Integrated Scheduling of Multi-Objective Job Shops and Material Handling Robots with Reinforcement Learning Guided Meta-Heuristics DOI Creative Commons

Zhangying Xu,

Qi Jia,

Kaizhou Gao

и другие.

Mathematics, Год журнала: 2024, Номер 13(1), С. 102 - 102

Опубликована: Дек. 30, 2024

This study investigates the integrated multi-objective scheduling problems of job shops and material handling robots (MHR) with minimising maximum completion time (makespan), earliness or tardiness, total energy consumption. The collaborative MHR machines can enhance efficiency reduce costs. First, a mathematical model is constructed to articulate concerned problems. Second, three meta-heuristics, i.e., genetic algorithm (GA), differential evolution, harmony search, are employed, their variants seven local search operators devised solution quality. Then, reinforcement learning algorithms, Q-learning state–action–reward–state–action (SARSA), utilised select suitable during iterations. Three reward setting strategies designed for algorithms. Finally, proposed algorithms examined by solving 82 benchmark instances. Based on solutions analysis, we conclude that GA integrating SARSA first strategy most competitive one among 27 compared

Язык: Английский

Supplier Encroachment Channel Selection on an Online Retail Platform DOI Creative Commons

Zongyu Mou,

Kaidi Ding, Yaping Fu

и другие.

Systems, Год журнала: 2025, Номер 13(1), С. 66 - 66

Опубликована: Янв. 20, 2025

Online retail platforms offer encroachment opportunities for suppliers to directly sell products consumers on the online market. However, how select appropriate channels poses a significant challenge suppliers. To solve this problem, we take one supplier selling through an indirect reselling channel third-party platform (TORP) as base model, and further consider that can choose TORP agency selling, owned channel, or both encroach onto We hereby establish game-theoretical models analyze optimal strategy of encroachment, preference, equilibrium strategy. The findings show is always willing market its own channel. Additionally, when commission rate low, will via provides only exceeds certain threshold. If competition not very fierce (the intensity lower than 0.852) moderate, dual-channel strategy; otherwise, supplier-owned-channel extend our main by incorporating blockchain adoption cost differences between parties enhance practical applicability.

Язык: Английский

Процитировано

1
An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems DOI Creative Commons
Xinshuo Cui, Qingbo Meng, Jiacun Wang

и другие.

Mathematics, Год журнала: 2025, Номер 13(2), С. 256 - 256

Опубликована: Янв. 14, 2025

In order to protect the environment, an increasing number of people are paying attention recycling and remanufacturing EOL (End-of-Life) products. Furthermore, many companies aim establish their own closed-loop supply chains, encouraging integration disassembly assembly lines into a unified production system. this work, hybrid line that combines processes, incorporating human–machine collaboration, is designed based on traditional line. A mathematical model proposed address collaboration balancing problem in layout. To solve model, evolutionary learning-based whale optimization algorithm developed. The experimental results show significantly faster than CPLEX, particularly for large-scale instances. Moreover, it outperforms CPLEX other swarm intelligence algorithms solving problems while maintaining high solution quality.

Язык: Английский

Процитировано

0
Green fourth-party logistics network design under carbon cap-and-trade policy DOI
Yuxin Zhang, Min Huang,

Yaoxin Wu

и другие.

International Journal of Production Economics, Год журнала: 2025, Номер unknown, С. 109540 - 109540

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Addressing Due Date and Storage Restrictions in the S-Graph Scheduling Framework DOI Creative Commons
Krisztián Attila Bakon, Tibor Holczinger

Machines, Год журнала: 2025, Номер 13(2), С. 131 - 131

Опубликована: Фев. 9, 2025

This paper addresses the Flexible Job Shop Scheduling Problem (FJSP) with objective of minimizing both earliness/tardiness (E/T) and intermediate storage time (IST). An extended S-graph framework that incorporates E/T IST minimization while maintaining structural advantages original approach is presented. The further enhanced by integrating linear programming (LP) techniques to adjust machine assignments operation timings dynamically. following four methodological approaches are systematically analyzed: a standalone for minimization, an combined hybrid LP comprehensive addressing IST. Computational experiments on benchmark problems demonstrate efficacy proposed methods, showing efficiency smaller instances offering improved solution quality more complex scenarios. research provides insights into trade-offs between computational across different problem configurations policies. work contributes field production scheduling versatile capable multi-objective nature modern manufacturing environments.

Язык: Английский

Процитировано

0
Efficient stochastic framework for availability improvement of doormat manufacturing plants using grey wolf optimization algorithm DOI
Monika Saini, Naveen Kumar, Ashish Kumar

и другие.

Quality & Quantity, Год журнала: 2025, Номер unknown

Опубликована: Фев. 18, 2025

Язык: Английский

Процитировано

0
Optimized Green Unrelated Parallel Machine Scheduling Problem Subject to Preventive Maintenance DOI Creative Commons
Najat Almasarwah

Designs, Год журнала: 2025, Номер 9(2), С. 26 - 26

Опубликована: Фев. 25, 2025

Manufacturing areas typically conduct machine maintenance to prevent early failures and ensure a safe working environment efficient production. In this study, the green unrelated parallel scheduling problem (GUPMSP) is studied. Besides preventive maintenance, availability non-preemption are considered. A globally optimal solution (mathematical model) local (a modified Moore heuristic algorithm) used optimize number of products returned in GUPMSP. Three datasets, namely, most favorable case, an average least created test performance two solutions’ approaches. The results demonstrate ability mathematical model dominate Moore’s algorithm tested datasets. However, optimizing UPMSP with reduces costs as step support concept sustainability enhance efficiency.

Язык: Английский

Процитировано

0
Hybrid Disassembly Line Balancing of Multi-Factory Remanufacturing Process Considering Workers with Government Benefits DOI Creative Commons

Xiaoyu Niu,

Xiwang Guo, Peisheng Liu

и другие.

Mathematics, Год журнала: 2025, Номер 13(5), С. 880 - 880

Опубликована: Март 6, 2025

Optimizing multi-factory remanufacturing systems with social welfare considerations presents critical challenges in task allocation and process coordination. This study addresses this gap by proposing a hybrid disassembly line balancing optimization problem, considering workers government benefits. A mixed-integer programming model is formulated to maximize profit, its correctness verified using the CPLEX solver. Furthermore, discrete zebra algorithm proposed solve model, integrating survival-of-the-fittest strategy improve capabilities. The effectiveness convergence of are demonstrated through experiments on cases, comparisons made six peer algorithms CPLEX. experimental results highlight importance research improving resource utilization efficiency, reducing environmental impacts, promoting sustainable development.

Язык: Английский

Процитировано

0
Mathematical modeling and optimization of multi-period fourth-party logistics network design problems with customer satisfaction-sensitive demand DOI
Yuxin Zhang, Min Huang, Yaping Fu

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127219 - 127219

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Advancing Optimization Strategies in the Food Industry: From Traditional Approaches to Multi-Objective and Technology-Integrated Solutions DOI Creative Commons
Esteban Arteaga-Cabrera, César Ramírez‐Márquez, Eduardo Sánchez‐Ramírez

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(7), С. 3846 - 3846

Опубликована: Апрель 1, 2025

Optimization has become an indispensable tool in the food industry, addressing critical challenges related to efficiency, sustainability, and product quality. Traditional approaches, such as one-factor-at-a-time analysis, have been supplanted by more advanced methodologies like response surface methodology (RSM), which models interactions between variables, identifies optimal operating conditions, significantly reduces experimental requirements. However, increasing complexity of modern production systems necessitated adoption multi-objective optimization techniques capable balancing competing goals, minimizing costs while maximizing energy efficiency Advanced methods, including evolutionary algorithms comprehensive modeling frameworks, enable simultaneous multiple offering robust solutions complex challenges. In addition, artificial neural networks (ANNs) transformed practices effectively non-linear relationships within datasets enhancing prediction accuracy system adaptability. The integration ANNs with Industry 4.0 technologies—such Internet Things (IoT), big data analytics, digital twins—has enabled real-time monitoring optimization, further aligning processes sustainability innovation goals. This paper provides a review evolution tracing transition from traditional univariate approaches advanced, integrated emerging technologies, examining current future perspectives.

Язык: Английский

Процитировано

0
Scheduling Reentrant FlowShops: Reinforcement Learning‐guided Meta‐Heuristics DOI Creative Commons

J.Q. Yuan,

Kaizhou Gao, Adam Słowik

и другие.

IET Collaborative Intelligent Manufacturing, Год журнала: 2025, Номер 7(1)

Опубликована: Янв. 1, 2025

ABSTRACT The reentrant flowshop scheduling problems (RFSP) are ubiquitous in high‐tech industries such as semiconductor manufacturing and liquid crystal display (LCD) production. Given the complexity of RFSP, it is significant to improve production efficiency using effective intelligent optimisation techniques. In this study, four meta‐heuristics assisted by two reinforcement learning (RL) algorithms proposed minimise maximum completion time (makespan) for RFSP. First, a mathematical model RFSP established. Second, improved. Nawaz–Enscore–Ham (NEH) heuristic utilised population initialisation. Based on problem characteristics, we design six local search operators, which integrated into meta‐heuristics. Third, RL algorithms, Q‐learning state–action‐reward–state–action (SARSA), employed select appropriate operator during iterations enhance convergence space. Finally, results solving 72 instances indicate that perform effectively. RL‐guided can significantly overall performance particular, artificial bee colony algorithm (ABC) combined with SARSA‐guided yields highest performance.

Язык: Английский

Процитировано

0