International Journal of Information Technology & Decision Making

In most long-lasting conflicts, each party's grievances increase while the concessions they are willing to make decline in number, quality, and perceived value. Both parties lose sight of what they are willing to settle for, generally exaggerate their own needs, and minimize the needs of the other side over time. But, it is precisely the matter of trading that needs to be made more concrete and of higher priority for both sides, if a meaningful resolution is to be found. Without a formal way of trading off the concessions and packages of concessions, both sides are likely to suspect that they are getting the short end of the bargain. After the parties have agreed to a trade, very specific binding language about the terms of the agreement, clear implementation policies, and outside guarantors are needed. The worth of the concessions traded, as perceived by both the giver and receiver, need to be accurately determined and recorded. All of this requires going beyond verbal descriptions of the concessions to more broadly include their economic, social, geographic, humanitarian, and historical worth. It is critical that all of these need to be translated into priorities derived in terms of the different values and beliefs of the parties. Priorities are universal and include the diversity of measures in terms of which economic, social, and other values are measured. The Analytic Hierarchy Process (AHP) provides a way to perform such an assessment with the participation of negotiators for the parties. It is a positive approach that makes it possible to reason and express feelings and judgments with numerical intensities to derive priorities. It has been used productively in the past to deal with the conflicts in South Africa and Northern Ireland and with other controversies throughout the world. With the assistance of panels of Israeli participants and Palestinian participants brought together in 2009 and 2010, AHP was applied for the first time to the Palestinian–Israeli conflict. The process makes it clear that moderation in different degrees by both sides is essential to arrive at acceptable agreements on concessions proposed and agreed upon by both sides. AHP makes it possible to evaluate moderate and extreme viewpoints and determine their effect on the trading of concessions. The results obtained encourage us to advocate its use in the negotiation process.

This study presents a variant representation of pivot in simplex, which performs cone-cutting on a cone C in dual space to match the pivot performed on a basis B, while the edge-vectors of C are indicated by the row vectors of the feature matrix F = B^-1 in the simplex table. Under this representation, we can see the dual cone C of basis B through the feature matrix F directly, and we can perform pivot motivated by the monitor viewing toward the dual space. As an example, a constraint plane in the dual space is delete-able for the optimal searching if it does not pass through the dual optimal point, while such a plane corresponds to a variable being not in the optimal basis. Motivated by the cone-cutting's vision, a variable-sifting algorithm is presented in Sec. 3, which marks those variables corresponding to delete-able planes into a list to forbid them enter pivot and put zero to their components in the final solution.

Several studies have focused on mining changes in different time-period databases. Analyzing these change behaviors provides useful information for managers to develop better marketing strategies and decision making. Although some researchers have developed efficient methods for association rule change detection, no attempt has been made to analyze time-interval sequential pattern changes in databases collected over time. Therefore, this research proposes a time-interval sequential pattern change detection framework to derive the change trends in customer behaviors in two periods. First, two time-interval sequential pattern sets are generated from two time-period databases respectively using the proposed DTI-Apriori algorithm. Different from previous mining methods that require users to manually define a set of time-interval ranges in advance, the DTI-Apriori algorithm automatically arranges the time-interval range and then generates time-interval sequential patterns. The degree of change for each pair of time-interval sequential patterns from different time periods is evaluated next. Based on the degree of change, a time-interval sequential pattern is clarified as one of the following three change types: an emerging time-interval sequential pattern, an unexpected time-interval sequential pattern, or an added/perished time-interval sequential pattern. Significant change patterns are returned to users for further analysis if the degree of change is large enough.

We propose a multiple-criteria decision-making (MCDM) method based on Maximum A Posteriori (MAP) estimation to analyze users' physiological status either normal or abnormal. The decision-making problem is formulated using MAP estimation and is turned out to be MCDM problem given the assumption that all probability density functions (pdfs) follow exponential forms, especially Gaussian. It indicates that this MCDM equation is decomposed into direct sum of group's physiological status distribution. Group distribution is estimated by probabilistic approach using population from the same age or same sex. For verification, we applied the proposed method to public heart rate database. According to experimental results, the proposed method considering group context reduced overall classification errors by 20.42% compared to typical decision-making (TDM) method. This method is applicable to various personalized health monitoring applications, which estimates user's physiological status by referring other group distribution without prior knowledge about previous health records.

Multi-stage multi-attribute group decision making (MS-MAGDM) as a familiar decision activity that usually occurs in our daily life, such as multi-stage investment decision making, medical diagnosis, personnel dynamic examination, military system efficiency dynamic evaluation, etc. The aim of this paper is to investigate MS-MAGDM problems in which both the weight information on a collection of predefined attributes and the decision information on a finite set of alternatives with respect to the attributes are collected at different stages. We first propose a Poisson distribution based method to determine the weight vector associated with a time-weighted averaging (TWA) operator. Furthermore, we use a hybrid weighted aggregation (HWA) operator to fuse all individual decision information into group opinions at different stages, and then utilize the TWA operator to aggregate the derived group opinions at different stages into the complex group ones so as to rank the given alternatives. After that, we further investigate MS-MAGDM problems where all decision information at different stages cannot be given in exact numerical values, but value ranges can be obtained. An approach based on the uncertain time-weighted averaging (UTWA) operator and the uncertain hybrid weighted aggregation (UHWA) operator is developed for solving MS-MAGDM problems under interval uncertainty. Finally, a practical example is provided to illustrate the developed approaches.

The practice of solely relying on the human resources department in the selection process of external training providers has cast doubts and mistrust across other departments as to how trainers are sourced. There are no measurable criteria used by human resource personnel, since most decisions are based on intuitive experience and subjective market knowledge. The present problem focuses on outsourcing of private training programs that are partly government funded, which has been facing accountability challenges. Due to the unavailability of a scientific decision-making approach in this context, a 12-step algorithm is proposed and tested in a Japanese multinational company. The model allows the decision makers to revise their criteria expectations, in turn witnessing the change of the training providers' quota distribution. Finally, this multi-objective sensitivity analysis provides a forward-looking approach to training needs planning and aids decision makers in their sourcing strategy.

In a study on stochastic inventory systems, Chopra et al. (Decision Sciences35(1) (2004) 1–24) argue that decreasing lead time is the right lever if they want to cut inventories, not reducing lead time variability. According to Chopra et al., reducing the mean lead time, μ, is more important than reducing the lead time variance, σ², to reduce total inventory cost via a reduced safety stock. This paper is a criticism of Chopra et al., where the optimal z was derived based upon a predetermined Q, instead of solving the optimal z and Q simultaneously in a (z, Q) inventory system. We argue that such an approach is inappropriate because the two decision variables, z and Q, are in general interdependent, and, moreover, reducing reorder point (safety stock), z, does not necessarily decrease the total inventory cost. We demonstrate by means of a truncated lead time (z, Q) model that it is lead time variability, not mean lead time, that affects the inventory policy and total supply chain cost.

Classification algorithms that help to identify software defects or faults play a crucial role in software risk management. Experimental results have shown that ensemble of classifiers are often more accurate and robust to the effects of noisy data, and achieve lower average error rate than any of the constituent classifiers. However, inconsistencies exist in different studies and the performances of learning algorithms may vary using different performance measures and under different circumstances. Therefore, more research is needed to evaluate the performance of ensemble algorithms in software defect prediction. The goal of this paper is to assess the quality of ensemble methods in software defect prediction with the analytic hierarchy process (AHP), which is a multicriteria decision-making approach that prioritizes decision alternatives based on pairwise comparisons. Through the application of the AHP, this study compares experimentally the performance of several popular ensemble methods using 13 different performance metrics over 10 public-domain software defect datasets from the NASA Metrics Data Program (MDP) repository. The results indicate that ensemble methods can improve the classification results of software defect prediction in general and AdaBoost gives the best results. In addition, tree and rule based classifiers perform better in software defect prediction than other types of classifiers included in the experiment. In terms of single classifier, K-nearest-neighbor, C4.5, and Naïve Bayes tree ranked higher than other classifiers.