Four decades of research on the open-shop scheduling problem to minimize the makespan

This article examines the problem of scheduling preemptive open shops to minimize the total completion time. The problem is known to be NP-hard. An efficient constructive heuristic is developed for solving large sized problems. A new lower bound scheme based on the solution of another special type of preemptive open shop problem is presented. Both of the proposed upper and lower bounds are incorporated into a branch-and-bound algorithm to solve medium sized problems. Computational results are reported. The branch-and-bound algorithm can handle problems of up to 14 jobs and 14 machines in size within a reasonable amount of time. The solution obtained by the heuristic has an average deviation of less than 0.14% from the optimal value, while the initial lower bound has an average deviation of less than 2.12% from the optimal value. Moreover, the heuristic finds approved optimal solutions for over 70% of the problem instances completely solved.

http://ir.lib.cyut.edu.tw:8080/bitstream/310901800/22983/2/paper-201310.pdf

An improved branch-and-bound algorithm for the preemptive open shop total completion time scheduling problem

This article considers the problem of scheduling preemptive open shops to minimize the number of tardy jobs A mixed integer program is presented for solving small-sized problems An efficient heuristic is constructed for solving large-sized problems This heuristic can be viewed as a generalization of the well-known Moore’s algorithm for the single machine non-preemptive scheduling problem to the preemptive open shop scheduling problem A lower bound based on assignment problem is proposed for performance evaluation of the heuristic for large-sized problems Computational results for randomly generated problems are reported For small-sized problems, the solution obtained by the heuristic has an average deviation of about 402% from the optimal value The lower bound has an average deviation of about 2103% from the optimal value For large-sized problems, the solution obtained by the heuristic has an average deviation of less than 54% from the lower bound

A fast heuristic to minimize number of tardy jobs in preemptive open shops

Agile Earth Observing Satellite (AEOS) scheduling problem consists of selecting a subset of tasks and developing observation plans for a set of agile satellite resources with the purpose of maximizing the total reward of arranged mission observations. This problem has attracted much attention in recent years since AEOS is a new generation satellite being developed all over the world. Due to its NP-hardness, heuristic methods are widely adopted when solving the AEOS scheduling problem (AEOSSP). In this paper, we propose a temporal conflict network-based heuristic algorithm (TBHA), for AEOSSP. The novelty of TBHA lies in the fact that the heuristics are extracted from a temporal conflict network, which characterizes the overlaps (conflicts) of the visible time windows of the problem. These heuristics are highly effective since they well address the time window conflicts which otherwise pose a significant challenge on the choice of the imaging start time for satellite observations. The extensive simulation experiments with the comparison to a number of heuristic algorithm variants and sophisticated meta-heuristic algorithms are conducted to show that the TBHA algorithm performs very well in terms of both solution quality and computational efficiency.

/pdf/a-heuristic-algorithm-based-on-temporal-conflict-network-for-20yyfpgi5w.pdf

A Heuristic Algorithm Based on Temporal Conflict Network for Agile Earth Observing Satellite Scheduling Problem

并行机的生产调度问题是生产调度理论的基本问题之一,具有NP-hard的特性。对于制造期可中断的并行机调度问题(Pm|prmp|Cmax和Qm|prmp|Cmax),目标是最小化最大完成时间,现有此类问题解决算法(LRPT和LRPT-FM)存在缺陷,即工作中断个数往往无限多的。针对此类问题提出一种多目标的混合整数规划模型,使用MLeap建模语言和商用求解器CPLEX对其进行精确求解,对传统算法的求解结果进行改善,能够减少工作中断的次数。采用文献中算例进行求解验证,求解结果表明该改进混合整数规划模型能够有效解决此类问题,并使结果得到优化。

Multi-objective optimization research on the makespan with preemptions for parallel machine problem

Preemptive open-shop scheduling problem was studied, and a network flow based algorithm was presented. Firstly, based on the characteristics of the preemptive open-shop, the scheduling problem was formulated as a mixed-integer programming model with the objective to minimize the make-span. The maximum flow model of the preemptive open-shop was developed to model the machine resource allocation and time constraints. Moreover a new preflow push algorithm for the maximum flow model was put forward. Based on the solution of machine resource allocation problem got by preflow push algorithm, the sequences of the tasks processed by each machine were determined by calculating the matrix of the processing times and decrementing set. Finally, the validity of the developed scheduling algorithm is illustrated by randomly generated example.

Preemptive Open-Shop Scheduling: Network Flow Based Algorithm

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

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

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

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.

Yong Zhan

Author Tools

Chat about Author

Papers

Preemptive Open-Shop Scheduling: Network Flow Based Algorithm