A New Model for The Multi-Objective Multiple Allocation Hub Network Design and Routing Problem

In this paper, we propose a new model for the multi-objective multiple allocation hub network design and routing problem which contains determining the location of hubs, the design of hub network, and the routing of commodities between source-destination pairs in the given network. The selected hubs are not assumed to be fully connected, and each node and arc in the network has capacity constraints. The multiple objectives of the problem are the minimization of total xed and transportation costs and the minimization of the maximum travel time required for routing. We propose a mathematical formulation for the multiobjective problem and present a meta-heuristic solution based on a well-known multi-objective evolutionary algorithm. Using the proposed formulation, we are able to nd the optimal solution for small networks of ve nodes and seven nodes. To evaluate the performance of our heuristic approach on real data, the computational experiments are conducted on Turkish postal system data set. The results demonstrate that our heuristic approach can nd feasible solutions to the problem in reasonable execution time, which is less than 10 min

Un modelo bi-objetivo de localización de hubs considerando costos y cobertura

Tesis (Magíster en Ciencias de la Ingeniería mención Logística y Gestión de Operaciones)La localización de hubs busca determinar las mejores ubicaciones para este tipo de instalación. La red resultante es llamada hub-and-spoke, y permite conectar a múltiples pares origen-destino. Esta investigación se enfoca en definir y formular un nuevo modelo de localización hub para un problema de maximización de cobertura, definida por un tiempo límite de cobertura; y a su vez minimizar los costos totales. El modelo fue resuelto de forma exacta a través del software AMPL, utilizando el método NISE (Non-Inferior Set Estimation) para ver las curvas trade-off entre ambos objetivos. Para esto se definieron diferentes escenarios para analizar la incidencia de diferentes parámetros en las curvas de trade-off obtenidas, utilizando los primeros 40 nodos de la red de Turquía. Se obtuvo que el nivel de servicio es el parámetro determinante del modelo. Además como investigaciones futuras se podría determinar exógenamente la cantidad de hubs a localizar o disminuir los tiempos computacionales para instancias de tamaño considerables

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

Multi-objective genetic algorithm for robust flight scheduling

Master'sMASTER OF ENGINEERIN

Modelling and Analysis of Hub-and-Spoke Networks under Stochastic Demand and Congestion

Motivated by the strategic importance of congestion management, in this paper we present a model to design hub-and-spoke networks under stochastic demand and congestion. The proposed model determines the location and capacity of the hub nodes and allocate non-hub nodes to these hubs while minimizing the sum of the ?xed cost, transportation cost and the congestion cost. In our approach, hubs are modelled as spatially distributed M/G/1 queues and congestion is captured using the expected queue lengths at hub facilities. A simple transformation and a piecewise linear approximation technique are used to linearize the resulting nonlinear model. We present two solution approaches: an exact method that uses a cutting plane approach and a novel genetic algorithm based heuristic. The numerical experiments are conducted using CAB and TR datasets. Analysing the results obtained from a number of problem instances, we illustrate the impact of congestion cost on the network topology and show that substantial reduction in congestion can be achieved with a small increase in total cost if congestion at hub facilities is considered at the design stage. The computational results further confirm the stability and e?ciency of both exact and heuristic approaches

Variations in the Flow Approach to CFCLP-TC for Multiobjective Supply Chain Design

We review the problem for the design of supply chains called Capacitated Fixed Cost Facility Location Problem with Transportation Choices (CFCLP-TC). The problem is based on a production network of two echelons with multiple plants, a set of potential distribution centers, and customers. The problem is formulated as an optimization model with two objective functions based on time and cost. This paper proposes three changes to the original model to compare the sets of efficient solutions and the computational time required to obtain them. The main contribution of this paper is to extend the existing literature by incorporating approaches for the supply of product to customers through multiple sources, the direct flow between plants and customers, without this necessarily implying removing the distribution centers, and the product flow between distribution centers. From these approaches, we generate mathematical programming models and propose to solve through the epsilon-constraint approach for generating Pareto fronts and thus compare each of these approaches with the original model. The models are implemented in GAMS and solved with CPLEX

Bi-objective p-hub Location Problems

In this thesis, we introduce, model, and solve bi-objective hub location problems. The two well-known hub location problems from the literature, the p-hub median and p-hub center problems, are uni ed under a bi-objective setting considering the single, multiple, and r-allocation strategies. We developed a 3-index and a 4-index mixed-integer programming formulation for each of the allocation strategies. All the formulations are tested on the CAB dataset from the literature using a commercial optimization software. We observe the effect of different priorities given to the objectives on the locations of hub nodes, allocations, and the CPU time requirements with different allocation strategies under different values of problem parameters

A concise guide to existing and emerging vehicle routing problem variants

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges