A Dual-Population Genetic Algorithm with Q-Learning for Multi-Objective Distributed Hybrid Flow Shop Scheduling Problem

Article Engineering, Industrial

A novel shuffled frog-leaping algorithm with reinforcement learning for distributed assembly hybrid flow shop scheduling

Jingcao Cai et al.

Summary: This study considers the distributed assembly hybrid flow shop scheduling (DAHFS) problem with actual processing constraints and proposes a new algorithm with reinforcement learning to minimize makespan.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2023)

添加到收藏夹

Article Operations Research & Management Science

An effective multi-objective whale swarm algorithm for energy-efficient scheduling of distributed welding flow shop

Guangchen Wang et al.

Summary: This paper proposes a multi-objective scheduling method based on a multi-objective whale swarm algorithm to optimize the energy efficiency of distributed welding flow shop. By solving the problem of allocating jobs among factories, scheduling jobs in each factory, and determining the number of machines for each job, the proposed method shows superior performance in real-life cases. The experimental results demonstrate the effectiveness of the proposed algorithm.

ANNALS OF OPERATIONS RESEARCH (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

An adaptive artificial bee colony with reinforcement learning for distributed three-stage assembly scheduling with maintenance

Jing Wang et al.

Summary: The study proposed a new artificial bee colony algorithm with Q-learning for the distributed three-stage assembly scheduling problem, aiming to minimize maximum tardiness. By utilizing an effective Q-learning algorithm and an adaptive communication and competition process between two employed bee swarms, exploration ability was intensified and search efficiency was improved. The computational results demonstrated that the new strategies of QABC indeed enhanced its search performance and positioned QABC as a competitive algorithm for the problem at hand.

APPLIED SOFT COMPUTING (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A Cooperative Memetic Algorithm With Learning-Based Agent for Energy-Aware Distributed Hybrid Flow-Shop Scheduling

Jing-Jing Wang et al.

Summary: This article addresses the energy-aware distributed hybrid flow-shop scheduling problem and proposes a cooperative memetic algorithm with a reinforcement learning-based policy agent. By utilizing a reasonable encoding scheme, problem-specific heuristics, optimization and selection strategies, the goal of minimizing both makespan and energy consumption simultaneously is achieved.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

An improved iterated greedy algorithm for the energy-efficient blocking hybrid flow shop scheduling problem

Hao-Xiang Qin et al.

Summary: With the development of national economies, attention has been drawn to the issues of energy consumption and pollution emissions in manufacturing. Most existing research has focused on reducing economic costs and energy consumption, with limited studies on the energy-efficient hybrid flow shop scheduling problem, especially with blocking constraints. This paper presents a mathematical model for the blocking hybrid flow shop problem with an energy-efficient criterion and proposes a modified Iterative Greedy algorithm to optimize the model.

SWARM AND EVOLUTIONARY COMPUTATION (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Novel MILP and CP models for distributed hybrid flowshop scheduling problem with sequence-dependent setup times

Leilei Meng et al.

Summary: This study addresses the problem of distributed hybrid flow shop scheduling with sequence-dependent setup times. Three novel mathematical models are proposed and compared with existing models, demonstrating their effectiveness. Experimental results show that the model based on sequence-based modeling performs the best, while the existing model performs the worst. Additionally, the constraint programming model is more efficient and effective than the mathematical models.

SWARM AND EVOLUTIONARY COMPUTATION (2022)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Green scheduling of distributed two-stage reentrant hybrid flow shop considering distributed energy resources and energy storage system

Jun Dong et al.

Summary: In this study, a distributed two-stage reentrant hybrid flow shop bi-level scheduling model is established to minimize makespan, total carbon emissions, and total energy consumption costs. The experiments demonstrate that the use of distributed energy resources and energy storage systems can effectively reduce carbon emissions and energy costs.

COMPUTERS & INDUSTRIAL ENGINEERING (2022)

添加到收藏夹

Article Automation & Control Systems

KMOEA: A Knowledge-Based Multiobjective Algorithm for Distributed Hybrid Flow Shop in a Prefabricated System

Jun-Qing Li et al.

Summary: This article discusses a distributed hybrid flow shop scheduling problem with variable speed constraints and proposes a knowledge-based adaptive reference points multiobjective algorithm (KMOEA) to solve it. Each solution is represented by a 3-D vector encoding the factory assignment, machine assignment, operation scheduling, and speed setting. Several problem-specific lemmas are proposed to guide the algorithm's main components, including initialization, global, and local search procedures, resulting in an efficient and effective algorithm.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

添加到收藏夹

Article Engineering, Chemical

Hybrid Memetic Algorithm to Solve Multiobjective Distributed Fuzzy Flexible Job Shop Scheduling Problem with Transfer

Jinfeng Yang et al.

Summary: This research investigates the distributed flexible job shop scheduling problem with uncertain processing environment and proposes a hybrid decomposition variable neighborhood memetic algorithm for solving it. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in terms of convergence and uniformity of non-dominated solution set distribution.

PROCESSES (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A multi-neighborhood-based multi-objective memetic algorithm for the energy-efficient distributed flexible flow shop scheduling problem

Weishi Shao et al.

Summary: This paper proposes a multi-objective memetic algorithm with a two-level encoding scheme to solve the energy-efficient distributed flexible flow shop scheduling problem. Through comprehensive experiments, the effectiveness of the algorithm in optimizing total weighted tardiness and energy consumption is verified.

NEURAL COMPUTING & APPLICATIONS (2022)

添加到收藏夹

Article Engineering, Industrial

A discrete artificial bee colony algorithm for distributed hybrid flowshop scheduling problem with sequence-dependent setup times

Yingli Li et al.

Summary: This paper presents a mathematical model and a discrete artificial bee colony algorithm for optimizing the distributed hybrid flowshop scheduling problem with sequence dependent setup times. The proposed algorithm utilizes two-level encoding and effective solution update techniques, showing superior performance in experiments.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2021)

添加到收藏夹

Article Engineering, Industrial

A shuffled frog-leaping algorithm with memeplex quality for bi-objective distributed scheduling in hybrid flow shop

Jingcao Cai et al.

Summary: This study proposes a new shuffled frog-leaping algorithm with memeplex quality (MQSFLA) to address the DHFSP problem, integrating factory assignment and machine assignment and demonstrates promising results in improving scheduling efficiency.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A cooperated shuffled frog-leaping algorithm for distributed energy-efficient hybrid flow shop scheduling with fuzzy processing time

Jingcao Cai et al.

Summary: In this paper, a cooperated shuffled frog-leaping algorithm (CSFLA) is proposed to optimize fuzzy makespan, total agreement index and fuzzy total energy consumption for the distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP). Extensive experiments validate the promising advantages of CSFLA in solving DEHFSP.

COMPLEX & INTELLIGENT SYSTEMS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A matrix-cube-based estimation of distribution algorithm for the distributed assembly permutation flow-shop scheduling problem

Zi-Qi Zhang et al.

Summary: This paper introduces an innovative three-dimensional matrix-cube-based estimation algorithm to solve the DAPFSP problem, which improves computational efficiency through global exploration and local exploitation, achieving significantly better results than existing algorithms.

SWARM AND EVOLUTIONARY COMPUTATION (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multi-objective evolutionary algorithm based on multiple neighborhoods local search for multi-objective distributed hybrid flow shop scheduling problem

Weishi Shao et al.

Summary: This paper studies a multiobjective distributed hybrid flow shop scheduling problem (MDHFSP) and proposes a multi-objective evolutionary algorithm to solve it, optimizing solutions effectively through multiple neighborhoods local search operators and an adaptive weight updating mechanism.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

A cooperative water wave optimization algorithm with reinforcement learning for the distributed assembly no-idle flowshop scheduling problem

Fuqing Zhao et al.

Summary: This study introduces a cooperative water wave optimization algorithm CWWO to solve the distributed assembly no-idle flow-shop scheduling problem DANIFSP, aiming to minimize the maximum assembly completion time. The algorithm uses a reinforcement learning mechanism in the propagation phase, introduces a combination of path-relinking and VNS method as the modified breaking operator in the improvement phase, and applies a multi-neighborhood perturbation strategy in the refraction phase to increase the probability of escaping the local optimal.

COMPUTERS & INDUSTRIAL ENGINEERING (2021)

添加到收藏夹

Article Computer Science, Information Systems

Decomposition-Based Multi-Objective Optimization for Energy-Aware Distributed Hybrid Flow Shop Scheduling with Multiprocessor Tasks

Enda Jiang et al.

Summary: This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks (EADHFSPMT) and proposes a Novel Multi-Objective Evolutionary Algorithm based on Decomposition (NMOEA/D) to solve it. The algorithm includes special designs such as decoding scheme, local intensification operators, and dynamic adjustment strategy for weight vectors, which have been proven effective through extensive computational experiments. The NMOEA/D has superior performances compared to existing algorithms, as verified by statistical comparisons.

TSINGHUA SCIENCE AND TECHNOLOGY (2021)

添加到收藏夹

Article Automation & Control Systems

Solving Type-2 Fuzzy Distributed Hybrid Flowshop Scheduling Using an Improved Brain Storm Optimization Algorithm

Junqing Li et al.

Summary: An improved optimization algorithm has been developed to solve the distributed hybrid flowshop scheduling problem efficiently, aiming to minimize the completion time in uncertain environments. The study introduces a vector structure for representing solutions, a constructive heuristic based on the NEH method, and various local search heuristics considering problem features and objectives.

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Integrated production and distribution scheduling in distributed hybrid flow shops

Hu Qin et al.

Summary: This paper focuses on a novel integrated production and distribution scheduling problem with consideration of factory eligibility and third-party logistics. Three fast heuristics and an adaptive human-learning-based genetic algorithm are proposed to minimize earliness, tardiness and delivery costs. Computational experiments show the superiority of the adaptive human-learning-based genetic algorithm.

MEMETIC COMPUTING (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A Bi-Population Cooperative Memetic Algorithm for Distributed Hybrid Flow-Shop Scheduling

Jing-Jing Wang et al.

Summary: In this article, the distributed hybrid flow-shop scheduling problem is addressed with an optimization framework comprising a mixed integer linear programming model and a bi-population cooperative memetic algorithm (BCMA). Collaborative initialization and intensification search are used to generate diverse solutions and balance exploration and exploitation. Extensive computational tests show the effectiveness of the BCMA in solving the DHFSP and verifying the optimization capabilities of the specific designs.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2021)

添加到收藏夹

Article Automation & Control Systems

A Genetic Programming-Based Scheduling Approach for Hybrid Flow Shop With a Batch Processor and Waiting Time Constraint

Ming Qin et al.

Summary: This study addresses a hybrid flow shop scheduling problem with a batch processor and a discrete processor, proposing a mixed-integer linear programming model and a learning-based scheduling approach to generate high-quality solutions through optimization.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2021)

添加到收藏夹

Article Engineering, Industrial