Emir Demirović,Nysret Musliu
Emir Demirović
High school timetabling (HSTT) is a well known and wide spread problem. The problem consists of coordinating resources (e.g. teachers, rooms), times, and events (e.g. lectures) with respect to various constraints. Unfortunately, HSTT is har...
Adam N Guetz,Susan P Holmes
Adam N Guetz
Network Growth Models such as Preferential Attachment and Duplication/Divergence are popular generative models with which to study complex networks in biology, sociology, and computer science. However, analyzing them within the framework of...
Developing policy analytics for public health strategy and decisions-the Sheffield alcohol policy model framework [0.03%]
完善政策分析方法，促进公共卫生策略和决策——谢菲尔德酒精政策模型框架研究
Alan Brennan,Petra Meier,Robin Purshouse et al.
Alan Brennan et al.
This paper sets out the development of a methodological framework for detailed evaluation of public health strategies for alcohol harm reduction to meet UK policy-makers needs. Alcohol is known to cause substantial harms, and controlling it...
Distribution-dependent robust linear optimization with applications to inventory control [0.03%]
基于分布的鲁棒线性优化及其在库存控制中的应用
Seong-Cheol Kang,Theodora S Brisimi,Ioannis Ch Paschalidis
Seong-Cheol Kang
This paper tackles linear programming problems with data uncertainty and applies it to an important inventory control problem. Each element of the constraint matrix is subject to uncertainty and is modeled as a random variable with a bounde...
Matthew P Johnson,Alexander Gutfraind,Kiyan Ahmadizadeh
Matthew P Johnson
In network interdiction problems, evaders (e.g., hostile agents or data packets) are moving through a network toward targets and we wish to choose locations for sensors in order to intercept the evaders. The evaders might follow determinist...
Batch Mode Reinforcement Learning based on the Synthesis of Artificial Trajectories [0.03%]
基于人工轨迹合成的批量模式强化学习
Raphael Fonteneau,Susan A Murphy,Louis Wehenkel et al.
Raphael Fonteneau et al.
In this paper, we consider the batch mode reinforcement learning setting, where the central problem is to learn from a sample of trajectories a policy that satisfies or optimizes a performance criterion. We focus on the continuous state spa...