Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

Li, Zhi; Chen, Yingjian

doi:10.1038/s41598-023-33615-z

Cited by 4 publications

(2 citation statements)

References 46 publications

(45 reference statements)

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“…The non-dominant sorting Genetic algorithm II (NSGA-II) method was used to solve the problem. Aiming at minimizing completion time and total carbon emissions, Li [29] focused on the study of green scheduling in flexible job-shop systems. Considering energy consumption and worker learning efficiency, an improved multi-objective sparrow search algorithm (IMOSSA) is used to find the optimal solution.…”

Section: Dynamic Job Shop Scheduling Based On Conventional Methodsmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

Pu,

Li,

Rahimifard

2024

Sustainability

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In response to the challenges of dynamic adaptability, real-time interactivity, and dynamic optimization posed by the application of existing deep reinforcement learning algorithms in solving complex scheduling problems, this study proposes a novel approach using graph neural networks and deep reinforcement learning to complete the task of job shop scheduling. A distributed multi-agent scheduling architecture (DMASA) is constructed to maximize global rewards, modeling the intelligent manufacturing job shop scheduling problem as a sequential decision problem represented by graphs and using a Graph Embedding–Heterogeneous Graph Neural Network (GE-HetGNN) to encode state nodes and map them to the optimal scheduling strategy, including machine matching and process selection strategies. Finally, an actor–critic architecture-based multi-agent proximal policy optimization algorithm is employed to train the network and optimize the decision-making process. Experimental results demonstrate that the proposed framework exhibits generalizability, outperforms commonly used scheduling rules and RL-based scheduling methods on benchmarks, shows better stability than single-agent scheduling architectures, and breaks through the instance-size constraint, making it suitable for large-scale problems. We verified the feasibility of our proposed method in a specific experimental environment. The experimental results demonstrate that our research can achieve formal modeling and mapping with specific physical processing workshops, which aligns more closely with real-world green scheduling issues and makes it easier for subsequent researchers to integrate algorithms with actual environments.

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Section: Dynamic Job Shop Scheduling Based On Conventional Methodsmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

Pu,

Li,

Rahimifard

2024

Sustainability

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“…[ 33 ] analyze a production line in an automobile assembly plant, using simulation and dispatching rules, to define a production planning strategy for the company. [ 34 ] focus on the green scheduling problem in a flexible job shop system. The authors formulate a mixed integer linear multiobjective optimization model.…”

Section: Introductionmentioning

confidence: 99%

Two hybrid flow shop scheduling lines with assembly stage and compatibility constraints

Muñoz-Sánchez,

Martínez-Salazar,

González-Velarde

et al. 2024

PLoS ONE

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Two hybrid flow shop scheduling lines must be coordinated to assemble batches of terminated products at their last stage. Each product is thus composed of two jobs, each produced in one of the lines. The set of jobs is to be processed in a series of stages to minimize the makespan of the scheduling, but jobs forming a product must arrive at the assembly line simultaneously. We propose a mixed integer linear programming model. Then, based on the model, we propose a pull-matheuristic algorithm. Finally, we present two metaheuristics, a greedy randomized adaptive search procedure and a biased random key genetic algorithm, and compare all the methodologies with real-based instances of a production scheduling problem in the automobile manufacturing industry. The greedy algorithm yields high-quality solutions, while the genetic one offers the best computational times.

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A Pareto-optimality based black widow spider algorithm for energy efficient flexible job shop scheduling problem considering new job insertion

Akram,

Bhutta,

Butt

et al. 2024

Applied Soft Computing

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Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

Cited by 4 publications

References 46 publications

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

Two hybrid flow shop scheduling lines with assembly stage and compatibility constraints

A Pareto-optimality based black widow spider algorithm for energy efficient flexible job shop scheduling problem considering new job insertion

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