Open Vehicle Routing Problem Research Articles

An improved tabu search algorithm for solving heterogeneous fixed fleet open vehicle routing problem with time windows

The heterogeneous fixed fleet open vehicle routing problem with time windows is a very significant type of the vehicle routing problem (VRP) that aims to find the minimum fixed and variable cost of transportation for a heterogeneous fleet with a fixed number in which the capacity of every vehicle and usage of the vehicles should not be ignored. Also, in this problem, each customer has a special time window for servicing and each vehicle starts its route from the warehouse and ends up in one of the customers. We propose a mixed integer linear programming model of this problem. Since this problem, as well as open VRP and VRP with fixed heterogeneous fleet are hard NP problem, an improved tabu search algorithm is proposed to solve the problem. Our proposed algorithm uses a modified sweep algorithm to generate some initial solutions. Besides, a variable tabu list and some new mechanisms for intensification and diversification mechanisms are used. Numerical results are presented to show the correctness of our model and finally, the efficiency of the proposed algorithm is compared with an exact algorithm, classic tabu search and simulated annealing. The obtained results prove the efficiency of the proposed algorithm.

The Multi-Depot Traveling Purchaser Problem with Shared Resources

Using shared resources has created better opportunities in the field of sustainable logistics and procurement. The Multi-Depot Traveling Purchaser Problem under Shared Resources (MDTPPSR) is a new variant of the Traveling Purchaser Problem (TPP) in sustainable inbound logistics. In this problem, each depot can purchase its products using the shared resources of other depots, and vehicles do not have to return to their starting depots. The routing of this problem is a Multi-Trip, Open Vehicle Routing Problem. A tailored integer programming model is formulated to minimize the total purchasers’ costs. Considering the complexity of the model, we have presented a decomposition-based algorithm that breaks down the problem into two phases. In the first phase, tactical decisions regarding supplier selection and the type of collaboration are made. In the second phase, the sequence of visiting is determined. To amend the decisions made in these phases, two heuristic algorithms based on the removing and insertion of operators are also proposed. The experimental results show that not only can purchasing under shared resources reduce the total cost by up to 29.11%, but it also decreases the number of dispatched vehicles in most instances.

Intelligent evacuation model in disaster mitigation

Mitigation is a pre-disaster action that aims to prepare for a disaster situation. One of the mitigation activities is evacuation, which aims to reduce disaster-related losses. Because disaster damage cannot be predicted, dealing with evacuation efforts requires a dynamic model. This study will utilize a dynamic model that combines the game theory model for choosing the evacuation location and the open vehicle routing problem (OVRP) model for selecting evacuation routes to create an intelligent system. The game theory model will be used to supplement the selection of alternate evacuation locations by taking into account geographical features that are very uncertain in the event of a crisis. An evacuation route equipped with an OVRP model with the goal of optimizing travel time is required to mobilize disaster-affected people. With the development of the Intelligent Evacuation System idea, combining the two models will create a new model. The simulation model is evaluated using linear, interactive, and discrete optimizer (LINDO), which may minimize the evacuation time from the evacuation route to a safe destination by 50 minutes if there is no contra-flow to allow more people to be evacuated.

The Two Echelon Open Vehicle Routing Problem: Optimization of Crowdshipping Based Parcel Delivery

Crowdshipping is a revolutionary concept of the sharing economy. In this study, two carriers are used to perform the following expedition: the truck starts from the depot to complete part of the deliveries, shares part of the load with the crowdshipper at the relay point, and the private driver selected as the crowdshipper continues from that point onward. This study proposes the two-echelon open vehicle routing problem with crowdshipping (2EOVRP-CS) and formulates a mathematical model to determine the crowdshipper, parcel relay location, truck route, and crowdsource route. A tangible nested genetic algorithm (NGA) is proposed, and its efficiency is demonstrated by comparison with CPLEX and genetic algorithm (GA). A real case study is investigated in Xi'an city to test the applicability of the proposed model. The results show that using crowdshipping instead of truck delivery alone can save approximately 14% of the total cost and 26% of truck vehicle miles traveled (VMT). Moreover, several sensitivity analyses are performed. The results show that crowdshipping is sensitive to the detour limit and the time value of carriers. For the detour limit, after the acceptable detour distance increases by 8%, the total cost can be reduced by up to 5.94%.

Solving open vehicle problem with time window by hybrid column generation algorithm

This paper addresses the open vehicle routing problem with time window (OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task. The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process. We propose a hybrid column generation algorithm (HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time.

Solving the Green Open Vehicle Routing Problem Using a Membrane-Inspired Hybrid Algorithm

The green open vehicle routing problem with time windows has been widely studied to plan routes with minimal emissions in third-party logistics. Due to the NP-hardness, the performance of the general heuristics significantly degrades when dealing with large-scale instances. In this paper, we propose a membrane-inspired hybrid algorithm to solve the problem. The proposed algorithm has a three-level structure of cell-like nested membranes, where tabu search, genetic operators, and neighbourhood search are incorporated. In particular, the elementary membranes (level-3) provide extra attractors to the tabu search in their adjacent level-2 membranes. The genetic algorithm in the skin membrane (level-1) is designed to retain the desirable gene segments of tentative solutions, especially using its crossover operator. The tabu search in the level-2 membranes helps the genetic algorithm circumvent the local optimum. Two sets of real-life instances, one of a Chinese logistics company, Jingdong, and the other of Beijing city, are tested to evaluate our method. The experimental results reveal that the proposed algorithm is considerably superior to the baselines for solving the large-scale green open vehicle routing problem with time windows.

Penerapan Seagulls Optimization Algorithm untuk Menyelesaikan Open Vehicle Routing Problem

This paper aims to solve the problem of Open Vehicle Routing Problem using Seagulls Optimization Algorithm. Open Vehicle Routing Problem (OVRP) is a variation of Vehicle Routing Problem (VRP) which will not return to the depot after visiting the last customer, is different from VRP which requires the vehicle to return to the depot because the company have insufficient number of vehicles for the distribution of products to customers so they must to rent vehicles and this OVRP aims to minimize the total cost of distributing products with the shortest optimal distance to meet the demands of each customer with private vehicles and rental vehicles. Seagulls Optimization Algorithm (SOA) is the algorithm inspired by the behaviour of seagulls in migrating and ways of attacking the pray of seagulls in nature. In general, the process begins with generating the initial position, evaluating the objective function, the migration process, the attacking process to get a new position, compare the objective function for the new position and the old position, update the position and save the best seagulls in each iteration until the maximum iteration is met. The program used to complete OVRP with Seagulls Optimization Algorithm is Borland C++ and implemented using 3 case examples, small data with 18 customers, medium data 50 customers and large data 100 customers. Based on the implementation results, it can be concluded that the higher number of seagulls, iterations and the smaller the control variable value tend to effect minimum cost gained.

A hybrid multi-objective evolutionary algorithm for open vehicle routing problem through cluster primary-route secondary approach

ABSTRACT This study focuses on the cluster primary-route secondary approach to solve the multi-objective open green vehicle routing problem under a sustainable environment. An open green vehicle routing problem involves distributing products or services from a single depot to several customers placed at different geographical locations using third-party logistics to reduce pollution. The proposed model considers two conflicting realistic objectives: minimizing the operating costs and minimizing the carbon emission due to fuel consumption by the service vehicles. Unlike existing multi-objective problems, this multi-objective model chooses an optimal route based on the decision maker’s choice from the set of alternative solutions. Initially, it clusters all the customers by applying a modified -means algorithm. Each cluster is served by one vehicle only. Then a multi-objective evolutionary algorithm is employed to search for the best subroute to cover all the customers belonging to a cluster. We employ the extended Strength Pareto Evolutionary Algorithm (SPEA2) and Non-dominated Sorting-based Genetic Algorithm (NSGA-II) separately to obtain different approximate fronts. The VIKOR method is used to identify the decision maker’s choice-based solution for each cluster. In the next step, all the compromise solutions are combined to produce the final result of the proposed problem. Some statistical analyses are performed to compare the performance of SPEA2 and NSGA-II. SPEA2 has shown better results compared to NSGA-II.

A Hybrid Intelligent Algorithm for the Fuzzy Open Vehicle Routing Problem with Risk Preference

A Hybrid Intelligent Algorithm for the Fuzzy Open Vehicle Routing Problem with Risk Preference

Design an Effective Blood Distribution Network with Minimal Impacts on the Environment and Blood Supply Assurance

As the world's population grows, resulting in the aggravating trend of aging population, it brings with it an increase in the demand for blood. Nowadays, in most cities, the blood distribution network is based on a single distribution centre pattern, with the blood centre acting as distribution centre for one-to-one distribution. However, despite its convenience, this pattern has a high frequency of delivery, increased risk of blood shortage, and generates high carbon emissions. This paper aims to understand the real-life problems of the current blood distribution network and to design a more rational blood distribution network by taking the characteristics of the blood supply chain into account. Two blood distribution network patterns are considered, the current single distribution centre pattern and the proposed multiple distribution centre pattern. In order to minimise environmental impacts, we introduce open vehicle routing problem for blood delivery routes planning, using mixed integer programming for modelling, to compare the carbon emissions between the two blood distribution network patterns. Numerical experimental results demonstrate that applying the proposed BDN can reduce carbon emissions by an average of 25.84% and up to 29.59%, and the delivery time in emergency situations is significantly reduced by an average of 33.15%. Such studies are essential for both reducing carbon emissions and safeguarding patients' lives.

A Coordination-Based Algorithm for Dedicated Destination Vehicle Routing in B2B E-Commerce

This paper proposes a solution to the open vehicle routing problem with time windows (OVRPTW) considering third-party logistics (3PL). For the typical OVRPTW problem, most researchers consider time windows, capacity, routing limitations, vehicle destination, etc. Most researchers who previously investigated this problem assumed the vehicle would not return to the depot, but did not consider its final destination. However, by considering 3PL in the B2B e-commerce, the vehicle is required back to the nearest 3PL location with available space. This paper formulates the problem as a mixed integer linear programming (MILP) model with the objective of minimizing the total travel distance. A coordinate representation particle swarm optimization (CRPSO) algorithm is developed to obtain the best delivery sequencing and the capacity of each vehicle. Results of the computational study show that the proposed method provides solution within a reasonable amount of time. Finally, the result compared to PSO also indicates that the CRPSO is effective.

Learning Whale Optimization Algorithm for Open Vehicle Routing Problem with Loading Constraints

This paper addresses the two-dimensional loading open vehicle routing problem with time window (2L-OVRPTW). We propose a learning whale optimization algorithm (LWOA) to minimize the total distance; an improved skyline filling algorithm (ISFA) is designed to solve the two-dimensional loading problem. In LWOA, the whale optimization algorithm is used to search the solution space and get the high-quality solution. Then, by learning and accumulating the block structure and customer location information in the high-quality solution individuals, a three-dimensional matrix is designed to guide the updating of the population. Finally, according to the problem characteristics, the local search method based on fleet and vehicle is designed and performed on the high-quality solution region. IFSA is used to optimize the optimal individual. The computational results show that the proposed algorithm can effectively solve 2L-OVRPTW.

A multi-objective open vehicle routing problem with overbooking: Exact and heuristic solution approaches for an employee transportation problem

We address a bus routing problem, where individuals need to be gathered from spatially distributed pickup points and transported to a workplace. The problem is modeled as an open vehicle routing problem. Overbooking is allowed because the total seat capacity of the buses is limited. The problem is treated as a multi-objective optimization problem, where the total distance traveled by the buses and the total number of straphangers in the buses are minimized. We develop a mixed integer programming (MIP) model and employ the ε-constraint method to generate the Pareto-optimal frontier. Due to the high computational requirements of the exact model, two heuristic approaches are developed: a heuristic algorithm that is based on a cluster-first and route-second algorithm and a multi-objective evolutionary algorithm. We also develop another MIP model that provides an alternative bound to evaluate the quality of the heuristics’ solutions. Experiments on a small and a moderate-sized problem show that the heuristics are fast and approximate the optimal solutions well. The heuristic approaches are then used to solve the actual problem having 103 pickup points, 50 buses and 1986 individuals. A set of approximate solutions whose total transportation distances are at most 14% worse than the best lower bounds are obtained.

Integrating vehicle scheduling and open routing decisions in a cross-docking center with multiple docks

Society is facing a series of challenges, as the growth in urban population, the expansion of e-commerce, the pandemic moment (COVID-19), and many others leading to changes in companies’ supply chain, like reducing product delivery time and attention to consumer welfare, the environmental impact, to mention a few. The efficient management of logistic solutions such as cross-docking can contribute to improving the supply chain performance. Here we focus on the integration of scheduling of trucks and routing decisions; the integration of these strategies can significantly reduce costs and help organize the distribution centers and the customers’ services. This article analyzes the integrated problem in which trucks’ scheduling in a cross-docking center with multiple docks is combined with the associated open vehicle routing problem, called Open Vehicle Routing Problem With Cross-Docking (OVRPCD). This approach aims to minimize penalties caused by delays in servicing customers. First, a mixed-integer linear programming model is proposed to solve small instances optimally. Next, two heuristics are proposed to contribute to the solution of the two problems in an integrated way. These heuristics are: the Vehicle Routing Cross-Docking Heuristic (VRCDH) and the Cross-Docking Vehicle Routing Heuristic (CDVRH), each focusing on one of the problems. We also propose a Prioritization Lagrangian Heuristic (PLH) based on a model decomposition to improve the solutions found. These three heuristics are compared, considering two search approaches (i) a constructive version (HC) using the swap heuristic; and (ii) a version using the Variable Neighborhood Search (VNS) metaheuristic framework. The VNS-enhanced versions of the heuristics outperform the previous ones. Still, the same relation holds regarding the three heuristics, i.e.: the PLH heuristic outperforms the VRCDH one, while the latter outperforms the CDVRH one. Finally, we propose a polynomial-time framework, called Robust Dynamic Prioritization Lagrangian Heuristic (RDPLH), which extends PLH, considering trucks’ release dates and travel times uncertainties, approximating our problem to a real cross-docking center. The framework’s simplicity and the quality of the results allow us to assert that this approach can be used in real cross-docking centers (CDCs).

Research on Open Vehicle Routing Problem based on Multi-Objective and Multi-Constrain Genetic Algorithm

With the advancement of urbanization, the efficiency of take-out delivery has become one of the focuses of urban residents. In order to comprehensively meet the needs of both the delivery system managers and the delivery customers, this paper designs a multiobjective and constraint linear programming model that takes into account the minimum total cost of delivery and is based on the timeliness of delivery to maximize customer satisfaction. At the same time, considering the high concurrency of customers’ demands for delivery, this paper designs a set of multi-objective multi-constraint genetic algorithm based on the control evolution direction, which is carried out in feasible domain first, and then to the optimal solution, and elaborates the constraint control evolution operator and sorting operator. Finally, an example is given to verify the feasibility and effectiveness of the algorithm, and a new efficient solution method is proposed for the delivery route planning problem.

Open Vehicle Routing Problem with Robust Optimization Approach

Open Vehicle Routing Problem with Robust Optimization Approach

Robust counterpart open capacitated vehicle routing problem with hard time windows on waste transportation problems

Based on the problem of garbage transportation routes, this research proposes a robust counterpart open capacitated vehicle routing problems (RCOCVRP) with hard time windows. Garbage transport vehicles sometimes have delays. This model is a generalization of robust capacitated vehicle routing problems, robust open vehicle routing problems and robust vehicle routing problems with time windows. RCOCVRP with hard time windows for waste transportation is a robust optimization problem that aims to minimize the distance of vehicle routes for waste volume and time windows with uncertainty The mathematical formulation built is completed using a sequential insertion algorithm. Then, the waste transportation route obtained is completed using LINGO software. The results of this model on the problem of transporting garbage show that the total distance using the sequential insertion algorithm is more minimum than using LINGO.

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 light-robust-optimization model and an effective memetic algorithm for an open vehicle routing problem under uncertain travel times

This paper addresses an open vehicle routing problem with predetermined time windows under uncertain travel times (OVRP-UT). A novel light-robust-optimization model is proposed by integrating the goal programming formulations with set-based descriptions of the problem data, which can enable as many customers as possible to meet their demands within a group of predetermined time windows. An effective memetic algorithm (MA) is presented for solving the OVRP-UT model. We design a heuristic-based initialization mechanism to generate an initial population with a high level of quality and diversity. We design a timely-vertices based crossover operator and mutation operator to give birth to the offspring with high quality and good structure built in the search process. We provide a hybrid selection mechanism and a population updating strategy to remain the diversity of the population. We develop a self-adapted crossover and mutation rate to help the MA suit the different phases during the search process. A comprehensive simulation experiment based on the 320 benchmark instances demonstrates the effectiveness of the proposed algorithm.

An Adaptive Genetic Algorithm for a New Variant of the Gas Cylinders Open Split Delivery and Pickup with Two-dimensional Loading Constraints

This paper studies a combination of two well-known problems in distribution logistics, which are the truck loading problem and the vehicle routing problem. In our context, a customer daily demand exceeds the truck capacity. As a result, the demand has to be split into several routes. In addition, it is required to assign customers to depots, which means that each customer is visited just once by any truck in the fleet. Moreover, we take into consideration a customer time windows. The studied problem can be defined as a Multi-depots open split delivery and pickup vehicle routing problem with two-dimensional loading constraints and time windows (2L-MD-OSPDTW). A mathemat-ical formulation of the problem is proposed as a mixed-integer linear programming model. Then, a set of four class instances is used in a way that reflects the real-life case study. Furthermore, a genetic algorithm is proposed to solve a large scale dataset. Finally, preliminary results are reported and show that the MILP performs very well for small test instances while the genetic algorithm can be efficiently used to solve the problem for a wide-reaching test instances.