Optimization Days 2019
HEC Montréal, May 1315, 2019
JOPT2019
HEC Montréal, 13 — 15 May 2019
TD5 Risk Averse Decision Making
May 14, 2019 03:30 PM – 05:10 PM
Location: HélèneDesmarais
Chaired by Erick Delage
4 Presentations

03:30 PM  03:55 PM
A BranchandCut Approach for the Distributionally Robust ChanceConstrained Assignment Problem
We study a class of assignment problems with chance constraints that provide a probabilistic guarantee for bin capacity constraints, in which a set of items with random weights are assigned to the bins while minimizing the assignment cost. We firstly formulate chanceconstrained assignment problem (CAP) as a 01 integer program with discrete distribution of random weights. We then extend (CAP) into distributionally robust chanceconstrained assignment problem (DRCAP), and robustify chance constraints by introducing a general family of ambiguity sets (e.g. moment matching set and Wasserstein set) with finite support. Moreover, we derive the bilinear integer reformulations for (CAP) and (DRCAP) and propose two classes of valid inequalities. A BranchandCut solution framework is proposed to solve (CAP) and (DRCAP) respectively. An extensive study of surgery assignment problem using real data from a hospital is conducted.
Keywords: Chance constrained assignment problem, distributionally robust optimization, BranchandCut, valid inequalities 
03:55 PM  04:20 PM
Equal risk pricing under worstcase risk measure
We study the equal risk framework in which the fair price of an option is the price that exposes both sides of a contract to the same level of risk. This framework takes into consideration both the perspective of the option writer and that of the buyer, which results in separate hedging strategies for each of them. In particular, we study the use of recursive risk measures by the option writer and the buyer for pricing and hedging options.

04:20 PM  04:45 PM
Probabilistic envelope constrained multiperiod stochastic EMS location model and decomposition scheme
This paper considers a multiperiod Emergency Medical Services (EMS) location problem and introduces two twostage stochastic programming formulations that account for emergency demand uncertainty. While the first model considers both a constraint on the probability of covering the realized emergency demand and minimizing the expected cost of doing so, the second one employs probabilistic envelope constraints which allows us to the degradation of coverage under the more severe scenarios. These models give rise to large MIPs, which can be tackled by BranchandBendersCut method directly or using conservative approximation scheme. Finally, a practical study is conducted using historical data.

04:45 PM  05:10 PM
Adjustable robust optimization reformulations of twostage worstcase regret minimization problems
Within the context of optimization under uncertainty, a wellknown alternative to minimizing expected value or the worstcase scenario, a.k.a. robust optimization, consists in minimizing regret. We demonstrate that twostage worstcase regret minimization problems can be reformulated as twostage robust optimization models. This empowers us to employ recent advanced approximate and exact solution schemes for these hard problems.
Keywords: Robust optimization, worstcase regret minimization, sequential decision making