A method for minimizing the energy consumption of machining system: integration of process planning and scheduling

Zhang, Zhongwei; Tang, Renzhong; Peng, Tao; Li-yan, Tao; Jia, Shi

doi:10.1016/j.jclepro.2016.03.101

Cited by 104 publications

(36 citation statements)

References 42 publications

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“…Then, based on an energy consumption model for a flexible flow-shop, a genetic algorithm is used to optimize both the make span and the total energy consumption simultaneously. In another way, the issue of integration of pro- [58,85,86,87] cesses planning and scheduling is studied by Zhang et al [60]. In this work, an integration model based on Nonlinear Process Planning (NLPP) is proposed to select the suitable process planning and scheduling.…”

Section: Strategies and Technologies Of Energy Efficiencymentioning

confidence: 99%

Energy efficiency in discrete-manufacturing systems: Insights, trends, and control strategies

Diaz

Ocampo‐Martínez

2019

Journal of Manufacturing Systems

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Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.

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Section: Strategies and Technologies Of Energy Efficiencymentioning

confidence: 99%

Energy efficiency in discrete-manufacturing systems: Insights, trends, and control strategies

Diaz

Ocampo‐Martínez

2019

Journal of Manufacturing Systems

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“…Seker et al [19] proposed a hybrid heuristic algorithm based on a GA and artificial neural network (ANN) to solve IPPS problem. [20] established an IPPS model with the total energy consumption as the objective. A genetic algorithm-based approach was proposed to solve the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A Mathematical Model for Multiworkshop IPPS Problem in Batch Production

Yang

et al. 2018

Mathematical Problems in Engineering

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Integrated Process Planning and Scheduling (IPPS) problem is an important issue in production scheduling. Actually, there exit many factors affecting scheduling results. Many types of workpieces are commonly manufactured in batch production. Moreover, due to differences among process methods, all processes of a workpiece may not be performed in the same workshop or even in the same factory. For making IPPS problem more in line with practical manufacturing, this paper addresses an IPPS problem with batches and limited vehicles (BV-IPPS). An equal batch splitting strategy is adopted. A model for BV-IPPS problem is established. Makespan is the objective to be minimized. For solving the complex problem, a particle swarm optimization (PSO) with a multilayer encoding structure is proposed. Each module of the algorithm is designed. Finally, case studies have been conducted to validate the model and algorithm.

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“…In this paper, a fast non-dominated sorting method is adopted [1]. Select the feasible solution with less dominated number to do the search.…”

Section: Crossover Operator and Mutation Operatormentioning

confidence: 99%

Clustering and Differential Evolution Algorithm for Solving Multi-objectives IPPS Problem

Du¹,

Wang²,

Pan³

2018

dtcse

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Aiming at the multi-objective integrated process planning and scheduling problem with make span, tardiness and equipment load optimization objectives, building a multi-objective non-chain process planning integration model. Combined with the clustering algorithm, differential evolution algorithm and genetic algorithm operations, the diversity in the feasible solution space has been kept, the rapid updating of Pareto non-dominated solutions has been realized. Finally, the feasibility and superiority of the algorithm are verified by an example.

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A method for minimizing the energy consumption of machining system: integration of process planning and scheduling

Cited by 104 publications

References 42 publications

Energy efficiency in discrete-manufacturing systems: Insights, trends, and control strategies

Energy efficiency in discrete-manufacturing systems: Insights, trends, and control strategies

A Mathematical Model for Multiworkshop IPPS Problem in Batch Production

Clustering and Differential Evolution Algorithm for Solving Multi-objectives IPPS Problem

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