A generalized permutation approach to job shop scheduling with genetic algorithms

Bierwirth, Christian

doi:10.1007/bf01719250

Cited by 270 publications

(141 citation statements)

References 4 publications

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“…. , n. This is a permutation of the set of tasks as proposed in [56] for the JSP, where each task is represented by its job number. For example, the topological order θ 21 , θ 11 , θ 22 , θ 31 , θ 32 , θ 12 is encoded as (2 1 2 3 3 1).…”

Section: Genotype Coding and Decodingmentioning

confidence: 99%

Coevolutionary makespan optimisation through different ranking methods for the fuzzy flexible job shop

Palacios

González-Rodríguez

Vela

et al. 2015

Fuzzy Sets and Systems

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In this paper we tackle a variant of the flexible job shop scheduling problem with uncertain task durations modelled as fuzzy numbers. To minimise the schedule's makespan, we consider different ranking methods for fuzzy numbers. We then propose a cooperative co-evolutionary algorithm with two different populations evolving the two components of a solution: machine assignment and task relative order. Additionally, we incorporate a specific local search method for each population. The resulting hybrid algorithm is then evaluated on existing benchmark instances, comparing favourably with the state-of-theart methods. The experimental results also serve to analyse the influence in the robustness of the resulting schedules of the chosen ranking method.

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Section: Genotype Coding and Decodingmentioning

confidence: 99%

Coevolutionary makespan optimisation through different ranking methods for the fuzzy flexible job shop

Palacios

González-Rodríguez

Vela

et al. 2015

Fuzzy Sets and Systems

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“…Subsequently, the two genes generated, will constitute the initial chromosomes [40], which will be the parents of the coming generations through three probable genetic operators: A crossing reproductive operator denominated partial matching crossover (PMX) with a 95% probability of occurring, a genetic mutation operator with a 5% probability of occurring [41], or substitute operator by inclusion and a stochastic selection operator. The use of the previous operators in a repeated form during a predetermined number of iterations, will allow the generation of a gene, which represents a feasible solution to the problems with the minimum possible makespan and idle time.…”

Section: Methodsmentioning

confidence: 99%

Design of a Hyperheuristic for Production Scheduling in Job Shop Environments

Castrillón

Sarache

Giraldo

2010

Ingeniare. Rev. chil. ing.

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RESUMENEl objetivo del presente trabajo es disminuir el tiempo de proceso (Makespan) e incrementar el tiempo de trabajo de las máquinas, diminuyendo el tiempo de ocio en ambientes de job shop, a través del diseño de una hiperheurística basada en colonia de hormigas y algoritmos genéticos. Este trabajo se desarrolla en dos etapas: en la primera se realiza la definición e identificación de una hiperheurística para la secuenciación de procesos en ambientes job shop. En la segunda etapa es mostrada la efectividad del sistema en la programación de la producción. En el proyecto de investigación se seleccionó una empresa del sector metalmecánico, donde por medio de una combinación de colonia de hormigas y algoritmos genéticos se programa la ruta óptima para un pedido, logrando la optimización o suboptimización de su respectivo tiempo total de proceso en un porcentaje superior al 95%.Palabras clave: Taller de trabajos, programación, heurísticas, simulación, función evaluación, tiempo total de proceso, tiempo ocioso. ABSTRACTThe objective of the present work is to diminish the total process time (Makespan)

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“…Among different proposals available in the literature, the two most popular encoding schemes are the conventional permutations (CP) [2] and permutations with repetition (PR) [1]. In both cases, a chromosome expresses a total ordering of all operations of the problem [26].…”

Section: An Evolutionary Approach To the Fuzzy Jspsmentioning

confidence: 99%

“…New chromosome will then be created and selected to the next generation based on their fitness values-which are the expected makespans of the schedule they encode-by the well-known binary tournament selection strategy. To create new offspring, the job order crossover (JOX) [1,12] and a simple swap mutation are employed. Specifically, given any two parents, the JOX selects a random subset of jobs from the first parent and copies the associated genes to the offspring at the same positions as they appear in that parent.…”

Section: An Evolutionary Approach To the Fuzzy Jspsmentioning

confidence: 99%

Solving Fuzzy Job-Shop Scheduling Problems with a Multiobjective Optimizer

Tran

Varela²,

González-Rodríguez³

et al. 2014

Advances in Intelligent Systems and Computing

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Abstract. In real-world manufacturing environments, it is common to face a job-shop scheduling problem (JSP) with uncertainty. Among different sources of uncertainty, processing times uncertainty is the most common. In this paper, we investigate the use of a multiobjective genetic algorithm to address JSPs with uncertain durations. Uncertain durations in a JSP are expressed by means of triangular fuzzy numbers (TFNs). Instead of using expected values as in other work, we consider all vertices of the TFN representing the overall completion time. As a consequence, the proposed approach tries to obtain a schedule that optimizes the three component scheduling problems (corresponding to the lowest, most probable, and largest durations) all at the same time. In order to verify the quality of solutions found by the proposed approach, an experimental study was carried out across different benchmark instances. In all experiments, comparisons with previous approaches that are based on a single-objective genetic algorithm were also performed.

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A generalized permutation approach to job shop scheduling with genetic algorithms

Cited by 270 publications

References 4 publications

Coevolutionary makespan optimisation through different ranking methods for the fuzzy flexible job shop

Coevolutionary makespan optimisation through different ranking methods for the fuzzy flexible job shop

Design of a Hyperheuristic for Production Scheduling in Job Shop Environments

Solving Fuzzy Job-Shop Scheduling Problems with a Multiobjective Optimizer

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