a b s t r a c tWe study a single machine scheduling problem with availability constraints and sequencedependent setup costs, with the aim of minimizing the makespan. To the authors' knowledge, this problem has not been treated as such in the operations research literature. We derive in this paper a mixed integer programming model to deal with such scheduling problem. Computational tests showed that commercial solvers are capable of solving only small instances of the problem. Therefore, we propose two ways for reducing the execution time, namely a valid inequality that strengthen the linear relaxation and an efficient heuristic procedure that provides a starting feasible solution to the solver. A substantial gain is achieved both in terms of the linear programming relaxation bound and in terms of the time to obtain an integer optimum when we use the enhanced model in conjunction with providing to the solver the solution obtained by the proposed heuristic.

Purpose: This research paper introduces an integrated employee scheduling problem that considers various real-life problems such as varying employee demand, different employee working conditions, and individual preferences regarding schedules.Design/methodology/approach: The proposed model, which is a combination of Analytic Hierarchy Process and Mixed Integer Linear Programming, is used to solve the problem with multi-dimensional objectivesFindings: Results show that the proposed model generates optimal and feasible solutions for weekly employee schedules.Originality/value: Many employee scheduling problems in literature are able to solve the employee scheduling problem to a large extent but still do not fully reflect current realistic organizational problems such as varying employee demand per hour inteval, different employee working conditions on disjoint shifts and breaks, and individual preferences regarding schedules all at the same time.

a b s t r a c tIn this paper we study a problem of sequencing jobs in a machine with programmed preventive maintenance and sequence-dependent setup times. To the authors' knowledge, this problem has not been treated as such in the operations research literature. Computational experiments show that it is very hard to solve the problem by exact methods. Therefore, the contribution of this paper is to design and implement a solution approach based on metaheuristic procedures. The proposed method finds high quality solutions in very short computational times.

Background: Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) share metabolic alterations such as abnormal insulin and lipid metabolism and have some common genetic factors such as APOE genotype. Taking this into account, we hypothesized that we could identify common genetic factors involved in the development of diabetes and cardiovascular diseases. Methodology: We first genotyped 48 single nucleotide polymorphisms (SNPs) previously associated with AD in a cohort composed of 330 patients with cognitive impairment (CI) to assess their association with plasma lipids. Second, we conducted pleiotropy-informed conjunctional false discovery rate (FDR) analysis designed to identify shared variants between AD and plasma lipid levels. Finally, we used the SNPs to be found associated with lipid parameters and AD to search for associations with lipoprotein parameters in 281 patients with cardiometabolic risk. Results: Five SNPs were significantly associated with lower levels of cholesterol transported in remnant lipoprotein particles (RLPc) in subjects with CI; among these SNPs was the rs73572039 variant in PVRL2. Stratified QQ-plots were conducted on GWAS designed for AD and triglycerides (TG). The cross-trait analysis resulted in a total of 22 independent genomic loci associated with both AD and TG levels with a conjFDR < 0.05. Among these loci, two pleiotropic variants were located in PVRL2 (rs12978931 and rs11667640). The three SNPs in PVRL2 were significantly associated with RLPc, TG, and number of circulating VLDL and HDL particles in subjects with cardiometabolic risk. Conclusions: We have identified three variants in PVRL2 that predispose individuals to AD that also influence the lipid profile that confers cardiovascular risk in T2DM subjects. PVRL2 is a potential new modulating factor of atherogenic dyslipidemia.

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