A Discrete Artificial Bee Colony Algorithm for the Energy-Efficient No-Wait Flowshop Scheduling Problem

Taşgetiren, M. Fatih; Yüksel, Damla; Gao, Liang; Pan, Quan-Ke; Li, Peigen

doi:10.1016/j.promfg.2020.01.347

Cited by 11 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A variable neighborhood strategy based on insert neighborhood structure and block neighborhood structure is adopted to locate the best solution in the vicinity of the current solution. Tasgetiren et al 31 aimed to minimize the total flow time and total energy consumption by using a mixed integer programming model to identify Pareto optimal sets, which were then tested on Taillard instances. To obtain a non-dominated set of solutions for large instances, the researchers developed three heuristics: a Discrete Artificial Bee Colony, an Energy-efficient genetic algorithm, and another variant of energy-efficient genetic algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems

Khurshid,

Maqsood,

Khurshid

et al. 2024

Sci Rep

View full text Add to dashboard Cite

This study investigates the no-wait flow shop scheduling problem and proposes a hybrid (HES-IG) algorithm that utilizes makespan as the objective function. To address the complexity of this NP-hard problem, the HES-IG algorithm combines evolution strategies (ES) and iterated greedy (IG) algorithm, as hybridizing algorithms helps different algorithms mitigate their weaknesses and leverage their respective strengths. The ES algorithm begins with a random initial solution and uses an insertion mutation to optimize the solution. Reproduction is carried out using (1 + 5)-ES, generating five offspring from one parent randomly. The selection process employs (µ + λ)-ES, allowing excellent parent solutions to survive multiple generations until a better offspring surpasses them. The IG algorithm’s straightforward search mechanism aids in further improving the solution and avoiding local minima. The destruction operator randomly removes d-jobs, which are then inserted one by one using a construction operator. The local search operator employs a single insertion approach, while the acceptance–rejection criteria are based on a constant temperature. Parameters of both ES and IG algorithms are calibrated using the Multifactor analysis of variance technique. The performance of the HES-IG algorithm is calibrated with other algorithms using the Wilcoxon signed test. The HES-IG algorithm is tested on 21 Nos. Reeves and 30 Nos. Taillard benchmark problems. The HES-IG algorithm has found 15 lower bound values for Reeves benchmark problems. Similarly, the HES-IG algorithm has found 30 lower bound values for the Taillard benchmark problems. Computational results indicate that the HES-IG algorithm outperforms other available techniques in the literature for all problem sizes.

show abstract

Section: Literature Reviewmentioning

confidence: 99%