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Sustainability 2016, 8(12), 1268; doi:10.3390/su8121268

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

1
The State Key Lab of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
School of Economics and Management, Hubei University of Automotive Technology, Shiyan 442002, China
*
Author to whom correspondence should be addressed.
Academic Editor: Tomonobu Senjyu
Received: 1 September 2016 / Revised: 25 November 2016 / Accepted: 25 November 2016 / Published: 7 December 2016
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Abstract

Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT) production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T), cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA) is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multiobjective Particle Swarm Optimization (OMOPSO), and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems. View Full-Text
Keywords: multi-objective evolutionary algorithm; energy efficient scheduling; controllable processing times; single machine scheduling; just-in-time multi-objective evolutionary algorithm; energy efficient scheduling; controllable processing times; single machine scheduling; just-in-time
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Yin, L.; Li, X.; Lu, C.; Gao, L. Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm. Sustainability 2016, 8, 1268.

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