Abstract

In this paper, we propose an efficient matheuristic approach for solving the problem of scheduling the Brazilian OR conference. The event has traditionally around 300 presentations across a period of 3 to 4 days, and building a schedule for the technical sessions is an arduous task. The developed algorithm integrates the concepts of iterated local search and simulated annealing with two mathematical programming-based procedures. The idea is to group the presentations via a clustering procedure, and handle the side constraints in a subproblem via an integer programming formulation. A set partitioning procedure is applied at the end of the algorithm to find the optimal combination of clusters found during the search. We first assess the performance of the method by comparing our results with those attained by other algorithms from the literature on two existing sets of artificial instances derived from two other conferences. Next, we executed our approach on real-life instances derived from different SBPO editions, and compared the solutions with the manual solutions, when available, or with upper bounds (we solve a maximisation problem) found by an exact algorithm from the literature. The results obtained show that the matheuristic is capable of achieving high quality solutions both on the artificial and real-life instances.

摘要

在本文中，我们提出了一种有效的数学方法来解决安排巴西 OR 会议的问题。传统上，该活动在 3 到 4 天的时间里有大约 300 场演讲，制定技术会议的时间表是一项艰巨的任务。所开发的算法将迭代局部搜索和模拟退火的概念与两个基于数学规划的程序相结合。这个想法是通过聚类过程对表示进行分组，并通过整数规划公式处理子问题中的边约束。在算法结束时应用集合划分过程以找到在搜索过程中找到的集群的最佳组合。我们首先通过将我们的结果与文献中其他算法获得的结果进行比较来评估该方法的性能，这些算法来自于来自其他两个会议的两个现有人工实例集。接下来，我们对源自不同 SBPO 版本的真实实例执行我们的方法，并将解决方案与手动解决方案（如果可用）或通过文献中的精确算法找到的上限（我们解决最大化问题）进行比较。获得的结果表明，数学能够在人工和现实生活实例上实现高质量的解决方案。或通过文献中的精确算法找到的上限（我们解决最大化问题）。获得的结果表明，数学能够在人工和现实生活实例上实现高质量的解决方案。或通过文献中的精确算法找到的上限（我们解决最大化问题）。获得的结果表明，数学能够在人工和现实生活实例上实现高质量的解决方案。