Job-shop scheduling problem with energy consideration

We review the literature about shop scheduling problems in manufacturing systems, revealing the concepts and methodologies that most impact the usage of scheduling theory in manufacturing environments. We focus our attention on the job shop and flow shop problems and their variants. We emphasize the interactions between the scheduling functions and manufacturing paradigms such as Industry 4.0, Computer Integrated Manufacturing, Computer‐Aided Process Planning, Advanced Planning and Scheduling, and Integrated Process Planning and Scheduling. We describe the main components and characteristics of the scheduling ecosystem, and we discuss how the scheduling interacts with the components that make it up and how it is affected by them. The metadata collected from the digital libraries on which the review was based (ScienceDirect, Scopus, and Elsevier) made it possible to characterize the historical evolution of the main concepts of the scheduling ecosystem in terms of scientific publications and research trends in the period 2000–2020.

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“…(2018b), Bürgy and Bülbül (2018), Masmoudi et al. (2019), Gong et al. (2019), Jamili (2019), Morikawa et al.…”

Section: An Overview Of Shop Scheduling Problems Characteristicsmentioning

confidence: 99%

Shop scheduling in manufacturing environments: a review

Márquez

Ribeiro

2022

Int Trans Operational Res

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“…Masmoudi et al [100] proposed two integer linear programming models to minimize the energy production costs under power peak constraints and used a heuristic method to solve the mathematical 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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“…In recent years, due to rising energy costs and environmental concerns, researchers have started working on energy-efficient scheduling problems as a main feature of JSSP. Two integer programming models were for example used in [4], namely a disjunctive and a time-indexed formulation, to solve the JSSP in order to minimize electricity cost. 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.…”

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 scheduling problem with energy consideration

Cited by 69 publications

References 25 publications

Shop scheduling in manufacturing environments: a review

Shop scheduling in manufacturing environments: a review

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

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

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