A cooperative iterated greedy algorithm for the distributed flowshop group robust scheduling problem with uncertain processing times

Swarm and Evolutionary Computation, Год журнала: 2023, Номер 79, С. 101320 - 101320

Опубликована: Апрель 24, 2023

Язык: Английский

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A hybrid genetic algorithm for distributed hybrid blocking flowshop scheduling problem

Journal of Manufacturing Systems, Год журнала: 2023, Номер 71, С. 390 - 405

Опубликована: Окт. 7, 2023

Язык: Английский

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BRCE: bi-roles co-evolution for energy-efficient distributed heterogeneous permutation flow shop scheduling with flexible machine speed

Complex & Intelligent Systems, Год журнала: 2023, Номер 9(5), С. 4805 - 4816

Опубликована: Фев. 15, 2023

Abstract Distributed manufacturing is the mainstream model to accelerate production. However, heterogeneous production environment makes engineer hard find optimal scheduling. This work investigates energy-efficient distributed permutation flow scheduling problem with flexible machine speed (DHPFSP-FMS) minimizing makespan and energy consumption simultaneously. In DHPFSP-FMS, local search misleads population falling into optima which reduces convergence diversity. To solve this problem, a bi-roles co-evolutionary algorithm proposed contains following improvements: First, global divided two swarms producer consumer balance computation. Second, three heuristic rules are designed get high-quality initialization population. Next, five problem-based strategies converging. Then, an efficient energy-saving strategy presented save energy. Finally, verify performance of algorithm, 22 instances generated based on Taillard benchmark, number numerical experiments adopted. The experiment results state that our superior state-of-arts more for DHPFSP-FMS.

Язык: Английский

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An effective iterated local search algorithm for the distributed no-wait flowshop scheduling problem

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 120, С. 105921 - 105921

Опубликована: Янв. 30, 2023

Язык: Английский

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Self-Adaptive Population-Based Iterated Greedy Algorithm for Distributed Permutation Flowshop Scheduling Problem with Part of Jobs Subject to a Common Deadline Constraint

Expert Systems with Applications, Год журнала: 2024, Номер 248, С. 123278 - 123278

Опубликована: Янв. 18, 2024

Язык: Английский

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

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 29, 2024

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

Язык: Английский

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A cooperative grey wolf optimizer for the joint flowshop scheduling problem with sequence-dependent set-up time

Engineering Optimization, Год журнала: 2024, Номер unknown, С. 1 - 23

Опубликована: Апрель 12, 2024

With the complexity involved in manufacturing products, many companies use multiple processes to complete product processing. Most studies have been concerned with single production but neglected widespread joint flowshop scheduling problem (JFSP). In this article, a cooperative grey wolf optimizer (CGWO) is developed solve JFSP. First, according features of JFSP, corresponding mathematical model constructed, and three collaborative strategies random generation are proposed initialize population. process searching for prey, discretized search prey update mechanism proposed, which conducive balancing exploration exploitation. An energy-saving strategy decrease energy consumption. Moreover, four local mechanisms different optimization objectives enhance performance method attacking prey. The results show that CGWO effective solving

Язык: Английский

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Intelligent Optimization Under Multiple Factories: Hybrid Flow Shop Scheduling Problem with Blocking Constraints Using an Advanced Iterated Greedy Algorithm

Complex System Modeling and Simulation, Год журнала: 2023, Номер 3(4), С. 282 - 306

Опубликована: Дек. 1, 2023

The distributed hybrid flow shop scheduling problem (DHFSP), which integrates manufacturing models with parallel machines, has gained significant attention. However, in actual scheduling, some adjacent machines do not have buffers between them, resulting blocking. This paper focuses on addressing the DHFSP blocking constraints (DBHFSP) based production conditions. To solve DBHFSP, we construct a mixed integer linear programming (MILP) model for DBHFSP and validate its correctness using Gurobi solver. Then, an advanced iterated greedy (AIG) algorithm is designed to minimize makespan, modify Nawaz, Enscore, Ham (NEH) heuristic constraints. balance global local search capabilities of AIG, two effective inter-factory neighborhood strategies swap-based strategy are designed. Additionally, each factory mutually independent, movement within one does affect others. In view this, specifically memory-based decoding method insertion operations reduce computation time objective. Finally, shaking incorporated into mitigate premature convergence. Five algorithms used conduct comparative experiments AIG 80 test instances, experimental results illustrate that makespan relative percentage increase (RPI) obtained by 1.0% 86.1% respectively, better than algorithms.

Язык: Английский

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An effective adaptive iterated greedy algorithm for a cascaded flowshop joint scheduling problem

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 121856 - 121856

Опубликована: Сен. 26, 2023

Язык: Английский

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A rapid population-based iterated greedy for distributed blocking group flowshop scheduling with delivery time windows under multiple processing time scenarios

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 110949 - 110949

Опубликована: Фев. 1, 2025

Язык: Английский

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