A foundation for competence set analysis

For each decision problem, there is a competence set consisting of knowledge, information and skills for its effective solution. How the decision-maker acquires and expands his/her competence set plays a key role in the process and quality of decision-making. This paper provides a mathematical foundation for studying competence sets, their expansion processes and stability. Time and cost functions for expansion, reachable domains, effective expansion using minimal spanning trees, random set decomposition of competence sets, marginal analysis, connectivity, metrization and stability of competence sets are some key concepts introduced.     

Multicriteria–multistage planning for the optimal path selection using hybrid genetic algorithms

The problem of the competence set expansion involves determining the optimal expansion path under the minimum cost and time. As we know, the conventional competence set model only considers the problems of the static situation and the single objective. However, the dynamic situation and the multicriteria should also be simultaneously considered in practice. In this paper, a multicriteria and multistage competence set model is proposed. In order to efficiently obtain an optimal expansion path, hybrid genetic algorithms (HGA) are employed. In addition, a numerical example is used to demonstrate the proposed method. On the basis of the numerical results, we can conclude that the proposed method can provide a sound competence set model by simultaneously considering the multicriteria and multistage situations.     

Optimal expansion and design of competence sets with asymmetric acquiring costs

This paper provides a method of finding the optimal expansion process and discusses the marginal analysis for expansion of the competence set when the cost functions are asymmetric. The concept of tree expansion process is introduced, and a method of finding the optimal tree expansion process is given. The paper also shows a way to identify the optimal competence set when both the expected return and cost are considered.     

Knowledge accelerator by transversal competences and multivariate adaptive regression splines

Transversal competences constitute a set of the knowledge, skills, and attitudes required for various positions and in different professions. Such competences include: entrepreneurship, teamwork, creativity, and communicativeness; they are increasingly listed by employers in different countries as the key requirements in the labor market. The article presents the model of accelerating the process of acquiring transversal competences, developed based on the analysis of data collected in four countries of the European Union: Poland, Finland, Slovakia, and Slovenia. In the analysis, multivariate additive regression spline method was used, along with artificial neural networks, in order to create the best model describing the influence of different variables on the acceleration of acquiring transversal competences. Herewith, we demonstrated that by accelerating the acquisition of the transversal competence of entrepreneurship is influenced by the following factors: rank of the training method in the developed matrix, student numbers and the weighted average of the pace of acceleration regarding the acquisition of the remaining transversal competences, i.e., teamwork, communicativeness and creativity by the given student. The results validate our new method of the acceleration of acquiring transversal competences by students. Students may be from various higher education institutions in different countries. Developed results may be used in the course of education within the framework of the already planned vocational courses and for developing the skills required by employers for various positions and in different professions.

     

Multiobjective strategies in power systems planning

The rapidly changing decision environment in electrical systems planning calls for a new approach in planning procedures and mathematical tools. The method proposed in this study decomposes the decision making into two strata, one at the strategy setting, the other at the project selection level. For the strategy setting level, a multiobjective dynamic linear programming model is used in generating system expansion decision alternatives. The suitability of the modelling approach is considered from the various aspects such as congruence, flexibility, transparency, and accuracy.An application of the model to the Turkish electrical system is also presented, where expansion alternatives to the year 2000 are generated. Three criteria were observed: cost, environmental impact and risk.     

Expanding competence sets for the consumer decision problem

The framework of competence set analysis provides a new approach to complement the existing models for the consumer decision problem. Since it is essential for the firm to improve the competence set of its product or service to fully address the consumer's truly needed benefits, the effective expansion of competence sets plays an important role in marketing reality. The previous studies regarding competence set expansion have thrown light on the tree expansion processes, but forest learning is more suitable for the acquisition of product benefits than tree learning. Thus, this study looses the assumption of tree learning and conducts forest learning to design an effective expansion program. In addition, this study explores a more general problem involving intermediate attributes, compound benefits, and experiential effects. An algorithm is also provided for effective expansion of competence sets in consumer decision analysis.     

CRM决策的序贯优化——以KDD98直邮决策为例

从多阶段、延迟回报的角度来看待CRM中的决策优化问题。以KDD98数据集为例,将邮寄序贯决策定义为一个部分可观察马尔可夫决策模型(POMDP)。提出了模型参数估计的EM算法并用MATLAB实现;用模型对数似然值、BIC统计量选择最佳模型;用向前一步预测对模型进行检验;用Incremental prune算法对模型求解。实证结果表明,POMDP模型可以很好的捕捉客户购买行为的动态变化,对客户的购买有很好的预测效果。在此基础上,说明了如何使用该模型以客户终生价值最大化为目标优化直邮策略。     

Optimal competence set expansion using deduction graphs

A competence set is a collection of skills used to solve a problem. Based on deduction graph concepts, this paper proposes a method of finding an optimal process so as to expand a decision maker's competence set to enable him to solve his problem confidently. Using the concept of minimum spanning tree, Yu and Zhang addressed the problem of the optimal expansion of competence sets. In contrast, the method proposed here enjoys the following advantages: it can deal with more general problems involving intermediate skills and compound skills; it can find the optimal solution by utilizing a 0–1 integer program; and it can be directly extended to treat multilevel competence set problems, and thus is more practically useful.This work was supported by the National Science Council, Taipei, Taiwan, Republic of China, Grant No. NSC-81-0301-H-009-501.     

A dynamic unreliability assessment and optimal maintenance strategies for multistate weighted k‐out‐of‐n:F systems

In this paper, a dynamic evaluation of the multistate weighted k‐out‐of‐n:F system is presented in an unreliability viewpoint. The expected failure cost of components is used as an unreliability index. Using failure cost provides an opportunity to employ financial concepts in system unreliability estimation. Hence, system unreliability and system cost can be compared easily in order to making decision. The components' probabilities are computed over time to model the dynamic behavior of the system. The whole system has been assessed by recursive algorithm approach. As a result, a bi‐objective optimization model can be developed to find optimal decisions on maintenance strategies. Finally, the application of the proposed model is investigated via a transportation system case. Matlab programming is developed for the case, and genetic algorithm is used to solve the optimization model. Copyright © 2016 John Wiley & Sons, Ltd.     

A column generation algorithm for the estimation of origin–destination matrices in congested traffic networks

The general problem of estimating origin–destination (O–D) matrices in congested traffic networks is formulated as a mathematical programme with equilibrium constraints, referred to as the demand adjustment problem (DAP). This approach integrates the O–D matrix estimation and the network equilibrium assignment into one process. In this paper, a column generation algorithm for the DAP is presented. This algorithm iteratively solves a deterministic user equilibrium model for a given O–D matrix and a DAP restricted to the previously generated paths, whose solution generates a new O–D trip matrix estimation. The restricted DAP is formulated via a single level optimization problem. The convergence on local minimum of the proposed algorithm requires only the continuity of the link travel cost functions and the gauges used in the definition of the DAP.     

A model of professional competences in mathematics to update mathematical and didactic knowledge of teachers

This paper describes part of a research and development project carried out in public elementary schools. Its objective was to update the mathematical and didactic knowledge of teachers in two consecutive levels in urban and rural public schools of Region de Los Lagos and Region de Los Rios of southern Chile. To that effect, and by means of an advanced training project based on a professional competences model, didactic interventions based on types of problems and types of mathematical competences with analysis of contents and learning assessment were designed. The teachers’ competence regarding the didactic strategy used and its results, as well as the students’ learning achievements are specified. The project made possible to validate a strategy of lifelong improvement in mathematics, based on the professional competences of teachers and their didactic transposition in the classroom, as an alternative to consolidate learning in areas considered vulnerable in two regions of the country.     

Selecting informative rules with parallel genetic algorithm in classification problem

The classification system is very important for making decision and it has been attracted much attention of many researchers. Usually, the traditional classifiers are either domain specific or produce unsatisfactory results over classification problems with larger size and imbalanced data. Hence, genetic algorithms (GA) are recently being combined with traditional classifiers to find useful knowledge for making decision. Although, the main concerns of such GA-based system are the coverage of less search space and increase of computational cost with the growth of population. In this paper, a rule-based knowledge discovery model, combining C4.5 (a Decision Tree based rule inductive algorithm) and a new parallel genetic algorithm based on the idea of massive parallelism, is introduced. The prime goal of the model is to produce a compact set of informative rules from any kind of classification problem. More specifically, the proposed model receives a base method C4.5 to generate rules which are then refined by our proposed parallel GA. The strength of the developed system has been compared with pure C4.5 as well as the hybrid system (C4.5 + sequential genetic algorithm) on six real world benchmark data sets collected from UCI (University of California at Irvine) machine learning repository. Experiments on data sets validate the effectiveness of the new model. The presented results especially indicate that the model is powerful for volumetric data set.     

Optimal Expansion of Competence Sets with Intermediate Skills and Compound Nodes

This paper provides a method for finding the optimal expansion process of the competence set in generalized situations in which the cost functions are asymmetric and the directed graphs, which depict all possible expansion processes, have cycles and contain intermediate skills and compound nodes.     

A particle swarm algorithm for inspection optimization in serial multi-stage processes

Implementing efficient inspection policies is much important for the organizations to reduce quality related costs. In this paper, a particle swarm optimization (PSO) algorithm is proposed to determine the optimal inspection policy in serial multi-stage processes. The policy consists of three decision parameters to be optimized; i.e. the stages in which inspection occurs, tolerance of inspection, and size of sample to inspect. Total inspection cost is adopted as the performance measure of the algorithm. A numerical example is investigated in two phases, i.e. fixed sample size and sample size as a decision parameter, to ensure the practicality and validity of the proposed PSO algorithm. It is shown that PSO gives better results in comparison with two other algorithms proposed by earlier works.     

Elective course planning

Efficient planning increasingly becomes an indispensable tool for management of both companies and public organizations. This is also the case for high school management in Denmark, because the growing individual freedom of the students to choose courses makes planning much more complex. Due to reforms, elective courses are today an important part of the curriculum, and elective courses are a good way to make high school education more attractive for the students. In this article, the problem of planning the elective courses is modeled using integer programming and three different solution approaches are suggested, including a Branch-and-Price framework using partial Dantzig-Wolfe decomposition. Explicit Constraint Branching is used to enhance the solution process, both on the original IP model and in the Branch-and-Price algorithm. To the best of our knowledge, no exact algorithm for the Elective Course Planning Problem has been described in the literature before. The proposed algorithms are tested on data sets from 98 of the 150 high schools in Denmark. The tests show that for the majority of the problems, the optimal solution can be obtained within the one hour time bound. Furthermore the suggested algorithms achieve better results than the currently applied meta-heuristic.     

人力资源约束下的项目群调度问题建模与求解

基于人员胜任力是影响工作绩效的关键因素,将资源受限项目调度问题中的可更新资源通过一系列科学合理的方法或者手段转变为存在胜任力差异的人力资源,由此构建起一个强调胜任力差异的人力资源约束项目调度问题模型,此模型最突出的优势在于选取了能够客观合理评估人员胜任力的指标,提供了严谨科学的关系式,将复杂的多项目总工期与总成本的双目标最小化问题转换为综合指标单目标最大化问题,建立数学优化模型,采用遗传算法求解。通过算例研究证实,相较于传统多模式模型,基于人员胜任力水平差异的模型明显更胜一筹,其优势集中表现为最优工期更短、最优成本更低。考虑了胜任力差异的数学优化模型更符合研发项目群管理实践,同时遗传算法在求解方面不仅效率高,并且更容易获得客观准确的结果。     

基于CVaR的危险品公铁联运路径选择研究

公铁联运在危险品的多式联运中扮演着重要角色,为了降低危险品公铁联运风险,提高危险品公铁联运的安全性,危险品公铁联运的路径选择至关重要。本文运用条件风险价值(CVaR)理论,在对危险品公铁联运网络进行变形的基础上构建了考虑决策者风险规避程度的危险品公铁联运路径选择模型,设计了求解该模型的算法,并进行了算例分析。研究结果表明:通过该模型及其算法,可根据决策者对所需运输的危险品的运输风险规避程度,在危险品公铁联运网络中快速地选出使危险品公铁联运风险最小的运输路径和运输方式;决策者的风险规避程度会对危险品公铁联运过程中的运输路径和运输方式的选择产生重要影响。     

Optimal multiple stage expansion of competence set

In this paper, we consider the expansion processes of competence sets which have asymmetric cost functions, intermediate skills, and compound skills; among the skills, cyclic connections are possible. We introduce the concept of the stage expansion process (SEP) of the competence set, and provide mathematical programming methods to find a minimal cost SEP and the ordering of expansion.     

A multicriteria accreditation system for information technology skills and qualifications

Employees must acquire new competences and qualifications throughout their lives, in order to be able to deal with the multiple changes in the labour market. The specific knowledge and competences, acquired either formally or non-formally, must be recognized so that they can be transferred and utilized. The existing titles of studies and accreditation mechanisms do not generally cover this need. This paper aims to propose an integrated approach for the evaluation of information technology knowledge and skills, regardless of where and how they have been acquired, so as to apply a continuous education and training policy. The proposed multicriteria methodology for the evaluation of qualifications and skills concerns candidates wishing to be accredited in an information technology specialization or profession. The methodology refers to the evaluation of the professional experience, studies and vocational training of the candidates for accreditation. It contains the analysis and modelling of the qualitative criteria as well as the implementation of multicriteria aggregation–disaggregation techniques attributing value to each criterion. Then, the candidates are classified in categories of professionals using the Electre Tri method, accepting as input data the multicriteria assessments on each criterion. The proposed evaluation approach has been adapted to the Greek educational system and is applied to a specific example of candidate.     

The signal model: A model for competing risks of opportunistic maintenance

This paper presents a competing risks reliability model for a system that releases signals each time its condition deteriorates. The released signals are used to inform opportunistic maintenance. The model provides a framework for the determination of the underlying system lifetime from right-censored data, without requiring explicit assumptions about the type of censoring to be made. The parameters of the model are estimated from observational data by using maximum likelihood estimation. We illustrate the estimation process through a simulation study. The proposed signal model can be used to support decision-making in optimising preventive maintenance: at a component level, estimates of the underlying failure distribution can be used to identify the critical signal that would trigger maintenance of the individual component; at a multi-component system level, accurate estimates of the component underlying lifetimes are important when making general maintenance decisions. The benefit of good estimation from censored data, when adequate knowledge about the dependence structure is not available, may justify the additional data collection cost in cases where full signal data is not available.