Methods and algorithms to solve the vehicle routing problem with time windows and further conditions

In this paper methods and algorithms are examined for solving one important problem of transport logistics, namely formation of roadmap. The proposed mathematical model is based on the well-known multi-depot heterogeneous vehicle routing problem with time windows algorithm. Modifications of the model provide additional conditions and restrictions. Algorithmic support of information transport system in an enterprise is connected with features of the vehicle routing problem. The suggested solution is based on modified Clarke and Wright Algorithm and Variable Neighborhood Search. The risk on the roads and the cost of toll roads are calculated, too

Economic and environmental concerns in planning recyclable waste collection systems

This paper addresses the planning of recyclable waste collection systems while accounting for economic and environmental concerns. Service areas and vehicle routes are defined for multiple-depot logistics networks where different products have to be collected. The problem is modeled as a multi-product, multi-depot vehicle routing problem with two objective functions: distance and CO2 emissions minimization. A decomposition solution method is developed and applied to a real case-study. Six scenarios are studied regarding different service areas configuration and different objective functions. Savings up to 22% in distance and 27% in CO2 emissions are achieved, excelling economical and environmental goals.info:eu-repo/semantics/publishedVersio

Vehicle routing for a complex waste collection problem

We consider a complex waste collection problem, where the residents of a certain region dispose of recyclable waste, which is collected using a fixed heterogeneous fleet of vehicles with different volume and weight capacities, fixed costs, unit distance running costs and hourly driver wage rates. Each tour starts and ends at one of several depots, not necessarily the same, and is a sequence of collections followed by disposals at the available recycling plants, with a mandatory disposal before the end of the tour. There are time windows and a maximum tour duration, which is interrupted by a break after a certain interval of continuous work. Moreover, due to the specificities of different collection regions, there are occasional site dependencies. The problem is modeled as a mixed binary linear program and the formulation is enhanced with several valid inequalities and elimination rules. To solve realistic instances, we develop a local search heuristic, which currently embeds much of the functionality of the mathematical model. The heuristic performs well, as indicated by an optimality gap of 2% compared to the exact solution on small instances. Future work will see improving the model formulation to solve larger instances to optimality and expanding the heuristic to include all of the features of the model

A Hybrid Local Improvement Algorithm for Large-scale Multi-depot Vehicle Routing Problems with Time Windows

One of the major research topics in the supply chain management field is the multi-depot vehicle routing problem with time windows (m-VRPTW). It aims to designing a set of minimum-cost routes for a vehicle fleet servicing many customers with known demands and predefined time windows. This paper presents an m-VRPTW local search improvement algorithm that explores a large neighborhood of the current solution to discover a cheaper set of feasible routes. The neighborhood structure comprises all solutions that can be generated by iteratively performing node exchanges among nearby trips followed by a node reordering on every route. Manageable mixed-integer linear programming (MILP) formulations for both algorithmic steps were developed. To further reduce the problem size, a spatial decomposition scheme has also been applied. A significant number of large-scale benchmark problems, some of them including up to 200 customers, multiple depots and different vehicle-types, were solved in quite reasonable CPU times.Fil: Dondo, Rodolfo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Cerda, Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

A robust solving strategy for the vehicle routing problem with multiple depots and multiple objectives

This document presents the development of a robust solving strategy for the Vehicle Routing Problem with Multiple Depots and Multiple Objectives (MO-MDVRP). The problem tackeled in this work is the problem to minimize the total cost and the load imbalance in vehicle routing plan for distribution of goods. This thesis presents a MILP mathematical model and a solution strategy based on a Hybrid Multi- Objective Scatter Search Algorithm. Several experiments using simulated instances were run proving that the proposed method is quite robust, this is shown in execution times (less than 4 minutes for an instance with 8 depots and 300 customers); also, the proposed method showed good results compared to the results found with the MILP model for small instances (up to 20 clients and 2 depots).MaestríaMagister en Ingeniería Industria

Modelación matemática del problema de ruteo de vehículos con restricciones de múltiples depósitos, flota heterogénea de vehículos y ventanas de tiempos 

Ingeniería IndustrialIn the present work we propose a mathematical method of mixed integer linear programming (MIP) to solve a vehicle routing problem with constrains of multiples depots, heterogeneous fleet of vehicles and time windows programmed in GAMS, a General Algebraic modeling Software. One of the difficulties presented in approximated methods proposed to solve vehicle routing problems is that the quality of their solutions is not always known and they often are only applicable to solve the specific problems for which they were designed. The presented model not only is capable to solve problems such MDHVRPTW to which it was originally designed but it’s also capable to solve less constrained problems like VRPTW, HVRPTW and MDVRPTW. Another valuable contribution of the presented model is that the model can work as a pattern to prove the quality of the solutions of the approximated methods. The model solve to optimality benchmark problems of 5 and 10 nodes and generates solutions near to optimality with a gap of less than 3% to 15 and 20 nodes problems.En el presente trabajo se propone un método matemático de programación entera mixta (MIP) para solucionar un problema de ruteo de vehículos con restricciones de múltiples depósitos, flota heterogénea de vehículos y ventanas de tiempo codificado en GAMS, un software de modelación algebraica general. Una de las dificultades que presentan los métodos aproximados para solucionar problemas de ruteo de vehículos es que no siempre se conoce que tan buenas son las soluciones que generan y adicionalmente por lo general solo aplican para resolver el problema específico para el cual fueron diseñados. El modelo presentado no solamente soluciona problemas del tipo MDHVRPTW para el cual fue diseñado sino también es capaz de solucionar problemas con menos restricciones como los VRPTW, HVRPTW y MDVRPTW. Otro aporte valioso del modelo presentado es que sirve de patrón para probar la calidad de las soluciones generadas por métodos aproximados. El modelo resuelve de forma óptima instancias de referencia de 5 y10 nodos y da soluciones muy cercanas al óptimo con una diferencia de menos del 3% para instancias de 15 y 20 nodos

Integrated Forward and Reverse Logistics Network Design

Many manufacturers are moving towards green manufacturing. One of the actions for environment friendly manufacturing is collection of end-of-life products (EOL). EOL products are transported to the proper facilities for reprocessing or proper disposal. Movement of collected products is performed through reverse logistics networks. Reverse logistics networks may be designed independent of forward logistics networks, or as integrated networks, known as integrated forward and reverse logistics (IFRL) networks. Recent research shows that IFRL networks are more efficient than independent networks. In this work, we study a number of IFRL networks. We present a comprehensive mathematical model to represent an assignment and location-routing IFRL network. Afterwards, this model is decomposed into a number of sub-models that represent different IFRL networks. For each network we develop a solution methodology to solve practical size problems. Two sub-models based on the comprehensive model are presented to design two IFRL location-routing networks. The first network considers decision on the location to establish a disassembly plant. The second network considers decisions on the location to establish a manufacturing facility. For both networks, routing decisions are assigning customers to vehicles, and establishing vehicles’ routes. We develop two heuristic methods to solve the models. The heuristics are able to reach optimal or near optimal solutions in reasonable computational times. The vehicle routing problem with simultaneous pickup and delivery and time windows (VRPSPD-TW) is studied in this work. We use a sub-model of the comprehensive model to represent the problem. Classic heuristics and intelligent optimization or metaheuristics are widely used to solve similar problems. Therefore, we develop a heuristic method to solve the VRPSPD-TW. Results of the heuristic serve as initial solutions for a simulated annealing (SA) approach. For most tested problems, the SA approach is able to improve the heuristic solutions, and reach optimal solutions. Computational times are reasonable for the heuristic and SA. We also study the multi-depot vehicle routing problem with simultaneous pickup and delivery and time windows (MDVRPSPS-TW). A sub-model of the comprehensive model represents the problem. The network considers assignment of customers and vehicles to depots, assignment of customers to vehicles and routing of vehicles within customers’ time windows. We develop a 2-phase heuristic and a SA approach to solve the problem. Heuristic solutions serve as initial solutions for the SA approach. SA is able to reach optimum or near optimum solutions. Computational times are reasonable for the heuristic and S

MATHEMATICAL PROGRAMMING ALGORITHMS FOR TRANSPORTATION PROBLEMS

The thesis deals with the study of transportation problems, and in particular focuses on developing new exact and heuristic algorithms for two interesting variants of the well known Vehicle Routing Problem: the multi-depot heterogeneous-fleet vehicle routing problem with time windows and the multi-depot heterogeneous-fleet pickup and delivery problem with soft time windows. The studied problems consider additional real-world requirements, often neglected in the literature. They lead to more involved problems but on the other hand more realistic ones, that call for powerful optimization methods in order to tackle such difficult applications. The proposed algorithms are based on mathematical programming techniques, such as branch-and-price, column generation and dynamic programming. The performance of the algorithms is analyzed with extensive computational experiments and compared with the most effective algorithms from the literature, showing the usefulness of the proposed methods