Stochastic programming approaches to stochastic scheduling

Practical scheduling problems typically require decisions without full information about the outcomes of those decisions. Yields, resource availability, performance, demand, costs, and revenues may all vary. Incorporating these quantities into stochastic scheduling models often produces diffculties in analysis that may be addressed in a variety of ways. In this paper, we present results based on stochastic programming approaches to the hierarchy of decisions in typical stochastic scheduling situations. Our unifying framework allows us to treat all aspects of a decision in a similar framework. We show how views from different levels enable approximations that can overcome nonconvexities and duality gaps that appear in deterministic formulations. In particular, we show that the stochastic program structure leads to a vanishing Lagrangian duality gap in stochastic integer programs as the number of scenarios increases.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44935/1/10898_2004_Article_BF00121682.pd

ALGORITMOS PARA O PROBLEMA NÃO CAPACITADO DE FLUXOS COM CUSTOS FIXOS NOS ARCOS: UMA COMPARAÇÃO ESTATÍSTICA

Este trabalho tem como propósito a apresentação de resultados de uma comparação empírica entre algoritmos, sendo este um dos assuntos mais recorrentes na área de desenvolvimento de algoritmos. Os algoritmos sob estudo são para resolver um problema de otimização em redes, importante pelas suas aplicações potenciais em sistemas de telefonia e transporte, o problema não capacitado de fluxos com custos fixos nos arcos (NCFCF), uma generalização do clássico problema de Steiner em grafos. Para tal, são utilizadas ferramentas estatísticas conhecidas tais como planejamento de experimentos, análise de variância e intervalos de confiança, mas não comumente empregadas neste tipo de estudo. O problema NCFCF é apresentado em uma modelagem de programação matemática inteira mista, baseada na qual os algoritmos sob consideração são apresentados. Uma descrição do planejamento de experimentos adequado a este tipo de estudo é apresentada e é ilustrado o uso da técnica estatística baseado em cuja análise foi possível classificar os algoritmos sob consideração.<br>This paper is concerned about empirical comparisons of algorithms, one of the most recurrent issues in the field of design of algorithms. The problem under consideration is the uncapacitated fixed-charge network flow (UFCNF) problem, a generalization of the classic Steiner problem in graphs. The UFCNF is very important in the practical point of view because of its potential applications for telecommunication and transportation system design. In order to compare the algorithms for the UFCNF problem, we make use of well known and well established statistical tools namely the design of experiments, analysis of variance, and confidence intervals, but rarely applied in such studies. A mixed-integer mathematical programming formulation is presented for the UFCNF problem and the design of experiments suitable for the empirical study is detailed. Finally, the statistical analysis based on which the algorithms could be classified is presented

Reformulation and Decomposition of Integer Programs

We examine ways to reformulate integer and mixed integer programs. Typically, but not exclusively, one reformulates so as to obtain stronger linear programming relaxations, and hence better bounds for use in a branch-and-bound based algorithm. First we cover reformulations based on decomposition, such as Lagrangean relaxation, the Dantzig-Wolfe reformulation and the resulting column generation and branch-and-price algorithms. This is followed by an examination of Benders' type algorithms based on projection. Finally we discuss extended formulations involving additional variables that are based on problem structure. These can often be used to provide strengthened a priori formulations. Reformulations obtained by adding cutting planes in the original variables are not treated here