Taxi Planner Optimization: A Management Tool

This work introduces taxi planning optimization (TPO) as a methodology to guide airport surface management operations. The optimization model represents competing aircraft using limited ground resources. TPO improves aircraft taxiing routes and their schedule in situations of congestion, minimizing overall taxiing time (TT), and helping taxi planners to meet prespecified goals such as compliance with take-off windows, TT limits, and trajectory conflicts. By considering all simultaneous trajectories during a given planning horizon, TPO's estimation of TT from the stand to the runways improves over current planning methods. The operational optimization model is a large-scale space-time multi-commodity network with capacity constraints. In addition to its natural use as a real-time taxi planning tool, a number of TPO variants can be used for design purposes, such as expansion of new infrastructure. TPO is demonstrated using Madrid-Barajas as test airport

Constrained Non-Monotone Submodular Maximization: Offline and Secretary Algorithms

Constrained submodular maximization problems have long been studied, with near-optimal results known under a variety of constraints when the submodular function is monotone. The case of non-monotone submodular maximization is less understood: the first approximation algorithms even for the unconstrainted setting were given by Feige et al. (FOCS '07). More recently, Lee et al. (STOC '09, APPROX '09) show how to approximately maximize non-monotone submodular functions when the constraints are given by the intersection of p matroid constraints; their algorithm is based on local-search procedures that consider p-swaps, and hence the running time may be n^Omega(p), implying their algorithm is polynomial-time only for constantly many matroids. In this paper, we give algorithms that work for p-independence systems (which generalize constraints given by the intersection of p matroids), where the running time is poly(n,p). Our algorithm essentially reduces the non-monotone maximization problem to multiple runs of the greedy algorithm previously used in the monotone case. Our idea of using existing algorithms for monotone functions to solve the non-monotone case also works for maximizing a submodular function with respect to a knapsack constraint: we get a simple greedy-based constant-factor approximation for this problem. With these simpler algorithms, we are able to adapt our approach to constrained non-monotone submodular maximization to the (online) secretary setting, where elements arrive one at a time in random order, and the algorithm must make irrevocable decisions about whether or not to select each element as it arrives. We give constant approximations in this secretary setting when the algorithm is constrained subject to a uniform matroid or a partition matroid, and give an O(log k) approximation when it is constrained by a general matroid of rank k.Comment: In the Proceedings of WINE 201

Two-stage stochastic minimum s − t cut problems: Formulations, complexity and decomposition algorithms

We introduce the two‐stage stochastic minimum s − t cut problem. Based on a classical linear 0‐1 programming model for the deterministic minimum s − t cut problem, we provide a mathematical programming formulation for the proposed stochastic extension. We show that its constraint matrix loses the total unimodularity property, however, preserves it if the considered graph is a tree. This fact turns out to be not surprising as we prove that the considered problem is NP-hard in general, but admits a linear time solution algorithm when the graph is a tree. We exploit the special structure of the problem and propose a tailored Benders decomposition algorithm. We evaluate the computational efficiency of this algorithm by solving the Benders dual subproblems as max-flow problems. For many tested instances, we outperform a standard Benders decomposition by two orders of magnitude with the Benders decomposition exploiting the max-flow structure of the subproblems

Testing Tablet Computers in Nursing Education: A Comprehensive Evaluation Framework

Background: Tablet Computers (TCs) and other mobile digital devices are rapidly changing the way we communicate and access information in our personal and professional lives. Scarce research exists regarding their effectiveness in promoting the learning of health professionals. This paper describes the evaluation framework used in a study to test TCs in a post-diploma baccalaureate nursing program in the Gulf Cooperation Council (GCC) state of Qatar.Purpose: The evaluation framework was structured around 10 objectives designed to assess the impact of TC integration into the evidence-based practice (EBP) and reflective practice (RP) components of a scholarship course. Evaluation variables included perceptions of knowledge, confidence, comfort, satisfaction and technical skill before and after the 7-week TC implementation; students’ usage patterns and attitudes about the usefulness of TCs in promoting their learning related to EBP and RP were also examined; in addition, students’ views about the impact of TCs on the learning environment and their engagement in the learning process were sought.Methods: A mixed method descriptive design was used to assess outcomes of interest. Qualitative methods (focus groups, participant observation, field notes and reflective journals) were used to capture subjective perspectives of TC users. Quantitative methods (pre-test/posttest, activity logs and skills labs) were used to assess change in knowledge, attitude and technical proficiency over time.Results: The evaluation framework used to assess process and outcome variables in this study combined structural, philosophical, theoretical, pedagogical and methodological elements. These included the logic model, participatory action, theory-based course concepts, as well as a learning taxonomy involving cognitive, affective and psychomotor competencies.Conclusion: The value of a comprehensive evaluation plan executed in tandem with TC implementation is highlighted

On Budget-Feasible Mechanism Design for Symmetric Submodular Objectives

We study a class of procurement auctions with a budget constraint, where an auctioneer is interested in buying resources or services from a set of agents. Ideally, the auctioneer would like to select a subset of the resources so as to maximize his valuation function, without exceeding a given budget. As the resources are owned by strategic agents however, our overall goal is to design mechanisms that are truthful, budget-feasible, and obtain a good approximation to the optimal value. Budget-feasibility creates additional challenges, making several approaches inapplicable in this setting. Previous results on budget-feasible mechanisms have considered mostly monotone valuation functions. In this work, we mainly focus on symmetric submodular valuations, a prominent class of non-monotone submodular functions that includes cut functions. We begin first with a purely algorithmic result, obtaining a $\frac{2e}{e-1}$-approximation for maximizing symmetric submodular functions under a budget constraint. We view this as a standalone result of independent interest, as it is the best known factor achieved by a deterministic algorithm. We then proceed to propose truthful, budget feasible mechanisms (both deterministic and randomized), paying particular attention on the Budgeted Max Cut problem. Our results significantly improve the known approximation ratios for these objectives, while establishing polynomial running time for cases where only exponential mechanisms were known. At the heart of our approach lies an appropriate combination of local search algorithms with results for monotone submodular valuations, applied to the derived local optima.Comment: A conference version appears in WINE 201

Perspective Cuts for the ACOPF with Generators

International audienceThe alternating current optimal power flow problem is a fundamental problem in the management of smart grids. In this paper we consider a variant which includes activation/deactivation of generators at some of the grid sites. We formulate the problem as a mathematical program, prove its NP-hardness w.r.t. ac-tivation/deactivation, and derive two perspective reformulations

A computational analysis of lower bounds for big bucket production planning problems

In this paper, we analyze a variety of approaches to obtain lower bounds for multi-level production planning problems with big bucket capacities, i.e., problems in which multiple items compete for the same resources. We give an extensive survey of both known and new methods, and also establish relationships between some of these methods that, to our knowledge, have not been presented before. As will be highlighted, understanding the substructures of difficult problems provide crucial insights on why these problems are hard to solve, and this is addressed by a thorough analysis in the paper. We conclude with computational results on a variety of widely used test sets, and a discussion of future research