Multi-period Closed-loop Supply Chain Research Articles

Multi-period Closed-loop Supply Chain Network Equilibrium with Distributor Engaging in Remanufacturing

Multi-period Closed-loop Supply Chain Network Equilibrium with Distributor Engaging in Remanufacturing

Performance analysis of a closed-loop supply chain with incentive-dependent demand and return

Due to environmental impacts and economic benefits, establishment of closed-loop supply chain (CLSC) system has been getting increasing attention both in industry as well as in academia. In order to improve the collection and recovery process, we develop a system dynamics framework for a CLSC under incentive-dependent demand and return. In this paper, first, a relationship between “the amount of incentive offered to a consumer” and “the probability with which the consumer would return the used product to retailer” is established. Then, a recovery framework is developed to model the return function of used products and the demand function for new products. Lastly, the developed framework is integrated with a system dynamics model of a multi-period CLSC, which investigates the system’s performance both in terms of profitability and customer satisfaction when an incentive offer is present. The performance analysis of the system dynamics simulation model indicates that the inclusion of incentive offer enhances the demand, collection, and remanufacturing process, which in turn results in more profit and better fill rate for the entire CLSC. The analysis suggests a trade-off between the extra revenue generated through remanufacturing of collected used products and the cost of collection due to the employment of incentive offers. The significance of important parameters, their interactions, and the type of their impact on profit and on fill rate performance are examined through sensitivity and statistical analysis. Additionally, we also compare the performances of the proposed CLSC model in presence as well as in absence of incentive offer.

Multi-period closed-loop supply chain network equilibrium with carbon emission constraints

One effective method to reduce industry's environmental footprint is the use of a closed-loop supply chain (CLSC). Based on the traditional forward supply chain, the CLSC incorporates collection and remanufacturing to reduce waste and satisfy environmental goals. In this study, we consider the CLSC network equilibrium, comprising manufacturers, retailers, demand markets and recyclers, in a multi-period planning horizon. In the scenario considered, all of the manufacturers make homogeneous products and have two types of mandatory carbon emission constraints during manufacturing/remanufacturing; one of which can be called a periodic carbon emission constraint, and the other, a global carbon emission constraint. Based on variational inequality and complement theory, the optimal behaviors and the equilibrium conditions of various players in the CLSC network are formulated, and the governing network equilibrium model is established. A modified projection and contraction algorithm is used to solve the model. The validity of the proposed model and the impact of the two types of carbon emission constraints on network equilibrium are demonstrated with numerical examples. The managerial insights obtained in this paper lead to areas of future research into CLSC networks.

Deterministic and Metaheuristic Solutions for Closed-loop Supply Chains with Continuous Price Decrease

In a global economy, an efficient supply chain as a main core competency empowers enterprises to provide products or services at the right time in the right quantity, at a low cost. This paper is to plan a single product, multi-echelon, multi-period closed loop supply chain for high-tech products (which have continuous price decrease). Ultimately, considering components rated to procurement, production, distribution, recycling and disposal, the final decisions are made. To solve the mixed integer linear programming (MILP) model for closed loop supply chain (CLSC) network plan of the paper, four heuristics-based methods including genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), and artificial bee colony (ABC) are proposed. Finally, the computational results of these four methods are compared with the solutions obtained by GAMS optimization software. The solution revealed that the ABC methodology performs comparatively better in terms of both quality of solutions and computational time.

Design and analysis of a closed-loop supply chain in presence of promotional offer

Closed-loop supply chain (CLSC) has been an area of increasing attention during the last decade due to its economic impact, strict legislations and social awareness. Profitability of any remanufacturing process is mainly driven by the effective and efficient acquisition of used products. In this paper, a market-driven recovery framework is proposed to acquire the used products from consumers by taking into account their buying patterns as well as the willingness to accept a promotional offer. The proposed framework is integrated with an optimisation model for a multi-period CLSC to maximise the overall profit of the system by determining the optimal discount amount along with the decision of finding optimal manufacturing, remanufacturing and disposal quantity. The findings of the study provide several insights to the decision-makers that lead to better performance of the entire closed-loop system. The analysis suggests a trade-off among manufacturing/remanufacturing cost, penalty cost for not fulfilling the legislation criterion and collection cost due to the employment of promotional offer. Sensitivity analysis is performed to observe the effect of various important parameters on the system’s performance. Finally, a comparative study has been conducted to examine the performance of the CLSC with vs. without promotional offer.