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• Data Envelopment Analysis as Nonparametric Least-Squares Regression AI翻译
  作者：Kuosmanen, Timo; Johnson, Andrew L.
  作者单位：Natural Resources Institute Finland (Luke); Aalto University; Texas A&M University System; Texas A&M University College Station
  摘要：Data envelopment analysis (DEA) is known as a nonparametric mathematical programming approach to productive efficiency analysis. In this paper, we show that DEA can be alternatively interpreted as nonparametric least-squares regression subject to shape constraints on the frontier and sign constraints on residuals. This reinterpretation reveals the classic parametric programming model by Aigner and Chu [Aigner, D., S. Chu. 1968. On estimating the industry production function. Amer. Econom. Rev....
• Robust Optimization for Unconstrained Simulation-Based Problems AI翻译
  作者：Bertsimas, Dimitris; Nohadani, Omid; Teo, Kwong Meng
  作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); National University of Singapore
  摘要：In engineering design, an optimized solution often turns out to be suboptimal when errors are encountered. Although the theory of robust convex optimization has taken significant strides over the past decade, all approaches fail if the underlying cost function is not explicitly given; it is even worse if the cost function is nonconvex. In this work, we present a robust optimization method that is suited for unconstrained problems with a nonconvex cost function as well as for problems based on ...
• Branch-and-Price-and-Cut for the Split-Delivery Vehicle Routing Problem with Time Windows AI翻译
  作者：Desaulniers, Guy
  作者单位：Universite de Montreal; Polytechnique Montreal; Universite de Montreal
  摘要：This paper addresses the split-delivery vehicle routing problem with time windows (SDVRPTW) that consists of determining least-cost vehicle routes to service a set of customer demands while respecting vehicle capacity and customer time windows. The demand of each customer can be fulfilled by several vehicles. For solving this problem, we propose a new exact branch-and-price-and-cut method, where the column generation subproblem is a resource-constrained elementary shortest-path problem combine...
• Acceleration Operators in the Value Iteration Algorithms for Markov Decision Processes AI翻译
  作者：Shlakhter, Oleksandr; Lee, Chi-Guhn; Khmelev, Dmitry; Jaber, Nasser
  作者单位：University of Toronto; University of Toronto
  摘要：We study the general approach to accelerating the convergence of the most widely used solution method of Markov decision processes (MDPs) with the total expected discounted reward. Inspired by the monotone behavior of the contraction mappings in the feasible set of the linear programming problem equivalent to the MDP, we establish a class of operators that can be used in combination with a contraction mapping operator in the standard value iteration algorithm and its variants. We then propose ...
• Acceleration Operators in the Value Iteration Algorithms for Markov Decision Processes (vol 58, pg 193, 2010) AI翻译
  作者：Shlakhter, Oleksandr; Lee, Chi-Guhn; Khmelev, Dmitry; Jaber, Nasser
• Percentile Optimization for Markov Decision Processes with Parameter Uncertainty AI翻译
  作者：Delage, Erick; Mannor, Shie
  作者单位：Universite de Montreal; HEC Montreal; McGill University
  摘要：Markov decision processes are an effective tool in modeling decision making in uncertain dynamic environments. Because the parameters of these models typically are estimated from data or learned from experience, it is not surprising that the actual performance of a chosen strategy often differs significantly from the designer's initial expectations due to unavoidable modeling ambiguity. In this paper, we present a set of percentile criteria that are conceptually natural and representative of t...
• Partially Observable Markov Decision Processes: A Geometric Technique and Analysis AI翻译
  作者：Zhang, Hao
  作者单位：University of Southern California
  摘要：This paper presents a novel framework for studying partially observable Markov decision processes (POMDPs) with finite state, action, observation sets, and discounted rewards. The new framework is solely based on future-reward vectors associated with future policies, which is more parsimonious than the traditional framework based on belief vectors. It reveals the connection between the POMDP problem and two computational geometry problems, i.e., finding the vertices of a convex hull and findin...
• Disjunctive Decomposition for Two-Stage Stochastic Mixed-Binary Programs with Random Recourse AI翻译
  作者：Ntaimo, Lewis
  作者单位：Texas A&M University System; Texas A&M University College Station
  摘要：This paper introduces disjunctive decomposition for two-stage mixed 0-1 stochastic integer programs (SIPs) with random recourse. Disjunctive decomposition allows for cutting planes based on disjunctive programming to be generated for each scenario subproblem under a temporal decomposition setting of the SIP problem. A new class of valid inequalities for mixed 0-1 SIP with random recourse is presented. In particular, we derive valid inequalities that allow for scenario subproblems for SIP with ...
• Linear Inflation Rules for the Random Yield Problem: Analysis and Computations AI翻译
  作者：Huh, Woonghee Tim; Nagarajan, Mahesh
  作者单位：Columbia University; University of British Columbia
  摘要：In this paper, we propose a simple heuristic approach for the inventory control problem with stochastic demand and multiplicative random yield. Our heuristic tries to find the best candidate within a class of policies that are referred to in the literature as the linear inflation rule (LIR) policies. Our approach is computationally fast, easy to implement, and intuitive to understand. Moreover, we find that in a significant number of instances our heuristic performs better than several other w...