A bi-level multi objective programming approach to solve grey problems: an application to closed loop supply chain network

Abstract

Purpose

This paper aims to develop a grey decentralized bi-level multi-objective programming (MOP) model. A solution approach is also proposed for the given model. A production and transportation plan for a closed loop supply chain network under an uncertain environment and different scenarios is also developed.

Design/methodology/approach

In this paper, we combined grey linear programming (GLP) and fuzzy set theory to present a solution approach for the problem. The proposed model first solves the given problem using GLP. Membership functions for the decision variables under the control of the leader and for the goals are created. These membership functions are then used to generate the final solutions.

Findings

This paper provides insight for fomenting the decision-making process while providing a more flexible approach in uncertain logistics problems. The deviations of the final solution from the individual best solutions of the two levels are very little. These deviations can further be reduced by adjusting the tolerances associated with the decision variables under the control of the leader.

Practical implications

The proposed approach uses the concept of membership functions of linear form, and thus, requires less computational efforts while providing effective results. Most of the organizations exhibit decentralized decision-making under the presence of uncertainties. Therefore, the present study is helpful in dealing with such scenarios.

Originality/value

This is the first time, formulation of a decentralized bi-level multi-objective model under a grey environment is carried out as per the best knowledge of the authors. A solution approach is developed for bi-level MOP under grey uncertainty.

Citation

Asim, Z., Jalil, S.A.A., Javaid, S. and Muneeb, S.M. (2021), "A bi-level multi objective programming approach to solve grey problems: an application to closed loop supply chain network", Journal of Modelling in Management, Vol. 16 No. 3, pp. 765-798. https://doi.org/10.1108/JM2-07-2018-0107