A hybrid genetic local and global search algorithm for solving no-wait flow shop problem with bi criteria

Keskin, Kenan; Engin, Orhan

doi:10.1007/s42452-021-04615-3

Cited by 15 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By combining these dispatch rules and factory assignment rules, several constructive heuristics are derived. Keskin and Engin 42 tackled the issue of scheduling in the NWFSSP with setup times, focusing on two performance metrics: total flow time (ΣCj) and makespan. To address this problem, a Hybrid Genetic Local and Global Search Algorithm was introduced.…”

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%

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

show abstract

“…Next, the performance of the proposed vCCEA is evaluated on the medium-large scale instances in β 2 . Here, we collected five metaheuristic algorithms for comparisons, namely, CVND [30], GA [40], GAR [24], VMBO [41], and DABC [42], which are those presented for the HFSP in the literature most recently and have been proven to have excellent performance. For the HFSP_ECS, three highly coupled subproblems need to be solved, i.e., lot sequence, machine assignment, and lot split.…”

Section: Evaluation Of Vccea On the Medium-large Scale Instancesmentioning

confidence: 99%

Energy-Efficient Hybrid Flowshop Scheduling with Consistent Sublots Using an Improved Cooperative Coevolutionary Algorithm

Zhang

Han

et al. 2022

Mathematics

View full text Add to dashboard Cite

Energy conservation, emission reduction, and green and low carbon are of great significance to sustainable development, and are also the theme of the transformation and upgrading of the manufacturing industry. This paper concentrates on studying the energy-efficient hybrid flowshop scheduling problem with consistent sublots (HFSP_ECS) with the objective of minimizing the energy consumption. To solve the problem, the HFSP_ECS is decomposed by the idea of “divide-and-conquer”, resulting in three coupled subproblems, i.e., lot sequence, machine assignment, and lot split, which can be solved by using a cooperative methodology. Thus, an improved cooperative coevolutionary algorithm (vCCEA) is proposed by integrating the variable neighborhood descent (VND) strategy. In the vCCEA, considering the problem-specific characteristics, a two-layer encoding strategy is designed to represent the essential information, and a novel collaborative model is proposed to realize the interaction between subproblems. In addition, special neighborhood structures are designed for different subproblems, and two kinds of enhanced neighborhood structures are proposed to search for potential promising solutions. A collaborative population restart mechanism is established to ensure the population diversity. The computational results show that vCCEA can coordinate and solve each subproblem of HFSP_ECS effectively, and outperform the mathematical programming and the other state-of-the-art algorithms.

show abstract

“…These algorithms draw inspiration from the biological process of evolution and prove particularly valuable for tackling search and optimization challenges in extensive and continuous search domains [7]. The effectiveness of GA extends notably to scheduling problems due to its ability to swiftly and effectively navigate intricate solution spaces [8].…”

Section: Introductionmentioning

confidence: 99%

The Effectiveness of Genetic Algorithm, And the CDS Method In Solving Flowshop Scheduling Problems

Prassetiyo,

Heryati

2024

E3S Web Conf.

View full text Add to dashboard Cite

Flow shop scheduling problem is considered NP-hard for m machines and n jobs. For such NP-hard combinatorial optimization problems, heuristics play a major role in searching for near-optimal solutions. In this paper we used Genetic Algorithm, and the CDS method for solving flow shop scheduling problem with makespan as the criteria. The objective of this model is to obtain a sequence of jobs and the minimization of the total completion time (makespan). To test the effectiveness of the method, a dataset of case studies is used to compare the makespan values obtained for each method.

show abstract

A hybrid genetic local and global search algorithm for solving no-wait flow shop problem with bi criteria

Cited by 15 publications

References 41 publications

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

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

Energy-Efficient Hybrid Flowshop Scheduling with Consistent Sublots Using an Improved Cooperative Coevolutionary Algorithm

The Effectiveness of Genetic Algorithm, And the CDS Method In Solving Flowshop Scheduling Problems

Contact Info

Product

Resources

About