A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines

Mir, Mir Saber Salehi; Rezaeian, Javad

doi:10.1016/j.asoc.2015.12.035

Cited by 57 publications

(6 citation statements)

References 59 publications

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“…Also, Cheng & Huang (2017) relied on the elitist strategy to create the next generation based on the top 10% of paternal chromosomes with the best offspring chromosomes. While the Mir & Rezaeian (2016) utilized two selection methods to create the new generation roulette wheel selection method and tournament selection procedure. Moreover, the chromosomes with the highest fitness values receive the highest probability of creating the next generation.…”

Section: Literature Reviewmentioning

confidence: 99%

An Accurate and Robust Genetic Algorithm to Minimize the Total Tardiness in Parallel Machine Scheduling Problems

RAMADAN,

ALMASARWAH,

ABDELALL

et al. 2023

Management and Production Engineering Review

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This paper uses a Genetic Algorithm (GA) to reduce total tardiness in an identical parallel machine scheduling problem. The proposed GA is a crossover-free (vegetative reproduction) GA but used for four types of mutations (Two Genes Exchange mutation, Number of Jobs mutation, Flip Ends mutation, and Flip Middle mutation) to make the required balance between the exploration and exploitation functions of the crossover and mutation operators. The results showed that use of these strategies positively affects the accuracy and robustness of the proposed GA in minimizing the total tardiness. The results of the proposed GA are compared to the mathematical model in terms of the time required to tackle the proposed problem. The findings illustrate the ability of the propounded GA to acquire the results in a short time compared to the mathematical model. On the other hand, increasing the number of machines degraded the performance of the proposed GA.

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Section: Literature Reviewmentioning

confidence: 99%

An Accurate and Robust Genetic Algorithm to Minimize the Total Tardiness in Parallel Machine Scheduling Problems

RAMADAN,

ALMASARWAH,

ABDELALL

et al. 2023

Management and Production Engineering Review

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“…TAFOA [3] and a multi-pass heuristic (MPH) [7] are chosen as comparative algorithm because they can be directly used to solve UPMSPR with PM. Salehi Mir and Rezaeian [47] presented a hybrid particle swarm optimization and genetic algorithm (HPSOGA), which can be directly applied to our UPMSPR after the decoding procedure of ABC-AC is adopted; therefore, we selected it as a comparative algorithm.…”

Section: Test Instances and Comparative Algorithmsmentioning

confidence: 99%

“…ABC has following parameters: N = 100 and Limit = 8. Parameter settings of three comparative algorithms are directly selected from references [3,7,47] except that the stopping condition. We also test these settings for each comparative algorithm by Taguchi method.…”

Section: Parameter Settingsmentioning

confidence: 99%

An Artificial Bee Colony with Adaptive Competition for the Unrelated Parallel Machine Scheduling Problem with Additional Resources and Maintenance

Xiong

Lei

2022

Symmetry

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The unrelated parallel machine scheduling problem (UPMSP) is a typical production scheduling problem with certain symmetries on machines. Additional resources and preventive maintenance (PM) extensively exist on parallel machines; however, UPMSP with additional resources and PM has been scarcely investigated. Adaptive competition is also seldom implemented in the artificial bee colony algorithm for production scheduling. In this study, UPMSP with additional resources and PM is investigated, which has certain symmetries with machines. An artificial bee colony with adaptive competition (ABC-AC) is proposed to minimize the makespan. Two employed bee swarms are constructed and evaluated. In the employed bee phase, adaptive competition is used to dynamically decide two cases. The first is the shifting of search resources from the employed bee swarm with a lower evolution quality to another one, and the second is the migration of solutions from the employed bee swarm with a higher evolution quality to another one. An adaptive onlooker bee phase and a new scout phase are given. Extensive experiments are conducted on 300 instances. The computational results demonstrate that the new strategies of ABC-AC are effective, and ABC-AC provides promising results for the considered UPMSP.

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“…A problem with deteriorating job effects, where processing times of jobs increases after they are released into the system, is analysed in [194]. The authors apply a GA, PSO, and a hybrid method which combines the previous two metaheuristics.…”

Section: B Metaheuristicsmentioning

confidence: 99%

“…A complete classification of the reviewer research including the problem variant, solution method, and short notes is given in Table V. R|R|Cmax heuristics matheuristics that combine mathematical programming with heuristic procedures [85] R|R|Cmax heuristics two heuristic types, one which consider resources when scheduling, and the second which does not and then performs a repair mechanism [86] R||T EC heuristic jobs have an electricity consumption rate when executing, and electricity prices are changing during the time horizon [87] R|sij , Mj | Tj heuristic, CLONALG, VND, GRASP several novel heuristics adapted for the considered, CLONALG combined with VNS and GRASP problem [88] R||Cmax GA, TS, SA, ID, MDDR GA with neighbourhood operators, initialisation of starting solutions with MCT [89] R||Cmax descent method, TS greedy initialisation [90] R||Cmax, Tmax TS bi-criteria optimisation [91] R||Cmax TS TS with hashing for control of tabu restrictions [92] R|s ijk , s − batch| Tj SA setup times are not considered between jobs belonging to the same batch [93] R|s R||Cmax GA matrix encoding of solutions [133] R|s ijk |Cmax GA, SA dominance properties included in the metaheuristics [134] R||Cmax VND combination od VND with a commercial solver [135] R||Cmax VND problem where not all machines and jobs need to be considered for scheduling [136] R|s ijk |Cmax GA combination of GA with local search operators [137] R|s ijk | Tj IG IG with destruction and reconstruction operators [138] R|s IG algorithm for multi-objective optimisation and integration with concepts from TS [187] R|s ijk | Tj GA, SA SA embedded in GA as a local search procedure [188] R|M d |Cmax GA machines deteriorate over time, but can be fixed with maintenance activities [189] R|s ijk |Cmax GA, heuristic GA coupled with LS [190] R|s ijk , Mj | Fj GA, MDDRs GA which sequences jobs and a DR that assigns them to machines [191] R|s ijk , Mj , brkdwn, rj , batch| wj Cj + wj Tj TS [192] R|sij , Mj , rj , prec|T LT GA GA coupled with the SA solution acceptance test [193] R|rj | wj Tj , Cmax MDDR, heuristic, TS TS and heuristic adapted for bi-criteria optimisation [194] R|rj , s ijk , pc| Fj GA, PSO a hybrid metaheuristic combining PSO and GA [195] R|rj , sij , Mj , prec, R|Cmax GA,...…”

Section: Constraints Referencesmentioning

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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A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines

Cited by 57 publications

References 59 publications

An Accurate and Robust Genetic Algorithm to Minimize the Total Tardiness in Parallel Machine Scheduling Problems

An Accurate and Robust Genetic Algorithm to Minimize the Total Tardiness in Parallel Machine Scheduling Problems

An Artificial Bee Colony with Adaptive Competition for the Unrelated Parallel Machine Scheduling Problem with Additional Resources and Maintenance

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

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