Diseño de una técnica de solución que optimice la distribución de alimentos desde múltiples centros de procesamiento a colegios

Las operaciones de distribución y transporte juegan un papel muy importante en la logística. El problema de enrutamiento de vehículos tiene el objetivo de programar un conjunto de rutas que atenderán a un grupo de clientes geográficamente dispersos. En otras palabras, debe decidirse qué vehículo atiende a cada cliente y desde qué depósito. Este artículo propone una técnica de solución para optimizar la distribución de almuerzos escolares en Bogotá, teniendo en cuenta las restricciones del problema, reduciendo costos y logrando el nivel de servicio esperado (cantidad de escuelas donde se entrega el almuerzo dentro del horario). Vale la pena señalar que la mayoría de los colegios que participan en el programa del gobierno colombiano PAE están ubicadas en el sur de la ciudad, formando parte de una población vulnerable. La propuesta adicional de este documento es incluir las variaciones Multi-Depot, Capacitado, Ventanas de Tiempo y Estocácidad del VRP tradicional en un problema, con el fin de respetar las restricciones de la aplicación de la vida real e integrar estos problemas previamente estudiados en uno mismo. El proceso de diseño se basó en ISO 13053 de 2012. La solución propuesta es una heurística de Solomon combinada con un algoritmo Tabú Granular programada en C ++. Para probar el rendimiento de la técnica determinista en un entorno estocástico, se realizó un simheuristic para comparar esos resultados teniendo en cuenta los Indicadores clave de rendimiento (KPI) seleccionados (Costo total, Nivel de servicio y Tiempo de demora). Como resultado, se encontraron soluciones favorables al combinar la heurística seleccionada con la meta heurística de Tabú Granular. La solución tuvo una brecha promedio de 5.97% en comparación con el valor óptimo, con un tiempo de ejecución aceptable. La evaluación entre la solución determinista y estocástica mostró una mejora en los KPI cuando se tiene en cuenta el entorno estocástico. Además, diferentes factores (como la Cantidad de Nodo, la Desviación del tiempo de tránsito y el Costo de Retardo Fijo) se evaluaron en SPSS y se demostró que tienen una influencia significativa en los KPI's, lo que indica que el modelo es sensible a esos factores.Distribution and Transport operations play a very important role in logistics. The Vehicle Routing Problem has the goal to schedule a set of routes that will attend a set of customers geographically dispersed. In other words, it needs to be decided which vehicle attends each customer and from which depot. This paper proposes a solution technique to optimize the distribution of school's lunches in Bogota, while considering the problem's restrictions, reducing costs and accomplishing the expected service level (amount of schools where lunch is delivered inside the time windows). It is worth noting that most of the schools involved in the Colombian government program PAE are in the south of the city, being part of a vulnerable population. The additional proposal of this paper is to include the Multi-Depot, Capacitated, Time Window dependent and Stochastic modifications of the traditional VRP in one problem, in order to respect the restrictions of the real-life application and to integrate these previously studied problems in one. The design process was based on ISO 13053 of 2012. As a solution a Solomon heuristic combined with a Granular Tabu Search algorithm was used and programmed in C++. To test the performance of the deterministic technique in a stochastic environment, a Simheuristic was made to compare those results taking into account the Key Perfomance Indicators (KPIs) selected (Total Cost, Service Level and Time of Delay). As a result, favorable solutions were found when combining the selected heuristic with the Granular Tabu. The solution had an average gap of 6,38% in comparison to the optimal value, with an acceptable execution time. The evaluation between the deterministic and stochastic solution showed an improvement in the KPIs when the stochastic environment is considered. Also, different factors (such as Number of Nodes, Deviation of transit time and Fixed Cost of Delay) were evaluated in SPSS and demonstrated to have significant influence in the KPIs, indicating that the model is sensitive to those factors.Ingeniero (a) IndustrialPregrad

A branch-and-price based heuristic for the stochastic vehicle routing problem with hard time windows

In the Vehicle Routing Problem with Hard Time Windows and Stochastic travel times, a disruption occurs if, due to stochastic events, a vehicle arrives too late at a customer. In this case a recourse action is required such that the service starts within the time window, and a relevant penalty cost is incurred. Despite the problem has been inspired by a real-life application in airport ground handling optimization, it has never been addressed before in literature, to the best of our knowledge. We discuss how the expected penalty cost can be evaluated and how this computation can be integrated in a branch-and-price procedure to obtain heuristic solutions. Preliminary tests on literature instances show the effectiveness of the approach

Service scheduling and vehicle routing problem to minimise the risk of missing appointments

This research studies a workforce scheduling and vehicle routing problem where technicians drive a vehicle to customer locations to perform service tasks. The service times and travel times are subject to stochastic events. There is an agreed time window for starting each service task. The risk of missing the time window for a task is defined as the probability that the technician assigned to the task arrives at the customer site later than the time window. The problem is to generate a schedule that minimises the maximum of risks and the sum of risks of all the tasks considering the effect of skill levels and task priorities. A new approach is taken to build schedules that minimise the risks of missing appointments as well as the risks of technicians not being able to complete their daily tours on time.We first analyse the probability distribution of the arrival time to any customer location considering the distributions of activities prior to this arrival. Based on the analysis, an efficient estimation method for calculating the risks is proposed, which is highly accurate and this is verified by comparing the results of the estimation method with a numerical integral method.We then develop three new workforce scheduling and vehicle routing models that minimise the risks with different considerations such as an identical standard deviation of the duration for all uncertain tasks in the linear risk minimisation model, and task priorities in the priority task risk minimisation model. A simulated annealing algorithm is implemented for solving the models at the start of the day and for re-optimisation during the day. Computational experiments are carried out to compare the results of the risk minimisation models with those of the traditional travel cost model. The performance is measured using risks and robustness. Simulation is used to compare the numbers of missed appointments and test the effect of re-optimisation.The results of the experiments demonstrate that the new models significantly reduce the risks and generate schedules with more contingency time allowances. Simulation results also show that re-optimisation reduces the number of missed appointments significantly. The risk calculation methods and risk minimisation algorithm are applied to a real-world problem in the telecommunication sector.</div

Algorithms for vehicle routing problems with heterogeneous fleet, flexible time windows and stochastic travel times

Orientador: Vinícius Amaral ArmentanoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho aborda três variantes multiatributo do problema de roteamento de veículos. A primeira apresenta frota heterogênea, janelas de tempo invioláveis e tempos de viagem determinísticos. Para resolvê-la, são propostos algoritmos ótimos baseados na decomposição de Benders. Estes algoritmos exploram a estrutura do problema em uma formulação de programação inteira mista, e três diferentes técnicas são desenvolvidas para acelerá-los. A segunda variante contempla os atributos de frota heterogênea, janelas de tempo flexíveis e tempos de viagem determinísticos. As janelas de tempo flexíveis permitem o início do serviço nos clientes com antecipação ou atraso limitados em relação às janelas de tempo invioláveis, com custos de penalidade. Este problema é resolvido por extensões dos algoritmos de Benders, que incluem novos algoritmos de programação dinâmica para a resolução de subproblemas com a estrutura do problema do caixeiro viajante com janelas de tempo flexíveis. A terceira variante apresenta frota heterogênea, janelas de tempo flexíveis e tempos de viagem estocásticos, sendo representada por uma formulação de programação estocástica inteira mista de dois estágios com recurso. Os tempos de viagem estocásticos são aproximados por um conjunto finito de cenários, gerados por um algoritmo que os descreve por meio da distribuição de probabilidade Burr tipo XII, e uma matheurística de busca local granular é sugerida para a resolução do problema. Extensivos testes computacionais são realizados em instâncias da literatura, e as vantagens das janelas de tempo flexíveis e dos tempos de viagem estocásticos são enfatizadasAbstract: This work addresses three multi-attribute variants of the vehicle routing problem. The first one presents a heterogeneous fleet, hard time windows and deterministic travel times. To solve this problem, optimal algorithms based on the Benders decomposition are proposed. Such algorithms exploit the structure of the problem in a mixed-integer programming formulation, and three algorithmic enhancements are developed to accelerate them. The second variant comprises a heterogeneous fleet, flexible time windows and deterministic travel times. The flexible time windows allow limited early and late servicing at customers with respect to their hard time windows, at the expense of penalty costs. This problem is solved by extensions of the Benders algorithms, which include novel dynamic programming algorithms for the subproblems with the special structure of the traveling salesman problem with flexible time windows. The third variant presents a heterogeneous fleet, flexible time windows and stochastic travel times, and is represented by a two-stage stochastic mixed-integer programming formulation with recourse. The stochastic travel times are approximated by a finite set of scenarios generated by an algorithm which describes them using the Burr type XII distribution, and a granular local search matheuristic is suggested to solve the problem. Extensive computational tests are performed on instances from the literature, and the advantages of flexible windows and stochastic travel times are stressed.DoutoradoAutomaçãoDoutor em Engenharia Elétrica141064/2015-3CNP