A Genetic Algorithm Approach to a Terminus Re-Scheduling Problem

1999 ◽  
pp. 493-502
Author(s):  
S. J. Massey ◽  
J. F. Boyce
Keyword(s):  
Genetic Algorithm ◽  
Scheduling Problem ◽  
Genetic Algorithm Approach

A genetic algorithm approach for a single hoist scheduling problem with time windows constraints

2013 ◽  
Vol 26 (7) ◽  
pp. 1761-1771 ◽  
Author(s):  
Adnen El Amraoui ◽  
Marie-Ange Manier ◽  
Abdellah El Moudni ◽  
Mohamed Benrejeb
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Scheduling Problem ◽  
Hoist Scheduling ◽  
Genetic Algorithm Approach

scholarly journals Genetic Algorithm Approach to a Scheduling Problem for a Metal Mold Assembly Process

1999 ◽  
Vol 12 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Kazuhiro ASHIZAWA ◽  
Hitoshi IIMA ◽  
Nobuo SANNOMIYA ◽  
Kagayaki SUZUKI ◽  
Yasunori KOBAYASHI
Keyword(s):  
Genetic Algorithm ◽  
Scheduling Problem ◽  
Assembly Process ◽  
Metal Mold ◽  
Genetic Algorithm Approach

A MILP and Genetic Algorithm Approach for a Furniture Manufacturing Flow Shop Scheduling Problem

2019 ◽  
pp. 238-259
Author(s):  
Marcell S. Kalman ◽  
Omar G. Rojas ◽  
Elias Olivares-Benitez ◽  
Samuel Moisés Nucamendi-Guillén
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Solution Time ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Algorithm Optimization ◽  
Shop Scheduling ◽  
Processing Times ◽  
Furniture Manufacturing ◽  
Genetic Algorithm Approach

A MILP and genetic algorithm optimization model for the sequencing of jobs in a medium-sized factory, dedicated to the manufacturing of home furniture, where different categories and types of articles are produced and whose routes and manufacturing processing times vary widely, are proposed. Different scenarios are considered for the objective function based on minimizing makespan and tardiness. The results of the optimization for an instance of 24 jobs on five machines, chosen as a representative instance of the order sizes that are handled by the company, show important reductions in the productive system's usage times, oscillating between 10% and 20% with respect to a random initial sequence in the production plan. Improvements were similar in both techniques, the main difference being the solution time of each one.

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