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

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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 integration of constrained resources into top-down project control

Earned value management (EVM), a technique mainly used to manage project cost, was developed during the second half of the twentieth century by an agency of the USA government. It was enriched, at the beginning of the current century, with an extension named earned schedule (ES) (Lipke, 2003), which added to EVM the capability to manage the project schedule.In 2014, earned duration management (EDM), a novel, and monetary value free technique for project schedule management, was proposed by Khamooshi and Golafshani (2014). Earned duration (ED), the project schedule indicator from EDM uses only activity dates and durations from EVM, and aims at replacing ES.  By not using cost as a proxy for time, ED overcomes one of the most criticized aspect of ES.This paper intends to (i) compare ED with ES to learn some fundamental lessons, (ii) propose the combination of ED and planned value (PV) from EVM to produce an EDM-based project-level earned value (EV〗^d), and (iii) assess the adequacy of 〖EV〗^d as an alternative to earned value (EV) from EVM in project time and cost management.  The comparison between the project real cost and both EVM and EDM-based independent estimates at completion, on a set of real projects, was the vehicle for assessing the adequacy of 〖EV〗^d  as a viable alternative to EV.Workload avoidance by replacing activities earned value calculations with the simpler and more consistent determination of the activities earned durations, and the enhanced visibility of potential schedule problems caused by lower cost/duration ratio activities, are benefits that 〖EV〗_d offers to project managers.

Combining EDM and EVM: a proposed simplification for project time and cost management

In this paper, we propose different complexity indicators for the well-known nurse scheduling problem (NSP). The NSP assigns nurses to shifts per day taking both hard and soft constraints into account. The objective is to maximize the nurses’ preferences and to minimize the total penalty cost from violations of the soft constraints. The problem is known to be NP-hard. Due to its complexity and relevance in practice, the operations research literature has been overwhelmed by different procedures to solve the problem. The complexity has resulted in the development of several (meta-)heuristic procedures, able to solve a NSP instance heuristically in an acceptable time limit. The practical relevance has resulted in a never-ending amount of different NSP versions, taking practical, case-specific constraints into account. The contribution of this paper is threefold. First, we describe our complexity indicators to characterize a nurse scheduling problem instance. Secondly, we develop a NSP generator to generate benchmark instances to facilitate the evaluation of existing and future research techniques. Finally, we perform some preliminary tests on a simple IP model to illustrate that the proposed indicators can be used as predictors of problem complexity.

/pdf/characterisation-and-generation-of-nurse-scheduling-problem-j04hf7aakt.pdf

Characterisation and Generation of Nurse Scheduling Problem Instances

In recent years, machine learning techniques, especially genetic programming (GP), have been a powerful approach for automated design of the priority rule-heuristics for the resource-constrained project scheduling problem (RCPSP). However, it requires intensive computing effort, carefully selected training data and appropriate assessment criteria. This research proposes a GP hyper-heuristic method with a duplicate removal technique to create new priority rules that outperform the traditional rules. The experiments have verified the efficiency of the proposed algorithm as compared to the standard GP approach. Furthermore, the impact of the training data selection and fitness evaluation have also been investigated. The results show that a compact training set can provide good output and existing evaluation methods are all usable for evolving efficient priority rules. The priority rules designed by the proposed approach are tested on extensive existing datasets and newly generated large projects with more than 1,000 activities. In order to achieve better performance on small-sized projects, we also develop a method to combine rules as efficient ensembles. Computational comparisons between GP-designed rules and traditional priority rules indicate the superiority and generalization capability of the proposed GP algorithm in solving the RCPSP. 

An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem

In this paper, we present a new genetic algorithm for the resource-constrained project scheduling problem with discounted cash flows and investigate the trade-off between a project’s net present value and its corresponding makespan. We consider a problem formulation where the pre-specified project deadline is not set as a hard constraint, but rather as a soft constraint that can be violated against a certain penalty cost. the genetic algorithm creates children from parents taken from three different populations, each containing relevant information about the (positive or negative) activity cash flows. We have tested various parent selection methods based on four crossover operators taken from literature and present extensive computational results.

Mario Vanhoucke

Papers

The integration of constrained resources into top-down project control

Combining EDM and EVM: a proposed simplification for project time and cost management

Characterisation and Generation of Nurse Scheduling Problem Instances

An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem

A genetic algorithm for net present value maximization for resource constrained projects