Perception data-driven optimization of manufacturing equipment service scheduling in sustainable manufacturing

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Perception data-driven optimization of manufacturing equipment service scheduling in sustainable manufacturing. / Xu, Wenjun; Shao, Luyang ; Yao, Bitao ; Zhou, Zude; Pham, Duc.

In: Journal of Manufacturing Systems, Vol. 41, 10.2016, p. 86-101.

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@article{ea247da1f5f648b89e4da999ff74785a,
title = "Perception data-driven optimization of manufacturing equipment service scheduling in sustainable manufacturing",
abstract = "Both sustainable manufacturing and manufacturing service are the trends in industry because they are regarded as ways to reduce the resource cost and energy consumption in manufacturing process, to improve the flexibility and responding speed to customers{\textquoteright} demand, and to improve the production efficiency. In order to improve the sustainability of manufacturing equipment services in job shop, this paper presents a multi-objective joint model of energy consumption and production efficiency. The model is related to multi-conditions of manufacturing equipment services. The conditions are monitored in real-time to drive a multi-objective dynamic optimized scheduling of manufacturing services. In order to solve the multi-objective problem, an enhanced Pareto-based bees algorithm (EPBA) is proposed. In order to ensure the variety of population, to prevent the premature convergence, and to improve the searching speed, several key technologies are utilized such as variable neighborhood searching, mutation and crossover operation, fast non-dominated ranking, critical path local search, archive Pareto set, critical path taboo set, etc. Finally, the proposed method is evaluated and shows better performance in static and dynamic scenarios compared with the existing optimization algorithms.",
keywords = "Sustainable manufacturing, Manufacturing equipment service, Perception data-driven, Bees algorithm, Optimized scheduling",
author = "Wenjun Xu and Luyang Shao and Bitao Yao and Zude Zhou and Duc Pham",
year = "2016",
month = oct,
doi = "10.1016/j.jmsy.2016.08.001",
language = "English",
volume = "41",
pages = "86--101",
journal = "Journal of Manufacturing Systems",
issn = "0278-6125",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Perception data-driven optimization of manufacturing equipment service scheduling in sustainable manufacturing

AU - Xu, Wenjun

AU - Shao, Luyang

AU - Yao, Bitao

AU - Zhou, Zude

AU - Pham, Duc

PY - 2016/10

Y1 - 2016/10

N2 - Both sustainable manufacturing and manufacturing service are the trends in industry because they are regarded as ways to reduce the resource cost and energy consumption in manufacturing process, to improve the flexibility and responding speed to customers’ demand, and to improve the production efficiency. In order to improve the sustainability of manufacturing equipment services in job shop, this paper presents a multi-objective joint model of energy consumption and production efficiency. The model is related to multi-conditions of manufacturing equipment services. The conditions are monitored in real-time to drive a multi-objective dynamic optimized scheduling of manufacturing services. In order to solve the multi-objective problem, an enhanced Pareto-based bees algorithm (EPBA) is proposed. In order to ensure the variety of population, to prevent the premature convergence, and to improve the searching speed, several key technologies are utilized such as variable neighborhood searching, mutation and crossover operation, fast non-dominated ranking, critical path local search, archive Pareto set, critical path taboo set, etc. Finally, the proposed method is evaluated and shows better performance in static and dynamic scenarios compared with the existing optimization algorithms.

AB - Both sustainable manufacturing and manufacturing service are the trends in industry because they are regarded as ways to reduce the resource cost and energy consumption in manufacturing process, to improve the flexibility and responding speed to customers’ demand, and to improve the production efficiency. In order to improve the sustainability of manufacturing equipment services in job shop, this paper presents a multi-objective joint model of energy consumption and production efficiency. The model is related to multi-conditions of manufacturing equipment services. The conditions are monitored in real-time to drive a multi-objective dynamic optimized scheduling of manufacturing services. In order to solve the multi-objective problem, an enhanced Pareto-based bees algorithm (EPBA) is proposed. In order to ensure the variety of population, to prevent the premature convergence, and to improve the searching speed, several key technologies are utilized such as variable neighborhood searching, mutation and crossover operation, fast non-dominated ranking, critical path local search, archive Pareto set, critical path taboo set, etc. Finally, the proposed method is evaluated and shows better performance in static and dynamic scenarios compared with the existing optimization algorithms.

KW - Sustainable manufacturing

KW - Manufacturing equipment service

KW - Perception data-driven

KW - Bees algorithm

KW - Optimized scheduling

U2 - 10.1016/j.jmsy.2016.08.001

DO - 10.1016/j.jmsy.2016.08.001

M3 - Article

VL - 41

SP - 86

EP - 101

JO - Journal of Manufacturing Systems

JF - Journal of Manufacturing Systems

SN - 0278-6125

ER -