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The wild horse optimizer (WHO) is a novel metaheuristic algorithm, which has been successfully applied to solving continuous engineering problems. Considering the characteristics of the wild horse optimizer, a discrete version of the algorithm, named discrete wild horse optimizer (DWHO), is proposed to solve the capacitated vehicle routing problem (CVRP). By incorporating three local search strategies-swap operation, reverse operation, and insertion operation-along with the introduction of the largest-order-value (LOV) decoding technique, the precision and quality of the solutions have been enhanced. Experimental results conducted on 44 benchmark instances indicate that, in most test cases, the solving capability of discrete wild horse optimizer surpasses that of basic wild horse optimizer (BWHO), hybrid firefly algorithm, dynamic space reduction ant colony optimization (DSRACO), and discrete artificial ecosystem-based optimization (DAEO). The discrete wild horse optimizer provides a novel approach for solving the capacitated vehicle routing problem and also offers a new perspective for addressing other discrete problems. [ABSTRACT FROM AUTHOR]

The field of logistics encounters interconnected characteristics in vehicle routing problems (VRP), particularly evident in the many-task VRP (M-VRP) seen in routine food deliveries spanning consecutive days. While these similarities and distinctions frequently remain unexplored, scant research focuses on optimizing many-task scenarios. This study introduces the tripartite shared evolutionary multi-task algorithm (TSEMTA), an innovative approach to addressing M-VRP complexity. TSEMTA integrates three strategic pillars: knowledge, resource, and search sharing strategies. The knowledge sharing strategy promotes cross-task insights and solutions, enhancing concurrent optimization. The resource sharing strategy aligns computational efforts with task progression, optimizing efficiency. The search sharing strategy prioritizes elite solutions and intensifies local searches, thereby increasing diversity and effectiveness. These combined strategies create a cohesive framework that improves optimization: informed resource allocation refines the search process, initiating a cycle that boosts TSEMTA's performance. Comparative analyses with advanced algorithms on three series of benchmark test problems and four real-world problems demonstrate TSEMTA's superior capability in navigating complex optimization landscapes. [ABSTRACT FROM AUTHOR]

The field of logistics encounters interconnected characteristics in vehicle routing problems (VRP), particularly evident in the many-task VRP (M-VRP) seen in routine food deliveries spanning consecutive days. While these similarities and distinctions frequently remain unexplored, scant research focuses on optimizing many-task scenarios. This study introduces the tripartite shared evolutionary multi-task algorithm (TSEMTA), an innovative approach to addressing M-VRP complexity. TSEMTA integrates three strategic pillars: knowledge, resource, and search sharing strategies. The knowledge sharing strategy promotes cross-task insights and solutions, enhancing concurrent optimization. The resource sharing strategy aligns computational efforts with task progression, optimizing efficiency. The search sharing strategy prioritizes elite solutions and intensifies local searches, thereby increasing diversity and effectiveness. These combined strategies create a cohesive framework that improves optimization: informed resource allocation refines the search process, initiating a cycle that boosts TSEMTA's performance. Comparative analyses with advanced algorithms on three series of benchmark test problems and four real-world problems demonstrate TSEMTA's superior capability in navigating complex optimization landscapes. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of the electric vehicle routing problem with time windows, backup batteries, and battery swapping stations (EVRPTW-BB-BSS), reflecting complex issues in modern logistics. We treat each problem as an optimization task. Our main goal is to optimize various types of tasks at the same time. To achieve this, we present the double-assistant evolutionary multitasking algorithm (DAEMTA). This algorithm skillfully combines internal and external knowledge exchange. It helps in the transfer between different and similar types of tasks. DAEMTA includes a two-stage system for collaboration and adaptive methods for managing knowledge transfer. A population enhancement strategy deepens the search for valuable solutions. It also maintains diversity to avoid early convergence. Our experiments show that DAEMTA performs better than advanced EMTAs and traditional algorithms. We test it on 30 multitasking EVRPTW-BB-BSSs and four real-world problems. These findings highlight the practicality and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

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]

This article presents a variable neighborhood search (VNS) algorithm for the consistent vehicle routing problem (ConVRP). ConVRP is a variant of the vehicle routing problem (VRP). In ConVRP, vehicle routes must be designed for multiple days, and each customer must be visited by the same driver at approximately an identical time on each day. VNS is an efficient algorithmic framework and is widely used. The proposed algorithm consists of two stages. In the first stage, VNS is applied to obtain approximately optimized solutions. The solutions obtained might be infeasible. If a solution is of acceptable quality, the second stage is applied to make it feasible and optimize it further. Several techniques are employed to reduce computation time of the local search stage. A special shaking method is introduced and proofed to be more effective than ordinary methods by experiments. A new method for computing time difference excess is proposed to solve the problem that change of time difference excess caused by operations on an individual day is not obvious. The proposed algorithm is tested on the benchmark ConVRP data set and compared with extant ConVRP approaches from the literature. The results demonstrate that VNS outperforms all the extant ConVRP approaches in terms of quality of solutions obtained. [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]

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]