A Didactic Review On Genetic Algorithms For Industrial Planning And Scheduling Problems*

Neumann, Anas; Hajji, Adnène; Rekik, Monia; Pellerin, Robert

doi:10.1016/j.ifacol.2022.10.100

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…The number of obtained solutions depends on the size of the studied population; in this study a population of 100 is considered which generates a number of 35 final non-dominated solutions. This method has been widely studied in the literature [25], [26] and is already applied for energy optimization [27], [28], although reproducibility may be a minor issue.…”

Section: Optimisation Algorithmmentioning

confidence: 99%

Multi-Criteria Optimisation to Align Environmental Impacts of Industrial Heat Production with Sustainable Thresholds

Jovet

Lefèvre

Laurent

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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Human activities are currently at an unsustainable level and a significant reduction in greenhouse gas emissions is essential to limit global warming. Industry, which accounts for a large share of these emissions, has an important role to play, particularly in its energy consumption for heat production. One of the issues of this transformation is to assess, among all the possible energy solutions, those that are most likely to achieve the objective of reducing emissions of greenhouse gases without incidentally increasing other environmental impacts. The study proposes a method for classifying non-dominated solutions found using an optimisation model based on life cycle analysis (LCA), energy and economic indicators. This is complemented by integrating constraints represented by sustainability limits, i.e. an acceptable level of impact defined by planetary boundaries. A case study of a paper industry process in Italy is presented to highlight the capability of the method. For this example, the ranking shows thatwith the current LCA weighting factors -waste heat recovered using a heat pump powered by electricity has the highest ranking. However, a scoring for which exceeding sustainable levels is penalized gives a higher ranking to solutions composed of a mix of several energy sources. Moreover, if one focuses only on the global warming indicator, the most effective solutions far exceed sustainable levels for other indicators. It is therefore necessary to adopt a comprehensive environmental approach to avoid shifting environmental burdens to other impact categories. On the other hand, no solution compatible with all sustainable levels has been found. It is therefore necessary to go further by proposing a global approach detailing the level of impact that each sector can have while ensuring an overall sustainable level.

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Section: Optimisation Algorithmmentioning

confidence: 99%

Multi-Criteria Optimisation to Align Environmental Impacts of Industrial Heat Production with Sustainable Thresholds

Jovet

Lefèvre

Laurent

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

View full text Add to dashboard Cite

show abstract

“…such as tabu search (TS) [24,25], simulated annealing (SA) [25], and genetic algorithm (GA). [26,27], neural networks (NN), ant colony optimization (ACO) [28], particle swarm optimization (PSO) [42] and so on. More recently, variationalQuantum algorithms (VQA) running on superconducting quantum processors have been applied to the JSP [29].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Job Shop Scheduling Problem With New Job Arrivals Using Hybrid Genetic Algorithm

Ali,

Bechikh,

Louati

et al. 2024

IEEE Access

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The paper tackles the dynamic job shop scheduling problem (DJSSP), aiming to schedule a new set of jobs while minimizing the completion time of all operations. This paper proposes an optimal scheduling method based on a meta-heuristic particle swarm optimization algorithm. The difficulty of this problem is to comprehensively find the best direction of a candidate solution while maintaining the minimum global makespan. To adapt the system to changes and perform the scheduling of a new job, a local search could be an appropriate solution to fix and repair the problem by guiding the search directions following the job's arrival. The problem is classified as NP-hard complex combinatorial optimization problem. Experiments based statistical analysis show that the proposed model has better convergence and accuracy than state of the art algorithms.

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Integrated planning and scheduling of engineer-to-order projects using a Lamarckian Layered Genetic Algorithm

Neumann,

Hajji,

Rekik

et al. 2024

International Journal of Production Economics

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A Didactic Review On Genetic Algorithms For Industrial Planning And Scheduling Problems*

Cited by 5 publications

References 37 publications

Multi-Criteria Optimisation to Align Environmental Impacts of Industrial Heat Production with Sustainable Thresholds

Multi-Criteria Optimisation to Align Environmental Impacts of Industrial Heat Production with Sustainable Thresholds

Dynamic Job Shop Scheduling Problem With New Job Arrivals Using Hybrid Genetic Algorithm

Integrated planning and scheduling of engineer-to-order projects using a Lamarckian Layered Genetic Algorithm

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