Efficient Approaches for Solving a Multiobjective Energy-aware Job Shop Scheduling Problem

González, Miguel A.; Oddi, Angelo; Rasconi, Riccardo

doi:10.3233/fi-2019-1811

Cited by 3 publications

(2 citation statements)

References 67 publications

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“…In this subsection, we implemented a genetic algorithm to compare the performance between proposed methods and metaheuristic. Genetic algorithm (GA) is known as an effective metaheuristic to solve flexible job shop scheduling problems [46][47][48][49]. e overall GA framework in Zhang et al [50] was adopted.…”

Section: Resultsmentioning

confidence: 99%

Energy-Aware Flexible Job Shop Scheduling Using Mixed Integer Programming and Constraint Programming

Ham

Park

Kim

2021

Mathematical Problems in Engineering

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Compromising productivity in exchange for energy saving does not appeal to highly capitalized manufacturing industries. However, we might be able to maintain the same productivity while significantly reducing energy consumption. This paper addresses a flexible job shop scheduling problem with a shutdown (on/off) strategy aiming to minimize makespan and total energy consumption. First, an alternative mixed integer linear programming model is proposed. Second, a novel constraint programming is proposed. Third, practical operational scenarios are compared. Finally, we provide benchmarking instances, CPLEX codes, and genetic algorithm codes, in order to promote related research, thus expediting the adoption of energy-efficient scheduling in manufacturing facilities. The computational study demonstrates that (1) the proposed models significantly outperform other benchmark models and (2) we can maintain maximum productivity while significantly reducing energy consumption by 14.85% (w/o shutdown) and 15.23% (w/shutdown) on average.

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Section: Resultsmentioning

confidence: 99%

Energy-Aware Flexible Job Shop Scheduling Using Mixed Integer Programming and Constraint Programming

Ham

Park

Kim

2021

Mathematical Problems in Engineering

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“…4,5 The traditional solution method uses a linear weighting strategy to transform the multi-objective problem into a single-objective problem. 6 For example, González et al 7 and Ceylan et al 8 adopted this method to minimize production cost and carbon emissions in the scheduling process. However, the rationality of the weight coefficient of each objective needs to be improved, which limits the effective treatment of some multi-objective problems by this method.…”

Section: Introductionmentioning

confidence: 99%

Digital twin oriented multi-objective flexible job shop scheduling model and its hybrid particle swarm optimization

Chen

Zou

Wang

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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To solve the problems of low efficiency and insufficient dynamic response of job shop scheduling in the discrete manufacturing process, a multi-objective flexible job shop scheduling model for digital twin and its solution method are proposed. Firstly, a digital twin scheduling model with physical entity, virtual model and production plan is constructed, and four factors are taken as optimization goals. Then, a hybrid particle swarm optimization method is designed to increase the refined optimization ability, and the obtained Pareto optimal solution set is analyzed by grey relational analysis to obtain a satisfactory solution which coincides with the actual production. Finally, a three-dimensional model which is completely mapped with the real job shop scheduling is built by Plant Simulation software. The scheduling process is simulated and optimized by combining with the production data of an enterprise, which verifies the feasibility and applicability of this method, and will effectively guide the production practice.

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Optimal Designs for Antoine’s Equation: Compound Criteria and Multi-Objective Designs via Genetic Algorithms

2023

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Antoine’s Equation is commonly used to explain the relationship between vapour pressure and temperature for substances of industrial interest. This paper sets out a combined strategy to obtain optimal designs for the Antoine Equation for D- and I-optimisation criteria and different variance structures for the response. Optimal designs strongly depend not only on the criterion but also on the response’s variance, and their efficiency can be strongly affected by a lack of foresight in this selection. Our approach determines compound and multi-objective designs for both criteria and variance structures using a genetic algorithm. This strategy provides a backup for the experimenter providing high efficiencies under both assumptions and for both criteria. One of the conclusions of this work is that the differences produced by using the compound design strategy versus the multi-objective one are very small.

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Efficient Approaches for Solving a Multiobjective Energy-aware Job Shop Scheduling Problem

Cited by 3 publications

References 67 publications

Energy-Aware Flexible Job Shop Scheduling Using Mixed Integer Programming and Constraint Programming

Energy-Aware Flexible Job Shop Scheduling Using Mixed Integer Programming and Constraint Programming

Digital twin oriented multi-objective flexible job shop scheduling model and its hybrid particle swarm optimization

Optimal Designs for Antoine’s Equation: Compound Criteria and Multi-Objective Designs via Genetic Algorithms

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