Sustainable Closed-loop supply chain under uncertainty

TL;DR

This paper develops a multi-objective mixed integer non-linear programming model for a sustainable closed-loop supply chain integrated with smart manufacturing technologies, addressing uncertainties and aiming to minimize costs, environmental impact, and lead time.

Contribution

It introduces a novel optimization model for a smart manufacturing supply chain under uncertainty, incorporating real-time decision-making and sustainability considerations.

Findings

The model effectively balances cost, environment, and lead time.

The case study demonstrates the model's practical applicability.

Sensitivity analysis highlights key parameters affecting solutions.

Abstract

With the fast change of information and communication technologies and global economics manufacturing industry faces the challenges in both market and supply sides. The challenges in the market include short product life cycle, demand uncertainty, and product delivery. Accordingly, supply challenges are the dramatic increase of flexibility in productions and complexity in the supply chain, which result from the changes in the industry and rapid development of ICPT (Information, Communication, and Production Technologies). In this study, we consider a supply chain converged with ICPT, called Smart Manufacturing Supply Chain (SMSC). By investigating the attributes of SMSC, we identify the functional and structural characteristics of SMSC. Tactical supply planning in SMSC recognizes the ability of a pseudo real-time decision-making constrained by the planning horizon. In order to take advantages of SMSC a multi-objective multi-period mixed integer non-linear programming for closed-loop supply chain network design is presented. This model aims to minimizing overall costs environment effects and lead time. To solve the proposed model, considering uncertainties in the problem, the improved epsilon-constraint approach was adopted to transform the multi-objective model into a single-objective one. Then, the Lagrange relaxation method was employed for an effective problem-solving. In the following a case study in the real world was proposed to evaluate the models performance. Finally a sensitivity analysis was carried out to investigate the effects of important parameters on the optimal solution.