Multiple Criteria Programming Research Articles

Web-Based Forest Resources Management Decision Support System

In this paper, we present a web-based decision support system (DSS)—wSADfLOR—to facilitate the access of stakeholders to tools that may contribute to enhancing forest management planning. The emphasis is on a web-based architecture and a web graphic user interface (wGUI) that may effectively support the analysis of trade-offs between ecosystem services in order to address participatory and sustainable forest management objectives. For that purpose, the wGUI provides remote access to a management information system, enabling users to analyze environmental and biometric data and topological information as well. Moreover, the wGUI provides remote access to forest simulators so that users may define and simulate prescriptions such as chronological sequences of management options and the corresponding forest ecosystem services outcomes. Remote access to management planning methods is further provided so that users may input their objectives and constraints. The wGUI delivers information about tradeoffs between ecosystem services in the form of decision maps so that users in different locations may negotiate bundles of ecosystem services as well as the plan needed to provide them. The multiple criteria programming routines provide proposals for management plans that may be assessed further, using geographical and alphanumeric information provided by the wGUI. Results for an application to a forested landscape extending to 14,388 ha are presented and discussed. This landscape provides several ecosystem services and the development of its management plan involves multiple stakeholders. Results show that the web-based architecture and the wGUI provide effective access for stakeholders to information about the forest management planning area and to decision support tools that may contribute to addressing complex multi-objective and multiple-decision-maker management planning contexts. They also highlight that the involvement and participation of stakeholders in the design of the web-based architecture contributes to assuring the quality and the usability of the system.

Advertisement clicking prediction by using multiple criteria mathematical programming

In online advertisement industry, it is important to predict potentially profitable users who will click target ads (i.e., Behavioral targeting). The task selects the potential users that are likely to click the ads by analyzing user's clicking/web browsing information and displaying the most relevant ads to them. This paper proposes four multiple criteria mathematical programming models for advertisement clicking problems. First two are multi-criteria linear regression (MCLR) and kernel-based multiple criteria regression (KMCR) algorithms for click-through rate (CTR) prediction. The second two are multi-criteria linear programming (MCLP) and kernel-based multiple criteria programming (KMCP) algorithms, which are used to predict ads clicking events, such as identifying clicked ads in a set of ads. Using the experimental datasets from KDD Cup 2012, the paper first conducts a comparison of the proposed MCLR and KMCR with the methods of support vector regression (SVR) and logistic regression (LR), which shows that both MCLR and KMCR are good alternatives. Then the paper further studies the performance between the proposed MCLP and KMCP algorithms with known algorithms, including support vector machines (SVM), LR, radial basis function network (RBFN), k-nearest neighbor algorithm (KNN) and Naive Bayes (NB) in both prediction and selection processes. The studies show that the MCLP and KMCP models have better performance stability and can be used to effectively handle behavioral targeting application for online advertisement problems.

A Forest Planning Approach with Respect to the Creation of Overmature Reserved Areas in Managed Forests

Forest harvest planning to maximize economic benefits also has to consider additional criteria such as the biodiversity functioning of the managed forest. The biodiversity requirements are determined by the size, shape, and distribution of harvest units and forest stands. A multiple criteria approach is presented where the harvesting volume is maximized while the environmental aspects are also considered. Multiple criteria programming and integer programming techniques are used to find an optimal program of forest harvesting with respect to both economic and environmental requirements. The practicality of the model is shown in a case study for one particular forest management unit. Different optimal solutions are calculated depending on changes made to the criteria weights. This model includes strict spatial constraints, multiple objective functions with three objectives, and alternative solutions according to the real manager’s priority. The results show that the spatial pattern and other spatial demands affect the harvest possibilities. It was confirmed that a compromise solution from both forest management and nature conservation could be achieved using the presented harvest scheduling approach.

Goal Programming for Academic Plans Design

This article describes a project undertaken at the Islamic University in Gaza. The project aims at designing a general academic departmental plan of study using binary goal programming. The design process includes balancing the assignment of courses to semesters. Soft and hard constraints are first identified based on interviews with academic experts. Then, a model that uses multiple criteria programming is built and used to construct the plan of study of the industrial engineering department at the Islamic University in Gaza. To determine the weights of the criteria, the researcher attempted to use Analytic Hierarchy Process (AHP). However, after interviewing the experts, it was concluded that pre-emptive goal programming is recommended. The model was then solved using LP-Solve software. The resulting plan of study clearly outperforms the manually designed one. A comparison between the newly designed plan of study and the existing one in terms of each of the criteria shows that the overall objective function could be greatly improved. Furthermore, the systematic formulation of the problem revealed that some constraints are unaccounted for in the manually designed plan. The model can be easily extended to include more objectives and constraints and can also be applied to other departments.

Reverse Convex Programming Approach in the Space of Extreme Criteria for Optimization over Efficient Sets

The problem of minimizing a convex function over the difference of two convex sets is called ‘reverse convex program’. This is a typical problem in global optimization, in which local optima are in general different from global optima. Another typical example in global optimization is the optimization problem over the efficient set of a multiple criteria programming problem. In this article, we investigate some special cases of optimization problems over the efficient set, which can be transformed equivalently into reverse convex programs in the space of so-called extreme criteria of multiple criteria programming problems under consideration. A suitable algorithm of branch and bound type is then established for globally solving resulting problems. Preliminary computational results with the proposed algorithm are reported.

Multiple criteria programming models for VIP E-Mail behavior analysis

Excessive lose of customer account is becoming a major headache for VIP E-Mail hosting companies. Analysis of what kind of customer is more prone to lose and finding the appropriate measures to sustain those customers has become urgent needs. Recently, classification models based on mathematical programming have been widely used in business intelligence. The purpose of this paper is to propose several multiple criteria programming methods for classification and apply these methods to VIP E-Mail behavior classification. We first introduce a model for a generalized multiple criteria programming based classification method, specifically four particular forms, and then we use a cross-validation method to test the stability and accuracy of multiple criteria programming methods on VIP E-Mail accounts. Finally, we compare our models with Support Vector Machine (SVM). The results show that the classification models based on mathematical programming are satisfactorily accurate and stable on a VIP E-Mail dataset. Therefore, it can be concluded that applying the proposed method on VIP E-Mail behavior analysis can provide stable and credible results. This research has been supported by the National Science Foundation of China (NSFC) under Grants No. 60674109, No. 70621001, and No. 70871111, and BHP Billiton Co., Australia. In the process of building the multiple criteria programming classification models, the authors received much help from Professor Juliang Zhang. We express our endless gratitude to him.

Approximating Pareto frontier using a hybrid line search approach

The aggregation of objectives in multiple criteria programming is one of the simplest and widely used approach. But it is well known that this technique sometimes fail in different aspects for determining the Pareto frontier. This paper proposes a new approach for multicriteria optimization, which aggregates the objective functions and uses a line search method in order to locate an approximate efficient point. Once the first Pareto solution is obtained, a simplified version of the former one is used in the context of Pareto dominance to obtain a set of efficient points, which will assure a thorough distribution of solutions on the Pareto frontier. In the current form, the proposed technique is well suitable for problems having multiple objectives (it is not limited to bi-objective problems) and require the functions to be continuous twice differentiable. In order to assess the effectiveness of this approach, some experiments were performed and compared with two recent well known population-based metaheuristics namely ParEGO and NSGA II. When compared to ParEGO and NSGA II, the proposed approach not only assures a better convergence to the Pareto frontier but also illustrates a good distribution of solutions. From a computational point of view, both stages of the line search converge within a short time (average about 150 ms for the first stage and about 20 ms for the second stage). Apart from this, the proposed technique is very simple, easy to implement and use to solve multiobjective problems.

Multiple criteria optimization-based data mining methods and applications: a systematic survey

Support Vector Machine, an optimization technique, is well known in the data mining community. In fact, many other optimization techniques have been effectively used in dealing with data separation and analysis. For the last 10 years, the author and his colleagues have proposed and extended a series of optimization-based classification models via Multiple Criteria Linear Programming (MCLP) and Multiple Criteria Quadratic Programming (MCQP). These methods are different from statistics, decision tree induction, and neural networks. The purpose of this paper is to review the basic concepts and frameworks of these methods and promote the research interests in the data mining community. According to the evolution of multiple criteria programming, the paper starts with the bases of MCLP. Then, it further discusses penalized MCLP, MCQP, Multiple Criteria Fuzzy Linear Programming (MCFLP), Multi-Class Multiple Criteria Programming (MCMCP), and the kernel-based Multiple Criteria Linear Program, as well as MCLP-based regression. This paper also outlines several applications of Multiple Criteria optimization-based data mining methods, such as Credit Card Risk Analysis, Classification of HIV-1 Mediated Neuronal Dendritic and Synaptic Damage, Network Intrusion Detection, Firm Bankruptcy Prediction, and VIP E-Mail Behavior Analysis.

MCLP-based methods for improving “Bad” catching rate in credit cardholder behavior analysis

Cardholders’ behavior prediction is an important issue in credit card portfolio management. As a promising data mining approach, multiple criteria programming (MCLP) has been successfully applied to classify credit cardholders’ behavior into two groups. In order to better control credit risk for financial institutes, this paper proposes three methods based on MCLP to improve the “Bad” catching accuracy rate. One is called MCLP with unbalanced training set selection, the second is called fuzzy linear programming (FLP) method with moving boundary, and the third is called penalized multi criteria linear programming (PMCLP). The experimental examples demonstrate the promising performance of these methods.

Introducing oblique norms into multiple criteria programming

We propose to use block norms to generate nondominated solutions of multiple criteria programs and introduce the new concept of the oblique norm that is specially tailored to handle general problems. We prove the equivalence of finding the properly nondominated solutions of a multiple criteria program and solving its scalarization by means of oblique norms.

Sensitivity analysis in MCDM using the weight space

Sensitivity analysis of compromise solutions is a key issue in interactive multiple criteria programming, where changes in the initial data as well as in the decision maker's preference structure (which evolves as more knowledge about the problem is acquired throughout the decision process) must be considered. This paper presents an interactive approach to sensitivity analysis in multiple criteria linear programming problems based on the weight space.

Connecting expert system features to a multiple criteria programming based decision support system

We concentrate on the issue of utilizing artificial intelligence based computer systems as an aid to formulating and formalizing corporate planning problems. Our proposal involves cooperation between the analyst and expert system in order to find a suitable structure for the problem to be solved. The Al system is designed using logic programming. The study is an outgrowth of the experience gained in developing a strategic planning system with menu-driven simulation (heuristic, Monte Carlo), multiple criteria optimization and database query facilities for the concern management of Kansallis-Osake-Pankki (Finland).

Mediation in Long-Term Planning

The aim in many conflict situations is to move from a situation in which the goals of the contending parties are mutually incompatible to one in which a compromise solution acceptable to all can be found. This is usually achieved through a series of concessions on both sides. This paper demonstrates how the concession-making process can be structured by a mediator using an interactive multiple-criteria programming system (DIDASS) based on aspiration point and achievement function methodology and linked to a model of the system under study. The use of the approach is illustrated by simulated negotiations concerning the long-term development of the Austrian energy system.