A mixed-integer linear programming approach for circular economy-led closed-loop supply chains in green reverse logistics network design under uncertainty

Abstract

PurposeTo maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA.Design/methodology/approachThe design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected.FindingsThe results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased.Originality/value(1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network.

Facility Location Optimization For Technical Inspection Centers Using Multi-Objective Mathematical Modeling Considering Uncertainty

“…The problem of facility location and assignment falls under the category of highly difficult (NP-hard (Non-Polynomial Deterministic Hard problem)) problems, which means that no polynomial-time algorithm has been discovered to solve this complex problem efficiently. Therefore, employing mathematical programming models (such as MILP used by Gharibi et al, [58]) and using solvers like CPLEX to solve this problem in large-scale dimensions is ineffective and requires significant time and cost. Various metaheuristic algorithms have been proposed to solve the facility location and assignment problem in different topics [59].…”

Reverse Logistics Network Design to Estimate the Economic and Environmental Impacts of Take-back Legislation: A Case Study for E-waste Management System in Washington State

“…Kannan et al (2010) presented a development of genetic algorithm (GA) model for recycling spent batteries. Gharibi and Abdollahzadeh (2021) developed a mixed-integer linear programming (MILP) model to maximize the profit of a reverse logistics network and presented a case of mobile phones and digital camera remanufacturing. Sasikumar et al (2010) developed a mixed integer nonlinear programming (MINLP) model to maximize the profit of reverse logistics network and presented a case of truck tire remanufacturing.…”

A Monte Carlo simulation approach to the gap-time relationship in solving scheduling problem

“…On the other hand, in recent years, notable improvements have been observed in the development of modern heuristic approaches towards solving these problems almost completely (zero percent optimality gap) as well as new approximation methods that tend to reduce the consumption power (Gharibi, 2021;Ahmadinejad et. Al., 2020).…”