Support Vector Machines (SVM) is the state-of-the-art in Supervised Classification. In this paper the Cluster Support Vector Machines (CLSVM) methodology is proposed with the aim to reduce the complexity of the SVM classifier in the presence of categorical features. The CLSVM methodology lets categories cluster around their peers and builds an SVM classifier using the … Read more
This paper introduces a new vehicle routing problem transferring one commodity between customers with a capacitated vehicle that can visit a customer more than once,although a maximum number of visits must be respected. It generalizes the capacitated vehicle routing problem with split demands and some other variants recently addressed in the literature. We model the … Read more
This paper presents a new model for online decision making. Motivated by the health care delivery application of dynamically allocating patients to procedure rooms in outpatient procedure centers, the online stochastic extensible bin packing problem is described. The objective is to minimize the combined costs of opening procedure rooms and utilizing overtime to complete a … Read more
The Capacitated Vehicle Routing Problem is a much-studied (and strongly NP-hard) combinatorial optimization problem, for which many integer programming formulations have been proposed. We present some new multi-commodity flow (MCF) formulations, and show that they dominate all of the existing ones, in the sense that their continuous relaxations yield stronger lower bounds. Moreover, we show … Read more
In this paper we discuss stochastic optimal path problems where the goal is to find a path that has minimal expected cost and at the same time is less risky (in terms of travel time) than a given benchmark path. The model is suitable for a risk-averse traveler, who prefers a path with a more … Read more
This paper explores the classic single-item newsvendor problem under a novel setting which combines temporal dependence and tractable robust optimization. First, the demand is modeled as a time series which follows an autoregressive process AR(p), p>= 1. Second, a robust approach to maximize the worst-case revenue is proposed: a robust distribution-free autoregressive forecasting method, which … Read more
Enormous global popularity of online social network sites has initiated numerous studies and methods investigating different aspects of their use, so some concepts from network-based studies in optimization theory can be used for research into online networks. In Gaji\’c (2014) are given a several new mixed integer linear programming formulations for first and second problem … Read more
In this paper, we investigate the problem of linear joint probabilistic constraints. We assume that the rows of the constraint matrix are dependent and the dependence is driven by a convenient Archimedean copula. Further we assume the distribution of the constraint rows to be elliptically distributed, covering normal, $t$, or Laplace distributions. Under these and … Read more
Incident Management Teams (IMTs) are responsible for managing the response to wildfires. One of the IMT’s objectives is the protection of assets and infrastructure. In this paper we develop a mathematical model to assist IMTs in assigning resources to asset protection activities during escaped wildfires. We present a mixed integer programming model for resource allocation … Read more
The traveling umpire problem (TUP) consists of determining which games will be handled by each one of several umpire crews during a double round-robin tournament. The objective is to minimize the total distance traveled by the umpires, while respecting constraints that include visiting every team at home, and not seeing a team or venue too … Read more