A robust bi-level programming model to design a closed loop supply chain considering government collection’s policy

Abstract

This study aims in providing a new approach regarding design of a closed loop supply chain network through emphasizing on the impact of the environmental government policies based on a bi-level mixed integer linear programming model. Government is considered as a leader in the first level and tends to set a collection rate policy which leads to collect more used products in order to ensure a minimum distribution ratio to satisfy a minimum demands. In the second level, private sector is considered as a follower and tries to maximize its profit by designing its own closed loop supply chain network according to the government used products collection policy. A heuristic algorithm and an adaptive genetic algorithm based on enumeration method are proposed and their performances are evaluated through computational experiences. The comparison among numerical examples reveals that there is an obvious conflict between the government and CLSC goals. Moreover, it shows that this conflict should be considered and elaborated in uncertain environment by applying Min-Max regret scenario based robust optimization approach. The results show the necessity of using robust bi-level programming in closed loop supply chain network design under the governmental legislative decisions as a leader-follower configuration.

Impact of Adopting Quick Response and Agility on Supply Chain Competition with Strategic Customer Behavior

“…The results proved that selling price is sensitive to the social cooperation effort. A bi-level mixed integer linear model for closedloop supply chain to collect more used products [33]. In that research, the government and private centers were taken as a leader and followers, respectively.…”

“…The ordering capacity and available budget for the follower are limited by constraints (29)(30)(31)(32). And finally, the constraint (33) shows the positive variables.…”

Dynamic p+q maximal hub location problem for freight transportation planning with rational markets

“…Since its introduction in 1970s, 3,4 bi-level optimization has got tremendous research consideration and it has been broadly used in problems with hierarchical decision-making. These hierarchical decision-making problems arise so often in transportation planning and network capacity expansions, [5][6][7] government policy making, 8,9 and revenue management. 10,11 One of the studies on bi-level models for pricing and revenue management is done by Coˆte´et al 11 They have proposed a first attempt at tackling the airline revenue optimization problem with bi-level models seeking to find a solution for both capacity allocation and pricing sub-problems in a real case study performed at North American airlines.…”

A case study on the coordination scheduling for integrated production and distribution

“…In order to ensure that the operation managers can obtain a solution within a reasonable amount of time, we apply Genetic Algorithm (GA) to the proposed model. It has been successfully applied to multiple related issues, e.g., integrated scheduling and rescheduling of railway transportation [37,42], VRP solution [43], supply chain design problems [44], and transportation scheduling problems [45]; besides, the e ectiveness of the algorithm has been demonstrated. The procedure for the GA employed in this study is similar to the general procedure, as de ned in the following steps:…”