernoulli model with convolution integral NBGMC(1,n) improved by Particle Swarm Optimization (PSO) demonstrates superior accuracy over alternative forecasting models. The proposed model and its findings are delineated with the help of a case study utilizing Washington State e-waste data. The results indicate that population density has a major impact on the generated e-waste followed by household income level. The findings also show that the e-waste generation forms a saturated distribution in Washington State. These results can help decision makers plan for more effective reverse logistics infrastructures that would ensure proper collection, recycling and disposal of e-waste.

Pressure from legislation and customers has motivated companies to consider reverse logistics (RL) in their operations. Since it is a complex procedure that requires an adequate system, the recent trend consists in outsourcing RL to third-party reverse logistics providers (3PRLPs). This paper provides the background of sustainable triple bottom line theory with focus on economic, environmental, and social aspects under 3PRL concerns. The relevant sustainability criteria are used in a case study conducted in cooperation with an Indian automotive remanufacturing company. To select the most preferred service provider, we use a hybrid method combining a variant of ELECTRE I accounting for the effect of reinforced preference, the revised Simos procedure, and Stochastic Multi-criteria Acceptability Analysis. The incorporated approach exploits all parameters of an outranking model compatible with the incomplete preference information of the Decision Maker. In particular, it derives the newly defined kernel acceptability and membership indices that can be interpreted as a support given to the selection of either a particular subset of alternatives or a single option. The proposed ELECTRE-based method enriches the spectrum of multiple criteria decision analysis approaches that can be used to effectively approach the problem of the 3PRLP selection. As indicated by the extensive review presented in the paper, this application field was so far dominated by Analytic Hierarchy Process and TOPSIS, whose weaknesses can be overcome by applying the outranking methods.

As the number of scrap cars increases rapidly, the management of their reverse logistics (RL) process has become an urgent issue for decision-makers in contemporary China. The process, capable of generating profits as well as minimizing negative environmental effects, mainly includes products acquisition, collection, inspection and disposition. Although the selection of take-back patterns is an essential part in the process, it is not well addressed by existing studies. Involving numerous related factors, the evaluation and selection of the take-back patterns of scrap cars are a comprehensive problem that can be formulated as a multi-criteria decision making (MCDM) problem. In this paper, we propose a complete method to solve it systematically. Three patterns quite suitable for China are presented and a novel criteria system is developed for choosing the best one. The unavoidable uncertainty in practical situation is also considered in this work. We integrate the grey method and Decision-making and Trial Evaluation Laboratory (grey DEMATEL) to determine the weights of nine criteria, and a fuzzy method and Vlse Kriterijumska Optimizacija I Kompromisno Resenje (fuzzy VIKOR) are adopted to rank the three patterns based on expert evaluations. Sensitivity analysis is also used to test and verify the robustness of the proposed method. Finally, we draw a conclusion that the Producer Responsibility Organization Take-back pattern whose main body is formed by manufacturer union organization is most suitable for China. The study results could provide a theoretical guideline for decision-makers in China as well as other developing countries.