Improved scatter search algorithm based on meerkat clan algorithm to solve NP-hard problems

A modified Scatter Search (SS) algorithm based on Meerkat Clan Algorithm (MCA) has been presented in this paper. SS is one of the important metaheuristic algorithms, while the MCA is one of the recent swarm intelligence algorithms. The modified SS algorithm, including the main steps of MCA, through it the diversity and exploration of SS-MCA's solutions, have improved. The proposed algorithm has been applied to two important NP-Hard problems (Travelling Salesman Problem (TSP) and Flexible Job Shop Scheduling Problem (FJSSP)) to verify the performance of SS-MCA. The experimental results show that the performance of SS-MCA is better than both SS and MCA, respectively

Neutrosophic Genetic Algorithm for solving the Vehicle Routing Problem with uncertain travel times

The Vehicle Routing Problem (VRP) has been extensively studied by different researchers from all over the world in recent years. Multiple solutions have been proposed for different variations of the problem, such as Capacitive Vehicle Routing Problem (CVRP), Vehicle Routing Problem with Time Windows (VRP-TW), Vehicle Routing Problem with Pickup and Delivery (VRPPD), among others, all of them with deterministic times. In the last years, researchers have been interested in including in their different models the variations that travel times may experience when exposed to all kind of phenomena, mainly vehicle traffic. This article addresses the VRP from this perspective, proposing the design and implementation of a genetic algorithm based on neutrosophic theory for calculating the fitness function of each route, considering the variability and uncertainty present in travel times. A deterministic genetic algorithm is also implemented with the average travel times to compare it with the neutrosophic algorithm using simulation. As conclusion, a deterministic algorithm does not necessarily generate the best solution in the real world, full of uncertainty. Also, the quantification of uncertainty using neutrosophic theory can be used in route planning, opening a broad and interesting field of research for future investigations

Particle Swarm Optimization Algorithm to Solve Vehicle Routing Problem with Fuel Consumption Minimization

The Conventional Vehicle Routing Problem (VRP) has the objective function of minimizing the total vehicles’ traveling distance. Since the fuel cost is a relatively high component of transportation costs, in this study, the objective function of VRP has been extended by considering fuel consumption minimization in the situation wherein the loading weight and traveling time are restricted. Based on these assumptions, we proposed to extend the route division procedure proposed by Kuo and Wang [4] such that when one of the restrictions can not be met the routing division continues to create a new sub-route to find an acceptable solution. To solve the formulated problem, the Particle Swarm Optimization (PSO) algorithm is proposed to optimize the vehicle routing plan. The proposed methodology is validated by solving the problem by taking a particular day data from a bottled drinking water distribution company. It was revealed that the saving of at best 13% can be obtained from the actual routes applied by the company

The capacitated vehicle routing problem with soft time windows and stochastic travel times

A full multiobjective approach is employed in this paper to deal with a stochastic multiobjective capacitated vehicle routing problem (CVRP). In this version of the problem, the demand is considered to be deterministic, but the travel times are assumed to be stochastic. A soft time window is tied to every customer and there is a penalty for starting the service outside the time window. Two objectives are minimized, the total length and the time window penalty. The suggested solution method includes a non-dominated sorting genetic algorithm (NSGA) together with a variable neighborhood search (VNS) heuristic. It was tested on instances from the literature and compared to a previous solution approach. The suggested method is able to find solutions that dominate some of the previously best known stochastic multiobjective CVRP solutions

A Tabu Search algorithm for the vehicle routing problem with discrete split deliveries and pickups

The Vehicle Routing Problem with Discrete Split Deliveries and Pickups is a variant of the Vehicle Routing Problem with Split Deliveries and Pickups, in which customers’ demands are discrete in terms of batches (or orders). It exists in the practice of logistics distribution and consists of designing a least cost set of routes to serve a given set of customers while respecting constraints on the vehicles’ capacities. In this paper, its features are analyzed. A mathematical model and Tabu Search algorithm with specially designed batch combination and item creation operation are proposed. The batch combination operation is designed to avoid unnecessary travel costs, while the item creation operation effectively speeds up the search and enhances the algorithmic search ability. Computational results are provided and compared with other methods in the literature, which indicate that in most cases the proposed algorithm can find better solutions than those in the literature

Eş zamanlı topla dağıt araç rotalama problemi için yeni bir çözüm önerisi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Eş zamanlı topla dağıt araç rotalama problemi müşterilerin dağıtım ve toplama taleplerinin eş zamanlı olarak karşılandığı bir araç rotalama problemidir. Bu tez kapsamında bir ana depo üzerinden 76 müşteriye hizmet sağlayacak bir firmanın araç rotalama problemi ele alınmıştır. Minimum sayıda araç kullanımı ile gidilen mesafeyi en küçükleyecek araç rotalarının oluşturulması hedeflenmiştir. Problem çözümü için literatürde yer alan karışık tamsayılı matematiksel model kullanılmıştır ve sezgisel bir algoritma geliştirilmiştir. Farklı büyüklükteki veri setleri dikkate alınarak önerilen yöntemin etkinliği gösterilmiş ve regresyon analizi kullanılarak araç sayıları ve mesafeler arasındaki ilişki incelenmiştir.Pick up and delivery vehicle routing problem is that customers' demand are met using a vehicle with simultaneously pickup and delivery policies on each route. In this study, a vehicle routing problem consists of single depot and 76 customers is solved. The main objective is to create vehicle routes which minimize the distance travelled using the minimum number of vehicles. A Mixed Integer Linear Programming (MILP) from literature and a new heuristic algorithm are proposed to solve the problem. Effectives of new proposed algorithm is illustrated using different data set and a relationship among distances and number of vehicle is examined searched using a regression analysis

Algoritmo bi-objetivo para el problema de enrutamiento óptimo de vehículos, considerando flota heterogénea y efectos ambientales

En este trabajo se aborda el problema de ruteo de vehículos con flota heterogénea, más conocido por su sigla HFVRP (del inglés Heterogeneous Fleet Vehicle Routing Problem), considerando la minimización de efectos contaminantes. En este problema, una flota de vehículos de diversas capacidades y costos, parten de un depósito central para atender diferentes demandas, considerando la minimización de la distancia recorrida y del consumo de combustible, como objetivos principales, a través de las variables de distancia y peso del vehículo cargado. Un algoritmo bi-objetivo es propuesto para la solución del problema, el cual, se basa en un híbrido entre técnicas exactas y metaheurísticas para evaluar las alternativas de solución. Finalmente, se han usado datos de prueba que fueron tomados de instancias de la literatura, obteniendo resultados de buena calidad para validar el algoritmo y proponiendo soluciones para la minimización de emisiones contaminantes

Algoritmo bi-objetivo para el problema de enrutamiento óptimo de vehículos, considerando flota heterogénea y efectos ambientales

En este trabajo se aborda el problema de ruteo de vehículos con flota heterogénea, más conocido por su sigla HFVRP (del inglés Heterogeneous Fleet Vehicle Routing Problem), considerando la minimización de efectos contaminantes. En este problema, una flota de vehículos de diversas capacidades y costos, parten de un depósito central para atender diferentes demandas, considerando la minimización de la distancia recorrida y del consumo de combustible, como objetivos principales, a través de las variables de distancia y peso del vehículo cargado. Un algoritmo bi-objetivo es propuesto para la solución del problema, el cual, se basa en un híbrido entre técnicas exactas y metaheurísticas para evaluar las alternativas de solución. Finalmente, se han usado datos de prueba que fueron tomados de instancias de la literatura, obteniendo resultados de buena calidad para validar el algoritmo y proponiendo soluciones para la minimización de emisiones contaminantes

Estudio del problema de ruteo de vehículos con balance de carga :Aplicación de la meta-heurística Búsqueda Tabú.

92 páginasEl Problema de Ruteo de Vehículos (VRP – por su sigla en inglés) es uno de los problemas de optimización combinatoria más estudiados en las últimas décadas. Este consiste en determinar un conjunto de rutas para una flota de vehículos que parte de uno o más depósitos para satisfacer la demanda de clientes dispersos geográficamente. El enfoque tradicionalmente utilizado ha sido la optimización de un solo objetivo; sin embargo, en la realidad organizacional optimizar más de un objetivo permite la toma de decisiones con una visión de negocio más integral. El presente trabajo estudia el problema de ruteo de vehículos bajo un enfoque multi-objetivo, en el cual se incorpora además de la minimización de la distancia, el balance de carga como objetivo de optimización. Al hacer una exhaustiva revisión de la literatura del problema de ruteo de vehículos multi-objetivo se evidenció que el balance de carga es un objetivo que se ha estudiado poco y en la mayoría de los trabajos analizados, se ha considerado el balance de carga desde la perspectiva de la longitud de las rutas. Como consecuencia, en este trabajo se definió el balance de carga como la diferencia de carga entre los vehículos con mayor y menor cantidad de producto a transportar hacia los clientes. Para la caracterización del problema de ruteo de vehículos multi-objetivo, mono-depósito con balance de cargas, se desarrolló un modelo de programación entera mixta el cual se implementó en GAMS y se probó con las primeras siete instancias de Augerat et al. (1998) obteniendo resultados prometedores tanto en el enfoque mono-objetivo como en el multi-objetivo. Por otra parte, teniendo en cuenta la complejidad del problema estudiado, se desarrolló un algoritmo de Búsqueda Tabú con tamaños de lista tabú fija y dependiente del número de nodos, el cual se probó con todas las instancias de Augerat et al