Open Vehicle Routing Problem Research Articles

An assignment-based decomposition approach for the vehicle routing problem with backhauls

In the context of the Vehicle Routing Problem with Backhauls, which involves delivering to linehaul and picking up from backhaul customers, we propose a novel mathematical model that can decompose the main problem into three sub-problems: two Open Vehicle Routing Problems and one Assignment Problem. In our proposed model, Open Vehicle Routing Problems optimize routes for homogeneous vehicles serving linehaul/backhaul customers, while the Assignment Problem matches linehaul and backhaul routes. We utilize Lagrangian decomposition approach and solve subproblems in parallel and sequential layouts. We measure the performance of the foregoing arrangements (in terms of solution quality and computational efficiency) by testing our model on two benchmark datasets, proposed by Goetschalckx and Jacobs-Blecha (1989) and Toth and Vigo (1997) and are known as GJ and TV datasets in the extant literature, respectively. Our model matches best known solutions in 35 % and 33 %, with most within 2 % deviation, for GJ and TV instances, respectively. We also showcase our model on a real-world transportation network containing 100, 250, and 500 customers and geographically located in Lansing, Michigan. To reduce the computational burden of solving the Vehicle Routing Problem with Backhauls on the Lansing dataset, we present a cluster first-route second algorithm and then analyze the impact of vehicle capacity on the solution quality of our proposed algorithm.

New Model for an Open Vehicle Routing Problem to Optimize the Blood Sample Collection Process within a Network of Clinical Laboratories

New Model for an Open Vehicle Routing Problem to Optimize the Blood Sample Collection Process within a Network of Clinical Laboratories

Constructing selection hyper-heuristics for open vehicle routing with time delay neural networks using multiple experts

Hyper-heuristics are general purpose search methods for solving computationally difficult problems. A selection hyper-heuristic is composed of two key components: a heuristic selection method and move acceptance criterion. Under an iterative single-point search framework, a solution is modified by selecting and applying a predefined low-level heuristic, with a decision then taken to accept or reject the resulting solution. Designing a selection hyper-heuristic is an extremely challenging task. In this study, we investigate computer-aided design of a selection hyper-heuristic for the open vehicle routing problem. A time delay neural network is used as an offline apprenticeship learning method. Our approach first observes the search behaviour of multiple expert human-designed selection hyper-heuristics on a selected sample of training instances, before automatically generating a selection hyper-heuristic capable of solving unseen instances effectively. The proposed approach is tested on open vehicle routing problem instances of different sizes to examine the performance and generality of the selection hyper-heuristics generated. Improved performance is demonstrated over a set of well-known benchmarks from the literature when compared to using the existing expert systems directly.

A Multi-Objective Learning Whale Optimization Algorithm for Open Vehicle Routing Problem with Two-Dimensional Loading Constraints

With the rapid development of the sharing economy, the distribution in third-party logistics (3PL) can be modeled as a variant of the open vehicle routing problem (OVRP). However, very few papers have studied 3PL with loading constraints. In this work, a two-dimensional loading open vehicle routing problem with time windows (2L-OVRPTW) is described, and a multi-objective learning whale optimization algorithm (MLWOA) is proposed to solve it. As the 2L-OVRPTW is integrated by the routing subproblem and the loading subproblem, the MLWOA is designed as a two-phase algorithm to deal with these subproblems. In the routing phase, the exploration mechanisms and learning strategy in the MLWOA are used to search the population globally. Then, a local search method based on four neighborhood operations is designed for the exploitation of the non-dominant solutions. In the loading phase, in order to avoid discarding non-dominant solutions due to loading failure, a skyline-based loading strategy with a scoring method is designed to reasonably adjust the loading scheme. From the simulation analysis of different instances, it can be seen that the MLWOA algorithm has an absolute advantage in comparison with the standard WOA and other heuristic algorithms, regardless of the running results at the scale of 25, 50, or 100 datasets.

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

Modeling open vehicle routing problem with real life costs and solving via hybrid civilized genetic algorithm

Many companies prefer to use third party logistics firms to deliver their goods and as such planning the return of the vehicles to the depot is not required. This is called open vehicle routing problem (OVRP). In literature, the OVRP is handled with minimum distance as objective function like vehicle routing problem. But in the real world, the objective function achieves minimum many costs like standard routing cost, stopping by cost and the deviation cost. The standard routes are previously defined under free market conditions by third party logistic firms. The deviation from the standard route is required to arrive cities which are not on the standard route. The stop by cost occurs on the delivery points. In this paper mentioned three costs are considered in the objective function while many papers consider only distance related costs in the literature. This paper proposes a new mathematical model for the OVRP. In the constraints, the last points of the routes are researched in detail. The standard route costs are determined by considering the last point of the route. Because of the NP-hard structure of the OVRP, the proposed mathematical model is solved with a hybrid metaheuristic called Civilized Genetic Algorithm (CGA). CGA is developed by hybridizing a modified genetic algorithm and a local search algorithm. The application of this study is implemented for the delivery routing of a combi boiler producer in Turkey. The third party logistic firms may use this proposed model and the solution approach for the real life applications.

A hybrid heuristic algorithm for the fuzzy open vehicle routing problem with risk preference

A hybrid heuristic algorithm for the fuzzy open vehicle routing problem with risk preference

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.

Fixed fleet open vehicle routing problem: Mathematical model and a modified ant colony optimization

Fixed fleet open vehicle routing problem: Mathematical model and a modified ant colony optimization

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

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

Multi-objective medical supplies distribution open vehicle routing problem with fairness and timeliness under major public health emergencies

Fair and timely delivery of supplies plays a critical role under major public health emergencies. In this paper, aiming at fairness and timeliness, an optimization model of open vehicle routing problem for medical supplies distribution is established considering the urgency of the demand. We adopt a differential evolutionary algorithm with fast non-dominated solution sorting to solve the proposed model, obtaining an approximate Pareto optimal solution set. Through the comparison of algorithms, the results showed that the differential evolutionary algorithm with non-dominated sorting is superior with a shorter runtime and more diverse solutions, while the epsilon constraint method has more accurate solutions. In the case verification, the quality of the solutions of both algorithms was within the acceptable range, but the runtime of the epsilon constraint method was too long to be applicable. The results can provide theoretical suggestions and practical guidance for decision-makers in emergency supplies distribution.

Open vehicle routing problem with moving shipments at the cross-docking terminal

In this study, one of the internal operations at the Cross-docking Terminal (CDT), moving shipments (MS) from receiving doors to shipping doors of CDT is integrated with Open Vehicle Routing Problem (OVRP) and the problem is indicated here as OVRPCD-MS. As an additional feature, asymmetric distance between any two customers is assigned by incorporating a characteristic of one-way routes between cities in real-life transportation. The objective is to minimize the total transportation cost which incurs travelling cost between customers, service cost at customer points, service cost at the receiving and shipping doors of CDT, cost of moving shipments inside the CDT and finally the cost of hiring fleets of vehicles. To solve the OVRPCD-MS problem, a Mixed Integer Linear Programming (MILP) model is developed. The programming models are implemented in LINGO (version 18) optimization software. Branch and Bound algorithm is employed to solve ten small-scale instances generated randomly. The applicability of the proposed MILP model is observed. The required fleets of vehicles to be hired and run time to reach the optimal solution are determined. The study revealed that the average run time is exponential for small-scale instances. Thus, it can be concluded that this proposed model can be used for last time planning for small-scale instances. Also, the combinatorial nature of the vehicle routing problem makes OVRPCD-MS as NP-hard. Therefore, this study recommends that heuristic or meta-heuristic methods are more appropriate for the large-scale instances of OVRPCD-MS to reach near-optimum solutions.

Research on the Distribution of Emergency Supplies during the COVID-19 Epidemic

The COVID-19 virus spreads quickly and is highly contagious, and the timeliness of emergency supplies is extremely demanding, so adequate and efficient emergency supplies have become a necessary prerequisite for strict prevention and control during the outbreak. However, the available research results are still relatively few, and using the brain storm optimization algorithm to solve the emergency supplies distribution problem is still blank. In order to optimize the emergency supplies distribution system in the context of COVID-19, this paper studies the open vehicle routing problem by considering the load weight constraints, demand constraints, and time window constraints. With the shortest total distribution distance as the objective function, a mathematical model of mixed integer linear programming is established, and the crossover operation of the genetic algorithm, the destruction operator and the repair operator of the large neighborhood search algorithm are introduced in the brain storm optimization algorithm to enhance the diversity of the solution while making the solution toward the optimal direction. Through the verification of two sets of examples, we found that compared with the other 13 solution algorithms, the solution capability and solution efficiency of the brain storm optimization algorithm are better than the comparison algorithms, which can significantly reduce the length of the distribution route and lower the distribution cost, and has excellent convergence and stability. Meanwhile, it can effectively improve the distribution efficiency of emergency supplies, which is of great significance to help the emergency reception and regular prevention and control during the outbreak of the COVID-19 epidemic. In addition, it has the necessary guidance for the improvement of personnel efficiency in the supply chain and logistics industry.

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.

An Effective Combination of Tabu Search, Elite Ant System and Local Search Algorithms for Solving the Heterogeneous Fixed Fleet Open Vehicle Routing Problem with Time Windows

An Effective Combination of Tabu Search, Elite Ant System and Local Search Algorithms for Solving the Heterogeneous Fixed Fleet Open Vehicle Routing Problem with Time Windows

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

A Hybrid Algorithm for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem with Time Windows

Nowadays, using a rental fleet to transport goods for delivering to customers at a particular time frame is very important in the services and industry. That is why, in this study, we consider the heterogeneous fixed fleet open vehicle routing problem with time windows, which is one version of the vehicle routing problem with time windows. The problem has not attracted attention so much in the operational research literature than the usual vehicle routing problem. The problem consists of determining the minimum cost routes for a fleet of a fixed number of vehicles with various capacities in order to fulfil the demands of the customer population. Moreover, the vehicles start at the headquarters and terminate at one of the customers. In this study, we introduce a mixed integer programming model and then integrate an exact algorithm to solve this model. Furthermore, a hybrid algorithm (HA) based on modified rank-based ant system is developed and then its efficiency is compared with the exact method and some metaheuristic methods on some standard instances in literature. The results proved the effectiveness of our proposed HA.

A variable neighborhood search for Open Vehicle Routing Problem

AbstractIn this study, the Open Vehicle Routing Problem, which the companies widely employ in practice using third‐party logistics services, is discussed. This problem is in the class of high‐dimensional and complex optimization problems. A three‐phase Variable Neighborhood Search Algorithm was proposed to solve large‐scale problems efficiently. In this proposed method, eight different neighborhoods are used, four within the route and four between the routes. In addition to this, wise shaking strategies are designed to escape local optimal solutions. The method's performance was tested on literature test instances and comparatively reported its competitiveness.

A School Bus Routing Heuristic Algorithm Allowing Heterogeneous Fleets and Bus Stop Selection

This paper addresses a school bus routing problem formulated as a capacitated and time-constrained open vehicle routing problem with a heterogeneous fleet and single loads. This problem incorporates several realistic features, such as student eligibility, maximum walking distances, bus stop selection, maximum riding times, different types of buses, multistops, and bus dwell times. A heuristic algorithm based on an iterated local search approach is proposed for this problem. It determines the selection of bus stops from a set of potential stops, the assignment of students to the selected bus stops, and the routes along the selected bus stops. The main objectives are minimizing the number of buses used, the total student walking distance, and the total route journey time. Other aims are balancing route journey times between buses and minimizing the total number of empty seats. A set of 20 real-world problem instances are used to evaluate the performance of the algorithm. Results indicate that the algorithm finds high-quality solutions in very short amounts of computational time.

A decision support system for consolidated distribution of a ceramic sanitary ware company

This study proposes a model-driven decision support system (DSS) for a consolidated distribution problem of a ceramic sanitary ware company with two factories producing different types of products. The primary motivation for consolidating loads of the these factories is to better use the empty capacity of the vehicles in terms of weight and volume. For transportation, the company has made contracts for each factory with a third-party logistics provider. In these contracts, there are special conditions according to the characteristics of the products. We suggest two kinds of transportation approaches that determine how to combine loads of two factories and how to solve the routing problem that arises to distribute the remaining loads. The DSS includes a mathematical model that determines full-truck loads and a hybrid genetic algorithm that solves the open vehicle routing problem. Both of these approaches have been developed specifically for the problem within the scope of the study. The mathematical model provides the best use of vehicles in terms of volume and weight. At the same time, the meta-heuristic approach offers the opportunity to address the complex cost structure of the emerging routing problem. The proposed DSS receives all order data from the enterprise resource planning system and constructs a dynamic distribution problem. Then it generates solution alternatives by using the models developed in this study and presents cost-effective and visualizable distribution plans.