Hybrid evolutionary method for obstacle location-allocation
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Simulation and optimization of sugar cane transportation in harvest season
Proceedings of the 32nd conference on Winter simulation
Self-organizing feature maps for solving location-allocation problems with rectilinear distances
Computers and Operations Research
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
A decision support approach for cane supply management within a sugar mill area
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Network Models and Optimization: Multiobjective Genetic Algorithm Approach
Network Models and Optimization: Multiobjective Genetic Algorithm Approach
Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Uncertainty Analysis and Decision Making; Guest Editors: Yan-Kui Liu, Baoding Liu, Jinwu Gao
Coupled modelling of sugarcane supply planning and logistics as a management tool
Computers and Electronics in Agriculture
A simulation model for capacity planning in sugarcane transport
Computers and Electronics in Agriculture
Ensemble strategies with adaptive evolutionary programming
Information Sciences: an International Journal
Differential evolution algorithm with ensemble of parameters and mutation strategies
Applied Soft Computing
Multi-operator based evolutionary algorithms for solving constrained optimization problems
Computers and Operations Research
Location allocation modeling for healthcare facility planning in Malaysia
Computers and Industrial Engineering
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
On an evolutionary approach for constrained optimization problem solving
Applied Soft Computing
Self-adaptive differential evolution incorporating a heuristic mixing of operators
Computational Optimization and Applications
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This paper presents a computational tool for operational planning of sugarcane loading stations. The loading stations are used to facilitate the supply of sugarcane to a sugar mill, especially for small-sized sugarcane growers whose fields are located over 30km away from the sugar mill. The objective of this research is to solve the problems involved in transportation planning and allocation of sugarcane from grower's fields to the loading stations, and from the loading stations to the sugar mill. The decisions consist basically of the determination of proper loading station's locations, the optimal type and number of transloaders in each loading station, and sugarcane field allocation to guarantee a continuous and uniform feeding of sugarcane to the sugar mill. Furthermore, this study is different from that of the general location problem in that it determines suitable types and the number of different multi-facility services (i.e. transloaders) at each of the sugarcane loading stations. In order to solve the problem of sugarcane loading stations with multi-facility services, we apply a mixed-integer programming model that can handle small-scale problems (less than 300 sugarcane fields or nodes). Additionally for large-scale problems, we present a comprehensive decision support system (DSS) with geographical information system (GIS) based on the proposed method, adaptive genetic algorithm (AGA), to solve this problem. Numerical experimental results of the AGA were compared with those obtained from the MPL/CPLEX, traditional genetic algorithm (GA), and the current practices in the sugar mill of our case study. The results demonstrated that the AGA is not only useful for reducing cost when compared to the traditional GA and the current practices, but also for efficient management of a sugarcane supply system. Furthermore, the method of this research should prove beneficial to other similar agro-food sectors in Thailand and around the world.