Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives

Abstract

In recent years, the application of artificial intelligence has been revolutionizing the manufacturing industry, becoming one of the key pillars of what has been called Industry 4.0. In this context, we focus on the job shop scheduling problem (JSP), which aims at productions orders to be carried out, but considering the reduction of energy consumption as a key objective to fulfill. Finding the best combination of machines and jobs to be performed is not a trivial problem and becomes even more involved when several objectives are taken into account. Among them, the improvement of energy savings may conflict with other objectives, such as the minimization of the makespan. In this paper, we provide an in-depth review of the existing literature on multi-objective job shop scheduling optimization with metaheuristics, in which one of the objectives is the minimization of energy consumption. We systematically reviewed and critically analyzed the most relevant features of both problem formulations and algorithms to solve them effectively. The manuscript also informs with empirical results the main findings of our bibliographic critique with a performance comparison among representative multi-objective evolutionary solvers applied to a diversity of synthetic test instances. The ultimate goal of this article is to carry out a critical analysis, finding good practices and opportunities for further improvement that stem from current knowledge in this vibrant research area.

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

“…Since seven of these papers are reviews, we ended up with 172 papers to be further synthesized, analyzed, and reported on. This is almost four times the number of JSP and FJSP papers reviewed in the most recently published literature reviews [2][3][4]. Moreover, of these 172 papers, 115 have been published since 2019.…”

“…The search was conducted on the two most well-established bibliographic databases: Web of Science™ and Scopus ® . To avoid missing relevant papers due to varying authors' keyword choices, papers were also gathered through backward/forward reference search, including from the most recent review papers [2][3][4][5].…”

“…However, researchers have proved energy-efficient scheduling to be an effective way of reducing energy consumption. Additionally, energy-efficient scheduling has the advantage of not requiring a significant investment, if at all, which is particularly relevant for small and medium enterprises [2][3][4][5][6].…”

Energy-efficient job shop scheduling problem with transport resources considering speed adjustable resources

“…Although energy efficiency in manufacturing systems can be addressed in many ways, such as adopting renewable resources, using improved machinery, and redesigning products and production processes, researchers have proved energy-efficient scheduling to be an effective way of reducing energy consumption. Additionally, scheduling optimization is easier to apply to existing systems and requires far less capital investment, if at all, making it more widely applicable; especially for small and medium enterprises (Fernandes, Homayouni, and Fontes 2022;Para, Del Ser, and Nebro 2022;Gahm et al 2016).Two solution methods were developed to solve the proposed EEJSPT-MS: a bi-objective mixed-integer linear programming model (MILP), and a multi-objective multi-population biased random key genetic algorithm (mpBRKGA). MILP's can provide exact optimal solutions but are usually too computationally demanding and slow to solve large instances in a reasonable timeframe, and thus are deemed unsuitable for real-world applications for this problem.…”

“…Although energy efficiency in manufacturing systems can be addressed in many ways, such as adopting renewable resources, using improved machinery, and redesigning products and production processes, researchers have proved energy-efficient scheduling to be an effective way of reducing energy consumption. Additionally, scheduling optimization is easier to apply to existing systems and requires far less capital investment, if at all, making it more widely applicable; especially for small and medium enterprises (Fernandes, Homayouni, and Fontes 2022;Para, Del Ser, and Nebro 2022;Gahm et al 2016).…”