A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows

This study describes a pickup and delivery vehicle routing problem, considering time windows in reality. The problem of tractor truck routes is formulated by a mixed integer programming model. Besides this, three algorithms - a guided local search, a tabu search, and simulated annealing - are proposed as solutions. The aims of our study are to optimize the number of internal tractor trucks used, and create optimal routes in order to minimize total logistics costs, including the fixed and variable costs of an internal vehicle group and the renting cost of external vehicles. Besides, our study also evaluates both the quality of solutions and the time to find optimal solutions to select the best suitable algorithm for the real problem mentioned above. A novel mathematical model is formulated by OR tools for Python. Compared to the current solution, our results reduced total costs by 18%, increased the proportion of orders completed by internal vehicles (84%), and the proportion of orders delivered on time (100%). Our study provides a mathematical model with time constraints and large job volumes for a complex distribution network in reality. The proposed mathematical model provides effective solutions for making decisions at logistics companies. Furthermore, our study emphasizes that simulated annealing is a more suitable algorithm than the two others for this vehicle routing problem.

Vehicle routing problem with fuzzy time windows

A dynamic vehicle routing problem with time-dependent travel times

Over the last years, various extensions of Vehicle Routing Problem, Considering the pick -up and delivery vehicle routing problem have been studied. In this work we tackle the pickup and delivery of passengers namely Demand Responsive Transport. Its idea is to group a number of passengers in the same vehicle with the respecting of the capacity of each vehicle in order to reduce operational cost with the maintaining of high quality service. This paper presents an exact Method which is the Branch and Bound Algorithm to solve a new variant of Demand Responsive Transport which is based on service quality but not as an option it is an obligation. The Multi-Vehicle Demand Responsive Transport Problem Based on Service Quality in the case of Oneto-One(MVSDRTPBSQ1-t-1) under study presented the following characteristics: Users specify transportation requests between origins and destinations, each customer have his own origin and own destination, they may provide a time window on their desired departure or arrival time. All demands are knows before the pacification of vehicles tours and the transportation is supplied by a fleet of vehicle based at a common depot. The aim of this paper is to find the best routing scheduled of all receipt requests with the respecting of hard Time Windows of each customer, its mean that the most important is the service quality of each customer. The novelty of our paper is presented in the new manner of algorithm application. However, we will trying to take the general architecture of Branch & Bound algorithm and using valuation rules in a different way which can adapted for such problem to find an optimal solution.

PurposeIn this paper, the author introduces a new variant of the pickup and delivery transportation problem, where one commodity is collected from many pickup locations to be delivered to many delivery locations within pre-specified time windows (one–to many–to many). The author denotes to this new variant as the 1-commodity pickup-and-delivery vehicle routing problem with soft time windows (1-PDVRPTW).Design/methodology/approachThe author proposes a hybrid genetic algorithm and a scatter search to solve the 1-PDVRPTW. It proposes a new constructive heuristic to generate the initial population solution and a scatter search (SS) after the crossover and mutation operators as a local search. The hybrid genetic scatter search replaces two steps in SS with crossover and mutation, respectively.FindingsSo, the author proposes a greedy local search algorithm as a metaheuristic to solve the 1-PDVRPTW. Then, the author proposes to hybridize the metaheuristic to solve this variant and to make a good comparison with solutions presented in the literature.Originality/valueThe author considers that this is the first application in one commodity. The solution methodology based on scatter search method combines a set of diverse and high-quality candidate solutions by considering the weights and constraints of each solution.

Traditional vehicle routing problems implicitly assume that only one crew operates a vehicle for the entirety of its journey. However, this assumption is violated in many applications arising in humanitarian and military logistics. This paper considers a joint vehicle and crew routing and scheduling problem in which crews are able to interchange vehicles, resulting in space and time interdependencies between vehicle routes and crew routes. The problem is formulated as a mixed integer programming (MIP) model and a constraint programming (CP) model that overlay crew routing constraints over a standard vehicle routing problem. The constraint program uses a novel optimization constraint to detect infeasibility and to bound crew objectives. This paper also explores methods using large neighborhood search over the MIP and CP models. Experimental results indicate that modeling the vehicle and crew routing problems jointly and supporting vehicle interchanges for crews may bring significant benefits in cost reduction compared with a method that sequentializes these decisions.

This study examines the performance of genetic algorithms (GAs) on solving a pickup and delivery vehicle routing problem, typically faced by home delivery service providers. A number of combinations of GA's three main algorithmic operators, namely, selection, crossover, and mutation is implemented, and the data envelopment analysis (DEA) is adopted to evaluate and rank these various combinations of GA operators. The numerical results demonstrate that DEA is appropriate in determining the efficient combinations of GA operators. Among the combinations under consideration, the combination using tournament selection and simple crossover is generally more efficient. The findings also contribute to algorithm development and evaluation of vehicle routing problems from the operations research perspective.

With the emphasis of the exhaust gas emission of the transportation vehicles, the mode of picking up and delivering products simultaneously has become a challenging issue in the vehicle routing problem (VRP). To remedy this issue, we investigate a special VRP with realistic constraints including product classification, pickup-delivery, and time window (PC-VRPSPDTW). Then, a hybrid algorithm combining tabu search and artificial immune algorithm (TS-AIA) is proposed. In the proposed algorithm, the earliest time and residual capacity (ETRC) heuristic is designed to generate the initial population. Then, two metaheuristics including variable neighborhood search and large neighborhood search are cooperated to balance the exploration and exploitation abilities. Besides, a new crossover operator is designed to increase the population diversity. Finally, a series of comparative experiments on the extension version of the Solomon’s benchmarks are performed to verify the effectiveness of the proposed algorithm.

Simultaneous product and service delivery vehicle routing problem with time windows and order release dates

This paper proposes models and algorithms for the pickup and delivery vehicle routing problem with time windows and last-in-first-out LIFO loading constraints PDPTWL. The LIFO loading rule ensures ...

On dynamic pickup and delivery vehicle routing with several time windows and waiting times

The Vehicle Routing Problem (VRP) is a complex and high-level set of routing problems. Two of its important variants are the Dynamic Vehicle Routing Problem (DVRP) and the Green Vehicle Routing Problem (GVRP). The first one has become a challenging research topic in the last two decades, in which not all informations are known in advance, but are revealed as the system progresses. The second one is seen as a new application and solution in new logistics patterns, more specifically, for finding routes of vehicles to serve a set of customers while minimizing the total amount of CO 2 emissions, by increasing the loading rate and reducing the number of empty trips could reduce from 10% to 40% km traveled and therefore CO 2 emissions [1]. In this paper, we combine these two variants (DVRP and GVRP), where we try to minimize, in the dynamic environment, the greenhouse gas especially the carbon dioxide CO 2 , which is the immediate consequence of the depletion of the ozone layer and time duration. We will call this combination the Dynamic Green Vehicle Routing Problem (DGVRP). To study this new problem, we present the technique employed to estimate the amount of CO 2 emissions, the emission matrix and integrate them into the DGVRP model, then we propose the hybridization based on Ant Colony Optimization (ACO) algorithm with a Large Neighborhood Search (LNS) algorithm to solve our problem. The effectiveness of this approach is tested on a set of the dynamic green problems instances, which are adopted in this work from the static GVRP benchmark datasets.

In logistics, several algorithms can be implemented in order to solve the problems of the vehicle routing with variants in order to find near-optimal solutions. Waste Collection can be considered as an essential logistic activity within any area or city. This kind of paper is aimed to implement Iterated greedy (IG) and Adaptive Large Neighborhood Search (ALNS) to solve waste collection vehicle routing problem with time windows on a real-case study. The idea is to generate an efficient way to collect waste problems in an area located in Riyadh, Saudi Arabia. Moreover, generating a route plays a significant role in terms of serving all customers’ demands who have own different time windows of receiving goods. Also, the performance of the proposed algorithms according to all instances is examined and minimizing the total costs and meeting all constraints that related to capacity, time windows, and others. To evaluate the execution of the presented algorithms, the computational results showed essential improvements, and also ALNS algorithm generates reasonable solutions in terms of total costs and a reasonable amount of time, when compared to other algorithms. Â

This paper investigates the potential benefits of collaboration among less than truckload (LTL) carriers in fulfilling pickup and delivery jobs. It adopts the commonly used centralized collaborative planning scheme where a central authority pools all jobs and allocate them to the carriers with the objective of minimizing the total transportation cost. However, in this study, LTL carriers are allowed to retain some of the jobs. This extension to the centralized planning scheme is necessary to make collaboration practical. When the carriers are allowed to retain some of their jobs, the allocation of the pooled jobs by the central authority is more complex because it needs to consider the location of the retained jobs. To this end, a mathematical model and a solution method based on large neighborhood search (LNS) are proposed. The experimental results from hypothetical networks provide several important insights regarding cost savings for various collaboration scenarios.

This paper develops the PDP (pickup and delivery vehicle routing problem) models, where customers can both receive and send goods. The performance of these models is compared to that of the classical vehicle routing problem. The effects of e-commerce as well as of establishing pickup points, which is one of the measures for city logistics on urban freight transport, are investigated using a PDP model. Results indicate that the PDP models are effective in reducing the total costs of pickup and delivery. Applications of the models to a test road network also indicates that introducing e-commerce could lead to reduction in total travel distance and CO2 emissions as well as that the establishment of pickup point could be a good measure.