With the extensive application of automated guided vehicles in manufacturing system, production scheduling considering limited transportation resources becomes a difficult problem. At the same time, the real manufacturing system is prone to various disturbance events, which increase the complexity and uncertainty of shop floor. To this end, this paper addresses the dynamic flexible job shop scheduling problem with insufficient transportation resources (DFJSP-ITR) to minimize the makespan and total energy consumption. As a sequential decision-making problem, DFJSP-ITR can be modeled as a Markov decision process where the agent should determine the scheduling object and allocation of resources at each decision point. So this paper adopts deep reinforcement learning to solve DFJSP-ITR. In this paper, the multiobjective optimization model of DFJSP-ITR is established. Then, in order to make agent learn to choose the appropriate rule based on the production state at each decision point, a hybrid deep Q network (HDQN) is developed for this problem, which combines deep Q network with three extensions. Moreover, the shop floor state model is established at first, and then the decision point, generic state features, genetic-programming-based action space and reward function are designed. Based on these contents, the training method using HDQN and the strategy for facing new job insertions and machine breakdowns are proposed. Finally, comprehensive experiments are conducted, and the results show that HDQN has superiority and generality compared with current optimization-based approaches, and can effectively deal with disturbance events and unseen situations through learning. 

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

Flexible job shop scheduling problem under Industry 5.0: A survey on human reintegration, environmental consideration and resilience improvement

An effective MCTS-based algorithm for minimizing makespan in dynamic flexible job shop scheduling problem

Job shop scheduling is a process of optimising the use of limited resources to improve the production efficiency. Job shop scheduling has a wide range of applications such as order picking in the warehouse and vaccine delivery scheduling under a pandemic. In real-world applications, the production environment is often complex due to dynamic events such as job arrivals over time and machine breakdown. Scheduling heuristics, e.g., dispatching rules, have been popularly used to prioritise the candidates such as machines in manufacturing to make good schedules efficiently. Genetic programming, has shown its superiority in learning scheduling heuristics for job shop scheduling automatically due to its flexible representation. This survey firstly provides comprehensive discussions of recent designs of genetic programming algorithms on different types of job shop scheduling. In addition, we notice that in the recent years, a range of machine learning techniques such as feature selection and multitask learning, have been adapted to improve the effectiveness and efficiency of scheduling heuristic design with genetic programming. However, there is no survey to discuss the strengths and weaknesses of these recent approaches. To fill this gap, this paper provides a comprehensive survey on genetic programming and machine learning techniques on automatic scheduling heuristic design for job shop scheduling. In addition, current issues and challenges are discussed to identify promising areas for automatic scheduling heuristic design in the future. 

Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling

Multitask-oriented manufacturing service composition in an uncertain environment using a hyper-heuristic algorithm

Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e., the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this article, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and most accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multiobjective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling.

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Binzi Xu

Author Tools

Chat about Author

Papers

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling.