Variant Of Vehicle Routing Problem Research Articles

A rich vehicle routing problem arising in the replenishment of automated teller machines

This paper introduces, models, and solves a rich vehicle routing problem (VRP) motivated by the case study of replenishment of automated teller machines (ATMs) in Turkey. In this practical problem, commodities can be taken from the depot, as well as from the branches to efficiently manage the inventory shortages at ATMs. This rich VRP variant concerns with the joint multiple depots, pickup and delivery, multi-trip, and homogeneous fixed vehicle fleet. We first mathematically formulate the problem as a mixed-integer linear programming model. We then apply a Geographic Information System (GIS)-based solution method, which uses a tabu search heuristic optimization method, to a real dataset of one of the major bank. Our numerical results show that we are able to obtain solutions within reasonable solution time for this new and challenging practical problem. The paper presents computational and managerial results by analyzing the trade-offs between various constraints.

A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network

Modern-day logistics companies require increasingly shorter lead-times in order to cater for the increasing popularity of on-demand services. There is consequently an urgent need for fast scheduling algorithms to provide high quality, real-time implementable solutions. In this work we model a spare part delivery problem for an on-demand logistics company, as a variant of vehicle routing problem. For large delivery networks, the optimisation solution technique of column generation has been employed successfully in a variety of vehicle routing settings and is often used in combination with exact methods for solving problems with a large number of variables. Challenges may arise when the pricing subproblem is difficult to solve in a realistic period due to complex constraints or a large number of variables. The problem may become intractable when the network structure varies daily or is known with less certainty over longer period. In such instances, a high quality heuristic solution may be more preferable than an exact solution with excessive run time. We propose an improved version of column generation approach integrating an efficient genetic algorithm to obtain fast and high-quality solutions for a sustainable spare parts delivery problem. More specifically, we propose to retain the traditional column generation iterative framework, with master problem solved exactly, but with pricing subproblem solved using a metaheuristic. Computational results on a real dataset indicate that this approach yields improved solutions compared to the current best-case business-as-usual costs. It also substantially decreases the computational time; allowing for high-quality, tractable solutions to be obtained in few minutes. We propose to strike a balance between the practical and efficient solution aspects of metaheuristic algorithms, and the exact decomposition and iterative aspect of the column generation solution technique.

A Comprehensive Study of Vehicle Routing Problem With Time Windows Using Ant Colony Optimization Techniques

Ant Colony Optimization (ACO) is a nature-inspired swarm intelligence technique and a metaheuristic approach which is inspired by the foraging behavior of the real ants, where ants release pheromones to find the best and shortest route from their nest to the food source. ACO is being applied to various optimization problems till date and has been giving good quality results in the field. One such popular problem is known as Vehicle Routing Problem(VRP). Among many variants of VRP, this paper presents a comprehensive survey on VRP with Time Window constraints(VRPTW). The survey is presented in a chronological order discussing which of the variants of ACO is used in each paper followed by the advantages and limitations of the same.

A large neighborhood search based matheuristic for the tourist cruises itinerary planning

The planning of itineraries for tourist cruises is a complex process where several features, such as vessel selection, port services, and requirements for point of interest to be inserted in each tour, must be addressed. The present work models the tour planning problem as a variant of vehicle routing problem considering specific constraints such as: fixed number of tours, not mandatory visits of all nodes, multiple time windows, possibility to choose among different travel speed values. The resulting mathematical formulation lead to a complex model for which commercial solvers fail to solve large instances in a reasonable time. To overcome this issue we propose a Large Neighborhood Search based matheuristic, in which an over-constrained version of the mathematical model is used to exhaustively and efficiently explore large neighborhoods. Test results performed on a real case instances demonstrate effectiveness of the proposed approach.

A Multi-Stage Algorithm for a Capacitated Vehicle Routing Problem with Time Constraints

The Vehicle Routing Problem (VRP) is one of the most optimized tasks studied and it is implemented in a huge variety of industrial applications. The objective is to design a set of minimum cost paths for each vehicle in order to serve a given set of customers. Our attention is focused on a variant of VRP, the capacitated vehicle routing problem when applied to natural gas distribution networks. Managing natural gas distribution networks includes facing a variety of decisions ranging from human resources and material resources to facilities, infrastructures, and carriers. Despite the numerous papers available on vehicle routing problem, there are only a few that study and analyze the problems occurring in capillary distribution operations such as those found in a metropolitan area. Therefore, this work introduces a new algorithm based on the Saving Algorithm heuristic approach which aims to solve a Capacitated Vehicle Routing Problem with time and distance constraints. This joint algorithm minimizes the transportation costs and maximizes the workload according to customer demand within the constraints of a time window. Results from a real case study in a natural gas distribution network demonstrates the effectiveness of the approach.

Solving Capacitated Closed Vehicle Routing Problem with Time Windows (CCVRPTW) using BRKGA with local search

The main issue in vehicle routing problem (VRP) is finding the shortest route of product distribution from the depot to outlets to minimize total cost of distribution. Capacitated Closed Vehicle Routing Problem with Time Windows (CCVRPTW) is one of the variants of VRP that accommodates vehicle capacity and distribution period. Since the main problem of CCVRPTW is considered a non-polynomial hard (NP-hard) problem, it requires an efficient and effective algorithm to solve the problem. This study was aimed to develop Biased Random Key Genetic Algorithm (BRKGA) that is combined with local search to solve the problem of CCVRPTW. The algorithm design was then coded by MATLAB. Using numerical test, optimum algorithm parameters were set and compared with the heuristic method and Standard BRKGA to solve a case study on soft drink distribution. Results showed that BRKGA combined with local search resulted in lower total distribution cost compared with the heuristic method. Moreover, the developed algorithm was found to be successful in increasing the performance of Standard BRKGA.

Penentuan Rute Kendaraan Heterogen Menggunakan Algoritma Insertion Heuristic

Distribution process at PT XYZ is identical to one of the variants of Vehicle Routing Problem (VRP) which there are several vehicles with different capacities and operation cost, called as Heterogeneous Fleet Vehicle Routing Problem (HFVRP). There are two kinds of vehicle that have been used, namely, vehicle As and vehicle Bs. Vehicle As has the higher operation cost than vehicle Bs. Currently, the company implements policies where vehicle Bs is used if all vehicle As have been used. This policy may lead the distribution cost is so expensive and its equal with the operation cost of vehicle As that also high. Routing aims to combine the use of available vehicles to generate minimal distribution costs in servicing the customers. This research uses mathematical model according to the description of the distribution system in the company. The model is verified and tested using the optimization software. Then the constraints of the verifiable mathematical model are used as the basis for developing Insertion Heuristic (IH) algorithm. Comparison of model solutions with developed algorithm solutions showed a difference of 11.6%. This implies that the algorithm generated solution is quite good as one of the heuristic approaches that result in optimal local solutions. Implementation of IH algorithm provides cost savings of 5.97% towards the actual distribution system in the company.

The Line-haul Feeder Vehicle Routing Problem: Mathematical model formulation and heuristic approaches

This paper deals with a rather new version of the well known Vehicle Routing Problem (VRP) called the Line-haul Feeder Vehicle Routing Problem (LFVRP). It can be described as a VRP with synchronization constraints where two types of customers are served by two types of vehicles. These vehicles, contrary to the regular VRP, can meet and perform a transshipment to extend their travel. To achieve that, the vehicles need to synchronise – meaning that they have to be at the same place at the same time. The objective of the LFVRP is to minimize the total cost consisting of fixed vehicle cost as well as variable fuel and wage costs for drivers. For this problem we propose the first general mathematical model and derive two heuristics inspired by some structural insights about the problem. Using a thorough and comprehensive computational analysis we show the benefits of the LFVRP over simpler VRP variants, the quality of the heuristics compared with earlier work on the LFVRP and the relative performance of the two heuristics described as a function of different problem characteristics.

Vehicle routing problem: recent literature review of its variants

The vehicle routing problem is the most studied combinatorial optimisation problem. The purpose of this study is to provide an overview of the research to date in vehicle routing problem variants. The literature is reviewed with a focus on research in three major variants of the vehicle routing problem, namely capacitated vehicle routing problem, mixed depot vehicle routing problem and vehicle routing problem with pickup and delivery. Journal articles from three academic databases, namely Taylor and Francis, Elsevier and Emerald, are selected and reviewed. Ample literature is available on this problem so to restrict the scope, we screened the journal articles using the above mentioned variants precisely, excluding those that are in combination with other variants. This review takes a closer look at 117 research articles selected from various journals. By presenting the past literature, we hope to motivate further research in the field.

Dynamic Vehicle Routing Problems with Enhanced Ant Colony Optimization

As we all know, there are a great number of optimization problems in the world. One of the relatively complicated and high-level problems is the vehicle routing problem (VRP). Dynamic vehicle routing problem (DVRP) is a major variant of VRP, and it is closer to real logistic scene. In DVRP, the customers’ demands appear with time, and the unserved customers’ points must be updated and rearranged while carrying out the programming paths. Owing to the complexity and significance of the problem, DVRP applications have grabbed the attention of researchers in the past two decades. In this paper, we have two main contributions to solving DVRP. Firstly, DVRP is solved with enhanced Ant Colony Optimization (E-ACO), which is the traditional Ant Colony Optimization (ACO) fusing improved K-means and crossover operation. K-means can divide the region with the most reasonable distance, while ACO using crossover is applied to extend search space and avoid falling into local optimum prematurely. Secondly, several new evaluation benchmarks are proposed, which can objectively and comprehensively estimate the proposed method. In the experiment, the results for different scale problems are compared to those of previously published papers. Experimental results show that the algorithm is feasible and efficient.

Capacitated vehicle routing problem with pick-up and alternative delivery (CVRPPAD): model and implementation using hybrid approach

The paper presents an optimization model and its implementation using a hybrid approach for the Capacitated Vehicle Routing Problem with Pick-up and Alternative Delivery (CVRPPAD). The development of the CVRPPAD was motivated by postal items distribution issues. The model proposed combines various features of Vehicle Routing Problem variants. The novelty of this model lies in the introduction of alternativeness of item delivery points, differentiation of delivery point types in terms of their capacity (parcel lockers) and possibility of collecting the items during the execution of delivery routes. The model also takes into account a possibility of introducing time windows related to delivery time. The proposed data structure in the form of sets of facts facilitates the implementation of the model in all environments, Constraint Logic Programming (CLP), Mathematical Programming (MP), metaheuristics, databases, etc. The model is implemented in the MP, hybrid CLP/MP, and hybrid CLP/heuristic environments. The hybrid CLP/MP approach is the authors’ original solution, which has already been used to solve Supply Chain Management problems, scheduling problems, routing problems etc. For large size problems, considering their combinatorial character, the proposed CLP/MP approach is ineffective. Its effectiveness will improve when MP is replaced by a heuristic. All implementations were performed on the same data instances (facts), which made it possible to compare them according to the solution search time, number of decision variables and number of constraints. The directions into which the model can be further developed were also presented.

Two-echelon vehicle routing problem with simultaneous pickup and delivery: Mathematical model and heuristic approach

Abstract The vehicle routing problem is one of the most important areas of logistics management. This study considers two-echelon vehicle routing problem with simultaneous pickup and delivery (2E-VRPSPD) which is a variant of vehicle routing problem. In the 2E-VRPSPD, the pickup and delivery activities are performed simultaneously by the same vehicles through depot to satellites in the first echelon and from satellites to customers in the second echelon. To solve the problem, firstly, a node-based mathematical model is proposed and three valid inequalities from the literature are adapted to strengthen the model. Because of the NP-hardness of the 2E-VRPSPD, secondly, a hybrid heuristic algorithm based on variable neighborhood descent (VND) and local search (LS), called VND_LS, is developed to solve medium- and large-size instances of the 2E-VRPSPD. We conduct an experimental study to investigate the effectiveness of the valid inequalities on the mathematical model and also to evaluate the effectiveness and efficiency of the VND_LS. Computational results show that valid inequalities have significant effect to strengthen the mathematical formulation. Furthermore, the VND_LS finds good solutions for the problem efficiently. Finally, we apply the VND_LS to compare single- and two-echelon distribution systems for a supermarket chain located in Turkey. The results indicate that the VND_LS can easily be applied for real-world problems.

Time Dependent Heterogeneous Vehicle Routing Problem for Catering Service Delivery Problem

The heterogeneous vehicle routing problem (HVRP) is a variant of vehicle routing problem (VRP) which describes various types of vehicles with different capacity to serve a set of customers with known geographical locations. This paper considers the optimal service deliveries of meals of a catering company located in Medan City, Indonesia. Due to the road condition as well as traffic, it is necessary for the company to use different type of vehicle to fulfill customers demand in time. The HVRP incorporates time dependency of travel times on the particular time of the day. The objective is to minimize the sum of the costs of travelling and elapsed time over the planning horizon. The problem can be modeled as a linear mixed integer program and we address a feasible neighbourhood search approach to solve the problem.

Vehicle Routing Problem using Swarm Optimization Techniques

Vehicle Routing Problem (VRP) is a major research domain in optimization research area. In VRP produced goods are supplied to customers on their demand from one or more depot. Goal of VRP is to attain a group of delivery path satisfying several necessities or constraints and give out minimal overall cost. After introducing a basic description in relevance of VRP, how the applications are applied in real-life is represented. Numerous variations of VRP exist there such as: time windows, pickup and delivery, stochastic VRP, periodic deliveries and heterogeneous fleet etc. Review contains the paper published in between 2008 to 2016, which shows how heuristic techniques are used in solving different variants of VRP.

A heuristic approach for solving a rich min-max vehicle routing problem with mixed fleet and mixed demand

We propose a new heuristic approach for a new variant of vehicle routing problem with a min-max objective function and mixed types of service demands satisfied by a mixed fleet. It is assumed that vehicles with unlimited service capacity, which have different service and transfer speeds, operate on demand points grouped in regions with boundaries that cannot be passed. The proposed solution method is based on swarm intelligence. A numerical study is carried out to evaluate the performance of the proposed method.

Hybrid metaheuristic to solve the “one-to-many-to-one” problem

PurposeThe purpose of this paper is to present a specific variant of vehicle routing problem with simultaneous full pickup and delivery problem (VRPSFPD) known as one-to-many-to-one (1-M-1) problem with several vehicles, where every customer can receive and send goods simultaneously, which has added the notion of the totality for the pickup goods. Currently, hybrid metaheuristics have become more popular because they offer the best solutions to several combinatorial optimization problems. Therefore, due to the complexity of 1-M-1 a hybrid genetic algorithm with variable neighborhood descent (HGAVND) local search is proposed. To improve the solution provided by the HGAVND the authors suggest applying a structure OR-Opt. To test the performance of the algorithm the authors have used a set of benchmarks from the literature and apply the HGAVND algorithm to solve the real case of distribution of soft drink in Tunisia. The experimental results indicate that the algorithm can outperform all other algorithms proposed in literature with regard to solution quality and processing time. Moreover, the authors improve the best known solution of the majority of benchmark instances taken from the literature.Design/methodology/approachDue to the complexity of 1-M-1 a HGAVND local search is proposed.Originality/valueFirst, in the presence of full pickup constraints, the problem becomes more complex, this implies that the choice of a good metaheuristic can provide good results. Second, the best contribution consists in a specific variant of VRPSFPD problem as 1-M-1 which the paper present the first application of metaheuristics to solve the specific 1-M-1 and to apply it in real case of distribution of soft drink.

Developing decision support system for heterogeneous fleet vehicle routing problem using hybrid heuristic

In the last few years, there has been an increasing use of optimisation packages and decision support system (DSS) for the efficient and effective decision making. A significant variant of vehicle routing problem (VRP) arises when a fleet of vehicles is characterised by different capacities and operating costs, which is known as heterogeneous fleet vehicle routing problem (HFVRP). This paper aims at determining the optimal composition of fleet and set of routes to be accomplished by a fleet of heterogeneous vehicles for serving a given set of customers with the objective of minimising the total routing cost. To obtain the solution of the HFVRP, implementations of construction heuristics (savings algorithm, nearest neighbour, branch and cut) and improvement heuristics (2-opt and or-opt) are achieved through development of a DSS which eventually leads to proposal of a hybrid heuristic for route optimisation.

Developing decision support system for heterogeneous fleet vehicle routing problem using hybrid heuristic

In the last few years, there has been an increasing use of optimisation packages and decision support system (DSS) for the efficient and effective decision making. A significant variant of vehicle routing problem (VRP) arises when a fleet of vehicles is characterised by different capacities and operating costs, which is known as heterogeneous fleet vehicle routing problem (HFVRP). This paper aims at determining the optimal composition of fleet and set of routes to be accomplished by a fleet of heterogeneous vehicles for serving a given set of customers with the objective of minimising the total routing cost. To obtain the solution of the HFVRP, implementations of construction heuristics (savings algorithm, nearest neighbour, branch and cut) and improvement heuristics (2-opt and or-opt) are achieved through development of a DSS which eventually leads to proposal of a hybrid heuristic for route optimisation.

A Brief Note on Bus Based Evacuation Planning

Evacuation route planning is essential for emergency preparedness, especially in regions threatened by hurricanes, earthquake etc. The evacuees who do not travel on their own are gathered at few collection points, where they are brought on buses to take them to safe region in bus based evacuation. Bus based evacuation planning is more essential for developing country like Nepal. It is necessary to reach the destination as early as possible for bus based evacuation. The bus based evacuation planning (BEP) problem is a variant of vehicle routing problem that arises in emergency planning. The problem in this variant is that not all the evacuees can gather at the same time, elderly and handicaps may need special help like wheelchair and may need more pickup time. Another problem is that at the shelter, some people may need special care like medicine, oxygen etc. In this paper we review a mathematical model to minimize the duration of evacuation together with the dynamic programming and the branch and bound as solution procedures. Moreover, a brief report of a case study for Kathmandu has also been given.Journal of Institute of Science and TechnologyVolume 21, Issue 1, August 2016, page: 28-34

Quantum Inspired Algorithm for a VRP with Heterogeneous Fleet Mixed Backhauls and Time Windows

This paper presents a new Quantum Inspired Harmony Search algorithm with Variable Population Size QIHSVPS for a complex variant of vehicle routing problem (VRP), called HVRPMBTW (Vehicle Routing Problem with Heterogeneous fleet, Mixed Backhauls and Time Windows). This variant is characterized by a limited number of vehicles with various capacities and costs. The vehicles serve two types of customers: linehauls customers and backhauls customers. Each customer must be visited in a specific interval of time. The authors propose to use quantum principles to accelerate evolution process and variable population size to decrease the number of solution's evaluation, when the improvement is insignificant. This new approach was tested on benchmarks and produces satisfactory results compared to other approaches.