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.

Abstract
The editorial section of International Journal of Production Research provides information about industrial extensions and research opportunities in the fields of lot sizing and scheduling in industries. Some of the papers published in the journal reveal that the processing industry provides significant opportunities for conducting research in these areas. The scheduling of production lots and their sizing is an area of increasing research attention within the wider field of production planning and scheduling. The close relationship between lot sizing and scheduling in many industrial applications makes it essential that these decisions are made simultaneously to use capacity efficiently. Traditional models have been increasingly refined to incorporate more detail and integrate lot sizing with scheduling. Researchers and practitioners worldwide have been making efforts to incorporate more specificities of the production environment in their models besides the integration of several independent self-contained research fields.

Abstract
In this paper, we propose three novel mathematical models for the two-stage lot-sizing and scheduling problems present in many process industries. The problem shares a continuous or quasi-continuous production feature upstream and a discrete manufacturing feature downstream, which must be synchronized. Different time-based scale representations are discussed. The first formulation encompasses a discrete-time representation. The second one is a hybrid continuous-discrete model. The last formulation is based on a continuous-time model representation. Computational tests with state-of-the-art MIP solver show that the discrete-time representation provides better feasible solutions in short running time. On the other hand, the hybrid model achieves better solutions for longer computational times and was able to prove optimality more often. The continuous-type model is the most flexible of the three for incorporating additional operational requirements, at a cost of having the worst computational performance. Journal of the Operational Research Society (2012) 63, 1613-1630. doi:10.1057/jors.2011.159 published online 7 March 2012