An improved biogeography-based optimization for achieving optimal job shop scheduling solutions

Piroozfard, Hamed; Wong, Kuan Yew; Asl, Ali Derakhshan

doi:10.1016/j.procs.2017.09.073

Cited by 10 publications

(6 citation statements)

References 35 publications

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“…It is also interesting to apply other novel metaheuristic approaches in order to obtain more optimized solutions. 67,68 Power consumption data in idle state (Power in kW) …”

Section: Appendix A1mentioning

confidence: 99%

Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

Piroozfard¹,

Wong²,

Tiwari³

2018

IJCIS

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New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously minimize carbon emission and total late work criterion as sustainability-based and classical-based objective functions, respectively, in the multiobjective job shop scheduling environment. In order to solve the presented problem more effectively, a new multiobjective imperialist competitive algorithm imitating the behavior of imperialistic competition is proposed to obtain a set of non-dominated schedules. In this work, a three-fold scientific contribution can be observed in the problem and solution method, that are: (1) integrating carbon dioxide into the operational decision level of job shop scheduling, (2) considering total late work criterion in multi-objective job shop scheduling, and (3) proposing a new multi-objective imperialist competitive algorithm for solving the extended multi-objective optimization problem. The elements of the proposed algorithm are elucidated and forty three small and large sized extended benchmarked data sets are solved by the algorithm. Numerical results are compared with two well-known and most representative metaheuristic approaches, which are multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II, in order to evaluate the performance of the proposed algorithm. The obtained results reveal the effectiveness and efficiency of the proposed multi-objective imperialist competitive algorithm in finding high quality non-dominated schedules as compared to the other metaheuristic approaches.

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“…It is also interesting to apply other novel metaheuristic approaches in order to obtain more optimized solutions. 67,68 Power consumption data in idle state (Power in kW) …”

Section: Appendix A1mentioning

confidence: 99%

Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

Piroozfard¹,

Wong²,

Tiwari³

2018

IJCIS

Self Cite

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“…In recent years, several meta-heuristics approaches have been proposed to treat the JSSP, such as Greedy Randomized Adaptive Search Procedure (GRASP) [ 3 ], Local Search Genetic Algorithm (LSGA) [ 4 ], Parallel Agent-based Genetic Algorithm (PaGA) [ 5 ], Agent-based Local Search Genetic Algorithm (aLSGA) [ 6 ], Golden Ball Algorithm (GB) [ 7 ], Initial Population Based Genetic Algorithm (IPB-GA) [ 8 ], Memetic Algorithm (MA) [ 9 ], Improved Biogeography-Based Optimization (IBBO) [ 10 ], Grey Wolf Optimization (GWO) [ 11 ], Hybrid Grey Wolf Optimization (HGWO) [ 12 ], Memetic Chicken Swarm Optimization (MeCSO) [ 13 ], Genetic Algorithm with a critical-path-guided Giffler and Thompson crossover operator (GA-CPG-GT) [ 14 ] and the Discrete Wolf Pack Algorithm (DWPA) [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

Viana

Morandin

Contreras

2020

Sensors

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It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on local search schemes and improvements to standard mutation and crossover operators. In this work, we propose a new GA within this line of research. In detail, we generalize the concept of a massive local search operator; we improved the use of a local search strategy in the traditional mutation operator; and we developed a new multi-crossover operator. In this way, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. Our method is evaluated in three different case studies, comprising 58 instances of literature, which prove the effectiveness of our approach compared to traditional JSSP solution methods.

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“…En una interesante extensión del problema Job Shop se consideró la integración de la programación de turnos de empleados con la programación de máquinas y del personal dedicado al transporte de productos entre las máquinas considerado como "transportadores" [8]. Se realiza la diferencia entre los empleados dedicados al procesamiento de las actividades y como se mencionó anteriormente, al personal dedicado al transporte de productos entre máquinas.…”

Section: Introduccionunclassified

“…El problema es catalogado como NP-HARD y se adaptó una metaheurística denominada Anarchic Society Optimization Algorithm (ASOA) con el fin de minimizar el Makespan. En otro trabajo se adaptó una metaheurística basada en la migración e inmigración de especies entre hábitat llamada Biogeography-Based Optimization (BBO) con el fin de minimizar el Makespan [9]. Una nueva adaptación del algoritmo colonia de abejas o ABC fue utilizado para mantener un balance entre las fases de exploración y explotación, e incluye el fenómeno de la espuma de la cerveza como método de generación de nuevas posiciones [10].…”

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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An improved biogeography-based optimization for achieving optimal job shop scheduling solutions

Cited by 10 publications

References 35 publications

Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

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

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