Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

Abstract

In recent years, people have paid more and more attention to traditional manufacturing's environmental impact, especially in terms of energy consumption and related emissions of carbon dioxide. Except for adopting new equipment, production scheduling could play an important role in reducing the total energy consumption of a manufacturing plant. Machine tools waste a considerable amount of energy because of their underutilization. Consequently, energy saving can be achieved by switching machines to standby or off when they lay idle for a comparatively long period. Herein, we first introduce the objectives of minimizing non-processing energy consumption, total weighted tardiness and earliness, and makespan into a typical production scheduling model-the job shop scheduling problem, based on a machine status switching framework. The multi-objective genetic algorithm U-NSGA-III combined with MME (a heuristic algorithm combined with the MinMax (MM) and Nawaz-Enscore-Ham (NEH) algorithms) population initialization method is used to solve the problem. The multi-objective optimization algorithm can generate a Pareto set of solutions so that production managers can flexibly select a schedule from these non-dominated schedules based on their priorities. Three sets of numerical experiments have been carried out on the extended Taillard benchmark to verify this three-objective model's effectiveness and the multi-objective optimization algorithm. The results show that U-NSGA-III has obtained better Pareto solutions in most test problem instances than NSGA-II and NSGA-III. Furthermore, the non-processing energy consumption is reduced by 46%-69%, which is 13-83% of the total energy consumption.INDEX TERMS Job shop scheduling, energy efficiency, unified multi-objective genetic algorithm, machine status switching.

Machine Scheduling Approach for the Sustainable Development of a Residential-Industrial Area

“…The selected 12 Pareto solutions are processed using a fuzzy decision-making technique that can incorporate the two sustainable criteria: non-processing energy consumption and total disturbance noise. Our proposed approach extends the current research boundary of the scheduling approach with TWET and NEC consideration [21] by introducing the novel TDN indicator and time-varying electricity price condition [22]. The analysis results comprise a set of optimality values for all Pareto solutions under 5 scenarios with varying weight importance.…”

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

“…The third strategy has been considered in 21 papers. Some of these papers include decisions on turning machines off while idle (e.g., [24][25][26][27][28][29][55][56][57]), and others on switching them to a stand-by mode (e.g., [58][59][60][61]). These works balance the energy saved from not having the machines running idle with that required to restart or warm up the machines, depending on whether the machines are switched off or switched to a power-saving mode, respectively.…”

“…The second most frequently considered objective is the tardiness, which measures the difference between the job due date and the job completion time. About 20% of the reviewed papers optimize some form of tardiness, namely: total tardiness (T) (e.g., [9,63,[102][103][104]), total weighted tardiness (wT) (e.g., [21,23,25,30,105]), total weighted tardiness and earliness (wTE) [59,69,97], tardiness cost (T cost ) [86,106], mean tardiness (T) [107], and maximum tardiness (T max ) [108,109].…”

An Improved Rapid Power and Energy Prediction Method of Drilling Process for Sustainable Manufacturing

“…More and more literatures have studied the energy-saving and sustainability improvement of manufacturing and remanufacturing processes [17][18][19][20][21][22][23][24]. Especially for the manufacturing process, a considerable amount of researches focus on energy monitoring, modeling, and energy-saving methods [25][26][27][28][29][30].…”