Modeling and solving the multi-objective energy-efficient production planning and scheduling with imperfect maintenance activities

Abstract

Purpose

The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.

Design/methodology/approach

This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.

Findings

The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.

Originality/value

This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.

Citation

Rastgar, I., Rezaeian, J., Mahdavi, I. and Fattahi, P. (2024), "Modeling and solving the multi-objective energy-efficient production planning and scheduling with imperfect maintenance activities", Journal of Quality in Maintenance Engineering, Vol. 30 No. 1, pp. 26-50. https://doi.org/10.1108/JQME-10-2022-0068