Multiple-objective heuristics for scheduling unrelated parallel machines

Lin, Yang-Kuei; Fowler, John W.; Pfund, Michele E.

doi:10.1016/j.ejor.2012.10.008

Cited by 43 publications

(33 citation statements)

References 36 publications

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“…We compare the proposed TSA with four existing GAs, namely the GA and PGA described by Lin et al [25], the MPGA described by Cochran et al [7], and the GA (denoted as GA T ) described by Tavakkoli-Moghaddam et al [45]. The GA and PGA [25] variant was originally designed to minimize makespan and TWT for the unrelated parallel machine scheduling (Rj j C max ; P w j T j ) problem.…”

Section: Comparison Of the Tsa With The Gasmentioning

confidence: 98%

“…However, none of them considered release dates. There is some research that uses genetic algorithm (GA)-based approaches to solve parallel machines with bicriteria objective scheduling problems [6,12,25,43], and some research that uses simulated annealing to solve parallel machines with bicriteria objective scheduling problems [17,37]. Additionally, there is some research that uses tabu searches to solve parallel machines with bicriteria objective scheduling problems [4,42].…”

Section: Introductionmentioning

confidence: 98%

“…In the last few decades, more and more researchers have studied bicriteria parallel machine scheduling problems. Many considered makespan-or total-completion-time-related objectives [1,2,6,8,[10][11][12][14][15][16]19,23,24,38,40], while several of them considered due-date-related objectives [4,25,17,37,38,43]. However, none of them considered release dates.…”

Section: Introductionmentioning

confidence: 99%

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Bicriteria scheduling problem for unrelated parallel machines with release dates

Lin

2015

Computers & Operations Research

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Section: Comparison Of the Tsa With The Gasmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Bicriteria scheduling problem for unrelated parallel machines with release dates

Lin

2015

Computers & Operations Research

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“…Pareto-based metaheuristic is an effective approach for the multiobjective scheduling problem [11]. For the unrelated parallel machine environment, Lin et al [12] proposed two heuristics and a Pareto-based genetic algorithm for unrelated parallel machine scheduling problem to minimize the makespan, total weighted completion time, and total weighted tardiness. Lin and Ying [13] presented a multiobjective multipoint simulated annealing (MOMSA) algorithm and Lin et al [14] proposed a Tabu-enhanced iterated Pareto greedy algorithm for the same problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiobjective Order Acceptance and Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints

Wang

2018

Mathematical Problems in Engineering

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This paper studies the order acceptance and scheduling problem on unrelated parallel machines with machine eligibility constraints. Two objectives are considered to maximize total net profit and minimize the makespan, and the mathematical model of this problem is formulated as multiobjective mixed integer linear programming. Some properties with respect to the objectives are analysed, and then a classic list scheduling (LS) rule named the first available machine rule is extended, and three new LS rules are presented, which focus on the maximization of the net profit, the minimization of the makespan, and the trade-off between the two objectives, respectively. Furthermore, a list-scheduling-based multiobjective parthenogenetic algorithm (LS-MPGA) is presented with parthenogenetic operators and Pareto-ranking and selection method. Computational experiments on randomly generated instances are carried out to assess the effectiveness and efficiency of the four LS rules under the framework of LS-MPGA and discuss their application environments. Results demonstrate that the performance of the LS-MPGA developed for trade-off is superior to the other three algorithms.

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“…When tackling multiple objectives, decisions become even more complex and decisions to complete several criteria usually involve the decision of single experienced person in a facility. Several heuristic approaches have been employed for multi-objective optimization in scheduling problems [1], [2], [3], [4], but metaheuristics are considered to be the best in terms of efficiency and robustness [5], [6], [7] and [8].…”

Section: Introductionmentioning

confidence: 99%

A bi-criteria optimization model for parallel machine scheduling: game theoretic vs genetic algorithms

Rios¹,

Pinto²,

Martinez³

et al. 2016

IJMSOR

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Abstract--This paper considers a problem for scheduling jobs on two identical parallel machines, the aim was to minimize two criteria in particular, makespan and total flow time. In order to solve this problem, two approaches were considered. A mechanism was proposed as an approach to solve this type of problem with a setting of a 2-player non-cooperative game, under the framework of a 2x2 non-sum zero matrix; each player looking after one of the criteria suggested in the scheduling problem. On the other hand, a Genetic Algorithm, known as Strength Pareto Evolutionary Algorithm (SPEA), was applied to the problem. The comparison between both approaches suggests a complementarity among rational agents approach models and machine enforced solution approaches. The resulting Pareto Front set of points were plotted and curves were compared, showing promising results for game theoretic applications to scheduling under multiple objectives.Keywords-identical parallel machines, makespan, total flow time, non-cooperative game, SPEA, Pareto Front.Resumen--Este articulo contempla el problema de programación de la producción en una configuracion de maquinas en paralelo con el objetivo de minimizar dos criterios en particular: el lapso y el tiempo total de flujo. En este problema en particular, el incremento de uno de estos objetivos resulta en la reduccion del otro, por lo que se propone su solucion bajo enfoques metaheuristicos. Dos tipos de algoritmos fueron considerados: uno basado en la teoria de juegos y el otro en los algoritmos genéticos. Para el primero se diseña un mecanismo de juego no cooperativo entre dos jugadores, en donde cada jugador busca optimizar cada criterio de programación de las máquinas. Para el segundo enfoque se implementa el algortimo genético SPEA, en donde se seleccionan aquellas soluciones dominantes en ambos objetivos. Resultados de ambos enfoques resultan en un Frente de Pareto, las cuales representan las soluciones dominantes para ambos objetivos. Estos resultados demuestran que ambos enfoques son complementarios: SPEA arroja resultados que cubren todo el frente de Pareto, mientras que el algoritmo de Juegos No Cooperativo indica la programación mas conveniente para cada agente en particular.

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Multiple-objective heuristics for scheduling unrelated parallel machines

Cited by 43 publications

References 36 publications

Bicriteria scheduling problem for unrelated parallel machines with release dates

Bicriteria scheduling problem for unrelated parallel machines with release dates

Multiobjective Order Acceptance and Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints

A bi-criteria optimization model for parallel machine scheduling: game theoretic vs genetic algorithms

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