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

Abstract

Energy efficiency has become a major concern for manufacturing companies not only due to environmental concerns and stringent regulations, but also due to large and incremental energy costs. Energy-efficient scheduling can be effective at improving energy efficiency and thus reducing energy consumption and associated costs, as well as pollutant emissions. This work reviews recent literature on energy-efficient scheduling in job shop manufacturing systems, with a particular focus on metaheuristics. We review 172 papers published between 2013 and 2022, by analyzing the shop floor type, the energy efficiency strategy, the objective function(s), the newly added problem feature(s), and the solution approach(es). We also report on the existing data sets and make them available to the research community. The paper is concluded by pointing out potential directions for future research, namely developing integrated scheduling approaches for interconnected problems, fast metaheuristic methods to respond to dynamic scheduling problems, and hybrid metaheuristic and big data methods for cyber-physical production systems.

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

“…As described in a literature review regarding EEJSP by Fernandes et al (2022), numerous papers have introduced additional problem features such as variable machine operation speeds (MS) (Salido et al, 2016), which considers machines may complete operations faster or slower by using more energy or less energy respectively; or vehicle transportation with a limited number of vehicles (VS) (Zhou & Lei, 2021), which incorporates vehicle routing decisions into the EEJSP. However, papers seldom combine multiple of these features at once.…”

“…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).…”

Enhanced memetic search for reducing energy consumption in fuzzy flexible job shops

“…Despite being a relatively new topic, it is possible to find in the literature several contributions concerned with reducing energy consumption in job shop scheduling problems [48]. They can be organized into three different non-exclusive high-level approaches.…”

CEA-FJSP: Carbon emission-aware flexible job-shop scheduling based on deep reinforcement learning

“…Production scheduling is a subclass of combinational optimization problems aiming to sequence jobs to machines toward the optimization of one or more scheduling objectives (Fernandes et al, 2022). Production scheduling can be classified into many types according to its inherent properties.…”