A genetic algorithm for job-shop scheduling with operators enhanced by weak Lamarckian evolution and search space narrowing

Mencía, Raúl; Sierra, María R.; Mencía, Carlos; Varela, Ramiro

doi:10.1007/s11047-013-9373-x

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…In addition to that, since it was rather fast, it had a lot of room for improvement. This genetic algorithm was later enhanced in [26] by integrating a weak Lamarckian evolution and a narrowing-search-space technique.…”

Section: Related Workmentioning

confidence: 99%

Scheduling job shop problems with operators with respect to the maximum lateness

2020

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This paper deals with the problem of assigning operators to jobs, within a free assignment-changing mode, in a job-shop environment subject to a fixed processing sequence of the jobs. We seek an assignment of operators that minimizes the maximum lateness. Within this model, a job needs an operator during the entire duration of its processing. We show that the problem is 𝒩𝒫-hard when the number of operators is arbitrary and exhibit polynomial time algorithms for the cases involving one and two operators, respectively.

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Section: Related Workmentioning

confidence: 99%

Scheduling job shop problems with operators with respect to the maximum lateness

2020

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“…El algoritmo propuesto es comparado con métodos de solución [15] obteniendo un eficiente rendimiento. Un enfoque similar [16], se aborda con el problema JSO bajo un algoritmo genético mejorado consistente en utilizar la Evolución lamarckiana modificando el esquema de generación de soluciones Og&t, con el fin de reducir el tiempo ocioso de las máquinas y operadores. Esta mejora permite al algoritmo genético trabajar problemas de gran tamaño.…”

Section: Introduccionunclassified

Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados

2019

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Introducción: El problema Job Shop Con Operadores Calificados o Job Shop With Skilled Operators (JSSO) es una extensión del problema clásico Job Shop en el cual, una operación debe ser ejecutada por un conjunto limitados de trabajadores, con el objetivo de minimizar el tiempo de terminación total de los trabajos o Makespan, situación que puede representar distintas aplicaciones en la vida cotidiana. Es un problema complejo y es catalogado como NP-HARD. Objetivo: En este artículo, se aborda el problema JSSO desde la adaptación de un algoritmo conocido como Squirrel Search Algorithm (SSA). Metodología: Se propone un esquema de codificación discreto para el algoritmo SSA utilizando el método Smallest Position Value (SPV). Además, para evitar soluciones que violen las relaciones de precedencia; se corrigen con el método Valid Particle Generator (VPG), el cual garantiza soluciones factibles. Dos versiones del algoritmo se colocan a prueba en 28 instancias propuesta en la literatura para validar su rendimiento. Resultados: Los experimentos computacionales realizados muestran que los dos algoritmos propuestos alcanzan soluciones óptimas en 25 de las 28 instancias analizadas. Además, para las instancias en donde no se logró soluciones óptimas, el gap promedio no supera el 2% para ambas versiones de los algoritmos propuestos. Conclusiones: El esquema de codificación propuesto garantiza la discretización del algoritmo, generando soluciones que convergen hacia el óptimo. Además, la codificación propuesta, permite utilizar de manera natural los operadores de movimiento propuestos originalmente para el algoritmo utilizado. El rendimiento obtenido por los algoritmos es adecuado y de alta calidad.

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“…Wang Feng [3] studies the problem of Job shop scheduling based on combination of machines, creates a mathematical model, and gives the solving methods based on Ant Colony International Conference on Manufacturing Science and Engineering (ICMSE 2015) Algorithm. R. Mencia [4] trys to solve The Job Shop Scheduling Problem (JSSP) with operators based on GA considering makespan minimization.…”

Section: Introductionmentioning

confidence: 99%

Coordinated scheduling technology of multi-level relevance resources based on improved genetic algorithm

Lu¹,

Wang

Chengtong

2015

Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering

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Abstract. In the actual process of production, there is a practical situation that multi-level relevance resources such as operators, machines, fixtures and cutters participate in the job-shop operation. Different from traditional production scheduling which focuses on machines, this scheduling problem add constraints among operators, fixtures and cutters. This paper presents a new method based on improved genetic algorithm(GA) for multi-level relevance resources coordinated scheduling. This method consists mainly of two parts: (1) dynamic combination for multi-level relevance resources based on improved GA, which meets the matching relationship between multi-level relevance resources and selects the best combination of multi-level relevance resources through fitness calculation. (2) scheduling time calculation for multi-level relevance resources processes(MRRP) is proposed, which according to the processes occupancy of multi-level relevance resources, and used for finding the earliest available time span to insert MRRP. Finally, the results have demonstrated the effectiveness of the method with practical example.

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A genetic algorithm for job-shop scheduling with operators enhanced by weak Lamarckian evolution and search space narrowing

Cited by 14 publications

References 18 publications

Scheduling job shop problems with operators with respect to the maximum lateness

Scheduling job shop problems with operators with respect to the maximum lateness

Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados

Coordinated scheduling technology of multi-level relevance resources based on improved genetic algorithm

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