4.6 Article

A deep reinforcement learning based algorithm for a distributed precast concrete production scheduling

Related references

Note: Only part of the references are listed.
Article Construction & Building Technology

A knowledge-driven multiobjective optimization algorithm for the transportation of assembled prefabricated components with multi-frequency visits

Rui Qi et al.

Summary: In modern construction industries, the efficient routing strategy for transportation and cost minimization is a challenging issue. To address this, a multi-frequency vehicle routing problem for prefabricated components (MFVRP-PC) is introduced. A knowledge-driven multiobjective optimization algorithm is developed to minimize total transportation cost and makespan, achieving effective results compared to state-of-the-art algorithms. It provides efficient Pareto solutions for decision-makers and extends the theoretical foundation for construction scheduling, optimization, and industrial applications.

AUTOMATION IN CONSTRUCTION (2023)

Article Computer Science, Interdisciplinary Applications

Intelligent factory many-objective distributed flexible job shop collaborative scheduling method

Yanwei Sang et al.

Summary: The Ma-ODFJCSP is a significant problem that has not been addressed in the literature, with a large scheduling scale that is difficult to optimize and coordinate. The proposed solution involves a many-objective distributed flexible job shop model and a high-dimensional many-objective memetic algorithm (HMOMA).

COMPUTERS & INDUSTRIAL ENGINEERING (2022)

Article Computer Science, Artificial Intelligence

Semiconductor final testing scheduling using Q-learning based hyper-heuristic

Jian Lin et al.

Summary: The paper introduces a Q-learning based hyper-heuristic algorithm to address the semiconductor final testing scheduling problem, which autonomously selects low-level heuristics to optimize the solution space and improve resource utilization, demonstrating the effectiveness and efficiency of the algorithm through computational simulation and comparison on a benchmark set.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

Article Mathematics

A Novel Constraint Programming Decomposition Approach for the Total Flow Time Fixed Group Shop Scheduling Problem

Francisco Yuraszeck et al.

Summary: This study addresses a specific case of the group shop scheduling problem and proposes a novel heuristic procedure to improve the solution. Experimental results demonstrate that the proposed algorithm performs significantly better on large-size instances.

MATHEMATICS (2022)

Article Computer Science, Interdisciplinary Applications

Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network

Yuxin Li et al.

Summary: This paper addresses the dynamic flexible job shop scheduling problem with insufficient transportation resources using deep reinforcement learning. A hybrid deep Q network is developed for this problem, showing superiority and generality compared with current optimization-based approaches through comprehensive experiments.

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2022)

Article Automation & Control Systems

A Hybrid Iterated Greedy Algorithm for a Crane Transportation Flexible Job Shop Problem

Jun-Qing Li et al.

Summary: This study proposed an efficient optimization algorithm, IGSA, to solve the flexible job shop scheduling problem with crane transportation processes. The algorithm simultaneously minimizes the maximum completion time and energy consumptions. Unlike previous methods, the algorithm considers crane lift operations and balances exploration abilities and time complexity.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2022)

Article Computer Science, Artificial Intelligence

A Reinforcement Learning Approach for Flexible Job Shop Scheduling Problem With Crane Transportation and Setup Times

Yu Du et al.

Summary: In this study, a DQN model is proposed to solve a multiobjective FJSP with crane transportation and setup times. The model optimizes makespan and total energy consumption simultaneously based on weighting approach. The DQN model uses 12 state features and seven actions to describe the scheduling process, and applies a novel structure in the DQN topology. Extensive computational tests and comparisons demonstrate the effectiveness and advantages of the proposed method in solving FJSP-CS.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

Article Engineering, Industrial

Electricity cost minimisation for optimal makespan solution in flow shop scheduling under time-of-use tariffs

Minh Hung Ho et al.

Summary: This paper aims to build an energy-cost-aware scheduling plan for two-machine flow shop scheduling, tackling the joint optimization of makespan and electricity cost. The study shows the contribution of generating several optimal equivalent solutions with different electricity costs but the same makespan. The proposed approach significantly improves electricity cost under optimal makespan, providing good solutions for managers and decision makers to achieve energy cost savings without sacrificing productivity.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2021)

Article Computer Science, Artificial Intelligence

New benchmark algorithms for No-wait Flowshop Group Scheduling Problem with Sequence-Dependent Setup Times

Chen-Yang Cheng et al.

Summary: This study successfully addressed the No-wait Flowshop Group Scheduling Problems, achieving a best-found solution rate of over 99.7% through the development of two metaheuristics. The results indicate that RMSA outperforms existing algorithms for solving the NWFGSP_SDST problem.

APPLIED SOFT COMPUTING (2021)

Article Green & Sustainable Science & Technology

Evolutionary game based real-time scheduling for energy-efficient distributed and flexible job shop

Jin Wang et al.

Summary: In this study, a distributed and flexible job shop real-time scheduling method based on edge computing and industrial internet of things is proposed to enhance the real-time decision-making capability of the scheduling system. By utilizing an evolutionary game-based solver method, the DFJS-RS method was shown to improve energy efficiency by up to 26% in a validation case study.

JOURNAL OF CLEANER PRODUCTION (2021)

Article Computer Science, Artificial Intelligence

An efficient scheduling approach for an iron-steel plant equipped with self-generation equipment under time-of-use electricity tariffs

Jianhua Cao et al.

Summary: This study addresses an integrated scheduling problem from an iron-steel plant equipped with self-generation equipment under time-of-use electricity tariffs. It develops a multi-objective mathematical model with energy-awareness to optimize production schedules and electricity cost jointly. By using an improved SPEA2 algorithm, the total electricity cost can be significantly reduced, providing high-quality scheduling schemes for energy-intensive industries.

SWARM AND EVOLUTIONARY COMPUTATION (2021)

Article Computer Science, Interdisciplinary Applications

Just-in-time scheduling for a distributed concrete precast flow shop system

Fuli Xiong et al.

Summary: This paper focuses on the distributed concrete precast flow shop scheduling problem, proposing a novel mixed integer linear programming model and solving the NP-hard problem by iterated greedy algorithm and tabu search algorithm. The computational analysis shows the effectiveness of the proposed algorithms.

COMPUTERS & OPERATIONS RESEARCH (2021)

Article Green & Sustainable Science & Technology

Flow shop optimization of hybrid make-to-order and make-to-stock in precast concrete component production

Wen Jiang et al.

Summary: This study investigates the flow shop precast component production scheduling problem under a hybrid MTO-MTS environment, develops a hybrid MTO-MTS precast component flow shop production model, and proposes an iterative job insertion algorithm based on whale optimization. The computational evaluation shows that the proposed algorithm effectively optimizes the precast component production.

JOURNAL OF CLEANER PRODUCTION (2021)

Article Automation & Control Systems

Group Scheduling With Nonperiodical Maintenance and Deteriorating Effects

Haiyan Xu et al.

Summary: This paper investigates single-machine group scheduling with nonperiodical maintenance and deteriorating effects, proposing batch-based heuristics and an iterated greedy algorithm as solutions. The study proves the NP-hardness of the problem and demonstrates the superiority of the proposed methods through comprehensive computational and statistical analyses.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2021)

Article Computer Science, Artificial Intelligence

A hybrid estimation of distribution algorithm for distributed flexible job shop scheduling with crane transportations

Yu Du et al.

Summary: This study proposed a hybrid algorithm to solve the distributed flexible job shop scheduling problem efficiently by combining EDA and VNS, achieving better performance.

SWARM AND EVOLUTIONARY COMPUTATION (2021)

Article Computer Science, Interdisciplinary Applications

A multi-objective scheduling method for distributed and flexible job shop based on hybrid genetic algorithm and tabu search considering operation outsourcing and carbon emission

Wenxiang Xu et al.

Summary: This study addresses the general distributed flexible job shop scheduling problem, considering operation sequencing, job-to-cell assignment, and operation-to-machine assignment. The model also takes into account the need for operation outsourcing and incorporates four optimization objectives including makespan, costs, quality, and carbon emission. By using the fuzzy analytical hierarchy process, the multi-objective problem is transformed into a single objective problem. A hybrid genetic algorithm and tabu search is developed to improve solving efficiency, showing performance advantages over traditional algorithms.

COMPUTERS & INDUSTRIAL ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning

Shu Luo et al.

Summary: This paper proposes an on-line rescheduling framework named as two-hierarchy deep Q network (THDQN) for the dynamic multi-objective flexible job shop scheduling problem with new job insertions. By optimizing two practical objectives including total weighted tardiness and average machine utilization rate, the trained THDQN has shown effectiveness and generality on a wide range of test instances.

COMPUTERS & INDUSTRIAL ENGINEERING (2021)

Article Automation & Control Systems

Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Ziyan Zhao et al.

Summary: This research introduces a new bi-objective group scheduling problem with multiple constraints and achieves satisfactory performance through a memetic algorithm, showing great potential for industrial-scale scheduling problems.

IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2021)

Article Green & Sustainable Science & Technology

Energy-efficient scheduling in an unrelated parallel-machine environment under time-of-use electricity tariffs

Hossein Saberi-Aliabad et al.

JOURNAL OF CLEANER PRODUCTION (2020)

Article Computer Science, Artificial Intelligence

Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning

Shu Luo

APPLIED SOFT COMPUTING (2020)

Article Computer Science, Interdisciplinary Applications

Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem

Leilei Meng et al.

COMPUTERS & INDUSTRIAL ENGINEERING (2020)

Article Green & Sustainable Science & Technology

Dynamic production scheduling model under due date uncertainty in precast concrete construction

Taehoon Kim et al.

JOURNAL OF CLEANER PRODUCTION (2020)

Article Computer Science, Artificial Intelligence

Multi-objective optimization based on decomposition for flexible job shop scheduling under time-of-use electricity prices

En-da Jiang et al.

KNOWLEDGE-BASED SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers

Qiang Luo et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

Article Computer Science, Interdisciplinary Applications

A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem

Ronghua Chen et al.

COMPUTERS & INDUSTRIAL ENGINEERING (2020)

Article Automation & Control Systems

Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network

Chun-Cheng Lin et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

Article Computer Science, Artificial Intelligence

An improved differential evolution algorithm for solving a distributed assembly flexible job shop scheduling problem

Xiuli Wu et al.

MEMETIC COMPUTING (2019)

Article Engineering, Industrial

Scheduling on a two-machine permutation flow shop under time-of-use electricity tariffs

Shijin Wang et al.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2018)

Article Construction & Building Technology

Optimized rescheduling of multiple production lines for flowshop production of reinforced precast concrete components

Zhiliang Ma et al.

AUTOMATION IN CONSTRUCTION (2018)

Article Computer Science, Artificial Intelligence

Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms

Hao-Chin Chang et al.

JOURNAL OF INTELLIGENT MANUFACTURING (2017)

Article Construction & Building Technology

Heuristic algorithm for ready-mixed concrete plant scheduling with multiple mixers

Zhenyuan Liu et al.

AUTOMATION IN CONSTRUCTION (2017)

Article Construction & Building Technology

Optimized flowshop scheduling of multiple production lines for precast production

Zhitian Yang et al.

AUTOMATION IN CONSTRUCTION (2016)

Article Construction & Building Technology

A multi-objective GA-based optimisation for holistic Manufacturing, transportation and Assembly of precast construction

B. Anvari et al.

AUTOMATION IN CONSTRUCTION (2016)

Article Multidisciplinary Sciences

Human-level control through deep reinforcement learning

Volodymyr Mnih et al.

NATURE (2015)

Article Engineering, Industrial

Hybrid flow shop scheduling considering machine electricity consumption cost

Hao Luo et al.

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS (2013)

Article Construction & Building Technology

GA-based decision support systems for precast production planning

Chien-Ho Ko et al.

AUTOMATION IN CONSTRUCTION (2010)

Article Management

An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem

L. De Giovanni et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2010)