A memetic algorithm for multi-objective distributed production scheduling: minimizing the makespan and total energy consumption

Gong, Guiliang; Chiong, Raymond; Deng, Qianwang; Luo, Qiang

doi:10.1007/s10845-019-01521-9

Cited by 41 publications

(9 citation statements)

References 49 publications

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“…Please insert Fig. 1 here Also, assumptions declared to describe the studied scheduling problem are organized as follows: (1) No job can be processed on two or more machines at a time, and a machine can only process one job at a time; (2) Once a job is processed on a specific machine, it will stay on the machine until finished; (3) Jobs travels sequentially through all stages and stage skipping of all jobs is not permitted; (4) A job will be transported to next processing stage instantly once it is finished at a specific stage, and transportation times for jobs between any two consecutive stages are ignored; (5) Machines are in good condition and random failures are not considered; (6) Setup times are sequence-independent, thus could be included in processing time; (7) Machines will be turned on once the first job assigned to them and will be shut down once all jobs assigned to them are finished.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Eq. (1) shows the total carbon emission in the shop, elements in brackets refers to total energy consumption in the period. According to [40], there exists a linear relationship between carbon emission and energy consumption in manufacturing.…”

Section: Two Optimization Objectivesmentioning

confidence: 99%

“…Production scheduling is taken as the key technique and core issue to the management of numerous manufacturing enterprises [1][2][3]. Nowadays, manufacturing industry has become the main source of resource consumption and pollutant emission all over the world, which leads to continuous deterioration of ecological environment and hinders the healthy development of global economy [4][5].…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective low-carbon hybrid flow shop scheduling via an improved teaching-learning-based optimization algorithm

et al. 2022

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In this article, for achieving an effective and environmental-friendly production scheduling, we investigate a multi-objective low-carbon hybrid flow shop scheduling problem (MLHFSP) with the consideration of machines with varied energy usage ratios. The problem is formulated by a multi-objective mathematical model with two optimization objectives, i.e., minimizing total carbon emission (TCE) and makespan (C max ). We primarily analyse on the formation of TCE and derive its mathematical expression. MLHFSP is NP-hard, therefore, to tackle the model, an improved multi-objective teaching-learning-based optimization (ITLBO) algorithm is proposed. The ITLBO algorithm mainly contains global search based teaching phase and local search based learning phase. In ITLBO, a solution is represented by two vectors, i.e., job sequence vector and machine assignment vector. Sigma method is utilized to quantify each individual, and to avoid local optimum, sequential neighbourhood search (SNS) method is also adopted. Experimental results validate the feasibility and effectiveness of proposed ITLBO in addressing MLHFSP. The research findings help manufacturing engineers to seek a sophisticated balance between carbon emission reduction and makespan reduction.

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Section: Problem Descriptionmentioning

confidence: 99%

Section: Two Optimization Objectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-objective low-carbon hybrid flow shop scheduling via an improved teaching-learning-based optimization algorithm

et al. 2022

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“…For scheduling objectives, the completion time-related and machine workload-related ones are the most evaluated. Energy consumption and low-carbon-related objectives are attracting increasing attention; they can be considered as one of multiple objectives and simultaneously optimized with traditional objectives [46,60,[85][86][87][90][91][92][93] .…”

Section: Problemmentioning

confidence: 99%

Distributed scheduling problems in intelligent manufacturing systems

Hou

Wang

et al. 2021

Tsinghua Sci. Technol.

127

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Currently, manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization. Hence, they have to extend their production mode into distributed environments and establish multiple factories in various geographical locations.Nowadays, distributed manufacturing systems have been widely adopted in industrial production processes. In recent years, many studies have been done on the modeling and optimization of distributed scheduling problems.This work provides a literature review on distributed scheduling problems in intelligent manufacturing systems. By summarizing and evaluating existing studies on distributed scheduling problems, we analyze the achievements and current research status in this field and discuss ongoing studies. Insights regarding prior works are discussed to uncover future research directions, particularly swarm intelligence and evolutionary algorithms, which are used for managing distributed scheduling problems in manufacturing systems. This work focuses on journal papers discovered using Google Scholar. After reviewing the papers, in this work, we discuss the research trends of distributed scheduling problems and point out some directions for future studies.

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“…Jiang et al (2020) proposed an effective modified multi-objective evolutionary algorithm with decomposition (MMOEA/D) to minimize energy consumption and makespan. While in all the abovementioned studies, it is assumed that all operations of a job should be performed on the machines of the same facility, Luo et al (2020) and Gong et al (2020) assume that operations of a job can be transferred among different facilities taking into account the jobs' transfer time and transfer energy consumption. For an overview of more related studies on production planning and scheduling problems in multi-factory production networks as well as a more comprehensive classification, see Lohmer and Lasch (2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed job-shop rescheduling problem considering reconfigurability of machines: a self-adaptive hybrid equilibrium optimiser

Mahmoodjanloo

Tavakkoli–Moghaddam

Baboli

et al. 2021

International Journal of Production Research

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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A memetic algorithm for multi-objective distributed production scheduling: minimizing the makespan and total energy consumption

Cited by 41 publications

References 49 publications

Multi-objective low-carbon hybrid flow shop scheduling via an improved teaching-learning-based optimization algorithm

Multi-objective low-carbon hybrid flow shop scheduling via an improved teaching-learning-based optimization algorithm

Distributed scheduling problems in intelligent manufacturing systems

Distributed job-shop rescheduling problem considering reconfigurability of machines: a self-adaptive hybrid equilibrium optimiser

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