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Organization of transport routes in the tasks of storage, distribution and supply of resources with the optimization model MILP

Organization of transport routes in the tasks of storage, distribution and supply of resources with the optimization model MILP

Abstract

The vehicle routing problem (VRP) is concerned with optimizing a set of routes, all beginning and ending at a given node (called the depot, center park or warehouse), to serve a given set of customers. The VRP was first introduced by Dantzig and Ramser (1959). It is a multivehicle version of the traveling salesman problem (TSP), and is therefore more applicable in practice since most organizations with substantial delivery operations use multiple vehicles simultaneously. Of course, it is also more difficult than the TSP since it involves decisions about how to assign customers to routes, in addition to how to optimize the sequence of nodes on each route. As a result, today’s «hard» VRP instances tend to involve, say, hundreds of nodes, whereas hard instances of the TSP involve thousands or tens of thousands of nodes. The VRP is used to model, in which a single vehicle delivers goods to multiple customer nodes with returning to the depot. The vehicle routing problem is a combinatorial optimization problem of integer programming. The goal: to organize an optimal set of local vehicle routes, several or one, according to their number, configuration and loading, to minimize the total transport costs for the delivery of orders from a single center to customers at their given location coordinates. VRP is a typical component of transport logistics and supply chains, the sphere of providing services for current maintenance of objects, prompt repair and elimination of accidents in network organizational structures. To solve this problem, the MILP model was developed and tested on specific examples using available Excel analytical tools in educational and research practice. The classic VRP (with orders) has a strictly decreasing sequence of flow values, which is relatively easy to divide into a certain number of subcircuits with the same decreasing sequence depending on the value of the TK potential. However, a VRP with customer resource reserves cannot have such a sequence, there is a simultaneous increase and decrease of current values in the chain of flows, so a single circuit is possible here by choosing a sufficient potential of TK, or, depending on the situation, the formation of several artificial sub-circuits from unreached groups of nodes. Fig.: 9. Refs: 8 titles.

Задача маршрутизації транспортних потоків (The vehicle routing problem, VRP) — це комбінаторна оптимізаційна задача цілочислового програмування. Мета: організувати оптимальну сукупність локальних маршрутів транспортних засобів (ТЗ), від кількох до одного, за їхньою кількістю, конфігурацією та завантаженням, для мінімізації загальних транспортних витрат на доставку замовлень із єдиного центру клієнтам за їхніми заданими координатами розташування. VRP є типовою складовою транспортної логістики та ланцюгів постачання, сфери надання послуг з поточного обслуговування об'єктів, оперативного ремонту та ліквідації аварій у мережевих організаційних структурах. За своєю постановкою це ускладнена та зважена модель класичної задачі комівояжера, де оптимізується не лише контур обходу, але й розподіл ресурсу згідно замовлень клієнті. У статті введено додаткове ускладнення — територіально розподілені пропозиції ресурсного забезпечення. Для розв’язання цієї задачі доступними аналітичними засобами Excel в освітньо-дослідницькій практиці розроблено та перевірено на конкретних прикладах модель MILP.

Keywords

мережеві організаційні структури, мережеве оптимізаційне моделювання, транспортна логістика, ланцюги постачання, електронно-табличне моделювання, оптимізаційні методи та моделі в Excel, network organizational structures, network optimization modeling, transport logistics, supply chains, spreadsheet modeling, optimization methods and models in Excel

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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