Hybrid genetic algorithm for multi-time period production/distribution planning

Authors:
Mitsuo Gen;Admi Syarif
Affiliations:
Graduate School of Information Production and System, Waseda University, Kitakyushu 808-0135, Japan;Department of Industrial and Information Systems Engineering, Ashikaga Institute of Technology, Ashikaga 326-8558, Japan and Department of Mathematics, Lampung University, Bandar Lampung 35145, In ...
Venue:
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
Year:
2005

Citing 7
Cited 11

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
A multi-commodity, multi-plant, capacitated facility location problem: formulation and efficient heuristic solution

Computers and Operations Research
Genetic Algorithms

Genetic Algorithms
Discrete Mathematics

Discrete Mathematics
Network design techniques using adapted genetic algorithms

Advances in Engineering Software
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques

Proceedings of the 5th International Conference on Genetic Algorithms
A multiobjective hybrid genetic algorithm for the capacitatedmultipoint network design problem

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Effective genetic approach for optimizing advanced planning and scheduling in flexible manufacturing system

Proceedings of the 8th annual conference on Genetic and evolutionary computation
A genetic algorithm approach for multi-objective optimization of supply chain networks

Computers and Industrial Engineering - Special issue: Computational intelligence and information technology applications to industrial engineering selected papers from the 33 rd ICC&IE
A hybrid genetic algorithm for the multi-depot vehicle routing problem

Engineering Applications of Artificial Intelligence
Pedestrian routing system using genetic algorithms suitable for mobile ad hoc networks

MATH'06 Proceedings of the 10th WSEAS International Conference on APPLIED MATHEMATICS
Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain

Computers and Industrial Engineering
Application of fuzzy sets to manufacturing/distribution planning decisions with multi-product and multi-time period in supply chains

Expert Systems with Applications: An International Journal
A memetic algorithm-based heuristic for a scheduling problem in printed circuit board assembly

Computers and Industrial Engineering
Integrated multistage logistics network design by using hybrid evolutionary algorithm

Computers and Industrial Engineering
A genetic algorithm approach for multi-objective optimization of supply chain networks

Computers and Industrial Engineering
Integrated production and distribution planning for single-period inventory products

International Journal of Computer Integrated Manufacturing
The hybrid planning algorithm for the distribution center operation using tabu search and decomposed optimization

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we deal with a production/distribution problem to determine an efficient integration of production, distribution and inventory system so that products are produced and distributed at the right quantities, to the right customers, and at the right time, in order to minimize system wide costs while satisfying all demand required. This problem can be viewed as an optimization model that integrates facility location decisions, distribution costs, and inventory management for multi-products and multi-time periods. To solve the problem, we propose a new technique called spanning tree-based genetic algorithm (hst-GA). In order to improve its efficiency, the proposed method is hybridized with the fuzzy logic controller (FLC) concept for auto-tuning the GA parameters. The proposed method is compared with traditional spanning tree-based genetic algorithm approach. This comparison shows that the proposed method gives better results.