An ant colony optimization approach for the proportionate multiprocessor open shop

Adak, Zeynep; Arıoğlu, Mahmure Övül; Bulkan, Serol

doi:10.1007/s10878-021-00798-y

Cited by 4 publications

(1 citation statement)

References 34 publications

(56 reference statements)

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“…In papers regarding ACO, e.g. in [3], the researchers indicate that the quantity of ants they utilized was what they discovered to be the most ideal. The fundamental reason for this argument is because the reasons for why a certain number is selected is not explored.…”

Section: Introductionmentioning

confidence: 99%

Determining the Number of Ants in Ant Colony Optimization

Arranz

2023

JBSHA

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The goal of this contribution article is to investigate the effect of the numbers of ants on the Ant Colony Optimization (ACO) metaheuristic's obtained solution while addressing the Traveling Salesman Problem. Within a restricted number of iterations, the purpose was to see how the duration of the calculated tours varied for various numbers of ants. Three well-known ACO algorithms: Elitist Ant System (EAS), Ranked Ant System (RAS), and Min-Max Ant System (MMAS), were developed and tested in this paper. The findings revealed comparable patterns across several test instances. EAS and RAS, both of which use specialized ants, demonstrated that the number of specialists had a significant impact on the duration of solutions. Normal ants, on the other hand, had no effect on the solutions. The response differed somewhat between EAS and MMAS, with a smaller number of ants being more preferred. When working with five specialists and ants, which are the same to the smart cities, however, RAS outperformed by a considerable margin.

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Section: Introductionmentioning

confidence: 99%

Determining the Number of Ants in Ant Colony Optimization

Arranz

2023

JBSHA

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The LPT heuristic for minimizing total load on a proportionate openshop

Gerstl

Mosheiov

2023

Discrete Applied Mathematics

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Bi-Objective, Dynamic, Multiprocessor Open-Shop Scheduling: A Hybrid Scatter Search–Tabu Search Approach

Abdelmaguid

2024

Algorithms

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This paper presents a novel, multi-objective scatter search algorithm (MOSS) for a bi-objective, dynamic, multiprocessor open-shop scheduling problem (Bi-DMOSP). The considered objectives are the minimization of the maximum completion time (makespan) and the minimization of the mean weighted flow time. Both are particularly important for improving machines’ utilization and customer satisfaction level in maintenance and healthcare diagnostic systems, in which the studied Bi-DMOSP is mostly encountered. Since the studied problem is NP-hard for both objectives, fast algorithms are needed to fulfill the requirements of real-life circumstances. Previous attempts have included the development of an exact algorithm and two metaheuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective gray wolf optimizer (MOGWO). The exact algorithm is limited to small-sized instances; meanwhile, NSGA-II was found to produce better results compared to MOGWO in both small- and large-sized test instances. The proposed MOSS in this paper attempts to provide more efficient non-dominated solutions for the studied Bi-DMOSP. This is achievable via its hybridization with a novel, bi-objective tabu search approach that utilizes a set of efficient neighborhood search functions. Parameter tuning experiments are conducted first using a subset of small-sized benchmark instances for which the optimal Pareto front solutions are known. Then, detailed computational experiments on small- and large-sized instances are conducted. Comparisons with the previously developed NSGA-II metaheuristic demonstrate the superiority of the proposed MOSS approach for small-sized instances. For large-sized instances, it proves its capability of producing competitive results for instances with low and medium density.

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An ant colony optimization approach for the proportionate multiprocessor open shop

Cited by 4 publications

References 34 publications

Determining the Number of Ants in Ant Colony Optimization

Determining the Number of Ants in Ant Colony Optimization

The LPT heuristic for minimizing total load on a proportionate openshop

Bi-Objective, Dynamic, Multiprocessor Open-Shop Scheduling: A Hybrid Scatter Search–Tabu Search Approach

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