Abstract
PurposeThe 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/approachThis 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.FindingsThe 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/valueThis 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.
Subject
Industrial and Manufacturing Engineering,Strategy and Management,Safety, Risk, Reliability and Quality
Reference33 articles.
1. New integration of preventive maintenance and production planning with cell formation and group scheduling for dynamic cellular manufacturing systems;Journal of Manufacturing Systems,2020
2. A hybrid algorithm for flexible job-shop scheduling problem with setup times;International Journal of Production Management and Engineering,2017
3. Material flow optimisation of flexible manufacturing system using real coded genetic algorithm (RCGA),2018
4. Multi-objective genetic algorithm for energy-efficient hybrid flow shop scheduling with lot streaming;Annals of Operations Research,2020
5. A memetic algorithm for energy-efficient scheduling of integrated production and shipping;International Journal of Computer Integrated Manufacturing,2022