Abstract This paper presents the Hybrid Particle Swarm Optimization and Neighborhood Strategy Search (HPSO-NS) to solve a tractor scheduling and routing problem with equipment allocation constraint in sugarcane field preparation, to help the growers catch the season and ensure advantageous production of sugar from sugarcane. This problem can be formulated as the flexible flow shop scheduling problem with machine eligibility, time windows, sequence dependent setup time (SDST), blocking, machine restriction and machine grouping (FFS | S smt , M j , Grouping, block, 6-stage, Tool, Tw | ∑ i N R i ). A mixed-integer programming model was developed to solve small-scale problems. The HPSO-NS was developed for large-scale problems, and three neighborhood strategies were added to the PSO procedure and developed. Moreover, two new formulae which were used to select the neighborhood strategy in HPSO-NS are presented in this paper to increase the performance of the proposed method. The computational results show that the HPSO-NS outperforms the original PSO and the lower bound obtained from the optimization software, while using 97% less computational time.

中文翻译：

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混合粒子群优化和邻域策略搜索调度机器和设备以及甘蔗田准备中拖拉机的路由

摘要 本文提出了混合粒子群优化和邻域策略搜索（HPSO-NS）来解决甘蔗田准备中设备分配约束的拖拉机调度和路由问题，以帮助种植者赶上季节并确保甘蔗糖的有利生产。 . 这个问题可以表述为灵活的流水车间调度问题，包括机器资格、时间窗口、序列相关设置时间 (SDST)、阻塞、机器限制和机器分组 (FFS | S smt , M j , Grouping, block, 6-stage , 工具, Tw | ∑ i NR i )。开发了混合整数编程模型来解决小规模问题。HPSO-NS 是针对大规模问题开发的，并且在 PSO 程序中添加了三个邻域策略并进行了开发。而且，本文提出了两个用于选择 HPSO-NS 中邻域策略的新公式，以提高所提出方法的性能。计算结果表明，HPSO-NS 优于原始 PSO 和从优化软件获得的下界，同时减少了 97% 的计算时间。