Multi-depot Open Vehicle Routing Problem Research Articles

Improved Harris Hawks Optimizer algorithm to solve the multi-depot open vehicle routing problem

Improved Harris Hawks Optimizer algorithm to solve the multi-depot open vehicle routing problem

A hybridization of granular adaptive tabu search with path relinking for the multi-depot open vehicle routing problem

The multi-depot open vehicle routing problem (MDOVRP) differs from the classical VRP in that there is more than one depot and the vehicle does not need to return to a depot after serving the last customer. For solving this challenging problem, we propose a hybrid metaheuristic algorithm (GATS-PR) which integrates the granular adaptive tabu search with path relinking. The main contributions of this work consist of introducing a solution-based tabu search technique in granular tabu search, designing an adaptive neighborhood selection method for the large neighborhoods with 22 kinds of move types, and adopting path relinking with a new similarity definition to the MDOVRP for the first time. Computational results on 24 public instances demonstrate that GATS-PR outperforms the previous state-of-the-art algorithms in the literature. Specifically, GATS-PR improved and matched the previous best known results on 4 and 19 instances, respectively.

On the exact solution of the multi-depot open vehicle routing problem

On the exact solution of the multi-depot open vehicle routing problem

A comprehensive mathematical model for quality integration in a project supply chain with concentrating on material flow and transportation

This study aims to investigate how quality integration affects a joint model of multi-project scheduling and material transportation. Therefore, the research problem not only considers the quality of project activities, but also focuses on quality in processes related to suppliers and customers. Accordingly, this study considers the quality of materials as a factor when determining the suppliers in a project-based organization. Furthermore, unlike the existing studies, it also assumes that the rework-time of projects depends on the quality of materials. The research problem is addressed using a mixed integer linear programming model, which minimizes the total normalized cost (TNC) as a measure of the balance between supply chain costs. Contrary to previous models, this model examines the simultaneous impacts of renewable and nonrenewable resources on project quality. Also, the material transportation is modeled based on the “multi-depot open vehicle routing problem” to increase the model’s applicability in real conditions. In order to solve the research problem, a tabu search algorithm is applied, which is verified based on instances of different sizes. Then, the proposed model is compared with one of the similar models in the literature to determine its efficiency. Additionally, a real-world case study is utilized to show the model application. The results show that the quality concept in the suggested model leads to more appropriate materials and shorter routes for transportation. Therefore, quality integration in this model reduces the total normalized cost by improving projects' delays and transportation costs.

A general VNS for the multi-depot open vehicle routing problem with time windows

A general VNS for the multi-depot open vehicle routing problem with time windows

An efficient variable neighborhood search with tabu shaking for a class of multi-depot vehicle routing problems

We present a Variable Tabu Neighborhood Search (VTNS) algorithm for solving a class of Multi-Depot Vehicle Routing Problems (MDVRP). The proposed algorithm applies a granular local search mechanism in the intensification phase and a tabu shaking mechanism in the diversification phase of Variable Neighborhood Search. Furthermore, it allows the violation of problem-specific constraints throughout the search in an attempt to escape from local optima and to converge to a high-quality feasible solution. VTNS is a flexible algorithm; with simple adaptations it can be implemented to solve MDVRP, MDVRP with Time Windows (MDVRPTW) and Multi-Depot Open Vehicle Routing Problem (MDOVRP). Our computational tests on these three problems show that VTNS provides promising results competitive with state-of-the-art algorithms from the literature in terms of both solution quality and run time. Overall, we achieve six new best-known solutions in the MDVRP, one in the MDVRPTW, and four in the MDOVRP benchmark data sets.

A memory-based iterated local search algorithm for the multi-depot open vehicle routing problem

The problem studied in this paper is the multi-depot open vehicle routing problem, which has the following two differences in relation to the classical vehicle routing problem: there are several depots; the vehicles do not return to the depot after delivering the goods to the customers, i.e., the end of the route is not the starting point. There are many practical applications for this problem, however the great majority of the studies have only addressed the open vehicle routing problem with a single depot. In this paper, we present an iterated local search algorithm, in which the moves performed during the local search are recalled and this historical search information is then used to define the moves executed inside the perturbation procedures. Therefore, it is recorded the number of times that each customer is moved during the local search. Since this information is continuously updated and changes in each iteration, the search is driven to potentially better regions of the solution space, and increases the chance of avoiding cycling, even when using deterministic perturbations. The performance of this algorithm was tested using a large set of benchmark problems and was compared with other algorithms, and the results show that it is very competitive.

Multi-depot open vehicle routing problem with fuzzy time windows

This paper presents one method and one hybrid genetic algorithm for multi-depot open vehicle routing problem with fuzzy time windows (MDOVRPFTW) without maximum time windows. For the method, the degree of customers’ willingness to accept goods (DCWAG) is firstly proposed, it’s one fuzzy vague and determines maximum time windows. Referring to methods to determine fuzzy membership function, the function between DCWAG and the starting service time is constructed. By setting an threshold for DCWAG, the starting service time that the threshold corresponds can be treated as the maximum time window, which meets the actual situation. The goal of the model is to minimize the total cost. For the algorithm, MDOVRPFTW without maximum time windows is an extension of the NP-hard problem, the hybrid genetic algorithm was designed, which is combination of genetic algorithm and Hungarian algorithm. When the hybrid genetic algorithm applied to one pharmaceutical logistics company in Beijing City, China, one optimal scheme is determined. Then the rationality and the stability of solutions by the hybrid genetic algorithm are proved. Finally, sensitivity analyses are performed to investigate the impact of someone factor on DCWAG and some suggestions are proposed.

A hybrid adaptive large neighbourhood search for multi-depot open vehicle routing problems

In this paper we address the multi-depot open vehicle routing problem (MDOVRP), a complex and difficult problem arising in several real-life applications. In the MDOVRP vehicles start from several depots and do not need to return to the depot at the end of their routes. We propose a hybrid adaptive large neighbourhood search algorithm to solve the MDOVRP coupled with improvement procedures yielding a hybrid metaheuristic. The performance of the proposed metaheuristic is assessed on various benchmark instances proposed for this problem and its special cases, containing up to 48 customers (single-depot version) and up to six depots and 288 customers. The computational results indicate that the proposed algorithm is very competitive compared with the state-of-the-art methods and improves 15 best-known solutions for multi-depot instances and one best-known solution for a single-depot instance. A detailed sensitivity analysis highlights which components of the metaheuristic contribute most to the solution quality.

Multi-Depot Open Vehicle Routing Problem with Time Windows Based on Carbon Trading.

In order to cut the costs of third-party logistics companies and respond to the Chinese government’s low-carbon economy plans, this paper studies the more practical and complex open vehicle routing problem, which considers low-carbon trading policies. A low-carbon multi-depot open vehicle routing problem with time windows (MDOVRPTW) model is constructed with minimum total costs, which include the driver’s salary, penalty costs, fuel costs and carbon emissions trading costs. Then, a two-phase algorithm is proposed to handle the model. In the first phase, the initial local solution is obtained with particle swarm optimization (PSO); in the second phase, we can obtain a global optimal solution through a further tabu search (TS). Experiments proved that the proposed algorithm is more suitable for small-scale cases. Furthermore, a series of experiments with different values of carbon prices and carbon quotas are conducted. The results of the study indicate that, as carbon trading prices and carbon quotas change, total costs, carbon emission trading costs and carbon emissions are affected accordingly. Based on these academic results, this paper presents some effective proposals for the government’s carbon trading policy-making and also for logistics companies to have better route planning under carbon emission constraints.

Multiple neighborhood search, tabu search and ejection chains for the multi-depot open vehicle routing problem

In this paper, we address the Multi-Depot Open Vehicle Routing Problem (MDOVRP), which is a generalization of the Capacitated Vehicle Routing Problem (CVRP) where vehicles start from different depots, visit customers, deliver goods and are not required to return to the depot at the end of their routes. The goal of this paper is twofold. First, we have developed a general Multiple Neighborhood Search hybridized with a Tabu Search (MNS-TS) strategy which is proved to be efficient and second, we have settled an unified view of ejection chains to be able to handle several neighborhoods in a simple manner. The neighborhoods in the proposed MNS-TS are generated from path moves and ejection chains. The numerical and statistical tests carried out over OVRP and MDOVRP problem instances from the literature show that MNS-TS outperforms the state-of-the-art methods.

An improved formulation for the multi-depot open vehicle routing problem

The multi-depot open vehicle routing problem (MDOVRP) is a recent hard combinatorial optimization problem that belongs to the vehicle routing problem family. In the MDOVRP, the vehicles depart from several depots and once they have delivered the goods to the last customers in their routes they are not required to return to the depots. In this work, we propose a new mixed integer programming formulation for the MDOVRP by improving some constraints from the literature and proposing new ones. The computational experience carried out over problem instances from the literature indicates that our proposed model outperforms the existing one.

Erratum to: A hybrid genetic algorithm for the multi-depot open vehicle routing problem

Erratum to: A hybrid genetic algorithm for the multi-depot open vehicle routing problem

Study on a Multi-Depot and Heterogeneous-Vehicle Open Vehicle Routing Problem to Reduce Fuel Consumption

An open vehicle routing problem is studied. In this problem, multi-depot, heterogeneous-vehicle, fuel consumption and start-up costs of vehicles are considered, thus a genetic algorithm is given to solve this hard problem. In order to improve the performance of the genetic algorithm, a heuristic algorithm is provided to produce the initial population and participate in crossover. An example is given to illustrate the genetic algorithm.

A hybrid genetic algorithm for the multi-depot open vehicle routing problem

This paper studies the multi-depot open vehicle routing problem (MDOVRP), a variant of the vehicle routing problem (VRP), in which vehicles start from several depots and are not required to return to the depot. Despite the vast amount of literature about VRPs, the MDOVRP has received very little attention from researchers. In this paper, a new hybrid genetic algorithm is presented for finding the routes that minimize the traveling cost of the vehicles. Computational results on a number of test instances indicate the proposed algorithm dominates the CPLEX solver and the existing approach in the literature. Meanwhile, experiments are conducted on multi-depot VRP benchmarks, and the results are compared with a sophisticated tabu search approach and an exact method.

Improved Ant Colony Optimization for Seafood Product Delivery Routing Problem

This paper deals with a real-life vehicle delivery routing problem, which is a seafood product delivery routing problem. Considering the features of the seafood product delivery routing problem, this paper formulated this problem as a multi-depot open vehicle routing problem. Since the multi-depot open vehicle routing problem is a very complex problem, a method is used to reduce the complexity of the problem by changing the multi-depot open vehicle routing problem into an open vehicle routing problem with a dummy central depot in this paper. Then, ant colony optimization is used to solve the problem. To improve the performance of the algorithm, crossover operation and some adaptive strategies are used. Finally, the computational results for the benchmark problems of the multi-depot vehicle routing problem indicate that the proposed ant colony optimization is an effective method to solve the multi-depot vehicle routing problem. Furthermore, the computation results of the seafood product delivery problem from Dalian, China also suggest that the proposed ant colony optimization is feasible to solve the seafood product delivery routing problem.