A decision support system to improve performances of airport check-in services

The recent remarkable increase in air passenger traffic has been fostering a considerable congestion of the airport facilities. In this context, traditional procedures employed for check-in operations have been supported by alternative methods, based on the use of self-service options (kiosks, web services, app for mobile phones, etc). However, even if such innovations are contributing to improve the service level provided to passengers, field investigations suggest that traditional procedures will be employed also in the future, especially for medium and long-haul flights, where baggage dropping is required. For this reason, the passengers allocation problem at check-in counters is attracting growing attention by the scientific community and several decision support tools, involving both optimization and simulation methods, have been proposed. Most of the available approaches aim at deciding the optimal number of check-in counters to be activated, in such a way to balance the operative costs and passengers waiting times. Such approaches assume that the service capacity (in terms of available check-in operators and counters) is given and determined on the basis of physical constraints (related to the available space in the terminal) and of staff scheduling decisions made at a tactical level. The present contribution tries to overcome this limitation, by proposing a decision support system, based on a mathematical model, capable of designing optimal check-in policies by also incorporating staff scheduling decisions. The model is tested on some real-world case studies; computational results are evaluated, along with the practical usability of the approach

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Improving police control rooms using simulation

Police command and control centres are the main point of contact for the public who require help. Like other areas of UK public services, police forces are set targets for their performance. Some of these targets relate to the speed at which they respond to calls for assistance from the public. In this paper we share our experience in improving the performance of command and control centres of a UK Police Force; a project which started as a classical simulation exercise and ended up with a significant reorganization in a UK Police Force

A Stochastic Approximation Algorithm for Quantile Estimation

In this paper, we present two new stochastic approximation algorithms for the problem of quantile estimation. The algorithms uses the characterization of the quantile provided in terms of an optimization problem in 1]. The algorithms take the shape of a stochastic gradient descent which minimizes the optimization problem. Asymptotic convergence of the algorithms to the true quantile is proven using the ODE method. The theoretical results are also supplemented through empirical evidence. The algorithms are shown to provide significant improvement in terms of memory requirement and accuracy

Optimal selection of contracts and work shifts in multi-skill call centers

This paper deals with the problem of finding the most suitable contracts to be used when hiring the operators of a call center and deciding their optimal working schedule, to optimize the trade-off between the service level provided to the customers and the cost of the personnel. In a previous paper (Cordone et al. 2011), we proposed a heuristic method to quickly build an integer solution from the solution of the continuous relaxation of an integer linear programming model. In this paper, we generalize that model to take into account a much wider class of working contracts, allowing heterogeneous shift patterns, as well as legal constraints related to continuously active working environments. Since our original rounding heuristic cannot be extended to the new model, due to its huge size and to the involved correlations between different sets of integer variables, we introduce a more sophisticated heuristic based on decomposition and on a multi-level iterative structure. We compare the results of this heuristic with those of a Greedy Randomized Adaptive Search Procedure, both on real-world instances and on realistic random instances

Monte carlo simulation

This chapter discusses the basic concept and techniques for Monte Carlo simulation. The simulation methods for a single random variable as well as those for a random vector (consisting of multiple variables) are discussed, followed by the simulation of some special stochastic processes, including Poisson process, renewal process, Gamma process and Markov process. Some advanced simulation techniques, such as the importance sampling, Latin hypercube sampling, and subset simulation, are also addressed in this chapter