An immune-inspired algorithm for an unrelated parallel machines’ scheduling problem with sequence and machine dependent setup-times for makespan minimisation

Diana, Rodney O. M.; Filho, Moacir Felizardo de França; Souza, Sérgio Ricardo de; Vitor, João Francisco de Almeida

doi:10.1016/j.neucom.2014.06.091

Cited by 34 publications

(24 citation statements)

References 29 publications

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“…CLONALG was developed to perform pattern recognition and optimization White and Garrett, 2003;Diana et al, 2015;Pérez-Cáceres and Riff, 2015). This specific algorithm is presented in this paper.…”

Section: Artificial Immune System (Ais) Algorithmmentioning

confidence: 99%

Resolving forward-reverse logistics multi-period model using evolutionary algorithms

Kumar

Brady

Garza‐Reyes

et al. 2017

International Journal of Production Economics

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Section: Artificial Immune System (Ais) Algorithmmentioning

confidence: 99%

Resolving forward-reverse logistics multi-period model using evolutionary algorithms

Kumar

Brady

Garza‐Reyes

et al. 2017

International Journal of Production Economics

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“…Considering the global optimization capabilities of AIS and the local exploitation characteristics of the trajectory methods, [13] proposed a variation of opt-aiNET for combinatorial optimization, called COPT-aiNET. Already [14] proposed a hybrid approach, based on the CLONALG and Variable Neighborhood Search (VNS) [21], for solving a job sequencing problem in parallel machines. This last approach presented high performance compared to the algorithms proposed in the literature for the problem.…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents a general purpose metaheuristic, based on characteristics of the immune network, of higher performance for combinatorial problems. The proposed metaheuristics is based on the strategies of exploitation of the search space employed by the aiNET family and characteristics of the trajectory methods found in [14]. This new metaheuristic, called VNS-aiNET, makes changes in the internal and external structure of the COPT-aiNET, modifying the behavior of the method, which results in a better search space coverage, but reduces the multimodal power of the same.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid metaheuristic for combinatorial optimization based on immune network for optimization and VNS

Diana

Souza

Wanner

et al. 2017

Proceedings of the Genetic and Evolutionary Computation Conference

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Metaheuristics for optimization based on the immune network theory are often highlighted by being able to maintain the diversity of candidate solutions present in the population, allowing a greater coverage of the search space. This work, however, shows that algorithms derived from the aiNET family for the solution of combinatorial problems may not present an adequate strategy for search space exploration, leading to premature convergence in local minimums. In order to solve this issue, a hybrid metaheuristic called VNS-aiNET is proposed, integrating aspects of the COPT-aiNET algorithm with characteristics of the trajectory metaheuristic Variable Neighborhood Search (VNS), as well as a new tness function, which makes it possible to escape from local minima and enables it to a greater exploration of the search space. The proposed metaheuristic is evaluated using a scheduling problem widely studied in the literature. The performed experiments show that the proposed hybrid metaheuristic presents a convergence superior to two approaches of the aiNET family and to the reference algorithms of the literature. In contrast, the solutions present in the resulting immunological memory have less diversity when compared to the aiNET family approaches.

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“…The authors also proposed a probability model that is based on the neighbor relations of the jobs, in order to assist the proposed algorithm to generate new solutions by sampling a promising search region. An immune-inspired algorithm was proposed by Diana et al (2015). The authors generated the initial population using the Greedy Randomized Adaptive Search Procedure (GRASP), used Variable Neighborhood Descent (VND) as local search heuristic, and proposed a population re-selection operator.…”

mentioning

confidence: 99%

A worm optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times

Arnaout

2019

Ann Oper Res

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This paper addresses the unrelated parallel machine scheduling problem with setup times, with an objective of minimizing the makespan. The machines are unrelated in the sense that the processing speed depends on the job being executed and not the machine. Each job will have different processing times for each of the available machines, is available at the beginning of the scheduling horizon, and can be processed on any of the machines but needs to be processed by one machine only. Sequence-dependent and machine-dependent setup times are also considered. A Worm Optimization (WO) algorithm is introduced and is applied to this NP-hard problem. The novel WO is based on the behaviors of the worm, which is a nematode with only 302 neurons. Nevertheless, these neurons allow worms to achieve several intricate behaviors including finding food, interchanging between solitary and social foraging styles, alternating between dwelling and roaming, and entering a type of stasis/declining stage. WO's performance is evaluated by comparing its solutions to solutions of six other known metaheuristics for the problem under study, and the extensive computational results indicated that the proposed WO performs best. IntroductionThis paper addresses the scheduling of N available jobs on M unrelated machines (R M ) using a novel Worm Optimization algorithm (WO) that emulates the worms' behaviors and an objective of minimizing the makespan, C max , without preemption.With its negligible neurons (only 3.02e−7% of human brain neurons), the worm remarkably is able to realize critical survival activities, especially in relation to switching between social and solitary food searching, locating nutrients, evading unhealthy food, varying between "roaming-global search" and "dwelling-local search", and converting from a reproductive stage to a declining one. WO was first introduced in Arnaout (2016) for the B Jean-Paul Arnaout

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An immune-inspired algorithm for an unrelated parallel machines’ scheduling problem with sequence and machine dependent setup-times for makespan minimisation

Cited by 34 publications

References 29 publications

Resolving forward-reverse logistics multi-period model using evolutionary algorithms

Resolving forward-reverse logistics multi-period model using evolutionary algorithms

Hybrid metaheuristic for combinatorial optimization based on immune network for optimization and VNS

A worm optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times

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