Fleet optimization considering overcapacity and load sharing restrictions using genetic algorithms and ant colony optimization

Abstract

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.

Mathematical model for optimizing the fleet of vehicles for performing land reclamation works

“…A mathematical model of the security of repair and technical impacts, taking into account the reliability indicators of basic and aggregated machines. The process of accumulation of technical failures, as a rule, is described on the basis of statistical data on the uptime of the unit [21][22][23]. In this case, when processing of experimental data by methods of mathematical statistics, it was determined that the law of distribution of operating time between technical failures is consistent with the exponential law of distribution with a probability density:…”

Criticality Analysis Based on Reliability and Failure Propagation Effect for a Complex Wastewater Treatment Plant

“…It is also important to note the difference between the equipment with very high EOI (CS1, DU1, GB2, and GB1) and most of the other pieces of equipment which present lower EOI indexes. This difference resides in the fact that the lower EOI indexes belong to subsystems under redundancy or load-sharing configurations, which dramatically reduces the criticality in the system [26] in contrast with the highest EOI indexes, which belong to subsystems without any kind of redundancy or overcapacity.…”