Adaptive genetic algorithm for solving sugarcane loading stations with multi-facility services problem
Adaptive genetic algorithm for solving sugarcane loading stations with multi-facility services problem
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DOI：
10.1016/j.compag.2013.07.016
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发表时间：
2013-10
影响因子：
8.3
通讯作者：
Woraya Neungmatcha;Kanchana Sethanan;M. Gen;S. Theerakulpisut
中科院分区：
文献类型：
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作者：
Woraya Neungmatcha;Kanchana Sethanan;M. Gen;S. Theerakulpisut
本文提出了一种计算工具，甘蔗装载站的业务规划。装载站用于促进向糖厂供应甘蔗，特别是对于田地距离糖厂超过30公里的小型甘蔗种植者。本研究的目的是解决甘蔗从种植园到装载站和从装载站到糖厂的运输规划和分配问题。这些决策基本上包括确定适当的装载站的位置，每个装载站的最佳类型和数量的转运机，以及甘蔗田的分配，以保证甘蔗连续和均匀地喂入糖厂。此外，这项研究是不同的，从一般的位置问题，因为它确定合适的类型和不同的多设施服务（即转运）在每个甘蔗装载站的数量。为了解决甘蔗装载站多设施服务的问题，我们采用了一个混合整数规划模型，可以处理小规模的问题（小于300甘蔗田或节点）。此外，对于大规模的问题，我们提出了一个综合的决策支持系统（DSS）与地理信息系统（GIS）的基础上提出的方法，自适应遗传算法（阿加），来解决这个问题。数值实验结果的阿加从MPL/CPLEX，传统的遗传算法（GA），并在我们的案例研究糖厂的现行做法得到的结果进行了比较。结果表明，阿加遗传算法不仅有利于降低成本时，与传统的遗传算法和目前的做法相比，但也有效地管理甘蔗供应系统。此外，这项研究的方法应该证明有利于泰国和世界各地的其他类似农业食品部门。
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 30 km 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.