Planning Of Closed-loop Supply Chain Research Articles

Short-lived product returns forecasting when customers are unwilling to return the product: A grey-graphical evaluation and review technique

Consumer electronics, such as mobile phones, laptops, and tablets, encounter the challenge of short product life cycles due to the rapid advancement of technology, marketing models, and rapidly changing consumer tastes. This study attempts to provide a forecasting model for a short-lived product for closed-loop supply chain planning. Importantly, we model the setting where the customers prefer to store the product instead of returning it for various reasons such as security and secondary use. We proposed a generic method that can be implemented for any technology-driven product's returns and remanufacturing process. We used the Graphical Evaluation and Review Technique to calculate the probability and quantity of product returns, the quantity of modules, parts, materials, and disposal. Then, we employed the Grey-Graphical Evaluation and Review Technique to calculate the expected time interval for core returns and the expected interval for the availability of module/parts/material for reconditioning/ remanufacturing of the product. An illustrative case study demonstrates the results of the forecasting model. Further, we analyzed the effect of customers' unwillingness to return the product on the reverse network. Our analysis asserts that the customer storing an end-of-use product has a greater impact on the probability of overall product return. Our analysis found that when customers tend to return the product instead of storing it for secondary use, the equivalence probability of returns can increase up to 91 %. In addition, we also address the effect of product return on the “ecological rucksack” of mobile phones. This can help policymakers design hassle-free return experiences and attractive customer incentives.

Integration of flexibility in reverse supply chain planning process

This research formulates reverse supply chain (RSC) flexibility criteria and integrates them in a closed loop supply chain planning model for improving overall business performances and sustainability. Proposed RSC flexibility criteria include: a) channel selection strategy to collect customer returned products (returnable); b) product design and recovery strategy to recover components, modules, and products from returnable; c) product categorisation strategy for marketing new, recovered, and mixed quality level product. Each flexibility criteria is formulated independently to provide options to supply chains (SCs) to incorporate all or select ones in their planning process. The benefits, applicability and integration procedures of proposed flexibility considerations are demonstrated through a numerical example.

Mathematical Formulation and Solving of Green Closed-loop Supply Chain Planning Problem with Production, Distribution and Transportation Reliability

In this paper, developed a new multi-product, multi-period, and multi-level closed-loop green supply chain planning model under uncertain conditions. The formulated model consists of five objective functions, which minimize the cost of the supply chain, minimize the CO2 emission of transportation vehicles, maximize the reliability of manufacturing and distribution centers, maximize the reliability of the transportation system, and maximize the level of service provided. Therefore, the problem of model formulated as a multi-objective mixed integer nonlinear programming. Also, since the proposed model is complex and NP-hard in large size, therefore, for the investigation of the results, we have used a Non-Dominated Sorting Genetic II Algorithm (NSGA-II). In addition, the small of size results of the problem achieved by GAMS software. Therefore, we try to solve these problems by analyzing and comparing them with the help of these algorithm. For this purpose, various size has been considered.

Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty

Abstract Awareness of environmental pollution, interest in recycling, and the importance of closed-loop supply chain management are all on the rise. In a closed-loop supply chain, production planning is influenced by uncertainty not only from customers’ demand, but also from collectors due to difficulties in the reverse logistics flow. Therefore, it is important to develop a robust closed-loop supply chain model to respond to uncertainty from reverse logistics. In this study, we develop a deterministic mixed-integer optimization model and robust counterparts to cope with the uncertainty of recycled products and customer demand in the fashion industry. We show that a robust counterpart with a budget of uncertainty is equivalent to a robust counterpart with a box uncertainty under special conditions. To avoid the conservatism of a robust solution, an alternative optimization problem is developed to take advantage of the budget of uncertainty. To verify the performance of the proposed model, numerical experiments are conducted. The simulation results show the proposed model responds robustly to uncertainty and is superior to a deterministic model and other robust counterparts.

An Adjusted Robust Optimization Method to an Integrated Production-Distribution Planning Problem in Closed-Loop Supply Chains under Uncertainty

In the last decade, planning of closed-loop supply chains in different strategic, tactical, and operational levels has attracted many interests due to economic reasons, environmental challenges, and government legislations. This paper presents a novel linear programming model for the integrated production and distribution planning in closed-loop supply chains under uncertainty. The proposed model involves multi-product and multi-period which considers multiple transportation modes, direct or indirect shipments, advertising costs, and several customer zones for different types of products and also attempts to integrate production and distribution plans in the forward and reverse sides of the closed-loop supply chain, simultaneously. To deal with uncertain input data, a robust optimization counterpart based on polyhedral uncertainty set is developed to obtain optimal solutions immunizing the problem for any realization of uncertain parameters in the given polyhedral uncertainty set. Computation results for a numerical example under different scenarios are discussed to give insights about the features of the proposed robust optimization model in handling the uncertainty of parameters. Finally, some sensitivity analyses are performed to show the behaviour of the robust and deterministic models respect to changes of uncertainty levels of parameters as well as the amounts of important parameters such as demands and returns.

Multi-period planning of closed-loop supply chain with carbon policies under uncertainty

Climate change and greenhouse gases emissions have caused countries to implement various carbon regulatory mechanisms in some industrial sectors around the globe to curb carbon emissions. One effective method to reduce industry environmental footprint is the use of a closed-loop supply chain (CLSC). The decision concerning the design and planning of an optimal network of the CLSC plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. In this context, this research proposes an optimization model for design and planning a multi-period, multi-product CLSC with carbon footprint consideration under two different uncertainties. The demand and returns uncertainties are considered by means of multiple scenarios and uncertainty of carbon emissions due to supply chain related activities are considered by means of bounded box set and solve using robust optimization approach. The model extends further to investigate the impact of different carbon policies such as including strict carbon cap, carbon tax, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. The model captures trade-offs that exist among supply chain total cost and carbon emissions. Also, the proposed model optimizes both supply chain total cost and carbon emissions across the supply chain activities. The numerical results reveal some insightful observations with respect to CLSC strategic design decisions and carbon emissions under various carbon policies and at the end we highlighted some managerial insights.

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.

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...

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.

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.

The impact of two-product joint lifecycles on capacity planning of remanufacturing networks

Abstract Capacity planning in the reverse channel of closed-loop supply chains (CLSCs) involves complex issues due to the different lifecycles of product offerings in combination with the variability regarding product usage time, quality level of used products and return patterns. (Georgiadis, P., Vlachos, D., Tagaras, G., 2006. The impact of product lifecycle on capacity planning of closed-loop supply chains with remanufacturing. Production and Operations Management 15; 514–527) developed a system dynamics (SD) model to study a CLSC with remanufacturing for a single product which incorporates a dynamic capacity modeling approach. We extend this SD model for two sequential product-types under two alternative scenarios regarding the market preferences over the product-types; in the first scenario, the market is considered showing no preferences, while in the second scenario, the demand over a product-type can be satisfied only by providing units of the specific type. We study how the joint lifecycles of two product-types, entry time of the second product-type to the market and used product return patterns affect the optimal policies regarding expansion and contraction of collection and remanufacturing capacities. The results of extensive numerical investigation are tested for their statistical significance using analysis of variance (ANOVA). In the first scenario, the results show that the system performs best when the two lifecycles form a trapezoid pattern for total demand while in the second scenario, when the two lifecycles form a triangular pattern.

The effect of uncertainty on the optimal closed-loop supply chain planning under different partnerships structure

In a global economy, the key to success is providing products around the world at the right time in the right quantity and quality, at a low cost. Efficient supply chains have an important role in guaranteeing this success. Optimized planning of such structures is required and uncertainties regarding product demands and prices, amongst other supply chain conditions, should also be considered. In this paper, we look into supply chain planning decisions that account for uncertainty on product portfolios demand and prices. A multi-period planning model is developed where the supply chain operational decisions on supply, production, transportation, and distribution at the actual period consider the uncertainty on products’ demand and prices. Different decision scenarios, involving the evaluation of the supply chain economical performance, are analyzed (e.g. global operating costs/profit realized) for different criteria on the importance of the partners within the global chain (i.e. partners’ structure). A Mixed Integer Linear Programming (MILP) formulation is formulated for each planning scenario and the optimal solution is reached using a standard Branch and Bound (B&B) procedure. The final results provide details on the supply chain partners production, transportation and inventory, at each planning period, while accounting for the importance of each partner in the global chain as well as demand/price uncertainties. The applicability of the developed formulation is illustrated through the solution of a real case-study involving an industrial chain in the pharmaceutical sector.