Flow shop rescheduling method

Abstract

The invention provides a flow shop rescheduling method which belongs to the technical field of shop scheduling. The method comprises the steps of: acquiring unexpected events, rescheduling workpieces, time available for machinery and manufacturing process completion time of a current flow shop in real time; for different unexpected events, grouping processes which are started and processes which are not started; determining rescheduling objectives and constraints of the flow shop; generating a rescheduling scheme of the flow shop using the particle swarm optimization; sending the rescheduling scheme of the flow shop to each process of the flow shop for rescheduling. The rescheduling method uses a particle swarm optimization to solve the problem of flow shop rescheduling, takes account of a variety of unexpected events, to guarantee rapid response among the unexpected events, uses inserted or exchanged neighborhood search mechanisms to effectively guarantee that the rescheduling scheme is used for processing in time between two unexpected events, constantly transmits search information to the subsequent migratory birds, and constantly swaps the lead bird, thereby ensuring the diversity and stability of scheduling schemes.

Description

technical field [0001] The invention belongs to the technical field of workshop scheduling, and in particular relates to a method for rescheduling a flow workshop. Background technique [0002] Flow shop scheduling problem is a kind of shop scheduling problem, which widely exists in various production processes, such as steelmaking and continuous casting, textile processing and so on. At present, scholars at home and abroad have carried out a lot of research on the static scheduling problem of assembly line scheduling, and obtained extensive research results and applied them in actual production. However, the actual production environment is often uncertain, and there are many unexpected events, such as random arrival of workpieces, random withdrawal of workpieces, changes in workpiece processing time, and random failures of equipment. Therefore, the flow shop rescheduling problem is more in line with the actual production process than the static scheduling problem. It has ...

AI Technical Summary

Problems solved by technology

The characteristics of these methods are that the rules are clear and easy to implement, but due to the uncertainty in the actual production process and too many random factors, the above methods cannot comprehensively consider various emergencies, and there is a big gap between them and the actual application

In recent years, with the development of computational intelligence methods, many intelligent optimization methods have been applied to solve flow shop scheduling problems, such as tabu search, simulated annealing, particle swarm optimization, genetic algorithm, neural network, meta-heuristic algorithm, expert system and For methods such as multi-agent technology, the above-mentioned algorithms are often unable to be applied to solve the flow shop rescheduling problem that comprehensively considers multiple emergencies due to insufficient convergence ability, or inability to solve large-scale problems, or inability to jump out of local optimum.

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