Integrated two-stage lot sizing and scheduling problem

Orientadores: Paulo Morelato França, Reinaldo MorabittoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: A presente tese de doutorado apresenta, modela matematicamente e soluciona úm problema multinível de dimensionamento de lotes e programação da produção em um ambiente industrial com máquinas paralelas que apresentam restrições de capacidade, custos e tempos de preparo dependentes da seqüência. O problema é motivado pela realidade encontrada em um setor industrial, em particular o de fabricação e engarrafamento de bebidas. Nesse tipo de indústria a produção envolve dois níveis interdependentes com decisões relativas à armazenagem das matérias-primas e ao engarrafamento das bebidas. As diversas matérias-primas são armazenadas em tanques de onde escoam para as linhas de engarrafamento. O desafio é determinar simultaneamente o dimensionamento e a programação das matérias-primas nos tanques e o envasamento de bebidas nas linhas, onde tempos e custos de trocas dependem do tipo de item previamente armazenado e envasado. O objetivo não foi apenas fornecer uma solução para o problema industrial, mas também estabelecer e solucionar o problema do ponto de vista acadêmico. Um modelo matemático inteiro-misto é proposto com diversas restrições combinadas que até então costumavam ser tratadas separadamente pela literatura. Inicialmente o modelo foi solucionado por meio do pacote GAMS/Cplex. A não existência de testes com modelos similares nos obrigou a criar um conjunto de instâncias para avaliar o modelo e as técnicas de solução desenvolvidas. A solução exata foi viável apenas em instâncias de pequena dimensão devido à complexidade do problema em estudo. Meta-heurísticas foram então propostas e se revelaram como uma alternativa para solucionar instâncias de média e grande dimensão. Os métodos foram capazes de fornecer soluções dentro de um tempo computacional razoávelAbstract: The present thesis establishes and solves a multi-Ievellot sizing and scheduling problem with parallel machines and sequence-dependent setup cost and time. The problem was motivated by a real situation found in some industrial settings mainly the soft drink industry. In this kind of industry, the production involves two interdependent levels with decisions about raw material storage and soft drink bottling. The several raw materiaIs are stored in tanks from which they ow to the bottling lines. The challenge is to determine simultaneously the lot sizing and scheduling of raw material in tanks and also in the bottling lines, where setup costs and time depend on the previous items stored and bottled. The objective is not only to provide an industrial problem solution, but also to establish and solve the problem by an academic point of view. Initially, a mathematical model is proposed with several combined constrains that use to be handled apart in the literature. This complex model was solved by the GAMS/Cplex software. The lack of similar models led us to create a set of instances to evaluate the model and the solution techniques developed. The exact model solution was possible only for small-sized instances because of the problem complexity. Therefore, meta-heuristics have been proposed and revealed as the only alternative to solve large instances. These methods have been able to provi de solutions with good quality in a reasonable computational timeDoutoradoAutomaçãoDoutor em Engenharia Elétric

A relax-and-fix with fix-and-optimize heuristic applied to multi-level lot-sizing problems

In this paper, we propose a simple but efficient heuristic that combines construction and improvement heuristic ideas to solve multi-level lot-sizing problems. A relax-and-fix heuristic is firstly used to build an initial solution, and this is further improved by applying a fix-and-optimize heuristic. We also introduce a novel way to define the mixed-integer subproblems solved by both heuristics. The efficiency of the approach is evaluated solving two different classes of multi-level lot-sizing problems: the multi-level capacitated lot-sizing problem with backlogging and the two-stage glass container production scheduling problem (TGCPSP). We present extensive computational results including four test sets of the Multi-item Lot-Sizing with Backlogging library, and real-world test problems defined for the TGCPSP, where we benchmark against state-of-the-art methods from the recent literature. The computational results show that our combined heuristic approach is very efficient and competitive, outperforming benchmark methods for most of the test problems

Service-Oriented Architecture to Integrate Flight Safety and Mission Management Subsystems into UAVs

This paper presents the design and implementation of a Service-Oriented Architecture (SOA) to tackle with the data exchange different elements onboard the Unmanned Aerial Vehicle (UAV) and the real-time safety critical operation of In-Flight Awareness Augmentation System (IFA2S) and non-safety critical Mission Oriented System Array (MOSA), respectively to take care of flight safety and mission accomplishment

Resolução de um problema dinamico de programação de maquinas paralelas com custo de troca de ferramentas dependente da sequencia e restrições de tempo

Oreintadores: Luiz Manoel Aguilera, Paulo Morelato FrançaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: O Problema de Roteamento de Veículos (PRV) procura estabelecer uma eficiente distribuição de bens de forma a atender as demandas existentes. Os atuais avanços em tecnologia de informação como rádio transmissores, telefonia celular, sistemas de localização via satélite, estão alterando o cenário em que um PRV pode ocorrer permitindo, por exemplo, a atualização de dados e localização de veículos em tempo real. Um PRV será considerado dinâmico caso seus dados não sejam conhecidos pelo usuário a priori e atualizados simultaneamente a determinação ou execução do conjunto de rotas. Um Problema de Roteamento Dinâmico de Veículos (PRDV) será estabelecido e um método de resolução, chamado algoritmo MORSS, será adaptado para resolver instâncias deste PRDV. Em seguida, um Problema Dinâmico de Programação (PDP) também será estabelecido e o algoritmo MORSS adaptado para resolver instâncias deste PDP. Um segundo método, baseado em heurísticas de busca em vizinhança e inserção, também será proposto para resolver as instâncias do PDP. O trabalho se propõe a resolver dois diferentes tipos de problemas dinâmicos procurando avaliar a adaptabilidade e desempenho do algoritmo MORSS enquanto método de resolução. No caso do PDP, o desempenho de um segundo método também é analisado e comparado ao desempenho obtido pelo algoritmo MORSSAbstract: The Vehicle Routing Problem (VRP) is the efficient distribution of products in order to attend customer requirement. Recently, the advances in information technology as radio transmission, cellular telephone, localization systems by satellite, are altering the scenarios in that VRP occurs and allowing update of information and vehicle localization occur in real time. The VRP is dynamic if the inputs of the problem are known by the decision-maker and are updated concurrentlY with the deterrnination or execution of the route's set. A Dynamic Vehicle Routing Problem (DVRP) will be established and a solution's method, called MORSS algorithm, will be adapted to solve DVRP instances. Next, a Dynamic Scheduling Problem (DSP) will be established and the MORSS algorithm will be adapted to solve DSP instances. A second method, based in heuristics of neighborhood search and insertion, will be also proposed to solve DSP instances. This work proposes to solve two different dynamic problems searching to evaluate the MORSS algorithm adaptability and performance as resolution method. In the PDP, the performance of the second method proposed also will be analyzed and compared with the performance obtained by MORSS algorithmMestradoMestre em Engenharia Elétric

Evaluating genetic algorithms with different population structures on a lot sizing and scheduling problem

This paper studies the use of different population structures in a Genetic Algorithm (GA) applied to lot sizing and scheduling problems. The population approaches are divided into two types: single-population and multi-population. The first type has a non-structured single population. The multi-population type presents non-structured and structured populations organized in binary and ternary trees. Each population approach is tested on lot sizing and scheduling problems found in soft drink companies. These problems have two interdependent levels with decisions concerning raw material storage and soft drink bottling. The challenge is to simultaneously determine the lot sizing and scheduling of raw materials in tanks and products in lines. Computational results are reported allowing determining the better population structure for the set of problem instances evaluated. Copyright 2008 ACM

Tabu search to solve the Synchronized and Integrated Two-Level Lot Sizing and Scheduling Problem

This paper proposes a tabu search approach to solve the Synchronized and Integrated Two-Level Lot Sizing and Scheduling Problem (SITLSP). It is a real-world problem, often found in soft drink companies, where the production process has two integrated levels with decisions concerning raw material storage and soft drink bottling. Lot sizing and scheduling of raw materials in tanks and products in bottling lines must be simultaneously determined. Real data provided by a soft drink company is used to make comparisons with a previous genetic algorithm. Computational results have demonstrated that tabu search outperformed genetic algorithm in all instances. Copyright 2011 ACM

Meta-heuristic approaches for a soft drink industry problem

The present paper evaluates meta-heuristic approaches to solve a soft drink industry problem. This problem is motivated by a real situation found in soft drink companies, where the lot sizing and scheduling of raw materials in tanks and products in lines must be simultaneously determined. Tabu search, threshold accepting and genetic algorithms are used as procedures to solve the problem at hand. The methods are evaluated with a set of instance already available for this problem. This paper also proposes a new set of complex instances. The computational results comparing these approaches are reported. © 2008 IEEE

(WIP) Tasks Selection Policies for Securing Sensitive Data on Workflow Scheduling in Clouds.

Scheduling is an important topic to support data security for workflow execution in clouds. Some workflow scheduling algorithms use security services such as authentication, integrity verification, and encryption for all workflow tasks. However, applying security services to no sensitive data does not make sense as no benefit is gained, yet it increases the makespan and monetary costs. In this paper, we introduce five policies for selection of tasks that handle sensitive data. We also propose a workflow scheduling algorithm based on a multi-populational genetic algorithm for minimizing cost while meeting a deadline. Experiments using four workflow applications show that our proposal can minimize both the makespan and cost, while maintaining the security of sensitive data compared to another approach in the literature