Optimal Solutions for the Vehicle Routing Problem with Split Demands

2019 ◽  
pp. 189-203
Author(s):  
Hipólito Hernández-Pérez ◽  
Juan-José Salazar-González
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimal Solutions ◽  
Routing Problem

scholarly journals The last-mile vehicle routing problem with delivery options

OR Spectrum ◽  
2021 ◽  
Author(s):  
Christian Tilk ◽  
Katharina Olkis ◽  
Stefan Irnich
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Resource Constraints ◽  
Time Window ◽  
Time Windows ◽  
Service Level ◽  
Optimal Solutions ◽  
Routing Problem ◽  
Common Location ◽  
Delivery Options

AbstractThe ongoing rise in e-commerce comes along with an increasing number of first-time delivery failures due to the absence of the customer at the delivery location. Failed deliveries result in rework which in turn has a large impact on the carriers’ delivery cost. In the classical vehicle routing problem (VRP) with time windows, each customer request has only one location and one time window describing where and when shipments need to be delivered. In contrast, we introduce and analyze the vehicle routing problem with delivery options (VRPDO), in which some requests can be shipped to alternative locations with possibly different time windows. Furthermore, customers may prefer some delivery options. The carrier must then select, for each request, one delivery option such that the carriers’ overall cost is minimized and a given service level regarding customer preferences is achieved. Moreover, when delivery options share a common location, e.g., a locker, capacities must be respected when assigning shipments. To solve the VRPDO exactly, we present a new branch-price-and-cut algorithm. The associated pricing subproblem is a shortest-path problem with resource constraints that we solve with a bidirectional labeling algorithm on an auxiliary network. We focus on the comparison of two alternative modeling approaches for the auxiliary network and present optimal solutions for instances with up to 100 delivery options. Moreover, we provide 17 new optimal solutions for the benchmark set for the VRP with roaming delivery locations.

A Hybrid Algorithm for the Multi-Objective Time Dependent Vehicle Routing Problem

2014 ◽  
Vol 505-506 ◽  
pp. 1071-1075
Author(s):  
Yi Sun ◽  
Yue Chen ◽  
Chang Chun Pan ◽  
Gen Ke Yang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Time Windows ◽  
Time Dependent ◽  
Ant Colony System ◽  
Optimal Solutions ◽  
Test Results ◽  
Routing Problem ◽  
Traffic Conditions

This paper presents a real road network case based on the time dependent vehicle routing problem with time windows (TDVRPTW), which involves optimally routing a fleet of vehicles with fixed capacity when traffic conditions are time dependent and services at customers are only available in their own time tables. A hybrid algorithm based on the Genetic Algorithm (GA) and the Multi Ant Colony System (MACS) is introduced in order to find optimal solutions that minimize two hierarchical objectives: the number of tours and the total travel cost. The test results show that the integrated algorithm outperforms both of its traditional ones in terms of the convergence speed towards optimal solutions.

scholarly journals Simultaneous Multi-Start Simulated Annealing for Capacitated Vehicle Routing Problem

2020 ◽  
Vol 8 ◽  
Author(s):  
Arman Davtyan ◽  
Suren Khachatryan
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Superior Performance ◽  
Metaheuristic Algorithm ◽  
Optimal Solutions ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Capacitated Vehicle

A new metaheuristic algorithm is proposed for Capacitated Vehicle Routing Problem. CVRP is one of the fundamental problems in combinatorial optimization that deals with transport route minimization. The algorithm combines Simulated Annealing, multi-start and simultaneous computing techniques. A series of computational tests are conducted on several CVRP benchmarks and near-optimal solutions are obtained. The results indicate superior performance compared with Simulated Annealing

scholarly journals A Multi-Objective Solution of Green Vehicle Routing Problem

2019 ◽  
Vol 10 (1) ◽  
pp. 31-44 ◽  
Author(s):  
Özgür Kabadurmuş ◽  
Mehmet Serdar Erdoğan ◽  
Yiğitcan Özkan ◽  
Mertcan Köseoğlu
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Alternative Fuel ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Routing Problem ◽  
Multi Objective

Abstract Distribution is one of the major sources of carbon emissions and this issue has been addressed by Green Vehicle Routing Problem (GVRP). This problem aims to fulfill the demand of a set of customers using a homogeneous fleet of Alternative Fuel Vehicles (AFV) originating from a single depot. The problem also includes a set of Alternative Fuel Stations (AFS) that can serve the AFVs. Since AFVs started to operate very recently, Alternative Fuel Stations servicing them are very few. Therefore, the driving span of the AFVs is very limited. This makes the routing decisions of AFVs more difficult. In this study, we formulated a multi-objective optimization model of Green Vehicle Routing Problem with two conflicting objective functions. While the first objective of our GVRP formulation aims to minimize total CO2 emission, which is proportional to the distance, the second aims to minimize the maximum traveling time of all routes. To solve this multi-objective problem, we used ɛ-constraint method, a multi-objective optimization technique, and found the Pareto optimal solutions. The problem is formulated as a Mixed-Integer Linear Programming (MILP) model in IBM OPL CPLEX. To test our proposed method, we generated two hypothetical but realistic distribution cases in Izmir, Turkey. The first case study focuses on an inner-city distribution in Izmir, and the second case study involves a regional distribution in the Aegean Region of Turkey. We presented the Pareto optimal solutions and showed that there is a tradeoff between the maximum distribution time and carbon emissions. The results showed that routes become shorter, the number of generated routes (and therefore, vehicles) increases and vehicles visit a lower number of fuel stations as the maximum traveling time decreases. We also showed that as maximum traveling time decreases, the solution time significantly decreases.

scholarly journals Solving practical waste collection with time windows in an urban area

2020 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Abdulwahab Almutairi
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Optimal Solutions ◽  
Waste Collection ◽  
Routing Problem ◽  
Iterated Greedy

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.  

AN ALGORITHM FOR THE GENERALIZED VEHICLE ROUTING PROBLEM WITH BACKHAULING

2005 ◽  
Vol 22 (02) ◽  
pp. 153-169 ◽  
Author(s):  
SUBRATA MITRA
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Mixed Integer Linear Programming ◽  
Upper Bounds ◽  
Mixed Integer ◽  
Optimal Solutions ◽  
Routing Problem ◽  
Construction Heuristic ◽  
Route Cost ◽  
Generalized Vehicle Routing Problem

The Vehicle Routing Problem with Backhauling deals with the supply of finished goods from a depot to a number of delivery points, and picking up returnable items and bringing them back to the depot using a fleet of trucks. Traditionally, the objective of the problem has been to determine the truck routes such that the total number of trucks and/or the total distance traveled/total route cost are minimized. Most of the papers available in the literature in this connection deal with problems where the linehaul (having a demand for finished goods) and backhaul (having items to be returned to the depot) customers are different, and a customer may be visited by at most one truck limiting demand and returns at a location by the capacity of the truck. In this paper, we allow the linehaul and backhaul customers to be the same leading to simultaneous delivery and pickup at a customer location, and also there is no restriction on the quantity demanded at (to be returned from) a customer location. As such a customer may be visited by more than one truck and more than once by the same truck. We developed a Mixed Integer Linear programming (MILP) formulation of the problem and a route construction heuristic. The heuristic averaged 80 ms for 110 problems tested, and in 78 of them the heuristic costs were either equal to the optimal costs or at most equal to the upper bounds on the optimal costs obtained after running the optimization package for 30 min. Optimal solutions were obtained for 28 problems at an average time of 295 ms. The heuristic could match the optimal solutions for 22 of these problems at an average time of 71 ms.

scholarly journals Quantum Walk-Based Vehicle Routing Optimisation

2021 ◽  
Vol 9 ◽  
Author(s):  
T. Bennett ◽  
E. Matwiejew ◽  
S. Marsh ◽  
J. B. Wang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Solution Space ◽  
Quantum Walk ◽  
Computational Effort ◽  
Optimal Solutions ◽  
Routing Problem ◽  
The Core ◽  
Core Components ◽  
Capacitated Vehicle

This paper demonstrates the applicability of the Quantum Walk-based Optimisation Algorithm (QWOA) to the Capacitated Vehicle Routing Problem (CVRP). Efficient algorithms are developed for the indexing and unindexing of the solution space and for implementing the required alternating phase-walk unitaries, which are the core components of QWOA. Results of numerical simulation demonstrate that the QWOA is capable of producing convergence to near-optimal solutions for a randomly generated eight location CVRP. Preparation of the amplified quantum state in this example problem is demonstrated to produce higher-quality solutions than expected from classical random sampling of equivalent computational effort.

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