This paper presents a fleet model explained through a complex configuration of load sharing that considers overcapacity and is based on a life cycle cost (LCC) approach for cost-related decision-making. By analyzing the variables needed to optimize the fleet size, which must be evaluated in combination with the event space method (ESM), the solution to this problem would normally require high computing performance and long computing times. Considering this, the combined use of an integer genetic algorithm (GA) and the ant colony optimization (ACO) method was proposed in order to determine the optimal solution. In order to analyze and highlight the added value of this proposal, several empirical simulations were performed. The results showed the potential strengths of the proposal related to its flexibility and capacity in solving large problems with a near optimal solution for large fleet size and potential real-world applications. Even larger problems can be solved this way than by using the complete enumeration approach and a non-family fleet approach. Thus, this allows for a more real solution to fleet design that also considers overcapacity, availability, and an LCC approach. The simulations showed that the model can be solved in much less time compared with the base model and allows for the resolution of a fleet of at least 64 trucks using GA and 130 using ACO, respectively. Thus, the proposed framework can solve real-world problems, such as the fleet design of mining companies, by offering a more realistic approach.

中文翻译：

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使用遗传算法和蚁群优化考虑过容量和负载共享限制的车队优化

本文介绍了一种车队模型，该模型通过复杂的负载共享配置来解释，该配置考虑了产能过剩，并基于生命周期成本 (LCC) 方法进行成本相关决策。通过分析优化车队规模所需的变量，必须结合事件空间方法（ESM）进行评估，该问题的解决方案通常需要高计算性能和长计算时间。考虑到这一点，为了确定最优解，提出了结合使用整数遗传算法（GA）和蚁群优化（ACO）方法。为了分析和突出该提案的附加值，进行了几次实证模拟。结果表明，该提案的潜在优势与其在解决大型问题方面的灵活性和能力有关，并为大型车队规模和潜在的实际应用提供了近乎最佳的解决方案。与使用完全枚举方法和非家庭车队方法相比，这种方法甚至可以解决更大的问题。因此，这为车队设计提供了更真实的解决方案，该解决方案还考虑了产能过剩、可用性和 LCC 方法。模拟表明，与基本模型相比，该模型可以在更短的时间内求解，并且允许分别使用 GA 解决至少 64 辆卡车的车队和使用 ACO 解决 130 辆卡车的车队。因此，所提出的框架可以通过提供更现实的方法来解决现实世界的问题，例如矿业公司的车队设计。