Minimizing tardiness and makespan for distributed heterogeneous unrelated parallel machine scheduling by knowledge and Pareto-based memetic algorithm

Wang, Hua; Li, Rui; Gong, Wenyin

doi:10.1016/j.eij.2023.05.008

Cited by 7 publications

(2 citation statements)

References 41 publications

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“…La programación de la producción o scheduling, es una de las tareas más relevantes realizadas en los sistemas de manufactura, responsable de la asignación de una serie de trabajos en unos recursos disponibles (Ruiz, Carlos García-Díaz y Maroto, 2007). El problema de programación de trabajos en máquinas paralelas no relacionadas ha sido altamente estudiado por muchos autores (Berthier et al, 2022;Wang S. et al, 2023;H. Wang, Li & Gong, 2023).…”

Section: Revisión De La Literaturaunclassified

Optimización de la tardanza total en máquinas paralelas: caso de estudio en la industria del café

Uribe,

López Duque,

Mesa López

et al. 2024

reveia

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La programación de producción es una de las tareas con mayor importancia en sistemas de manufactura ya que permite la asignación de trabajos con cierto número de recursos disponibles. En la industria de manufactura y de servicios, poder cumplir con los compromisos acordados con los diferentes clientes y con los plazos de producción puede generar mayores niveles de satisfacción y ventajas competitivas en el mercado. Por otra parte, una de las principales industrias a nivel mundial es la cafetera. El café es la segunda materia prima más negociada del mundo. Por lo tanto, en esta investigación se aborda la optimización de la tardanza total en la programación de trabajos en máquinas paralelas para un caso de estudio en una tostadora de café. Este es un problema NP-hard donde cada trabajo requiere del proceso de tostión y, además, cuenta con una fecha de entrega asociada. El tiempo de procesamiento de cada trabajo depende de la máquina que se designe y no todas las máquinas puede ejecutar todos los trabajos. Se presenta el modelo matemático para describir el problema mediante programación lineal entera mixta y se implementa en Python. Adicionalmente, se propone un modelo mate heurístico basado en la heurística ATC que permite reducir el tiempo de cómputo y obtener soluciones cercanas al óptimo. Este modelo fue evaluado con mil instancias de datos reales del proceso de tostión. Para la solución del problema se utilizó el optimizador Gurobi. El método exacto muestra altos tiempos de cómputo por lo que se propuso realizar primero la secuenciación de trabajos para luego asignarlos a las maquinas por medio de un modelo matemático simplificado. Al utilizar el modelo mate heurístico propuesto se logra reducir en promedio un 53.98% el tiempo de ejecución y con respecto a la tardanza de la solución, este modelo logra una mediana del 0.0% con respecto al óptimo.

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Section: Revisión De La Literaturaunclassified

Optimización de la tardanza total en máquinas paralelas: caso de estudio en la industria del café

Uribe,

López Duque,

Mesa López

et al. 2024

reveia

View full text Add to dashboard Cite

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“…Meanwhile, a QC is an expensive kind of equipment because of its high running and maintenance costs and energy consumption [2]. The QC scheduling problem (QCSP) is an important seaside operational planning problem with heterogeneous parallel machine characteristics in an automated container terminal [3][4][5][6][7]. It is very important to obtain a good QC scheduling plan that completes the loading and unloading operations with less time, high energy efficiency, and safety.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Multi-Objective Evolutionary Algorithm for Green Scheduling of Heterogeneous Quay Cranes Considering Cooperative Movement and Safety

Zhong,

He,

et al. 2023

JMSE

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Heterogeneous quay cranes (HQCs) are the main energy-consuming equipment of automated container terminals, and they need to move from one bay to another along the rail and maintain a safe distance from one another. Improving the operational efficiency of HQCs and reducing the ineffective walking distance of HQCs are key to reducing the energy consumption of QCs. In this paper, an energy-efficient HQC cooperative scheduling problem is studied, and the HQCs are required to ensure safe and efficient operation. A multi-objective scheduling model is formulated to minimize the maximum completion time of containers, the average completion time of HQCs, and the total energy consumption of HQCs simultaneously. An Enhanced Multi-Objective Evolutionary Algorithm (EMOEA) is designed to solve this problem using a problem-feature-based encoding method to encode and initialize the population, a cooperative strategy to ensure the safe operating distance of HQCs, and a novel multi-objective evaluation mechanism with effective evolutionary operators. The results indicate that the different operational capacities of HQCs had a significant impact on the three studied objectives, especially for some large-scale problems, and that our algorithm outperforms three other well-known multi-objective algorithms in solving the EHQCCSP.

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Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

Zhang,

Li,

et al. 2024

Complex Intell. Syst.

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This research is focused on addressing the energy-aware distributed heterogeneous welding shop scheduling (EADHWS) problem. Our primary objectives are to minimize the maximum finish time and total energy consumption. To accomplish this, we introduce a learning-based cooperative and competitive multi-objective optimization method, which we refer to as LCCMO. We begin by presenting a multi-rule cooperative initialization approach to create a population that combines strong convergence and diversity. This diverse population forms the foundation for our optimization process. Next, we develop a multi-level cooperative global search strategy that explores effective genes within solutions from different angles and sub-problems. This approach enhances our search for optimal solutions. Moreover, we design a competition and cooperation strategy for different populations to expedite convergence. This strategy encourages the exchange of information and ideas among diverse populations, thereby accelerating our progress. We also introduce a multi-operator cooperative local search technique, which investigates elite solutions from various directions, leading to improved convergence and diversity. In addition, we integrate Q-learning into our competitive swarm optimizer to explore different regions of the objective space, enhancing the diversity of the elite archive. Q-learning guides the selection of operators within the small-size population, contributing to more efficient optimization. To evaluate the effectiveness of LCCMO, we conduct numerical experiments on 20 instances. The experimental results unequivocally demonstrate that LCCMO outperforms six state-of-the-art algorithms. This underscores the potential of our learning and knowledge-driven evolutionary framework in enhancing performance and autonomy when it comes to solving EADHWS.

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Minimizing tardiness and makespan for distributed heterogeneous unrelated parallel machine scheduling by knowledge and Pareto-based memetic algorithm

Cited by 7 publications

References 41 publications

Optimización de la tardanza total en máquinas paralelas: caso de estudio en la industria del café

Optimización de la tardanza total en máquinas paralelas: caso de estudio en la industria del café

Enhanced Multi-Objective Evolutionary Algorithm for Green Scheduling of Heterogeneous Quay Cranes Considering Cooperative Movement and Safety

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

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