Hareketli müşterili araç rotalama problemi için Meta-Sezgisel algoritmaların deneysel analizi

Abstract

The time unit is a continuous value and the target number changes continuously at any time "t"  The behaviour of the UAVs when there is no target in the operation area has been included in the route plan  Using ideal number of vehicles in fleet. Figure A. Moving Customer Vehicle Routing Problem Purpose: In this study, Heterogeneous Fleet-Moving Customer Vehicle Routing Problem(MC-VRP) with Time Windows under constraint of vehicle capacity (endurance) has been aimed to be solved considering the minimum operation time and cost. Theory and Methods:In order to solve the problem, heuristic algorithms (ÇARA, RASA) were developed and metaheuristic algorithms (Genetic Algorithm, NSGA-II and Simulated Annealing) were used. Results:According to the results of experimental analysis, it has been found that Genetic Algorithm performs better than other algorithms. It has been determined that the Simulated Annealing Algorithm has difficulty in the simultaneous minimization of two objectives and the solution time of NSGA-II is high. ÇARA has produced suitable solutions in all scenarios in very short solution times. While RASA has found suitable solutions for small and medium-sized problems, large-scale problems have not been able to produce appropriate solutions within reasonable solution times. Conclusion:MC-VRP has been successfully solved within the acceptable solution times for the relevant purposes, taking into account the specified constraints. As a result of the analysis, it was determined that population based algorithms produced better results than single solution based algorithms and constructive heuristics. It is anticipated that the proposed methods can be applied to Dynamic VRP, Dynamic TSP, MTTSP and can be used successfully in Fleet Routing, Maritime Surveillance Operations, Logistics and Traffic Control.

Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft

“…In Genetic Algorithm, there are many parameters that make up the solution mechanism and affect the solution quality. The representation of the solution, the selection of new individuals, the crossover and mutation operators are the parameters that must be determined in the algorithm [ 37 ]. Information on the determination of these parameters is given in the following sections.…”

“…The structure of chromosomes and genes in the population may vary depending on the type of problem. Binary, integer, float and permutation are some of the solution representations used in the algorithm [ 37 ]. Float solution representation is used in this article and the related chromosome structure is shown in Fig.…”

“…The tournament selection mechanism is used in the algorithm developed in this article, since it prevents untimely convergence and is effective in minimization problems [ 37 ]. The solution steps of the algorithm are shown in Fig.…”

“…Different chromosome families are produced by using the crossover operator in the genetic algorithm in order to scan the solution space more broadly and to avoid the local optimum. In this article, the PMX (Partially Mapping Crossover) operator is used for the crossover operation [ 37 ]. The PMX crossover mechanism is shown in Fig.…”

“…Different mutation operators can be used according to the problem type and solution representation. In this article, the mutation process is performed by changing the value of the randomly selected gene in the chromosome [ 37 ].…”