The multicommodity traveling salesman problem with priority prizes: a mathematical model and metaheuristics

Artigo científico.The classic Traveling Salesman Problem (TSP) only considers the costs involved in the routes and does not differentiate products or customers. Logistic companies face conflict between operational costs, customers with different categories of products, and customer satisfaction, which is directly related to delivery time. This paper presents a new mathematical model for a TSP with variable costs and priority prizes, taking into account the customer’s product and preference values. This problem is denoted as the Multicommodity Traveling Salesman Problem with Priority Prizes (MTSPPP). Two versions of the Biased Random-Key Genetic Algorithm (BRKGA) are proposed to solve medium and large instances of the MTSPPP. Computational tests were performed, using modified instances based on classical TSP instances. The proposed methods have proved to be efficient in solving the MTSPPP.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Novas formulações de fluxo para problemas de otimização combinatória

Neste trabalho aborda-se o Problema de Minimização de Trocas de Ferramentas (PMTF) e o Problema do Caixeiro Viajante Multiproduto com Prioridades (PCVMP). O PMTF consiste em determinar um sequenciamento de tarefas, de tal modo que a quantidade de trocas de ferramentas entre as tarefas seja a menor possível. Cada tarefa requer um conjunto de ferramentas distinto, e supõe-se que cada um destes conjuntos não contenha mais ferramentas do que suporta a máquina. Já o PCVMP consiste em determinar uma rota de entrega de mercadorias considerando ao mesmo tempo, o cliente e o vendedor, ou seja, minimizando os custos totais do vendedor e maximizando as preferências dos clientes. Neste estudo tem-se como objetivo modelar, baseado em fluxo multicommodity, os problemas citados. Modelos matemáticos de otimização foram propostos assim como alguns resultados teóricos foram desenvolvidos. No caso do PMTF, o melhor modelo proposto foi comparado com os modelos existentes na literatura, mostrando um melhor desempenho tanto em quantidade de instâncias resolvidas na otimalidade, quanto no valor da relaxação linear e no tempo de execução. Mostrou-se que o valor da relaxação linear nos modelos propostos corresponde a diferença entre a quantidade de ferramentas e a capacidade da máquina. Algumas matheurísticas baseadas em busca por proximidade e um método exato enumerativo considerando eliminação de simetria foram propostos e comparados com os resultados da literatura. Já no caso do PCVMP, o modelo proposto se mostrou eficiente em resolver instâncias de pequeno e médio porte. Duas metaheurísticas, BRKGA e BRKGA adaptativo, ambas com busca local, também foram propostas para o PCVMP, apresentando bons resultados.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

A New Multicommodity Flow Model for the Job Sequencing and Tool Switching Problem

Artigo científico.In this paper a new multicommodity flow mathematical model for the Job Sequencing and Tool Switching Problem (SSP) is presented. The proposed model has a LP relaxation lower bound equal to the number of tools minus the tool machine’s capacity. Computational tests were performed comparing the new model with the models of the literature. The proposed model performed better, both in execution time and in the number of instances solved to optimality.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Cross-layer modeling and optimization of next-generation internet networks

Scaling traditional telecommunication networks so that they are able to cope with the volume of future traffic demands and the stringent European Commission (EC) regulations on emissions would entail unaffordable investments. For this very reason, the design of an innovative ultra-high bandwidth power-efficient network architecture is nowadays a bold topic within the research community. So far, the independent evolution of network layers has resulted in isolated, and hence, far-from-optimal contributions, which have eventually led to the issues today's networks are facing such as inefficient energy strategy, limited network scalability and flexibility, reduced network manageability and increased overall network and customer services costs. Consequently, there is currently large consensus among network operators and the research community that cross-layer interaction and coordination is fundamental for the proper architectural design of next-generation Internet networks. This thesis actively contributes to the this goal by addressing the modeling, optimization and performance analysis of a set of potential technologies to be deployed in future cross-layer network architectures. By applying a transversal design approach (i.e., joint consideration of several network layers), we aim for achieving the maximization of the integration of the different network layers involved in each specific problem. To this end, Part I provides a comprehensive evaluation of optical transport networks (OTNs) based on layer 2 (L2) sub-wavelength switching (SWS) technologies, also taking into consideration the impact of physical layer impairments (PLIs) (L0 phenomena). Indeed, the recent and relevant advances in optical technologies have dramatically increased the impact that PLIs have on the optical signal quality, particularly in the context of SWS networks. Then, in Part II of the thesis, we present a set of case studies where it is shown that the application of operations research (OR) methodologies in the desing/planning stage of future cross-layer Internet network architectures leads to the successful joint optimization of key network performance indicators (KPIs) such as cost (i.e., CAPEX/OPEX), resources usage and energy consumption. OR can definitely play an important role by allowing network designers/architects to obtain good near-optimal solutions to real-sized problems within practical running times

Novel Mixed Integer Programming Approaches to Unit Commitment and Tool Switching Problems

In the first two chapters, we discuss mixed integer programming formulations in Unit Commitment Problem. First, we present a new reformulation to capture the uncertainty associated with renewable energy. Then, the symmetrical property of UC is exploited to develop new methods to improve the computational time by reducing redundancy in the search space. In the third chapter, we focus on the Tool Switching and Sequencing Problem. Similar to UC, we analyze its symmetrical nature and present a new reformulation and symmetry-breaking cuts which lead to a significant improvement in the solution time. In chapter one, we use convex hull pricing to explicitly price the risk associated with uncertainty in large power systems scheduling problems. The uncertainty associated with renewable generation (e.g. solar and wind) is highlighting the need for changes in how power production is scheduled. It is known that symmetry in the integer programming formulations can slow down the solution process due to the redundancy in the search space caused by permutations. In the second chapter, we show that having symmetry in the unit commitment problem caused by having identical generating units could lead to a computational burden even for a small-scale problem. We present an effective method to exploit symmetry in the formulation introduced by identical (often co-located) generators. We propose a cut-generation approach coupled with aggregation method to remove symmetry without sacrificing feasibility or optimality. In the third chapter, we focus on the Job Sequencing and Tool Switching Problem (SSP), which is a well-known combinatorial optimization problem in the domain of Flexible Manufacturing Systems (FMS). We propose a new integer linear programming approach with symmetry-breaking and tightening cuts that provably outperformed the existing methodology described in the literature

Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

The minimization of tool switches problem (MTSP) seeks a sequence to process a set of jobs so that the number of tool switches required is minimized. The MTSP is well known to be NP-hard. This paper presents a new hybrid heuristic based on the Biased Random Key Genetic Algorithm (BRKGA) and the Clustering Search (CS). The main idea of CS is to identify promising regions of the search space by generating solutions with a metaheuristic, such as BRKGA, and clustering them to be further explored with local search heuristics. The distinctive feature of the proposed method is to simplify this clustering process. Computational results for the MTSP considering instances available in the literature are presented to demonstrate the efficacy of the CS with BRKGA. (C) 2015 Elsevier Ltd. All rights reserved.FAPESPCNPqUniv Fed Sao Paulo, BR-12231280 Sao Jose Dos Campos, BrazilNatl Inst Space Res, BR-12201970 Sao Jose Dos Campos, BrazilSao Paulo State Univ, BR-12516410 Guaratingueta, BrazilAmazon Com, MOP, Seattle, WA 98109 USAUniv Fed Sao Paulo, BR-12231280 Sao Jose Dos Campos, BrazilFAPESP: 2012/17523-3CNPq: 482170/2013-1CNPq: 304979/2012-0CNPq: 476862/2012-4CNPq: 300692-2009-9CNPq: 300692/2009-9Web of Scienc