Decentralized Decision System for Closed-Loop Supply Chain: A Bi-Level Multi-Objective Risk-Based Robust Optimization Approach

Over the course of the last decades, closed-loop supply chains (CLSC) and reverse logistics issues have attracted increasing attention owing to strict environmental laws, social responsibilities, economic interests, and customer awareness. Hence, the issue of closed-loop supply chain and reverse logistics has emerged as a field of research in the new era. This issue has received much attention because it allows recyclable products to return to their original cycle. Therefore, this study primarily intends to present a mathematical model for designing a supply chain network for recycled products. The multi-stage and multi-period objective function of the closed-loop supply chain is presented to meet that aim. In this chain, dismantling, recycling, and disposal centers are considered. The objective function is to reduce the total cost of the closed-loop supply chain. The results of optimizing the mathematical model demonstrate that this model has the necessary efficiency for use in recycled products.

This study develops a multi-echelon closed-loop supply chain (CLSC) optimization model for durable products by considering refurbishment, recycling processes, and carbon regulation. A mixed integer linear programming (MILP) is developed for CLSC network design involving suppliers, manufacturers, refurbishment centers, recycling centers, disposal centers, assembly centers, collection centers, and retailers. A carbon cap-and-trade policy is adopted to lessen the emissions emitted from some activities in CLSC. The objectives of the study are to determine the optimal allocations of echelons and to investigate the influences of collection rate, refurbishment rate, recycling rate and carbon policy on CLSC. A numerical example is presented to illustrate the application of the proposed model, and a sensitivity analysis is provided to investigate the effect of key parameters on the model’s behavior and system performance. The results show that the changes in collection, refurbishment, and recycling rates significantly influence the optimal allocation decisions. The results also show that by adopting a carbon cap-and-trade policy, CLSC can benefit both economically and environmentally. However, the level of benefits obtained will depend significantly on the size of the carbon cap and the selling or buying price of carbon in the market.

The remanufacturing warranty strategy has become an effective mechanism for reducing consumer risk and stimulating market demand in closed-loop supply chain management. Based on the characteristics of consumers’ behavior of purchase decisions, this paper studies the warranty decision model of remanufacturing closed-loop supply chain under the Stackelberg game model. The present study discussed and compared the decision variables, including remanufacturing product pricing, extended warranty service pricing, warranty period and supply chain system profit. The research shows that consumers’ decision-making significantly affirms the dual marginalization effect of the supply chain system while significantly affecting the supply chain warranty decision; the improved revenue sharing contract and the two charge contracts respectively coordinates the manufacturer-led and retail-oriented closed-loop supply chain system, which effectively implements the Pareto improvement of the closed-loop supply chain system with warranty services. In the present study, the model is verified and analyzed by numerical simulation.

This paper explores a dual-channel closed-loop supply chain (CLSC) system consisting of a manufacturer and a retailer. The manufacturer is the CLSC Stackelberg leader. We investigate the effect of exerting marketing effort on the optimal decisions and profits of supply chain members by considering several marketing effort supported models, namely the models when the manufacturer is the investor, the retailer is the investor, and the centralized CLSC system, respectively. We propose a two-part tariff contract for coordinating the CLSC. We analytically reveal that the two-part tariff contract can coordinate the CLSC when the manufacturer is the investor but unable to coordinate supply chain when the retailer is the investor. Finally, we present the numerical analysis to uncover insights on how the consumers' preference toward the direct channel affects the optimal decisions in the dual-channel CLSC system.

Metaheuristic-driven extended exergy accounting for sustainable closed-loop food supply chain management

In reality, there is often information asymmetry between upstream and downstream enterprises in a closed-loop supply chain (CLSC) system, which can have a profound impact on the decisions of member enterprises and the operation of the system. Under asymmetric market demand information, this study examines CSR input and recycling decision making in CLSC. Four decision-making models were developed for CLSC, and the effects of consumer sensitivity to CSR input and demand information asymmetry on CLSC optimization were studied. The results indicate that higher consumer sensitivity to CSR input enhances both CSR levels and recycling rates, benefiting both manufacturer and retailer by increasing profits. In terms of increasing CSR levels, the manufacturer achieves the best results when independently managing CSR input and recycling. However, for improving recycling rates and market demand, the retailer is more effective when responsible for CSR input, with the manufacturer handling recycling. Additionally, demand information asymmetry reduces the manufacturer’s profit but may not affect the retailer’s profit. The retailer–manufacturer cooperation model proves more beneficial for overall CLSC system performance compared to information symmetry.

Increasing environmental concerns require businesses to become more responsive to products that either have been returned or that are at the end of their useful lives. Organization's responsiveness and their reactions toward life cycles of products are critical to achieve sustained success once fluctuations are recurrent and the business environments are turbulent. Life cycles are getting shorter, and efficient handling can save large amounts of cash since many materials can be extracted, reused, and redistributed. Reverse Logistics (RL) and Closed Loop Supply Chains (CLSC) have garnered growing interest as a way to manage this reverse flow of products in a cost effective way. It is discernible that the degree of complexity in closed loop networks is usually higher than open networks. In a closed loop supply chain network, the attempt is to ensure a smooth flow of materials as well as extracting the maximum value from returning and end-of-life goods. Reduction of waste and generating profit for enterprises are two paramount achievements through integrating forward and reverse logistics. By considering various proposed conceptual models for CLSC, in this study at first we present a comprehensive applicable conceptual model. Then, we specifically focus on a CLSC network, which includes multiple plants, collection centers, demand markets, and products. To this aim, a generalized proposed mixed-integer linear programming model is presented that minimizes the total cost. The objective is to know how many, which production plants and Return Processors (Collection sites) should be open, which products, and in which quantities should have stuck. The goal of this model is to minimize the supply chain waste and reduce supply chain costs. In such a way, the supply chain system can be agile, integrated, robust, and lean. The proposed model can be extended to consider more environmental factors. Moreover, the efficacy and efficiency of that have been validated by case study and numerical example.

We propose a decision tree model that considers reverse and forward flows in a closed-loop supply chain (CLSC). Based on observations of three CLSCs, the model considers an environment where there is uncertainty in the quantity of returned used components (and new components from suppliers) with the decision being the incentive offered to each return source. Given that there are multiple suppliers, one must determine which supplier(s) to use and the corresponding capacity to reserve, in order to minimise total system costs. An example and a sensitivity analysis are presented to illustrate the model and to investigate multiple scenarios under various conditions. The analysis demonstrates that the supplier portfolio and returner incentive decisions are strongly linked to the supplier reliability, returned quantities, and the costs of not meeting the demand. Furthermore, the analysis suggests that understanding the behaviour of return sources relative to incentives is the most critical variable to implement the model.

The ever-increasing concerns of the growth in the volume of waste tires and new strict government legislations to reduce the environmental impact of the end-of-life (EOL) tires have increased interest among companies to design a sustainable and efficient closed-loop supply-chain (CLSC) network. In the real world, the CLSC network design is subject to a variety of uncertainties, such as random and fuzzy (epistemic) uncertainties. Designing a reliable and environmentally cautious CLSC with consideration of risks and the uncertainty of the parameters in the network is necessary for a successful supply-chain network. This study proposes a sustainable and environmentally cautious closed-loop supply-chain network for the tire industry, by considering several recovery options, including retreading, recycling, and energy recovery. This study aims to design and develop a robust multi-objective, multi-product, multi-echelon, multi-cycle, multi-capacity, green closed-loop supply-chain network under hybrid uncertainty. There are two types of uncertainties associated with the parameters in the network. There is an uncertainty associated with the demand, which is expressed in some future scenarios according to the probability of their occurrences, as well as fuzzy-based uncertainty associated with return rates, retreading rates, recycling rates, procurement, and production costs, which are expressed with possibilistic distributions. In order to deal with this hybrid uncertainty, a robust fuzzy stochastic programming approach has been proposed, and the proposed mixed integer programming model is applied to a case study in the tire industry to validate the model. The result indicates the applicability of the proposed model and its efficiency to control the hybrid uncertainties and the risk level in the network.

Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach

As environmental problems become more apparent, manufacturers need to balance environmental considerations with economic activities. This is where closed-loop supply chains are gaining attention. However, in addition to demand fluctuations, which are a problem in conventional supply chains, a circular supply chain is unstable in terms of supply, where end-of-life products are collected and reused. This destabilizes not only excess inventory and shortages but also production resources, such as manpower, facilities, and raw materials. This study focuses on the stabilization of the manufacturing system in a closed-loop supply chain. To confirm the dynamic changes in the manufacturing system, we designed a simulation model of a closed-loop manufacturing system and conducted numerical experiments under several scenarios, taking the variation of manufacturing quantity per unit period as an evaluation measure of stability. After showing that unplanned remanufacturing destabilizes the recovery of reusable end-of-life products, we demonstrate that the manufacturing system can be stabilized by appropriately limiting the amount of remanufacturing. However, excessive limits reduce opportunities for remanufacturing end-of-life products and generate adverse economic and environmental impacts. To determine appropriate restrictions, it is necessary to consider the product currently in use by the customer as a virtual inventory and to consider factors such as the quality of the products in the virtual inventory. In the future, we plan to study a system that can dynamically manage remanufacturing quantities based on the status of virtual inventories.

An integrated model for line planning and train scheduling based on the circulation of trains is proposed to reduce passenger dissatisfaction and operation costs for an urban rail transit line. The turnaround operations of trains and their departures from and arrivals at the depot were included in this model. Furthermore, binary variables were introduced to indicate whether train service existed, and a discrete event model was used to determine the order of the train services. In addition, a bi-level optimization approach is proposed to solve the integrated line-planning and train-scheduling problem, in which the number of required train services, the headways between train services, the departure times, and the arrival times were optimized simultaneously on the basis of passenger demand. The performance of the proposed integrated model and bi-level approach is illustrated with a case study of the Beijing Yizhuang line.

PurposeThe purpose of this paper is to unveil the circular economy (CE) values with an ultimate goal to provide tenets in a format or structure that can potentially be used for designing a circular, closed-loop supply chain and reverse logistics.Design/methodology/approachThis is desk-based research whose data were collected from relevant publication databases and other scientific resources, using a wide range of keywords and phrases associated with CE, reverse logistics, product recovery and other relevant terms. There are five main steps in the reformulation of CE principles: literature filtering, literature analysis, thematic analysis, value definition and value mapping.FindingsIn total, 15 CE values have been identified according to their fundamental concepts, behaviours, characteristics and theories. The values are grouped into principles, intrinsic attributes and enablers. These values can be embedded into the design process of product recovery management, reverse logistics and closed-loop supply chain.Research limitations/implicationsThe paper contributes to the redefinition, identification and implementation of the CE values, as a basis for the transformation from a traditional to a more circular supply chain. The reformulation of the CE values will potentially affect the way supply chain and logistics systems considering the imperatives of circularity may be designed in the future.Originality/valueThe reformulation principles, intrinsic attributes and enablers of CE in this paper is considered innovative in terms of improving a better understanding of the notion of CE and how CE can be applied in the context of modern logistics and supply chain management.

The various robust linear programming models investigated so far in the literature essentially appear to be based either on what is referred to as rowwise uncertainty models or on columnwise uncertainty models (these basically assume that the rows, or the columns, of the constraint matrix are subject to changes within a well-specified uncertainty set). In this chapter, we discuss a special case of columnwise uncertainty, namely the subclass of robust linear programming (LP) models with uncertainty limited to the right hand side (RHS) only (this subclass does not appear to have been significantly investigated so far). In this context we introduce the concept of a ‘two-stage robust LP model’ as opposed to the standard case (which might be referred to as a ‘single-stage robust LP model’) and we address the question of whether LP duality can be used to convert a LP problem with RHS uncertainty into a robust LP problem with uncertainty on the objective function. We show how to derive both statements of (a) the dual to the robust model and (b) the robust version of the dual. The resulting expressions of the objective function to be optimized in both cases, appear to be clearly distinct. Moreover, from a complexity point of view, one appears to be efficiently solvable (it reduces to a convex optimization problem), whereas the other, as a nonconvex optimization problem, is expected to be computationally difficult in the general case. As an application of the two-stage robust LP model introduced here, we next investigate the PERT (program evaluation and review technique) scheduling problem, considering two possible natural ways of specifying the uncertainty set for the task durations: the case where the uncertainty set is a scaled ball with respect to the L ∞ norm; the case where the uncertainty set is a scaled Hamming ball of bounded radius (which, though leading to a quite different model, bears some resemblance to the well-known Bertsimas–Sim approach to robustness). We show that in both cases the resulting robust optimization problem can be efficiently solved in polynomial time.

Supply chain management and closed-loop supply chain management (CLSCM) have developed into established concepts in recent years. The related material cycles and product returns form an important part of all related processes with high potential for reducing environmental burden. The paper proposes a framework for (environmentally triggered) closed-loop supply chain management, spanning three different levels: the societal or governance, the chain and the actor level. Within each level, a set of activities or processes can be identified. Taken together, the levels allow a comprehensive analysis of a closed-loop supply chain system. This is illustrated building on two case studies in the textile and apparel industry, where closed-loop supply chains have been designed to take specific apparel products back. The case studies are analyzed against all three levels and allow exemplification of related challenges and interrelations among the three levels. The three levels contribute to the further comprehension of the multiple issues having to be taken into account for successfully implementing closed-loop supply chains.