Abstract
Wafer slicing in photovoltaic industry is mainly done using multi-wire saw machines. The selection of set of bricks (parallelepiped block of crystalline silicon) to be sawn together poses difficult production scheduling decisions. The objective is to maximize the utilization of the available cutting length to improve the process throughput. We address the problem presenting a mathematical formulation and an algorithm that aims to solve it in very short running times while delivering superior solutions. The algorithm employs a reactive greedy randomized adaptive search procedure with some enhancements. Computational experiments proved its effectiveness and efficiency to solve real-world based problems and randomly generated instances. Implementation of an on-line decision system based on this algorithm can help photovoltaic industry to reduce slicing costs making a contribution for its competitiveness against other sources of energy.

Abstract
Driven by a real-world application in the beverage industry, this paper provides a design of a new VNS variant to tackle the annual production budget problem. The problem consists of assigning and scheduling production lots in a multi-plant environment, where each plant has a set of filling lines that bottle and pack drinks. Plans also consider final product transfers between the plants. Our algorithm fixes setup variables for family of products and determines production, inventory and transfer decisions by solving a linear programming (LP) model. As we are dealing with very large problem instances, it is inefficient and unpractical to search the entire neighborhood of the incumbent solution at each iteration of the algorithm. We explore the sensitivity analysis of the LP to guide the partial neighborhood search. Dual-reoptimization is also used to speed-up the solution procedure. Tests with instances from our case study have shown that the algorithm can substantially improve the current business practice, and it is more competitive than state-of-the-art commercial solvers and other VNS variants.

Abstract
We address a problem that often arises in industry, the multi-item capacitated-lot-sizing and scheduling problem with sequence-dependent setup times and costs. Powerful commercial solvers fail to solve even medium-sized instances of this NP-hard problem, therefore we employ a tabu search and a variable neighbourhood search meta-heuristic to solve it and compare the performance of these metaheuristics over time. In contrast to the majority of the literature on this topic, the solution representation explicitly considers production quantities and setup variables, which enables us to develop fast search heuristics. A comprehensive set of computational experiments shows the effectiveness and efficiency of the proposed approaches in solving medium-to large-sized problems.

Abstract
Hospital performance is a critical issue in society and its assessment must be tactfully studied in order to evaluate future decisions. In this paper we report an operating theatre study based on a simulation model, describing one of the biggest public hospitals in the north of Portugal. The model encompasses several operating rooms shared among different medical services and considers the arrival of both elective and emergency patients. We focus on a critical planning problem of the operating theatre, the allocation of medical services to operating rooms and shifts. With a discrete-event simulation model we assess the performance of the current schedule distribution and perform a set of tests in order to find a better master surgery schedule. Experiments show improvement opportunities by balancing surgical services capacities. © 2011 IEEE.

Abstract
Asthma patients incur a great cost in terms of loss of quality of life. The purpose of this study is to evaluate the relative contribution and relationship of several patient- and disease-related factors, measured by several variables, to the quality of life in adults with asthma. Two hundred and ten asthmatic outpatients over 18 years old, registered in a Family Health Unit, were randomly selected to complete the Asthma Quality of Life (AQLQ) and Short Form Generic questionnaires (SF-36), respectively. Single and multiple linear regression models were developed to explain the variability of the summary scores of AQLQ and Physical and Mental Health SF-36. As potential predictors, the following independent variables were used: gender, age, number of comorbidities, asthma severity following the Global Initiative for Asthma (GINA) criteria, asthma control (measured by ACQ questionnaire), %FEV1 (forced expiratory volume in the first second) and, for the first time, Graffar Score to assess socioeconomical features. The Graffar Score is an index that divides the population in 5 socioeconomic layers. We report the best Adjusted R Square of these models published in the literature, ranging from 0.40 to 0.76. Women showed poorer quality of life than men. The best predictor of AQLQ was ACQ, followed by Asthma Severity, Gender and %FEV1. The best predictors of Physical and Mental Health SF-36 were, by decreasing importance, ACQ, number of comorbidities, Gender and Graffar Score. We note that the variable Dumber of comorbidities was included in both SF-36 models, but not in AQLQ model. Asthma Severity and %FEV1 did not enter into SF-36 models. We conclude that besides clinical and functional measures, the evaluation process of the overall health status must incorporate quality-of-life measures.

Abstract
In this paper we present a novel approach to tackling the synchronisation of a secondary resource in lot-sizing and scheduling problems. This kind of problem occurs in various manufacturing processes (e. g. wafer testing in the semiconductor industry, production and bottling of soft drinks). We consider a scenario of parallel unrelated machines that have to be equipped with a tool or need a special kind of resource for processing. Our approach allows tracing the assignment of these secondary resources across different machines and synchronising their usage independently of the time period. We present extensions of the general lot-sizing and scheduling problem and of the capacitated lot-sizing problem. We prove that the latter model is a special case of the first, but it performs computationally much better.