Adaptive genetic algorithm for solving sugarcane loading stations with multi-facility services problem

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.

[1]  Map Taylor,et al.  A MODEL FOR SUGAR CANE ROAD TRANSPORT , 2001 .

[2]  Supachai Pathumnakul,et al.  Proportional self-organizing map (PSOM) based on flexible capacity buffer for allocating sugar cane loading stations , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[3]  Andrew Higgins,et al.  Opportunities for value chain research in sugar industries , 2007 .

[4]  Mitsuo Gen,et al.  Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation , 2008, Soft Comput..

[5]  Giovanni Righini.  A double annealing algorithm for discrete location/allocation problems☆ , 1994 .

[6]  Erhan Kozan,et al.  A job-shop scheduling approach for optimising sugarcane rail operations , 2011 .

[7]  Richard M. Soland,et al.  Exact and approximate solutions to the multisource weber problem , 1972, Math. Program..

[8]  Ponnuthurai N. Suganthan,et al.  Ensemble strategies with adaptive evolutionary programming , 2010, Inf. Sci..

[9]  Andrew Higgins,et al.  Assessing the potential benefits of alternative cane supply arrangements in the Australian sugar industry , 2003 .

[10]  Wen Du,et al.  GA based location models for physical distribution centers , 1997, 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335).

[11]  Anthony Chen,et al.  Constraint handling in genetic algorithms using a gradient-based repair method , 2006, Comput. Oper. Res..

[12]  Mitsuo Gen,et al.  A genetic algorithm for two-stage transportation problem using priority-based encoding , 2006, OR Spectr..

[13]  Caroline Lejars,et al.  A decision support approach for cane supply management within a sugar mill area , 2008 .

[14]  Mostafa Hajiaghaei-Keshteli,et al.  The allocation of customers to potential distribution centers in supply chain networks: GA and AIA approaches , 2011, Appl. Soft Comput..

[15]  Ruhul A. Sarker,et al.  Multi-operator based evolutionary algorithms for solving constrained optimization problems , 2011, Comput. Oper. Res..

[16]  L. Cooper.  Location-Allocation Problems , 1963 .

[17]  Ruhul A. Sarker,et al.  An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems , 2013, IEEE Transactions on Industrial Informatics.

[18]  Fang-Chih Tien,et al.  Self-organizing feature maps for solving location-allocation problems with rectilinear distances , 2004, Comput. Oper. Res..

[19]  Mitsuo Gen,et al.  Performance Analysis of Adaptive Genetic Algorithms with Fuzzy Logic and Heuristics , 2003, Fuzzy Optim. Decis. Mak..

[20]  Mitsuo Gen,et al.  Hybrid evolutionary method for capacitated location-allocation problem , 1997 .

[21]  N. Chiadamrong,et al.  Original papers: A methodology to support decision-making on sugar distribution for export channel: A case study of Thai sugar industry , 2008 .

[22]  Ruhul A. Sarker,et al.  Self-adaptive differential evolution incorporating a heuristic mixing of operators , 2013, Comput. Optim. Appl..

[23]  Nenad Mladenović,et al.  SOLVING THE CONTINUOUS LOCATION-ALLOCATION PROBLEM WITH TABU SEARCH , 1996 .

[24]  M. Goodchild,et al.  Discrete space location-allocation solutions from genetic algorithms , 1986 .

[25]  C. N. Bezuidenhout,et al.  Coupled modelling of sugarcane supply planning and logistics as a management tool , 2009 .

[26]  Andrew Higgins,et al.  A simulation model for capacity planning in sugarcane transport , 2005 .

[27]  Graciela Bueno,et al.  An activity simulation model for the analysis of the harvesting and transportation systems of a sugarcane plantation , 2001 .

[28]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[29]  A. Higgins,et al.  A framework for integrating a complex harvesting and transport system for sugar production , 2004 .

[30]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[31]  Carlos A. Coello Coello,et al.  THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART , 2002 .

[32]  Ruhul A. Sarker,et al.  On an evolutionary approach for constrained optimization problem solving , 2012, Appl. Soft Comput..

[33]  Mitsuo Gen,et al.  Network Models and Optimization: Multiobjective Genetic Algorithm Approach , 2008 .

[34]  R. C. Giles.  A simulation study of cane transport system improvements in the Sezela Mill area. , 2009 .

[35]  Reinaldo Morabito,et al.  A discrete simulation analysis of a logistics supply system , 2006 .

[36]  M. Gen,et al.  Hybrid evolutionary method for obstacle location-allocation , 1995 .

[37]  José A. Díaz,et al.  Simulation and optimization of sugar cane transportation in harvest season , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[38]  Mohd Omar,et al.  Location allocation modeling for healthcare facility planning in Malaysia , 2012, Comput. Ind. Eng..

[39]  A. T. Johns,et al.  Environmental/economic dispatch using fuzzy logic controlled genetic algorithms , 1997 .

[40]  Xinghuo Yu,et al.  Cane Railway Scheduling via Constraint Logic Programming: Labelling Order and Constraints in a Real-Life Application , 2001, Ann. Oper. Res..

[41]  A. Higgins,et al.  Scheduling of brand production and shipping within a sugar supply chain , 2006, J. Oper. Res. Soc..

[42]  Christopher R. Houck,et al.  Comparison of genetic algorithms, random restart and two-opt switching for solving large location-allocation problems , 1996, Comput. Oper. Res..

[43]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.