Dynamic flexible flow shop scheduling via cross-attention networks and multi-agent reinforcement learning

Journal of Manufacturing Systems, Год журнала: 2025, Номер 80, С. 395 - 411

Опубликована: Март 27, 2025

Язык: Английский

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Minimizing Makespan Scheduling on a Single Machine with General Positional Deterioration Effects

Axioms, Год журнала: 2025, Номер 14(4), С. 290 - 290

Опубликована: Апрель 12, 2025

This work studies single-machine scheduling with general position-dependent deterioration, where job processing times are non-decreasing functions dependent on their positions in a sequence. The goal is to find sequence such that makespan minimized. problem can be extended deal green environment time increases due additional carbon-reduction procedure. Under some optimal properties, we prove the solved by largest (denoted LPT) first rule.

Язык: Английский

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Scheduling an Energy-efficient Flexible Job Shop with Limited Waiting Time for Partial Operation Sequences

Journal of Intelligent & Fuzzy Systems, Год журнала: 2025, Номер unknown

Опубликована: Май 4, 2025

The classical energy-efficient flexible job shop problem (EFJSP) assumes that the waiting time between adjacent operations does not have strict requirements. However, in many actual industrial environments, phenomenon cannot exceed a specified value is very common. With regard to this, we propose an EFJSP with limited (EFJSP-LWT) minimization of makespan and total energy consumption. To solve this problem, develop knowledge-guided grouping artificial bee colony (KGGABC) algorithm it. In KGGABC, employed phase designed, modified selection method knowledge are given onlooker phase. addition, new principle proposed compare solutions. Finally, extensive experiments conducted results demonstrate KGGABC outperforms all compared algorithms over 90% instances solving EFJSP-LW. Our can help production managers who work manufacturing systems obtain feasible scheduling schemes considering may be useful for future research on energy-oriented problems realistic systems.

Язык: Английский

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An Artificial Bee Colony Optimization Algorithms for Solving Fuzzy Capacitated Logistic Distribution Center Problem

MethodsX, Год журнала: 2024, Номер 13, С. 102964 - 102964

Опубликована: Сен. 19, 2024

This paper presents a methodological approach to solving the fuzzy capacitated logistic distribution center problem, with focus on optimal selection of centers meet demands multiple plants. The are characterized by fixed costs and capacities, while plant modeled using triangular membership functions. problem is mathematically formulated converting into crisp values, providing structured framework for addressing uncertainty in planning. To support future research facilitate comparative analysis, 20 benchmark problems were generated, filling gap existing literature. Three distinct artificial bee colony algorithm variants hybridized heuristic: one best solution per iteration, another incorporating chaotic mapping adaptive procedures, third employing convergence diversity archives. An experimental design based Taguchi's orthogonal arrays was employed optimizing parameters, ensuring systematic exploration space. developed methods offer comprehensive toolkit complex, uncertain distribution, code provided reproducibility. Key contributions include:•Development model capacities under demands.•Generation advance domain.•Integration heuristic three ABC variants, each contributing unique insights.

Язык: Английский

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A constraint programming approach for resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery

Computers & Operations Research, Год журнала: 2024, Номер unknown, С. 106855 - 106855

Опубликована: Сен. 1, 2024

Язык: Английский

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Zone Oriented Binary Multi‐Objective Charged System Search Based Feature Selection Approach for Multi‐Label Classification

Expert Systems, Год журнала: 2024, Номер unknown

Опубликована: Дек. 5, 2024

ABSTRACT Multi‐label learning is used in situations when each instance has many labels. Due to the high‐dimensional feature space and noise multi‐label datasets, algorithms face substantial problems. Researchers have researched FS techniques minimise data dimensionality classification (MLC) Global optimization approaches, such as evolutionary algorithm (EA) optimizers, scale well This paper proposes a hybrid multi‐objective approach based on charged system search (CSS) grey wolf (GWO) methods for MLC problem. The first objective hamming loss (HLoss) value, second features from set. A novel concept zone informative non‐informative been added here. Here, we Preference Ranking Organisation METHod Enrichment of Evaluations (PROMETHEE) function approach. new velocity equation updated charge particles CSS algorithm. GWO property improve exploration exploiting For experimental verification, utilised six publically accessible datasets: CAL500 , Emotions Medical Enron Scene Yeast . findings show that proposed gets best value regarding various performance metrics. method achieves optimal Jaccard Score (JC) HLoss values 0.4408 0.0645 0.8169 0.0719 0.9486 0.0019 0.5950 0.0205 0.7391 0.0495 0.6452 0.0766 datasets. In particular, according empirical popular six‐label benchmark obtains competitive labels are constrained.

Язык: Английский

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Continuous charging assignment algorithm for heterogeneous robot clusters based on E-CARGO

Expert Systems with Applications, Год журнала: 2024, Номер 259, С. 125175 - 125175

Опубликована: Авг. 24, 2024

Язык: Английский

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The Rise of Artificial Bee Colony Algorithms in Data Science and Machine Learning is Notable

Springer tracts in nature-inspired computing, Год журнала: 2024, Номер unknown, С. 23 - 40

Опубликована: Янв. 1, 2024

Язык: Английский

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