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A Memetic Evolutionary Algorithm for Solving the Early-Delay Scheduling Problem

A technology for scheduling problems and algorithms, applied in computing, genetic models, data processing applications, etc., to achieve the effect of good calculation time and good optimization accuracy

Inactive Publication Date: 2017-04-19
NAT UNIV OF DEFENSE TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to provide a memetic evolution algorithm based on genetic algorithm to solve the early-delay scheduling problem in single machine and parallel machine environment

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  • A Memetic Evolutionary Algorithm for Solving the Early-Delay Scheduling Problem
  • A Memetic Evolutionary Algorithm for Solving the Early-Delay Scheduling Problem
  • A Memetic Evolutionary Algorithm for Solving the Early-Delay Scheduling Problem

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Embodiment Construction

[0021] The memetic evolution algorithm is a swarm intelligence optimization algorithm that introduced a local improvement strategy that appeared in the late 1980s. It is essentially a hybrid heuristic algorithm that combines a group search framework and a local neighborhood search strategy. The present invention Also using the genetic algorithm as the search framework, an iterative improved search strategy is introduced as a local search, and a memetic optimization algorithm is designed to solve the early-delay scheduling problem in single-machine and parallel-machine environments.

[0022] For the convenience of description, symbols are first introduced to represent the elements involved in the problem:

[0023] The number of workpieces to be processed;

[0024] a sufficiently large positive number;

[0025] workpiece release time, ;

[0026] workpiece required processing time, ;

[0027] workpiece delivery date, ;

[0028] workpiece The ear...

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Abstract

The invention provides a genetic algorithm-based meme evolutionary algorithm for solving the advancing-delay scheduling problem in a single-machine and parallel-machine environment. The invention is characterized in that genetic algorithm is adopted as a search framework and an iterative improvement search strategy is introduced as local search. The algorithm provided by the invention comprises steps of coding and population initialization, selecting operation, interlace operation, mutation operation and local search. The research in the invention takes into account problems of processing transfer time, workpiece releasing time and workpiece punishment in advance, which are more general than present research problems. According to the invention, the genetic algorithm is adopted as the search framework and the iterative improvement search strategy is introduced as local search, thus obtaining a good balance between optimization precision and computation time.

Description

technical field [0001] The invention relates to a meme evolution algorithm for solving the early-delay scheduling problem in the environment of single machine and parallel machine. Background technique [0002] In the just-in-time production concept of modern manufacturing, in order to maximize profits, enterprises need to arrange the completion time of processed products within the delivery date expected by users as much as possible. If the product is completed ahead of schedule or delayed, certain penalties will be incurred. This leads to an important class of problems: scheduling problems that consider early-late penalties, where the penalty for processing each job depends on the completion time of the job. Most of the existing studies on the early-late scheduling problem in a single-machine environment do not consider the processing conversion time between different workpieces processed by the machine, and the conversion time is a widely existing condition in scheduling ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06N3/12
Inventor 贺仁杰陈成李菊芳陈英武谭跃进姚锋邢立宁孙凯杨振宇王沛刘晓路李江成
Owner NAT UNIV OF DEFENSE TECH