Closed-loop Supply Chain Design Research Articles

Emission policies and their analysis for the design of hybrid and dedicated closed-loop supply chains

Government legislation and consumer awareness on carbon emissions have required industries to seek alternative ways to reorganize their supply chains. Therefore, industries have to consider not only the activities in the forward supply chain but also in the reverse supply chain. An integrated supply chain, that combines the activities of both the forward and reverse supply chains, is referred to as the closed loop supply chain (CLSC). There are two main types of CLSC structures: hybrid and dedicated. In this research, we analyze the effect of carbon emissions consideration on the design of both hybrid and dedicated CLSCs. In particular, we develop a set of integrated mixed integer linear programming (MILP) models to quantify and compare both of the environmental and economic impacts of carbon emissions policies (carbon cap, carbon tax, and carbon cap-and-trade), and market factors (scale effect, product return rate, and the customer demand) on the design of CLSCs with respect to both CLSC structures. Our analysis shows that when market conditions are considered, total cost and total emissions are more sensitive to customer demand than to the scale effect and the product return rate. When an industry faces fluctuation on the quantity of the returned products, adopting a dedicated CLSC would lead to a lower total cost. Under cap-and-trade policy, dedicated CLSC can produce less total emissions. We also observe that dedicated CLSC is more cost efficient under carbon cap policy and hybrid CLSC is more emissions efficient when the carbon tax is introduced.

A new bi-objective model for a closed-loop supply chain problem with inventory and transportation times

This paper considers a closed-loop supply chain design problem including several producers, distributors, customers, collecting centers, recycle centers, revival centers, raw materials customers considering several periods, existing inventory and shortage in distribution centers, and transportation cost and time. This problem is formulated as a bi-objective integer nonlinear programming model. The aim of this model is to determine numbers and locations of supply chain elements, their capacity levels, allocation structure, mode of transportation between them, amount of transported products between them, amount of existing inventory, and shortage in distribution centers in each period to minimize the sum of system costs and transportation time in the network. To validate this model and show the applicability of it for small-sized problems, GAMS software is used. Because this given problem is NP-hard, a Bee Colony Optimization (BCO) algorithm is proposed to solve medium and large-sized problems. Furthermore, to examine the efficiency of the proposed BCO algorithm, the associated results are compared with the results obtained by the Genetic Algorithm (GA). Finally, the conclusion is provided. (C) 2016 Sharif University of Technology. All rights reserved.

Advanced cross-entropy in closed-loop supply chain planning

Developing new methodologies for nondeterministic polynomial (NP-hard) problems such as supply chain network design is always a major consideration for academia and practitioners. In this paper a cross-entropy (CE) based solution methodology is developed in order to cope with complex combinatorial problems. The NP-hard problem of designing and planning a closed-loop supply chain (CLSC) is considered. Furthermore, a multi-product multi-period CLSC network in a mixed-integer programming structure is regarded. On the other side, cross-entropy is one of the newly developed and successful metaheuristic algorithms. Thus, in order to achieve better solutions in comparison with current solution methodologies, a cross-entropy algorithm is developed for the first time in CLSC design and planning. Then, the capabilities of the cross-entropy algorithm are elevated, in order to achieve solutions that are more robust. Therefore, an algorithm, which is called “advanced cross-entropy” (ACE) is proposed. Finally, two presented CE-based algorithms are compared with a developed genetic algorithm (GA) for the same problem. GA is the most well-known metaheuristic algorithm, which has been abundantly developed in CLSC. Results prove that both of proposed CE-based algorithms dominate current methodologies. Both can find acceptable solutions in comparison with GA. Furthermore, the proposed advanced cross-entropy performs even better than CE in the quality of solutions and time.

Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors

The growing concern for sustainability has forced the researchers and managers to incorporate the environmental and social factors along with the economical factors in the design of supply chains. This paper presents the design and optimization of a multi-objective closed-loop supply chain considering the economical and environmental factors with uncertainty in parameters. The proposed network is modeled as fuzzy multi-objective mixed integer linear programming problem considering multi-customer zones, multi-collection centers, multi-disassembly centers, multi-refurbishing centers, multi-external suppliers, and different product recovery processes; to take care for purchasing cost, transportation cost, processing cost, set-up cost, and capacity constraints simultaneously. The model is solved using an interactive $$\upvarepsilon $$ -constraint method. A case example is solved using LINGO 14.0 to demonstrate the significance and applicability of the developed fuzzy optimization model for closed-loop supply chain.

Design and Planning of Closed-Loop Supply Chains: A Risk-Averse Multistage Stochastic Approach

This paper presents a comprehensive risk-averse multistage model for dealing with the design and planning problem of a closed-loop supply chain (CLSC) considering adjustments in the supply chain structure during the planning horizon as well as uncertainty in supply and customer demands. The stochastic approach addresses a problem of a generic multiperiod multiproduct CLSC with a general network structure of ten types of entities, risk-averse objectives associated with both costs and revenues, a flexible network structure, transport capacity, salvage grade of returned products, CO2 emissions of the transport system, as well as minimum and maximum storage and processing capacity limits for network entities. A key aspect of the proposed work is to cope with a multiobjective function where the maximization of the expected profit is combined with the fundamental idea of risk management incorporated through the explicit trade-off between risk associated with costs and revenues. To the authors’ knowledge, the jo...

Robust environmental closed-loop supply chain design under uncertainty

With the fast developments in product remanufacturing to improve economic and environmental performance, an environmental closed-loop supply (ECLSC) chain is important for enterprises' competitiveness. In this paper, a robust ECLSC network is investigated which includes multiple plants, collection centers, demand zones, and products, and consists of both forward and reverse supply chains. First, a robust multi-objective mixed integer nonlinear programming model is proposed to deal with ECLSC considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. The first objective function aims to minimize the economical cost and the second objective function is to minimize the environmental influence. Then, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. Finally, numerical example has been presented to test the model. The results indicate that the proposed model is applicable in practice.

Integrating financial risk measures into the design and planning of closed-loop supply chains

In this paper, a mixed integer linear programming (MILP) formulation is proposed that integrates financial risk measures into the design and planning of closed-loop supply chains, considering demand uncertainty of final products. The goal is to maximize the supply chain expected net present value (ENPV), while simultaneously minimizing the associated risk. The augmented ɛ-constraint method is used to generate an approximation to the Pareto-optimal curve for each risk measure. Four different risk measures, most popular measures within the literature, are implemented, compared and directions for their usage by decision makers are discussed. Managerial insights are outlined based in decision makers’ risk profile and goal of the risk minimization. A European supply chain case study is explored.

A particle swarm approach for optimizing a multi-stage closed loop supply chain for the solar cell industry

In order to implement sustainable strategies in a supply chain, enterprises should provide highly favorable and effective solutions for reducing carbon dioxide emissions, which brings out the issues of designing and managing a closed-loop supply chain (CLSC). This paper studies an integrated CLSC network design problem with cost and environmental concerns in the solar energy industry from sustainability perspectives. A multi-objective closed-loop supply chain design (MCSCD) model has been proposed, in consideration of many practical characteristics including flow conservation at each production/recycling unit of forward/reverse logistics (FL/RL), capacity expansion, and recycled components. A deterministic multi-objective mixed integer linear programming (MILP) model capturing the tradeoffs between the total cost and total CO2 emissions was developed to address the multistage CSLC design problem. Subsequently, a multi-objective PSO (MOPSO) algorithm with crowding distance-based nondominated sorting approach is developed to search the near-optimal solution of the MCSCD model. The computational study shows that the proposed MOPSO algorithm is suitable and effective for solving large-scale complicated CLSC structure than the conventional branch-and-bound optimization approach. Analysis results show that an enterprise needs to apply an adequate recycling strategy or energy saving technology to achieve a better economic effectiveness if the carbon emission regulation is applied. Consequently, the Pareto optimal solution obtained from MOPSO algorithm may give the superior suggestions of CLSC design, such as factory location options, capacity expansion, technology selection, purchasing, and order fulfillment decisions in practice. An integrated CLSC problem by using PSO algorithm in the solar energy industry has been studied.A deterministic multi-objective mixed integer programming model has been established.The results Pareto CLSC solutions between CPLEX and MOPSO have been compared.MOPSO provides a rapid algorithm to search Pareto solution compare to CPLEX in large scale issues.

Supply chain design for unlocking the value of remanufacturing under uncertainty

Owing to the technological innovations and the changing consumer perceptions, remanufacturing has gained vast economic potential in the past decade. Nevertheless, major OEMs, in a variety of sectors, remain reluctant about establishing their own remanufacturing capability and use recycling as a means to satisfy the extended producer responsibility. Their main concerns seem to be the potential for the cannibalization of their primary market by remanufactured products and the uncertainty in the return stream in terms of its volume and quality. This paper aims at assisting OEMs in the development of their remanufacturing strategy, with an outlook of pursuing the opportunities presented by the inherent uncertainties. We present a two-stage stochastic closed-loop supply chain design model that incorporates the uncertainties in the market size, the return volume as well as the quality of the returns. The proposed framework also explicitly represents the difference in customer valuations of the new and the remanufactured products. The arising stochastic mixed-integer quadratic program is not amenable to solution via commercial software. Therefore, we develop a solution procedure by integrating sample average approximation with the integer L-shaped method. In order to gather solid managerial insights, we present a case study based on BSH, a leading producer of home appliances headquartered in Germany. Our analysis reveals that, while the reverse network configuration is rather robust, the extent of the firm’s involvement in remanufacturing is quite sensitive to the costs associated with each product recovery option as well as the relative valuation of the remanufactured products by the customers. In the context of the BSH case, we find that among the sources of uncertainty, the market size has the most profound effect on the overall profitability, and it is desirable to build sufficient expansion flexibility in the forward network configuration.

Coordination of the closed-loop supply chain for product line design with consideration of remanufactured products

Research on integrated products and the closed-loop supply chain design has focused on the competition and coordination of manufacturers and supply chain partners. However, the coordination of supply chain partners for product line design (PLD) with consideration of remanufactured products has not been addressed in previous studies. The present study investigates the coordination of a manufacturer and supply chain partners using a game theoretical model for the design of product lines that contain both new and remanufactured products. A multiobjective optimization model based on Stackelberg game theory is formulated to determine the product line solutions, pricing decisions of supply chain partners, and product return rate for remanufacturing. A non-dominated sorting genetic algorithm II is introduced to determine the Pareto optimal solutions of the multiobjective optimization problems. A case study is conducted on the coordination of a manufacturer, chain retailers, and a remanufacturer for the PLD of tablet PCs with consideration of remanufactured tablet PCs to illustrate the applicability and effectiveness of the proposed methodology.

Addressing environmental concerns in closed loop supply chain design and planning

This research integrates environmental concerns in a closed loop supply chain (SC) model to improve overall SC performance in terms of sustainability and business operational metrics. The model includes modular product design for facilitating faster manufacturing, disassembly, remanufacturing, refurbishing, and repairing using new, reusable and repairable components, and modular subassemblies. The model plans sustainable module formation; production process for components and products, and transportation and distribution routes to obtain optimum business performances and address environmental concerns for harmful emissions and spent energy. The research proposes to collect end-of-life and other customer returned products through retailers by motivating retailers and customers with an incentive scheme. Refurbishing, recovery, and repair works for the components and modules are managed by the SC through a pool of service providers trained by their manufacturing plants for such recovery works. Numerical example illustrates the applicability of the approach and the model.

The Impact of Carbon Policies on Closed-loop Supply Chain Network Design

Due to increase in environmental concerns along with stringent government legislations, forcing industry practitioners and policy makers to take a fresh look at the impact of their supply chain activities on the environment. Various carbon regulatory mechanisms have been proposed by governmental agencies around the globe, which aims to curb the carbon emission. In this paper, optimization models based on carbon regulatory policies for a closed-loop supply chain design and logistics operations are presented. Specifically, the following three common regulatory policies are considered: strict carbon caps, carbon tax, and carbon cap-and-trade. The proposed models optimize not only costs but also emissions in the supply chain operations. The models capture: the trade-offs that exist between location and transportation modes decisions; and the trade-offs between costs and emissions in the supply chain operations. Numerical experiment illustrates different policies and their impact on the costs and the effectiveness of emission reduction. The results from the models can help policy makers to predict the impact of regulatory policies on overall emissions in the supply chain operations.

Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS

Abstract Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network

Designing closed-loop supply chains with nonlinear dimensioning factors using ant colony optimization

Closed-loop supply chain (CLSC) design implies the modelling of the forward and the reverse flows of products in an integrated way. This paper introduces nonlinear dimensioning factors in the design of CLSC and uses ant colony optimization to optimize the design of the supply chain. The proposed algorithm is called SCAnt-NLDesign. The modelled nonlinear dimensioning factors are: cost variations in transportation distances between facilities (tapering principle), scale economies related to transported quantities, and scale economies regarding the facilities' capacity. Results show that the proposed SCAnt-NLDesign algorithm reduced the total cost in 44 %, when compared to a linear formulation of a CLSC. Note also that a mixed integer linear programming implementation of the nonlinear CLSC was not able to get closed to the optimal solution, given worse results than the linear CLSC.

An environmentally conscious robust closed-loop supply chain design

Strategic network planning activities of national and even more of multinational companies are recently influenced by an increasing number of laws and regulations regarding environmental issues. On top of that non-governmental bodies pose additional pressures on companies to comply with environmental as well as social standards. Therefore, this paper presents a multi-objective and robust optimization-based closed-loop supply chain design model. In the objective function both expected total costs as well as carbon dioxide equivalents (CO2e) are to be minimized. Customer demands and used-product return ratio are considered to be uncertain. The model supports the decision makers to better understand the influence of parameters and their values as well as the interrelationship between these when deciding on the structure of the entire value chain.

Multi-period design and planning of closed-loop supply chains with uncertain supply and demand

A design and planning approach is proposed for addressing general multi-period, multi-product closed-loop supply chains (CLSCs), structured as a 10-layer network (5 forward plus 5 reverse flows), with uncertain levels in the amount of raw material supplies and customer demands. The consideration of a multi-period setting leads to a multi-stage stochastic programming problem, which is handled by a mixed-integer linear programming (MILP) formulation. The effects of uncertain demand and supply on the network are considered by means of multiple scenarios, whose occurrence probabilities are assumed to be known. Several realistic supply chain requirements are taken into account, such as those related to the operational and environmental costs of different transportation modes, as well as capacity limits on production, distribution and storage. Moreover, multiple products are considered, which are grouped according to their recovery grade. The objective function minimizes the expected cost (that includes facilities, purchasing, storage, transport and emissions costs) minus the expected revenue due to the amount of products returned, from repairing and decomposition centers to the forward network. Finally, computational results are discussed and analyzed in order to demonstrate the effectiveness of the proposed approach. Due to the large size of the addressed optimization problem containing all possible scenarios for the two uncertain parameters, scenario reduction algorithms are applied to generate a representative, albeit smaller, subset of scenarios.

Closed-loop supply chain network design: A financial approach

The more common approaches used in the SCM consider only the physical logistic operations and ignore the financial aspects of the chain. This paper presents a financial approach to model a closed-loop supply chain design in which financial aspects are explicitly considered as exogenous variables. The model decides to determine the strategic decisions as well as the tactical decisions. The main contribution of this paper is to incorporate the financial aspects (i.e. current and fixed assets and liabilities) and a set of budgetary constraints representing balances of cash, debt, securities, payment delays, and discounts in the supply chain planning. Moreover, the financial approach applies the change in equity (instead of the measure of profit/cost in traditional approaches) as the objective function to be optimized in the presented model.To show the advantages of the presented approach, the results attributed to the financial approach and the traditional approach are compared, where the latter firstly decides on operations and fits finances afterwards. The results indicate that the traditional approach leads to lower change in equity compared to the financial approach. This fact illustrates the inadequacy of treating process operations and finances in isolated environments and pursuing as objective myopic performance indicators such as profit or cost. Moreover, a sensitivity analysis of the parameters using ANOVA for different levels of the parameters under different customer order patterns is performed to enhance the managerial insights of the study. The results clearly reveal the better improvement of using the financial approach over the traditional approach, and convince the decision makers to take advantage of the proposed approach.

Sustainable product and supply chain design decisions under uncertainties

Nowadays enterprises are forced to consider broader aspects while designing and manufacturing products. The scarcity of natural resources as well as stern environmental regulations have obliged enterprises to develop new products that can satisfy profit, people, and planet during its life cycle. In the last decade, many research efforts have incorporated supply chain and sustainability into product design. However, most of the studies provided deterministic based methodologies. Enterprises who intend to improve sustainability under various uncertainties couldn’t find an appropriate method. The method based on fuzzy set theory is proposed in this study to tackle the uncertainty of supply chain and highlight the sustainable design concepts considering product disassemblability, closed loop supply chain performance, and carbon footprint. A scooter company case study is presented to demonstrate the advantage of this methodology. Because the risk of impreciseness of data could be reduced, decision-makers only have to provide suitable regions to express the costs and lead-time instead of exact numbers. This method can support decision-makers to make the most sustainable decisions which contain both product and the closed loop supply chain design based on the concern of lead-time, cost, and carbon footprint. As a result, sustainable product and supply chain design under uncertainties can be addressed at the product design stage.

Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm

Designing and planning a closed-loop supply chain in a comprehensive structure is vital for its applicability. To cope with the design and planning issue of a comprehensive closed-loop supply chain network, this paper develops an extended model, which is multi-echelon, multi-product, and multi-period in a mixed integer linear programming framework. The word “comprehensive,” in our mathematical approach, in designing and planning a closed-loop supply chain problem, can be analyzed from two complementary angles: including all possible entities (facilities) of a real condition and considering minimum limitations on possible flows between entities. In our proposed model, customers can be supplied via manufacturers, warehouses, and distributors, as an example. The proposed model is solved by CPLEX optimization software and by a developed genetic algorithm. During this computational analysis, we compare results of proposed pretuned genetic algorithm with a global optimum of CPLEX solver. Then, a sufficient number of large-size instances are generated and solved by the proposed genetic algorithm. To the best of our knowledge, there has been no similar multi-period multi-product closed-loop supply chain design and planning problem utilizing any kind of meta-heuristics let alone genetic algorithms. Therefore, in this issue, it is an original research, and results prove the acceptable performances of the developed genetic algorithm.

Multi-level reverse logistics network design under uncertainty

Localising facilities and assigning product flows in a reverse logistics environment is a crucial but difficult strategic management decision, certainly when value decay plays an important part. Despite numerous publications regarding closed-loop supply chain design, very few addressed the impact of lead times and the high level of uncertainty in reverse processes. In this article, a single product reverse logistics network design problem with multiple layers and multiple routings is considered. To this end, a new advanced strategic planning model with integrated queueing relationships is built that explicitly takes into account stochastic delays due to various processes like collection, production and transportation, as well as disturbances due to various sources of variability like uncertain supply, uncertain process times, unknown quality, breakdowns, etc. Their impact is measured by transforming these delays into work-in-process, which affects profit through inventory costs. This innovative modeling approach is difficult to solve because of both combinatorial and nonlinear continuous relationships. The differential evolution algorithm with an enhanced constraint handling method is proposed as an appropriate heuristic to solve this model close to optimality. A number of scenarios for a realistic case illustrate the power of this optimisation tool.