A hybrid memetic algorithm for maximizing the weighted number of just-in-time jobs on unrelated parallel machines

Jolai, Fariborz; Amalnick, Mohsen Sadegh; Alinaghian, Mahdi; Shakhsi‐Niaei, Majid; Omrani, Hashem

doi:10.1007/s10845-009-0285-7

Cited by 15 publications

(8 citation statements)

References 17 publications

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“…Several design choices of the algorithm are then investigated during experiments. In [139] the authors consider the objective of maximising the weighted number of jobs that complete just in time. They propose a GA and a combination of a GA with LS approaches for inserting and swapping jobs.…”

Section: B Metaheuristicsmentioning

confidence: 99%

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Đurasević,

Jakobović

2021

Preprint

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Today scheduling problems have an immense effect on various areas of human lives, be it from their application in manufacturing and production industry, transportation, or workforce allocation. The unrelated parallel machines scheduling problem (UPMSP), which is only one of the many different problem types that exist, found its application in many areas like production industries or distributed computing. Due to the complexity of the problem, heuristic and metaheuristic methods are gaining more attention for solving it. Although this problem variant did not receive much attention as other models, recent years saw the increase of research dealing with this problem. During that time, many different problem variants, solution methods, or other interesting research directions were considered. However, no study has until now tried to systematise the research in which heuristic methods are applied for the UPMSP. The goal of this study is to provide an extensive literature review on the application of heuristic and metaheuristic methods for solving the UPMSP. The research was systematised and classified into several categories to enable an easy overview of the different problem and solution variants. Additionally, current trends and possible future research directions are also shortly outlined.

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Section: B Metaheuristicsmentioning

confidence: 99%

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Đurasević,

Jakobović

2021

Preprint

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“…It is the main operator of GA. During the past years, various crossover operators had been proposed such as partialmapped crossover (PMX), order crossover (OX), cycle crossover (CX), position-based crossover, etc. The two most popular crossover operators are partial mapped crossover (Liaw [32], Moon et al [42]) and order crossover [Arroyo and Armentano [2], França et al [18], Jolai et al [29]).…”

Section: Crossover Operatormentioning

confidence: 99%

“…In last years, a hybrid memetic algorithm for maximizing the weighted number of just-in-time jobs on unrelated parallel machines was also presented by Jolai et al [29]. Unrelated parallel machines can be charac-terized as machines that execute the same function but have different industrial unit may invest in related machines.…”

Section: Parallel Machines Scheduling Problemmentioning

confidence: 99%

“…Unrelated parallel machines can be charac-terized as machines that execute the same function but have different industrial unit may invest in related machines. A memetic algorithms (MA) is a genetic algorithm hybridized with a local search (LS) procedure used to intensify the search process (Jolai et al [29]). Besides, in the literature, a genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup limes was reported by Vallada and Ruiz R. [57].…”

Section: Parallel Machines Scheduling Problemmentioning

confidence: 99%

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Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems

Ławrynowicz

2011

Foundations of Management

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Genetic Algorithms for Solving Scheduling Problems in Manufacturing SystemsScheduling manufacturing operations is a complicated decision making process. From the computational point of view, the scheduling problem is one of the most notoriously intractable NP-hard optimization problems. When the manufacturing system is not too large, the traditional methods for solving scheduling problem proposed in the literature are able to obtain the optimal solution within reasonable time. But its implementation would not be easy with conventional information systems. Therefore, many researchers have proposed methods with genetic algorithms to support scheduling in the manufacturing system. The genetic algorithm belongs to the category of artificial intelligence. It is a very effective algorithm to search for optimal or near-optimal solutions for an optimization problem. This paper contains a survey of recent developments in building genetic algorithms for the advanced scheduling. In addition, the author proposes a new approach to the distributed scheduling in industrial clusters which uses a modified genetic algorithm.

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“…Liaw et al [18] proposed a branch-and-bound algorithm that incorporated efficient lower and upper bounds to minimize weighted total tardiness for unrelated parallel machine problems. Jolai et al [19] presented a hybrid memetic algorithm for the same scheduling problem with the objective of maximizing the weighted number of jobs that are completed exactly at their due dates. Ho and Chang [20] extended Moore's algorithm to solve problems with the number of tardy jobs as the objective.…”

Section: Introductionmentioning

confidence: 99%

Iterated hybrid metaheuristic algorithms for unrelated parallel machines problem with unequal ready times and sequence-dependent setup times

Chen¹

2011

Int J Adv Manuf Technol

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The problem considered in this paper involves a set of independent jobs on unrelated parallel machines with sequence-dependent setup times and unequal ready times, and the objective is to minimize the weighted number of tardy jobs. Iterated hybrid metaheuristic algorithms are proposed to address this problem. The algorithms begin with effective initial solution generators to generate initial feasible solutions; then, hybrid metaheuristics are applied to improve the initial solutions, integrating the principles of the variable neighborhood descent approach and tabu search. If the search becomes trapped at a local optimum, a perturbation operator is developed to help the search escape. To evaluate the performance of the suggested algorithms, heuristic rules and iterated local search algorithms are examined and compared. Computational experimental results show that the proposed algorithms outperform the other heuristics.

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A hybrid memetic algorithm for maximizing the weighted number of just-in-time jobs on unrelated parallel machines

Cited by 15 publications

References 17 publications

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems

Iterated hybrid metaheuristic algorithms for unrelated parallel machines problem with unequal ready times and sequence-dependent setup times

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