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Line-Prioritized Environmental Selection and Normalization Scheme for Many-Objective Optimization using Reference-Line-based Framework

  • Deepak Sharma
  • Pradyumn Kumar Shukla
    • Categories Multi-Criteria Optimization

    The Pareto-dominance-basedmulti-objective evolutionary algorithms (MOEAs) have been successful in solving many test problems and other engineering optimization problems. However, their performance gets affected when solving more than 3-objective optimization problems due to lack of sufficient selection pressure. Many attempts have been made by the researchers toward improving the environmental selection of those MOEAs. One such … Read more

    Distributionally Robust Partially Observable Markov Decision Process with Moment-based Ambiguity

  • Ruiwei Jiang
  • Siqian Shen
  • Hideaki Nakao
    • Categories Dynamic Programming, Robust Optimization Tags , , , ,

    We consider a distributionally robust Partially Observable Markov Decision Process (DR-POMDP), where the distribution of the transition-observation probabilities is unknown at the beginning of each decision period, but their realizations can be inferred using side information at the end of each period after an action being taken. We build an ambiguity set of the joint … Read more

    Assessment of Climate Agreements over the Long Term with Strategic Carbon Dioxyde Removal Activity

  • Frédéric Babonneau
  • Alain Haurie
  • Alain Vielle
    • Categories Applications - OR and Management Sciences, Game Theory Tags , , , ,

    In this paper we extend a game theoretic meta-model used to assess the future of Paris agreement to the time horizon 2100 and we include in the strategic decisions of the negotiating coalitions the use of Carbon Dioxyde Removal (CDR) technologies. The meta-game model is calibrated through statistical emulation of GEMINI-E3, a world computable general … Read more

    Solving Multiobjective Mixed Integer Convex Optimization Problems

  • Marianna De Santis
  • Gabriele Eichfelder
  • Julia Niebling
  • Stefan Rocktäschel
    • Categories (Mixed) Integer Nonlinear Programming, Global Optimization, Multi-Criteria Optimization Tags , ,

    Multiobjective mixed integer convex optimization refers to mathematical programming problems where more than one convex objective function needs to be optimized simultaneously and some of the variables are constrained to take integer values. We present a branch-and-bound method based on the use of properly defined lower bounds. We do not simply rely on convex relaxations, … Read more

    Risk-Sensitive Variational Bayes: Formulations and Bounds

  • Harsha Honnappa
  • Prateek Jaiswal
  • Vinayak A. Rao
    • Categories Other Topics, Stochastic Programming Tags , ,

    We study data-driven decision-making problems in a parametrized Bayesian framework. We adopt a risk-sensitive approach to modeling the interplay between statistical estimation of parameters and optimization, by computing a risk measure over a loss/disutility function with respect to the posterior distribution over the parameters. While this forms the standard Bayesian decision-theoretic approach, we focus on … Read more

    Pricing for Delivery Time Flexibility

  • Martin Savelsbergh
  • Barış Yıldız
    • Categories Dynamic Programming, Supply Chain Management, Transportation Tags , , , ,

    We study a variant of the multi-period vehicle routing problem, in which a service provider offers a discount to customer in exchange for delivery flexibility. We establish theoretical properties and empirical insights regarding the intricate and complex relation between the benefit from additional delivery flexibility, the discounts offered to customers to gain additional delivery flexibility, … Read more

    Stochastic Lipschitz Dynamic Programming

  • Shabbir Ahmed
  • Filipe G. Cabral
  • Bernardo Freitas Paulo da Costa
    • Categories Dynamic Programming, Integer Programming, Stochastic Programming Tags , , ,

    We propose a new algorithm for solving multistage stochastic mixed integer linear programming (MILP) problems with complete continuous recourse. In a similar way to cutting plane methods, we construct nonlinear Lipschitz cuts to build lower approximations for the non-convex cost to go functions. An example of such a class of cuts are those derived using … Read more

    Distributionally robust optimization with multiple time scales: valuation of a thermal power plant

  • Daniela Escobar
  • Martin Glanzer
  • Georg Ch. Pflug
  • Wim van Ackooij
    • Categories Dynamic Programming, Stochastic Programming Tags , , , , , ,

    The valuation of a real option is preferably done with the inclusion of uncertainties in the model, since the value depends on future costs and revenues, which are not perfectly known today. The usual value of the option is defined as the maximal expected (discounted) profit one may achieve under optimal management of the operation. … Read more

    Exact Multiple Sequence Alignment by Synchronized Decision Diagrams

  • Amin Hosseininasab
  • Willem-Jan van Hoeve
    • Categories Applications - OR and Management Sciences, Dynamic Programming, Integer Programming

    This paper develops an exact solution algorithm for the Multiple Sequence Alignment (MSA) problem. In the first step, we design a dynamic programming model and use it to construct a novel Multi-valued Decision Diagrams (MDD) representation of all pairwise sequence alignments (PSA). PSA MDDs are then synchronized using side constraints to model the MSA problem … Read more

    Planning for Dynamics under Uncertainty

  • Dicong Qiu
  • Karsh Tharyani
    • Categories Dynamic Programming, Systems governed by Differential Equations Optimization, Unconstrained Optimization Tags , ,

    Planning under uncertainty is a frequently encountered problem. Noisy observation is a typical situation that introduces uncertainty. Such a problem can be formulated as a Partially Observable Markov Decision Process (POMDP). However, solving a POMDP is nontrivial and can be computationally expensive in continuous state, action, observation and latent state space. Through this work, we … Read more