article k-opt in jssp
DESCRIPTION
Este artigo apresenta de forma inédita a utilização da heurística do k-opt para o Job Shop Schedule ProblemTRANSCRIPT
K-OPT heuristic for TSP and JSSP
Adriano Patrick
sábado, 16 de junho de 2012
Work Proposal
Traveling Salesman Problem
Job Shop Scheduling Problem
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Traveling Salesman Problem
The Traveling Salesman Problem consists in finding a circuit that has the shortdistance, starting in any city, among many, visiting each city exactly once and returning to the starting city (Nilsson, 1982).
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Job Shop Scheduling Problem
“Allocating machines and tasks to minimize the total time (makespan) manufacturing a production line.”
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Job Shop Scheduling Problem
Make 'n' tasks: J1, J2, ... , Jn (as wires), as follows:
Each task is processed by "m" machines M1, ..., Mm
The processing flow of the "n" tasks in "m" machines is the same for all tasks.
A machine processes only one operation at a time, and should not be interrupted until its completion.
Are known processing times for each task by machine.
A task => "m" operations.
Objective: To minimize the completion time of all tasks.
There are n! different possible sequences
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K-OPT
Lin and Kernighan in 1973 developed the k-opt
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K-OPT
In this proposal, k arcs are replaced in the circuit, other k arcs with the objective of reduce the total distance traveled. The higher the value of k, the better the accuracy of the method, but higher is the computational effort.
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K-OPT in TSP
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K-OPT in TSP
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K-OPT in JSSP
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K-OPT in JSSP
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K-OPT in JSSP
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K-OPT in JSSP
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K-OPT in JSSP
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K-OPT in JSSP
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Conclusions
3-OPT generates a limited number of threads in a short time
As the time passes increases the difficulty of finding new threads heuristics
But with the implementation of the method for generating new Shuffle random sequences based on sequence before showing the solution to more effectively the results
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sequência.
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