Search

In this paper, we introduce the Divide and Conquer (D&C) algorithm, a computationally attractive algorithm for determining classification rules which minimize the training sample misclassification cost in two-group classification. This classification rule can be derived using mixed integer programming (MIP) techniques. However, it is well-documented that the complexity of MIP-based classification problems grows exponentially as a function of the size of the training sample and the number of attributes describing the observations, requiring special-purpose algorithms to solve even small size problems within a reasonable computational time. The D&C algorithm derives its name from the fact that it relies, a.o., on partitioning the problem in smaller, more easily handled sub-problems, rendering it substantially faster than previously proposed algorithms. The D&C algorithm solves the problem to the exact optimal solution (i.e., it is not a heuristic that approximates the solution), and allows for the analysis of much larger training samples than previous methods. For instance, our computational experiments indicate that, on average, the D&C algorithm solves problems with 2 attributes and 500 observations more than 3 times faster, and problems with 5 attributes and 100 observations over 50 times faster than Soltysik and Yarnold's software, which may be the fastest existing algorithm. We believe that the D&C algorithm contributes significantly to the field of classification analysis, because it substantially widens the array of data sets that can be analyzed meaningfully using methods which require MIP techniques, in particular methods which seek to minimize the misclassification cost in the training sample. The programs implementing the D&C algorithm are available from the authors upon request.

This paper presents a method of decision making with returns in the form of discrete random variables. The proposed method is based on two approaches: stochastic orders and compromise programming used in multi-objective programming. Stochastic orders are represented by stochastic dominance and inverse stochastic dominance. Compromise programming uses the augmented Tchebycheff norm. This norm, in special cases, takes form of the Kantorovich and Kolmogorov probability metrics. Moreover, in the paper we show applications of the presented methodology in the following problems: projects selections, decision tree and choosing a lottery.

This paper reviews the problems of Boolean non-linear programming related to the half-product problem. All problems under consideration have a similar quadratic non-separable objective function. For these problems, we focus on the development of fully polynomial-time approximation schemes, especially of those with strongly polynomial time, and on their applications to various scheduling problems.

We discuss integrated chance constraints in their role of short-term risk constraints in a strategic ALM model for Dutch pension funds. The problem is set up as a multistage recourse model, with special attention for modeling short-term risk prompted by the development of new guidelines by the regulating authority for Dutch pension funds. The paper concludes with a numerical illustration of the importance of such short-term risk constraints.

In this paper, we pay our attention to geometric parameters and their applications in economics and finance. We discuss the multiplicative models in which a geometric mean and a geometric standard deviation are more natural than arithmetic ones. We give two examples from Warsaw Stock Exchange in 1995--2009 and from a bid of 52-week treasury bills in 1992--2009 in Poland as an illustrative example. For distributions having applications in finance and insurance we give their multiplicative parameters as well as their estimations. We consider, among others, heavy-tailed distributions such as lognormal and Pareto distribution, applied to modelling of large losses.

The aim of this paper is to develop a scheduling policy oriented towards minimizing setup times in the made-to-order furniture industry. The task is treated as a dynamic job shop scheduling problem, with the exception that customers’ orders collected over a specified period of time are combined into a production plan and released together. A simulation of a production flow based on technological routes of real subassemblies was performed. The proposed method of calculating a setup time eliminates the need to determine machine setup time matrices. Among the tested priority rules the best performance was observed in the case of the hierarchical rule that combines similar setup, the earliest due date and the shortest processing time. This rule allowed the setup time per operation to be reduced by 58 % compared to a combination of the earliest due date with the shortest setup and processing time rule and by over 70 % compared to the single shortest processing time rule.

This paper sets out the development of a methodological framework for detailed evaluation of public health strategies for alcohol harm reduction to meet UK policy-makers needs. Alcohol is known to cause substantial harms, and controlling its affordability and availability are effective policy options. Analysis and synthesis of a variety of public and commercial data sources is needed to evaluate impact on consumers, health services, crime, employers and industry, so a sound evaluation of impact is important. We discuss the iterative process to engage with stakeholders, identify evidence/data and develop analytic approaches and produce a final model structure. We set out a series of steps in modelling impact including: classification and definition of population subgroups of interest, identification and definition of harms and outcomes for inclusion, classification of modifiable components of risk and their baseline values, specification of the baseline position on policy variables especially prices, estimating effects of changing policy variables on risk factors including price elasticities, quantifying risk functions relating risk factors to harms including 47 health conditions, crimes, absenteeism and unemployment, and monetary valuation. The most difficult model structuring decisions are described, as well as the final results framework used to provide decision support to national level policymakers in the UK. In the discussion we explore issues around the relationship between modelling and policy debates, valuation and scope, limitations of evidence/data, how the framework can be adapted to other countries and decisions. We reflect on the approach taken and outline ongoing plans for further development.

This paper deals with power-aware scheduling of preemptable jobs on identical parallel processors to minimize schedule length when jobs are described by continuous, strictly concave functions relating their processing speed at time t to the amount of power allotted at the moment. Power is a continuous, doubly constrained resource, i.e. both: its availability at time t and consumption over scheduling horizon are constrained. Precedence constraints among jobs are represented by a task-on-arc graph. A methodology based on properties of optimal schedules is presented for solving the problem optimally for a given ordering of nodes in the graph. Heuristics for finding an ordering which leads to possibly short schedules are proposed and examined experimentally.

The prevalence of big data is starting to spread across the public and private sectors however, an impediment to its widespread adoption orientates around a lack of appropriate big data analytics (BDA) and resulting skills to exploit the full potential of big data availability. In this paper, we propose a novel BDA to contribute towards this void, using a fuzzy cognitive map (FCM) approach that will enhance decision-making thus prioritising IT service procurement in the public sector. This is achieved through the development of decision models that capture the strengths of both data analytics and the established intuitive qualitative approach. By taking advantages of both data analytics and FCM, the proposed approach captures the strength of data-driven decision-making and intuitive model-driven decision modelling. This approach is then validated through a decision-making case regarding IT service procurement in public sector, which is the fundamental step of IT infrastructure supply for publics in a regional government in the Russia federation. The analysis result for the given decision-making problem is then evaluated by decision makers and e-government expertise to confirm the applicability of the proposed BDA. In doing so, demonstrating the value of this approach in contributing towards robust public decision-making regarding IT service procurement. This study was partially funded by EUFP7 project Policy Compass (Project No. 612133).

In the past decade, there has been an increasing interest in green supply chain management, which integrates environmental thinking into supply chain management. Assessing a supplier’s potential for improvement is very important when an organization wants to achieve certain environmental targets concerning their supply base, taking into account the limited resources available. In this paper, incorporating environmental evaluation criteria into a comprehensive supplier segmentation approach called ‘supplier potential matrix’ (SPM), a green supplier segmentation is proposed to segment the suppliers. Two overarching dimensions—supplier’s capabilities and supplier’s willingness—are used to evaluate the supplier’s green potential. The two dimensions are measured by multiple criteria. A sorting method called ELECTRE TRI-rC is used to solve the resulted multi-criteria decision-making problem. In order to make a more meaningful distinction, a simple method is also proposed to assess the suppliers with respect to the carbon footprint of the raw materials they supply. The results of this assessment are combined with the ones of the SPM, resulting in a more useful segmentation. The proposed model is applied to a sample containing the suppliers of a large international company.

This paper presents a decision support system for the core problem of berth allocation decision in a container terminal. The allocation of berths to the calling vessels is complex with the fact that different service level requirements are required for different vessels. Terminal managers demand for effective decision support systems that would aid them with the allocation problem considering service priorities. Consequently, this study provides a DSS, built by a dynamic discrete-event simulation model embedded with an optimization tool that determines the priority controls for the berth allocation to the calling vessels. To show the practical application of the DSS, a comprehensive case study from a Turkish container terminal considering the current state and future expansion plans that also provides an indication of the usability aspect of the program on other ports around the world has been conducted. Further experiments are conducted based on data from the Port of Rotterdam. The DSS presented in this study may help port authorities in determining more efficient allocation decisions within a container terminal.