Search

In humanitarian aid scenarios, the model of cumulative capacitated vehicle routing problem can be used in vehicle scheduling, aiming at delivering materials to recipients as quickly as possible, thus minimizing their wait time. Traditional approaches focus on this metric, but practical implementations must also consider factors such as driver labor intensity and the capacity for on-site decision-making. To evaluate driver workload, the operation times of relief vehicles are typically used, and multi-objective modeling is employed to facilitate on-site decision-making. This paper introduces a multi-objective cumulative capacitated vehicle routing problem considering operation time (MO-CCVRP-OT). Our model is bi-objective, aiming to minimize both the cumulative wait time of disaster-affected areas and the extra expenditures incurred by the excess operation time of rescue vehicles. Based on the traditional grey wolf optimizer algorithm, this paper proposes a dynamic grey wolf optimizer algorithm with floating 2-opt (DGWO-F2OPT), which combines real number encoding with an equal-division random key and ROV rules for decoding; in addition, a dynamic non-dominated solution set update strategy is introduced. To solve MO-CCVRP-OT efficiently and increase the algorithm's convergence speed, a multi-objective improved floating 2-opt (F2OPT) local search strategy is proposed. The utopia optimum solution of DGWO-F2OPT has an average value of two fitness values that is 6.22% lower than that of DGWO-2OPT. DGWO-F2OPT's average fitness value in the algorithm comparison trials is 16.49% less than that of NS-2OPT. In the model comparison studies, MO-CCVRP-OT is 18.72% closer to the utopian point in Euclidean distance than CVRP-OT. [ABSTRACT FROM AUTHOR]

In this paper we consider two important objects of transportation, cost and customer satisfaction. The latter mainly depends on vehicle arrival time and expecting time of the customer. Whereas in the reality, road conditions varies at different time periods and affect the vehicle travelling speed. Meanwhile, transport cost, including fuel consumption, relate to load of vehicle. Correspondingly, mathematical model of vehicle routing problem with soft time windows and road factor (VRPSTWRF) was established in which transport cost, fuel consumption and customer satisfaction are considered. Multi-agent ant colony optimization is proposed in which the features of agent perceiving and reacting to the environment are applied reasonably. Adaptive information heuristic factor and pheromone expectation heuristic factor changing mechanism is used to improve global convergence ability. Pheromone is updated adaptively, the fuel consumption rate also considered, to ensure the convergence speed. 3-opt strategy was introduced to improve local search ability. Thus, multi-agent ant colony optimization (MACO) was constructed and used to solve 40-customer VRPSTWRF model. Experiments show that MACO proposed is feasible and valid.

To solve the fuzzy and unstable tactile similarity relationship between some sample points in the perception experiment, an improved non-metric multidimensional scaling (INMDS) is proposed in this paper. In view of the inconsistency of each sample's contribution, the maximum marginal decision when constructing the perception space to describe the tactile perception characteristics is also proposed. The corresponding constraints are set according to the degree of similarity, and controlling the relaxation variable factor is proposed to optimize the perception dimension and coordinate measurement. The effectiveness of the INMDS algorithm is verified by two perception experiments. The results show that compared with the metric multidimensional scaling (MDS) and non-metric multidimensional scaling (NMDS) algorithms, the perceptual space constructed by INMDS can more accurately reflect the difference relationship between different leather sample points perceived by people. Moreover, the relative position of sample points in the perceptual space is more consistent with subjective perception results. [ABSTRACT FROM AUTHOR]

For the disadvantage of particle swarm optimizer is not skillful at balancing the global and local search, a new algorithm is proposed in this paper, combining the time-varying acceleration coefficients. The new algorithm, based on particle swarm optimizer with constrict factor, employs the double constrict factors. The first constrict factor aims at adjusting the model of global and local search, and the second constrict factor concentrates on further balancing the global and local optima which influence the updates of the entire swarm. By comparing with particle swarm optimizer, particle swarm optimizer with constrict factor and chaos particle swarm optimizer, which evaluated on 8 Benchmark functions, with 3 kinds of tests, the results indicate the new algorithm owns a higher accurate level, and faster convergence velocity. The time varying acceleration coefficients are used, the new algorithm can balance the global and local search better. [ABSTRACT FROM AUTHOR]