Method For Vehicle Routing Problem Research Articles

An exact method for vehicle routing problem with backhaul discounts in urban express delivery network

The surge in e-commerce has led to an increased demand for urban express services, requiring the strategic development of delivery networks that are both efficient and cost-effective. This study addresses a practical vehicle routing problem (VRP) in an urban express delivery network to minimize transportation costs. Specifically, it considers the implementation of backhaul discounts, a factor disregarded in the existing literature. This VRP is further complicated by various realistic constraints, including pickup and delivery, time windows, multiple trips, heterogeneous fleets, and docking capacity limitations, which make most general VRP solvers inapplicable. This study proposes a trip-based formulation to overcome this challenge and develop a tailored branch-and-price algorithm. Feasible trips are classified into four types to simplify the computation of backhaul discounts, thereby enhancing solution efficiency. Validation with real-world data from SF Express substantiates the efficacy of our method and yields insights for sustainable city logistics management. Moreover, our simplified column generation algorithm exhibits competitive performance, achieving optimal solutions expeditiously for the tested instances.

Penentuan Rute Optimal dan Alokasi Kendaraan dalam Pendistribusian Beras di Provinsi Kalimantan Tengah

Central Kalimantan is one of the most populous provinces in Indonesia where rice has the greatest impact on the calculation of the poverty line. As of August 2022, 0.44 percent of the inflation in Palangkaraya is attributable to rice, which can contribute to an increase in the MPP rate. Margin perdagangan dan pengangkutan (MPP) is a percentage that indicates the increase in rice prices from producers to final consumers, taking into account transportation costs. The objective of this paper is to determine the optimal route and allocation of each vehicle fleet serving each depot, given several constraints using an open-source spreadsheet solver. Vehicle Routing Problem (VRP) is one of the most frequently encountered optimization problems in logistics. In this instance, we optimize the paddy distribution fleet in Central Kalimantan using the VRP method. The results of the data processing led to the choice of the best route that could adhere to all the limitations, demonstrating that the VRP spreadsheet solver could be used for the case in this study. Based on the computations of 13 vehicles, hereinafter referred to as vehicle (V) which serves 100 Customers, it is obtained that V1 will serve 4 Customers, V2 will serve 5 Customers, V3 will serve 26 Customers, V4 will serve 5 Customers, V5 will serve 5 Customers, V6 will serve 6 Customers, V7 serves 3 Customers, V8 serves 4 Customers, V9 serves 14 Customers, V10 serves 5 Customers, V11 serves 3 Customers, V12 serves 11 Customers, and V13 serves 9 Customers.

A Brief Survey on Learning Based Methods for Vehicle Routing Problems

Vehicle Routing Problem (VRP) is a kind of combinatorial optimization problem with extensive application scenarios. At present, many methods for solving VRPs have been proposed, which can be divided into exact methods and heuristic methods. However, due to the complexity of VRPs, exact methods are limited extending to large scale VRPs, and heuristic methods usually needs manually tuning parameters. In recent years, with the development of machine learning and deep learning, many researchers have been successfully applied Learning Based Methods (LBM) to solve VRPs. In this paper, we give a brief review of LBMs for VRPs, including end-to-end approaches and iterative improvement approaches. Then through experimental results, we analyzed the advantages and disadvantages of the two types of approaches. Finally, we summarize the characteristics of them, and look forward to the future research directions.

Model Rute Perjalanan Minimal Dengan Menggunakan Vehicle Routing Problem Pada PT X

In this research discusses the problem of distributing goods from a depot to several customer locations to minimize travel costs. The problem is solved by using the Vehicle Routing Problem method. The data used in this research is secondary data with 10 customers and one depot. The aim of this research is to form a model from the data that has been obtained and determine the optimal travel route based on the model that has been formed. The first step is to form the objective function of the problem, then form the constraints that may occur. After the model is formed, a solution to the problem is sought. The total cost of the trip is obtained by multiplying the cost of the trip per kilometer by the total distance traveled.

Workload Equity in Vehicle Routing Problems: A Survey and Analysis

Over the past two decades, equity aspects have been considered in a growing number of models and methods for vehicle routing problems (VRPs). Equity concerns most often relate to fairly allocating workloads and to balancing the utilization of resources, and many practical applications have been reported in the literature. However, there has been only limited discussion about how workload equity should be modeled in the context of VRPs, and various measures for optimizing such objectives have been proposed and implemented without a critical evaluation of their respective merits and consequences. This article addresses this gap by providing an analysis of classical and alternative equity functions for biobjective VRP models. In our survey, we review and categorize the existing literature on equitable VRPs. In the analysis, we identify a set of axiomatic properties that an ideal equity measure should satisfy, collect six common measures of equity, and point out important connections between their properties and the properties of the resulting Pareto-optimal solutions. To gauge the extent of these implications, we also conduct a numerical study on small biobjective VRP instances solvable to optimality. Our study reveals two undesirable consequences when optimizing equity with nonmonotonic functions: Pareto-optimal solutions can consist of non-TSP-optimal tours, and even if all tours are TSP optimal, Pareto-optimal solutions can be workload inconsistent, i.e., composed of tours whose workloads are all equal to or longer than those of other Pareto-optimal solutions. We show that the extent of these phenomena should not be underestimated. The results of our biobjective analysis remain valid also for weighted sum, constraint-based, or single-objective models. Based on this analysis, we conclude that monotonic equity functions are more appropriate for certain types of VRP models, and suggest promising avenues for further research on equity in logistics. The data are available at https://doi.org/10.1287/trsc.2017.0744 .

Vehicle Routing Problem Dengan Time Window Untuk Multiple Product Dan Multiple Route Menggunakan Algoritma Sequential Insertion

In this article is related to implemented a method in order to solve vehicle routing problem (VRP) for mineral water distribution activity from depot to customers which has particular constraints such as variant of product that has to be load at a vehicle (multiple product), vehicle’s capacity and time window of each customers. One of the company’s vehicle have 64 customers that must be visited in 6 days so there is a few route has been developed (multiple route). The constraint that set up sustained there might be few customers missed to be visited during horizon plan. Sequential Insertion Algorithm which is one of heuristic method for Vehicle Routing Problem with Time Window is choosen to built a vehicle routing solution. The result from this study show that this method in particular conditions and constraints give a better solusion than the company actual routes. Moreover, in this article, the comparasion of distance and completion time between solution route and actual route, and also the saving in cost is also analyzed. The using of Sequential Insertion Algorithm in this article can be used and applied because its perform an optimal result.

Research on Path Optimization with PSO for Unmanned Vehicle

The development of autonomous unmanned vehicles is of high interest to many organizations around the world and path planning is the key point of the navigation for the autonomous unmanned vehicle. Intelligent algorithms have been applied in this field and an essential aspect of unmanned vehicles autonomy is the ability for automatic path planning. In this paper, particle swarm optimization algorithm as one of new swarm intelligent optimization methods is introduced into a path planning for autonomous vehicle, which is constructed of a particle representation methods for vehicle routing problem with fast convergence speed. The results show that the particle swarm optimization algorithm can obtain the solution of the vehicle routing problem quickly and effectively. It is a good method for solving the vehicle routing problem.

A two-stage heuristic method for vehicle routing problem with split deliveries and pickups

The vehicle routing problem (VRP) is a well-known combinatorial optimization issue in transportation and logistics network systems. There exist several limitations associated with the traditional VRP. Releasing the restricted conditions of traditional VRP has become a research focus in the past few decades. The vehicle routing problem with split deliveries and pickups (VRPSPDP) is particularly proposed to release the constraints on the visiting times per customer and vehicle capacity, that is, to allow the deliveries and pickups for each customer to be simultaneously split more than once. Few studies have focused on the VRPSPDP problem. In this paper we propose a two-stage heuristic method integrating the initial heuristic algorithm and hybrid heuristic algorithm to study the VRPSPDP problem. To validate the proposed algorithm, Solomon benchmark datasets and extended Solomon benchmark datasets were modified to compare with three other popular algorithms. A total of 18 datasets were used to evaluate the effectiveness of the proposed method. The computational results indicated that the proposed algorithm is superior to these three algorithms for VRPSPDP in terms of total travel cost and average loading rate.

Order-first split-second methods for vehicle routing problems: A review

Cluster-first route-second methods like the sweep heuristic (Gillett and Miller, 1974) are well known in vehicle routing. They determine clusters of customers compatible with vehicle capacity and solve a traveling salesman problem for each cluster. The opposite approach, called route-first cluster-second, builds a giant tour covering all customers and splits it into feasible trips. Cited as a curiosity for a long time but lacking numerical evaluation, this technique has nevertheless led to successful metaheuristics for various vehicle routing problems in the last decade. As many implementations consider an ordering of customers instead of building a giant tour, we propose in this paper the more general name of ordering-first split-second methods. This article shows how this approach can be declined for different vehicle routing problems and reviews the associated literature, with more than 70 references.

時間枠と不確実性を同時に考慮した配送計画問題のモデル化と解法(<小特集>生産システム部門研究発表講演会2010)

時間枠と不確実性を同時に考慮した配送計画問題のモデル化と解法(<小特集>生産システム部門研究発表講演会2010)

A novel two-phase heuristic method for vehicle routing problem with backhauls

This paper raises a novel two-phase heuristic method to solve vehicle routing problems with backhauls. Differing from other vehicle routing problems, we consider the travel speed of vehicle to be time dependent, which will be used for the model of rush hour in an urban city. In the first phase, the original solution is generated by extending traditional heuristic methods and in the second phase, the reactive tabu search algorithm is used to optimize the original solution. We verified that this algorithm is efficient in a number of standard test cases. After comparison with the closest neighboring search algorithm, we found that the results of two-phase heuristic methods are more reasonable.

Particle Swarm Optimization for Vehicle Routing Problem with Time Windows

The investigation of the performance of the Particle Swarm Optimization (PSO) method for Vehicle Routing Problem with Time Windows is the main theme of the paper. “Exchange minus operator” is constructed to compute particle’s velocity. We use Saving algorithm, Nearest Neighbor algorithm, and Solomon insertion heuristics for parameter initialization and apply the “Routing first and Cluster second” strategy for solution generation. By PSO, customers are sorted in an ordered sequence for vehicle assignment and Nearest Neighbor algorithm is used to optimize every vehicle route. In our experiments, two different PSO algorithms (global and local), and three construct algorithms are investigated for omparison. Computational results show that global PSO algorithm with Solomon insertion heuristics is more efficiency than the others.

Heuristic methods for vehicle routing problem with time windows

This paper documents our investigation into various heuristic methods to solve the vehicle routing problem with time windows (VRPTW) to near optimal solutions. The objective of the VRPTW is to serve a number of customers within predefined time windows at minimum cost (in terms of distance travelled), without violating the capacity and total trip time constraints for each vehicle. Combinatorial optimisation problems of this kind are non-polynomial-hard (NP-hard) and are best solved by heuristics. The heuristics we are exploring here are mainly third-generation artificial intelligent (AI) algorithms, namely simulated annealing (SA), Tabu search (TS) and genetic algorithm (GA). Based on the original SA theory proposed by Kirkpatrick and the work by Thangiah, we update the cooling scheme and develop a fast and efficient SA heuristic. One of the variants of Glover's TS, strict Tabu, is evaluated and first used for VRPTW, with the help of both recency and frequency measures. Our GA implementation, unlike Thangiah's genetic sectoring heuristic, uses intuitive integer string representation and incorporates several new crossover operations and other advanced techniques such as hybrid hill-climbing and adaptive mutation scheme. We applied each of the heuristics developed to Solomon's 56 VRPTW 100-customer instances, and yielded 18 solutions better than or equivalent to the best solution ever published for these problems. This paper is also among the first to document the implementation of all the three advanced AI methods for VRPTW, together with their comprehensive results.

Vehicle Routing with Time Windows: Two Optimization Algorithms

We describe two optimization methods for vehicle routing problems with time windows. These are a K-Tree relaxation with time windows added as side constraints and a Lagrangian decomposition in which variable splitting is used to divide the problem into two subproblems—a semi-assignment problem and a series of shortest path problems with time windows and capacity constraints. We present optimal solutions to problems with up to 100 customers.

Parallel iterative search methods for vehicle routing problems

AbstractThis paper presents two partition methods that speed up iterative search methods applied to vehicle routing problems including a large number of vehicles. Indeed, using a simple implementation of taboo search as an iterative search method, every best‐known solution to classical problems was found. The first partition method (based on a partition into polar regions) is appropriate for Euclidean problems whose cities are regularly distributed around a central depot. The second partition method is suitable for any problem and is based on the arborescence built from the shortest paths from any city to the depot. Finally, solutions that are believed to be optimum are given for problems generated on a grid. © 1993 by John Wiley & Sons, Inc.