Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption

Zhang, Rui; Chiong, Raymond

doi:10.1016/j.jclepro.2015.09.097

Cited by 279 publications

(110 citation statements)

References 39 publications

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“…There are a number of recent studies, including those by the authors, who consider reduction of energy consumption in the framework of production scheduling optimization. 7,8 However, these studies give just an ancillary consideration to energy reduction when optimizing production schedules, not going as far as to consider optimal operation of energy plants. Yet, tertiary energy loads vary with production schedules, and optimal operation of energy plants must be considered with regard to such variation.…”

Section: Trends and Issues In Recent Researchmentioning

confidence: 99%

Improved Parallel Reactive Hybrid Particle Swarm Optimization using Improved Neighborhood Schedule Generation Method for the Integrated Framework of Optimal Production Scheduling and Operational Planning of an Energy Plant in a Factory

Kawaguchi

Fukuyama

2019

IEEJ Trans. IA

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This study proposes improved parallel reactive hybrid particle swarm optimization (IPRHPSO) using an improved neighborhood schedule generation method for the integrated framework of optimal production scheduling and operational planning of an energy plant in a factory. Conventionally, in an energy plant, fixed loads of various tertiary energies have been utilized to solve the optimal operational planning of an energy plant so far. Additionally, in production lines, only the minimization of production time has been yet considered. Therefore, the secondary energy cost of a factory cannot be reduced accurately. However, in this study, the loads of various tertiary energies are calculated according to the candidates of production scheduling and the optimal operational planning of an energy plant is determined using the tertiary energies. This can explicitly reduce the secondary energy cost of a factory. The proposed method was applied to 10 jobs and machine JSPs each. Accordingly, it was verified that it can minimize the secondary energy cost and production time simultaneously, and realize fast computation through parallel computation using IPRHPSO. K E Y W O R D Sjob-shop scheduling problem, metaheuristic method, optimal operational planning of an energy plant, secondary energy cost Electron Comm Jpn. 2020;103:37-48.

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Section: Trends and Issues In Recent Researchmentioning

confidence: 99%

Improved Parallel Reactive Hybrid Particle Swarm Optimization using Improved Neighborhood Schedule Generation Method for the Integrated Framework of Optimal Production Scheduling and Operational Planning of an Energy Plant in a Factory

Kawaguchi

Fukuyama

2019

IEEJ Trans. IA

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“…Constraints (9) and (10) force z kh to take 1 if the family type of B kh is different from that of B k(h−1) . Equation (11) calculates the finishing time of the first batch on each machine (F k1 ), and then Equation (12) calculates the finishing time of each subsequent batch (F kh ) by adding up the batch processing times and the setup times. Equation (13) defines the completion time (C i ) of job i, which is equal to F kh if job i has been allocated to B kh .…”

Section: The Linear Programming Formulationmentioning

confidence: 99%

“…Therefore, a trade-off must be made between production efficiency and energy usage. The speed scaling framework has been utilized in subsequent research such as [10][11][12][13] for single machine or job shop production environments. The above-mentioned and many other existing works all focus on the minimization of energy consumption for achieving sustainable manufacturing (based on the fact that electricity generation will normally produce carbon emissions).…”

Section: Introductionmentioning

confidence: 99%

Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search

Zhang

2017

Sustainability

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Abstract:The dyeing of textile materials is the most critical process in cloth production because of the strict technological requirements. In addition to the technical aspect, there have been increasing concerns over how to minimize the negative environmental impact of the dyeing industry. The emissions of pollutants are mainly caused by frequent cleaning operations which are necessary for initializing the dyeing equipment, as well as idled production capacity which leads to discharge of unconsumed chemicals. Motivated by these facts, we propose a methodology to reduce the pollutant emissions by means of systematic production scheduling. Firstly, we build a three-objective scheduling model that incorporates both the traditional tardiness objective and the environmentally-related objectives. A mixed-integer programming formulation is also provided to accurately define the problem. Then, we present a novel solution method for the sustainable scheduling problem, namely, a multi-objective genetic algorithm with tabu-enhanced iterated greedy local search strategy (MOGA-TIG). Finally, we conduct extensive computational experiments to investigate the actual performance of the MOGA-TIG. Based on a fair comparison with two state-of-the-art multi-objective optimizers, it is concluded that the MOGA-TIG is able to achieve satisfactory solution quality within tight computational time budget for the studied scheduling problem.

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“…The former configurations remained for fair comparison. Three metrics were used to indicate the MOO performance (Zhang and Chiong, 2016): (1) NS(ts), the number of non-dominated solutions given by time slot ts; (2) RN(ts), the degree of Pareto optimality of the non-dominated solutions output by ts; (3) TS(ts), the evenness of non-dominated solution distribution of ts.…”

Section: Scalability In Number Of Time Slotsmentioning

confidence: 99%

Energy-Efficient and Labor-Aware Production Scheduling based on Multi-Objective Optimization

Gong

Pessemier

Martens

et al. 2017

Computer Aided Chemical Engineering

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Manufacturing industry is a major energy consumer and greenhouse gas producer for the society. As recent literature points out, energy awareness can be integrated to production scheduling to enable industrial demand side management. Consequently, production loads can be shifted to periods with a lower electricity price for energy cost minimization. However, this may increase the overall production cost, because the labor compensation usually follows the opposite trend with the electricity price. To investigate this trade-off, this paper introduces a scheduling approach that considers both energy consumption and labor shifts. An efficient memetic algorithm is proposed for multi-objective optimization. Numerical experiments on a blow molding process demonstrated two general trade-offs: one between energy cost and labor cost, as well as one between overall production cost and makespan. Therefore, these trade-offs must be considered when performing energy-efficient production scheduling.

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Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption

Cited by 279 publications

References 39 publications

Improved Parallel Reactive Hybrid Particle Swarm Optimization using Improved Neighborhood Schedule Generation Method for the Integrated Framework of Optimal Production Scheduling and Operational Planning of an Energy Plant in a Factory

Improved Parallel Reactive Hybrid Particle Swarm Optimization using Improved Neighborhood Schedule Generation Method for the Integrated Framework of Optimal Production Scheduling and Operational Planning of an Energy Plant in a Factory

Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search

Energy-Efficient and Labor-Aware Production Scheduling based on Multi-Objective Optimization

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