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

Abstract

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.

Flexible job shop scheduling problem under Industry 5.0: A survey on human reintegration, environmental consideration and resilience improvement

“…are auxiliary energies used during a production process, which are also considered in FJSSP literature, e.g. Ham et al (2021). These energies are related to the makespan because the longer it takes to produce, the more power is required.…”

Joint maintenance planning and production scheduling optimization model for green environment

“…The bi-objectives considered are minimizing the completion time for all jobs and the power requirements (power threshold). Ham et al (2021) used mixed-integer problem (MIP) and constraint programming to address the problem of flexible job shop scheduling, seeking the minimum makespan and total energy cost by adopting the shutdown (on/off) option. Jiang et al (2016) introduced two heuristics to solve two problems for identical parallel machine scheduling with machine unavailability constraints due to PM.…”

Un Algoritmo Híbrido para la solución del Problema Job Shop Considerando Eficiencia Energética1

“…Usualmente, en cada Job Shop Problem JSP se consideran variables como el volumen de la producción, los costos, la calidad y variedad del producto, entre otras, sin embargo, el panorama respecto a la problemática del medio ambiente ha cambiado los últimos años, dando prioridad a la protección del planeta reduciendo el impacto ambiental utilizando diferentes métodos de cambio (Hidalgo & Perez, 2016); (Carlos Sornoza-Bravo & Felipe Sabando-Piguabe, 2021). Por esto, la eficiencia energética se ha consolidado como una variable importante a nivel mundial, debido al interés del ahorro de energía en los Problemas de Job Shop Scheduling, se han realizado varias investigaciones sobre esta temática (H. Zhang et al, 2022) ; (Ham et al, 2021); (Wang et al, 2021) Las metodologías utilizadas para resolver el Problema Job Shop con Eficiencia Energética (EJSP), se encuentran clasificadas como técnicas aproximadas las cuales se conocen como: métodos heurísticos y metaheurísticos, de estas se pueden destacar el Algoritmo Genético (Genetic Algorithm,GA), Optimización de Colonias de Hormigas (Ant Colony Optimization, ACO), Búsqueda Tabú (Tabú Search, TS), Optimización de Enjambre por Partículas (Particle Swarm Optimization, PSO), Recocido Simulado (Simulated Anealing, SA) (Coca, Castrillón, Ruiz, Mateo-Sanz, & Jiménez, 2019); (Mouzon & Yildirim, 2008). Liu et al, (2014)plantean un modelo para la reducción del consumo de energía para el clásico JSP, que busca minimizar el consumo total de electricidad y la tardanza ponderada total, para ello emplean el Algoritmo Genético de Clasificación No Dominante (Non-dominated Sorting Genetic Algorithm, NSGA-II) como la solución para obtener el frente de Pareto, este planteamiento se basó en un taller de trabajo 10*10 modificado para mostrar la efectividad del algoritmo.…”