Job-shop like manufacturing system with variable power threshold and operations with power requirements

Kemmoe, Sylverin; Lamy, Damien; Tchernev, Nikolay

doi:10.1080/00207543.2017.1321801

Cited by 24 publications

(11 citation statements)

References 46 publications

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“…Kemmoe et al [95] designed a mathematical model considering different power requirements in the initial phase and processing phase and selected the makespan as the objective function. The aim of the paper is to define a feasible production schedule that allows respecting the variable power threshold; the authors provided a randomized adaptive search procedure hybridized with an evolutionary local search to solve the problem.…”

Section: Classical Job Shopmentioning

confidence: 99%

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

Renna

Materi

2021

Applied Sciences

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Climate change mitigation, the goal of reducing CO2 emissions, more stringent regulations and the increment in energy costs have pushed researchers to study energy efficiency and renewable energy sources. Manufacturing systems are large energy consumers and are thus responsible for huge greenhouse gas emissions; for these reasons, many studies have focused on this topic recently. This review aims to summarize the most important papers on energy efficiency and renewable energy sources in manufacturing systems published in the last fifteen years. The works are grouped together, considering the system typology, i.e., manufacturing system subclasses (single machine, flow shop, job shop, etc.) or the assembly line, the developed energy-saving policies and the implementation of the renewable energy sources in the studied contexts. A description of the main approaches used in the analyzed papers was discussed. The conclusion reports the main findings of the review and suggests future directions for the researchers in the integration of renewable energy in the manufacturing systems consumption models.

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Section: Classical Job Shopmentioning

confidence: 99%

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

Renna

Materi

2021

Applied Sciences

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“…With this in mind, multiple articles depart from the simplified assumption of constant energy utilization and operation time in job processing (e.g., constant power demand presented as one energy bloc). One approach regarding this is the consideration of variations in an operation's power profile, as in [33,50,110]. In order to provide a more realistic modeling, all three articles divide an operation's power profile into two blocs with a power peak at the beginning of each process and a lower demand for the remaining part of the processing time.…”

Section: Various Energy Utilization Factorsmentioning

confidence: 99%

“…In order to provide a more realistic modeling, all three articles divide an operation's power profile into two blocs with a power peak at the beginning of each process and a lower demand for the remaining part of the processing time. For example, in the job shop scheduling approach in [110], this variable power demand in job processing allows the simultaneous scheduling operations to minimize makespan while still maintaining a power demand threshold. The authors argue that by assuming a constant power demand for the whole operation equal to the peak value instead, the energy demand threshold would not make it possible to schedule operations earlier.…”

Section: Various Energy Utilization Factorsmentioning

confidence: 99%

Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme

2021

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Scarcity of resources, structural change during the further development of renewable energy sources, and their corresponding costs, such as increasing resource costs or penalties due to dirty production, lead industrial firms to adapt ecological actions. In this regard, research on energy utilization in production planning has received increased attention in the last years, resulting in a large number of research articles so far. With the paper at hand, we review the literature on energy-oriented production planning. The aim of this study is to derive similar core issues and related properties along energy-oriented models within hierarchical production planning. For this, we carry out a systematic literature review and analyze and synthesize 375 research articles. We classify the underlying literature with a novel two-dimensional classification scheme and identify three key topics and five frequently found characteristics, which are presented in detail throughout this article. Based on these results, we state several potentials for further research.

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“…A scheduling model with the turn off/turn on of machines was introduced in [5], and a multi-objective genetic algorithm based on non-dominated sorting genetic algorithm NSGA-II was developed to minimize the energy consumption and total weighted tardiness simultaneously. A metaheuristic to solve the JSSP which includes a power threshold that must not be exceeded over time was also developed [6], with two power requirements considered for operations: a peak consumption at the beginning of the machining and a nominal consumption after. The aim of this work was to minimize the makespan while respecting the power threshold.…”

Section: Job Shop Energy-efficient Schedulingmentioning

confidence: 99%

A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021

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The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

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Job-shop like manufacturing system with variable power threshold and operations with power requirements

Cited by 24 publications

References 46 publications

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme

A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

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