Robust optimization approach to production system with failure in rework and breakdown under uncertainty: evolutionary methods

Abstract

Purpose

This paper aims to consider a multi-site production planning problem with failure in rework and breakdown subject to demand uncertainty.

Design/methodology/approach

In this new mathematical model, at first, a feasible range for production time is found, and then the model is rewritten considering the demand uncertainty and robust optimization techniques. Here, three evolutionary methods are presented: robust particle swarm optimization, robust genetic algorithm (RGA) and robust simulated annealing with the ability of handling uncertainties. Firstly, the proposed mathematical model is validated by solving a problem in the LINGO environment. Afterwards, to compare and find the efficiency of the proposed evolutionary methods, some large-size test problems are solved.

Findings

The results show that the proposed models can prepare a promising approach to fulfill an efficient production planning in multi-site production planning. Results obtained by comparing the three proposed algorithms demonstrate that the presented RGA has better and more efficient solutions.

Originality/value

Considering the robust optimization approach to production system with failure in rework and breakdown under uncertainty.

Citation

Rabbani, M., Manavizadeh, N. and Hosseini Aghozi, N.S. (2015), "Robust optimization approach to production system with failure in rework and breakdown under uncertainty: evolutionary methods", Assembly Automation, Vol. 35 No. 1, pp. 81-93. https://doi.org/10.1108/AA-05-2014-038