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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]

The defects in macro traffic model research are pointed out firstly. In order to remedy these defects, traffic movements at the grid intersection were analyzed, and with the basic framework of the traffic transmission model, the new macro traffic model used in the paper for control simulation and evaluation has been proposed. Secondly, the bi-level optimization control model is proposed, using minimal delay and maximal throughput as its upper objectives, and optimal traffic coordination on both sides as the lower objective. A corresponding heuristic ant algorithm is subsequently designed to solve the control model. Finally, the proposed method is tested at a suppositional road network, under three different demand scenarios, compared with Transyt-7F. The results show that the proposed method has better performance, especially under high demand scenarios.

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.

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]

Conditionally breeding real-coded genetic algorithm (CGAR) is effective approach for continue domain problems, in which crossover and mutation behaviors are performed by difference-degree between individuals instead of given probability. In this paper we present a novel exploitation scheme for CGAR to balance between two contradictory aspects of its performance: exploration and exploitation, which is aimed at improving its ability to converge to the near-optimal solutions. The proposed algorithms are evaluated on a number of benchmark functions and the simulation results show that the proposed algorithm performs quite well and outperforms other algorithms. [ABSTRACT FROM AUTHOR]

Considering these factors, such as suppliers have strict operating time limit, customers have the preset personalized delivery time, road conditions affect transportation cost, etc., this paper established the incident vehicle routing problem (IVRP) with many constraints mathematical model. At first, it constructed initial solution through cluster algorithm and saving algorithm, it could improve search speed, changed information heuristic factor and pheromone expectation heuristic factor adaptively, it could improve convergence speed. Secondly, it introduced genetic operators, and changed crossover probability and mutation probability adaptively, it could improve global search ability. At last, extracting core routes was conducive to late optimization, 3-opt and depots exchange could improve local search ability. Thus, it constructed adaptive ant colony optimization with genetic algorithm operators (AACOGAO), using this algorithm to solve 40 clients IVRP model. Experiments show that IVRP is superior to VRP, as a whole, AACOGAO is slightly better than AGA and AACO in convergence speed and optimal results, and the stability of AACOGAO is good. [ABSTRACT FROM AUTHOR]

Considering road condition affect transport cost and fuel consumption rate was associated with carry load in real life, dealing with crossing multi-period problem, this paper established vehicle routing problem in time-varying networks environment mathematical model. At first, constructing initial solution through cluster algorithm and saving algorithm could improve search speed, changing information heuristic factor and pheromone expectation heuristic factor adaptively could improve global convergence ability. Secondly, combining the fuel consumption rate to update pheromone adaptively ensured the convergence speed. At last, it used 3-opt strategy to improve local search ability. Thus, it constructed adaptive ant colony optimization (AACO), using this algorithm to solve VHP for 8 clients. Experiments show that AACO is better than adaptive genetic algorithm (AGA) and ant colony optimization (ACO) in convergence speed and optimal results. Considering the different carry load of fuel consumption, it provides a more accurate for estimating transportation cost. [ABSTRACT FROM AUTHOR]

Considering these facts, such as travel frequency seasons' preference of rural population, much more shifts in rush hours, enduring the longest time of getting the bus, continuous driving time limit of drivers, vehicles can be parked in other stations, vehicles can be shared between different depots etc., this paper established homogeneous vehicle open collaborative vehicle routing problem with leasing pattern model. Combining with saving algorithm, scanning algorithm and genetic algorithm, it constructed hybrid ant colony optimization. First of all, by scanning algorithm for grouping site, then applied saving algorithm for single TSP and got feasible solution, at last, applied the hybrid ant colony optimization to further optimized solution. The simulation results show that the algorithm is superior to genetic algorithm in convergence speeds and optimization ability. [ABSTRACT FROM AUTHOR]