Economic Control of Quality of Manufactured Product.

Polyphasic taxonomy, a consensus approach to bacterial systematics.

Brucellosis remains a major zoonosis worldwide Although many countries have eradicated Brucella abortus from cattle, in some areas Brucella melitensis has emerged as a cause of infection in this species as well as in sheep and goats Despite vaccination campaigns with the Rev 1 strain, B melitensis remains the principal cause of human brucellosis Brucella suis is also emerging as an agent of infection in cattle, thus extending its opportunities to infect humans The recent isolation of distinctive strains of Brucella from marine mammals has extended its ecologic range Molecular genetic studies have demonstrated phylogenetic affiliation to Agrobacterium, Phyllobacterium, Ochrobactrum, and Rhizobium Polymerase chain reaction and gene probe development may provide more effective typing methods Pathogenicity is related to production of lipopolysaccharides containing a poly N-formyl perosamine O chain, CuZn superoxide dismutase, erythrlose phosphate dehydrogenase, stress-induced proteins related to intracellular survival, and adenine and guanine monophosphate inhibitors of phagocyte functions Protective immunity is conferred by antibody to lipopolysaccharide and T-cell-mediated macrophage activation triggered by protein antigens Diagnosis still centers on isolation of the organism and serologic test results, especially enzyme immunoassay, which is replacing other methods Polymerase chain reaction is also under evaluation Therapy is based on tetracyclines with or without rifampicin, aminoglycosides, or quinolones No satisfactory vaccines against human brucellosis are available, although attenuated purE mutants appear promising

/pdf/brucellosis-an-overview-14k0fkto7f.pdf

Brucellosis: an overview.

Project scheduling under uncertainty: survey and research potentials

http://web.nchu.edu.tw/pweb/users/arborfish/lesson/8682.pdf

A survey of variants and extensions of the resource-constrained project scheduling problem

The purpose of this article is to give the reader a short yet complete overview of my new Project Management book that has recently been published by Springer. The general topic of this book is known as dynamic project scheduling and claims that project scheduling is a dynamic process that involves a continuous stream of changes and is a never ending process to support decisions that need to be made along the life of the project. Therefore, dynamic scheduling puts project control using Earned Value Management and project performance in a central place. More precisely, it focuses on the crucial role of the baseline schedule in order to assure a perfect integration with schedule risk analyses and an e!cient project control approach. It is conjectured that this integration is key to support a better corrective action decision making when the project is in trouble.

Project management using dynamic scheduling: baseline scheduling, risk analysis and project control

In this paper, we present the Integrated Physician and Surgery Scheduling Problem (IPSSP) as a new approach for solving operating room scheduling problems where staff rosters for the physicians are integrated in the optimization. A mixed integer linear programming formulation is created based on the most frequently observed objective and restrictions of the surgery scheduling and the physician rostering problem in the literature. We analyze schedules by relaxing both surgery and physician related constraints. We then measure the implications of setting these physician preferences on the surgery schedule. Our experiments show two main interesting insights for physician roster schedulers as well as operating theatre scheduling managers.

Analysis of the Integration of the Physician Rostering Problem and the Surgery Scheduling Problem

We propose 5 AI methods for predicting a project's duration.A methodology including PCA, cross-validation and grid search is presented.A large computational experiment shows the good performance of the AI methods.A sensitivity analysis reveals the weakness of the proposed methods. This paper presents five Artificial Intelligence (AI) methods to predict the final duration of a project. A methodology that involves Monte Carlo simulation, Principal Component Analysis and cross-validation is proposed and can be applied by academics and practitioners. The performance of the AI methods is assessed by means of a large and topologically diverse dataset and is benchmarked against the best performing Earned Value Management/Earned Schedule (EVM/ES) methods. The results show that the AI methods outperform the EVM/ES methods if the training and test sets are at least similar to one another. Additionally, the AI methods report excellent early and mid-stage forecasting results. A robustness experiment gradually increases the discrepancy between the training and test sets and demonstrates the limitations of the newly proposed AI methods.

A comparative study of Artificial Intelligence methods for project duration forecasting

The resource-constrained project scheduling problem (RCP- SP) is one of the most challenging problems in project scheduling. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions for more challenging problem instances. In this paper, we present a new genetic algorithm (GA) that, in contrast of a conventional GA, makes use of two separate populations. This bi-population genetic algorithm (BPGA) operates on both a population of left-justified schedules and a population of right-justified schedules in order to fully exploit the features of the iterative forward/backward scheduling technique. Comparative computational results reveal that this procedure can be considered as today's best performing RCPSP heuristic.

Mario Vanhoucke

Papers

Project management using dynamic scheduling: baseline scheduling, risk analysis and project control

Analysis of the Integration of the Physician Rostering Problem and the Surgery Scheduling Problem

A comparative study of Artificial Intelligence methods for project duration forecasting

A bi-population based genetic algorithm for the resource-constrained project scheduling problem

Hybrid tabu search and a truncated branch-and-bound for the unrelated parallel machine scheduling problem