Tabu Search for the Vehicle Routing Problem with Multiple Trips, Time Windows, and Simultaneous Delivery-Pickup

This paper studies the vehicle routing problem with multiple trips and time windows, in which vehicles are allowed to perform multiple trips during a scheduling period and each customer must be served within a given time interval. The problem is of particular importance for planning fleets of hired vehicles in common practices, such as e-grocery distributions, but this problem has received little attention in the literature. As a result of the multi-layered structure characteristic of the problem solution, we propose a pool-based metaheuristic in which various routes are first constructed to fill a pool, following which some of the routes are selected and combined to form vehicle working schedules. Finally, we conduct a series of experiments over a set of benchmark instances to evaluate and demonstrate the effectiveness of the proposed metaheuristic.

In this paper, we consider a variant of vehicle routing problem with multiple trip and time windows. In this alternative problem, we think about the scheduling of trucks to a number of customers in the presence of fixed fleet and time windows constraints. This type of problem can be described as determining a number of vehicle trips minimising total travelled distance complying with the time windows and the multiple use of vehicles. We call this problem a multiple trips fixed fleet vehicle routing problems with time windows (MTFFVRPTW). To solve this variant, a new hybrid evolutionary algorithm for transport optimisation problems, called estimation of distribution algorithm with local search is used. The proposed algorithm integrates a double structure of distribution technique, which is used to introduce the variables dependency. To evaluate our approach we provide a number of experimentation to a modified Solomon's instances for the case of multiple trips. The results show that the hybrid proposed algorithm offer a good quality solution in the term of objective functions, also in the running time.

This paper studies a set of rich vehicle routing problems incorporating various complexities found in real-life applications. The rich vehicle routing problem considers simultaneously four multiple constraints: multiple depots, multiple time windows, multiple trips, and multiple vehicle types. A metaheuristic algorithm called the general vehicle routing algorithm, based on the skewed variable neighborhood search, was designed to address the problem of any combination on five features. A slice of operators and heuristic approaches developed for the specific constraints is embedded in the general vehicle routing algorithm. Six combination vehicle routing problem types were investigated. The computational results demonstrated that the proposed algorithm is competitive for both benchmark instances and generated instances in accuracy of solution and computational time.

The Vehicle Routing Problem (VRP) is a known optimization problems falling under the category of NP-Hard set of problems. VRP, along with its variations, continue to be extensively explored by the research community due to their large domain of application (environment, agriculture, industry, etc.) and economic impact on improving the overall performance, Quality of Services and reducing the operational cost. In this paper, we focus on VRPMTW; a variant of VRP with Multiple Time Windows constraints. We introduce a novel Hybrid Genetic Variable Neighborhood Search (HGVNS) based heuristic for the optimization of VRPMTW. The proposed framework uses genetic cross-over operators on a list of best parents and new implementations of local search operators. Computational results on benchmark data show substantial performance improvement when using the newly introduced heuristic.

Vehicle routing problem (VRP) is about finding optimal routes for a fleet of vehicles so that they can meet the demands for a set of given customers by traveling through those paths. This problem is one of the most important and most applicable problems of transportation and logistics scope. In this paper, green vehicle routing and scheduling problem with a heterogeneous fleet, including reverse logistics in the form of collecting returned goods along with weighted earliness and tardiness costs considering multiple time windows, is studied to establish a trade-off between operational and environmental costs. In this regard, a mixed-integer non-linear programming (MINLP) model is proposed at the first stage; then its accuracy and correct functioning are evaluated by solving some examples. Demand is considered uncertain based on fuzzy numbers that robust possibilistic programming is employed regarding the other parameters uncertainty. Since this problem is categorized as an NP-hard problem, a genetic algorithm (GA) is suggested to find near-optimal solutions for large instances in a rational computational time. Eventually, the GA’s performance is evaluated compared to solving the mathematical model for small-sized problems. Analysis of the results considering two criteria, solutions quality and computational times, indicates the satisfactory operation of the proposed algorithm in a proper computational time.

In actual distribution process, the traffic flow varies with time, and each consumer has multiple fuzzy windows. To minimize the total distribution cost and mean consumer dissatisfaction, this paper sets up a vehicle routing problem (VRP) model with multiple fuzzy time windows, based on time-varying traffic flow. In addition, the Ito algorithm was improved based on time-varying traffic flow. The model and algorithm were verified through example simulation, in comparison with ant colony optimization (ACO). During the simulation, the improved Ito algorithm effectively reduced the distribution cost and consumer dissatisfaction, and outperformed the ACO in solving efficiency and solution quality. The results fully demonstrate the feasibility and effectiveness of the proposed algorithm. The research findings provide a desirable solution to VRPs with multiple fuzzy windows and time-varying traffic flow in the real world.

<p style='text-indent:20px;'>The Vehicle Routing Problem with Multiple Time Windows (VRPMTW) is a generalization of problems in real life logistics distribution, which has a wide range of applications and research values. Several neighborhood search based methods have been used to solve this kind of problem, but it still has drawbacks of generating numbers of infeasible solutions and falling into local optimum easily. In order to solve the problem of arbitrary selection for neighborhoods, a series of neighborhoods are designed and an adaptive strategy is used to select the neighborhood, which constitute the Adaptive Large Neighborhood Search(ALNS) algorithm framework. For escaping from the local optimum effectively in the search process, a local search based on destroy and repair operators is applied to shake the solution by adjusting the number of customers. The proposed method allows infeasible solutions to participate in the iterative process to expand the search space. At the same time, an archive is set to save the high-quality feasible solutions during the search process, and the infeasible solutions are periodically replaced. Computational experimental results on VRPMTW benchmark instances show that the proposed algorithm is effective and has obtained better solutions.</p>

Multi-trip time-dependent vehicle routing problem with time windows

Hybridization of tabu search with feasible and infeasible local searches for periodic home health care logistics

The Vehicle Routing Problem with Time Windows (VRPTW) consists of a homogenous set of vehicles and a set of customer located in a city. In VRPTW all the vehicles starts from the depot visit the customer and end at the depot. Each customer is visited exactly by one vehicle within the specified time window. The objective is to minimize the number of vehicles and total distance travelled simultaneously. This represents the multiobjective Vehicle Routing Problem with Time Windows. The proposed work consists of Hybrid Genetic Search with Diversity Control using the Genetic Algorithm for solving the VRPTW. The Pareto approach is used for finding the set of optimal solutions for achieving the multiobjective. The crossover operator is used for exchanging the best routes, which have shortest distance. Two mutation operators such as relocation mutation operator and split mutation operator were used in this application. In this, it accounts penalty for an infeasible solutions with respect to time-window and duration constrains. The computations are performed using the instances which are obtained from the VRPLIB.

This paper presents the first optimization study of multi-site transportation in the construction industry, which allows mutualizing building material delivery and construction waste removal. This study is inspired by a real-world problem encountered in the framework of the French R&D project DILC, in which a pooling platform must centralize the delivery of building materials to the construction sites and the pickup of their waste, using a limited and heterogeneous fleet that are allowed to perform multiple trips, under time and capacity limitation constraints. The problem under study, called the Multi-Trip Pickup and Delivery Problem, with Split loads, Profits and Multiple TimeWindows is a new extension of the vehicle routing problem with pickup and delivery, that considers new realistic constraints specific to the construction industry such as each construction site may have a priority on its delivery request or its pickup request or both, with a higher priority level for delivery request, and each construction site may have several time windows. To solve this problem, we propose new insertion criteria that takes into consideration several aspects of our problem, which we have embedded in a construction heuristic. Experiments performed on new real instances have shown the efficiency of our method.

A tabu search heuristic for the vehicle routing problem with time windows and split deliveries

This paper studied the VRPSPDTW (Vehicle Routing Problem with Simultaneous Pickups and Deliveries and Time-Windows). An integer programming model is constructed, its objective is to minimize the delivery time. To further improve the convergence speed and optimization capability of the genetic algorithm, an improved genetic algorithm with local search (IGALS) is proposed, which is based on a hybrid crossover operation with elitist strategy and a local search using the thought of allowed delay time. The paper analyzed the advantages of heuristic algorithm to solve the problem by comparing the computation complexity between exact algorithm and heuristic algorithm. Testing by Solomon Benchmarks instances, the results show the IGALS has better performance than simple GA, which has better global optimization capability and faster convergence speed.

The vehicle routing problem with time windows (VRPTW) is an extension of the well-known vehicle routing problem (VRP). It involves a fleet of homogeneous vehicles, originating and terminating at a central depot, with limited capacity and maximum travel time to service a set of customers with known demands and service-time windows. The objective is to find a set of feasible routes that minimizes the total costs using some measures of solution quality. This paper focuses on the study of a hybrid of two search heuristics, Tabu Search (TS) and Genetic Algorithm (GA) on VRPTW. TS is a local search technique that has been successfully applied to many NP-complete problems. On the other hand, GA which is capable of searching multiple search areas in a search space is good in diversification. In this paper, we create a hybrid that combines the strengths of the two search heuristics. Experimental results indicate that such a hybrid outperforms the individual heuristics alone.

In this paper vehicle routing problem with heterogeneous vehicle regarding the delivery time windows is examined . In the present case , each customer has a hard time window and a soft time window . Delivery outside the hardware time window is not possible. Delivery outside the soft time window earliness or delay costs imposed on the model . The mathematical model of the problem , innovative genetic algorithm for a given problem is developed . The results obtained from the sample solution by genetic algorithms , the exact solution of the model in the software Lingo is obtained and s uggests that genetic provided acceptable performance and low error gives an answer in a reasonable time .