A Hybrid Method of Biological Computation and Genetic Algorithms for Resolving Process-Focused Scheduling Problems

Authors:
Ikno Kim;Junzo Watada
Affiliations:
Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Japan 808-0135;Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Japan 808-0135
Venue:
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Year:
2009

Citing 5
Cited 0

Molecular computation of solutions to combinatorial problems

Science
Tabu Search

Tabu Search
Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks

Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
Applying Systematic Local Search to Job Shop Scheduling Problems: Basics, Concepts, and Methods

Applying Systematic Local Search to Job Shop Scheduling Problems: Basics, Concepts, and Methods
Scheduling: Theory, Algorithms, and Systems

Scheduling: Theory, Algorithms, and Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A huge number of different product types are managed through various processes in facilities with different approaches to scheduling. In this paper, we concentrate mainly on process-focused facilities. Sample groups of such facilities and processes were selected: its orders and times were investigated using both biological computation and genetic algorithms. First, biological computation was used to determine practical schedules. Second, genetic algorithms were used to identify which of the schedules determined by biological computation worked best. Here, we examine how combining these methods can be applied to solving process-focused scheduling problems.