Jennifer Shang

Enhancing data consistency in decision matrix: Adapting Hadamard model to mitigate judgment contradiction

Cardinal and ordinal inconsistencies are important and popular research topics in the study of decision making with pair-wise comparison matrices (PCMs). Few of the currently-employed tactics are capable of simultaneously dealing with both cardinal and ordinal inconsistency issues in one model, and most are heavily dependent on the method chosen for weight (priorities) derivation or the obtained closest matrix by optimization method that may change many of the original values. In this paper, we propose a Hadamard product induced bias matrix model, which only requires the use of the data in the original matrix to identify and adjust the cardinally inconsistent element(s) in a PCM. Through graph theory and numerical examples, we show that the adapted Hadamard model is effective in identifying and eliminating the ordinal inconsistencies. Also, for the most inconsistent element identified in the matrix, we develop innovative methods to improve the consistency of a PCM. The proposed model is only dependent on the original matrix, is independent of the methods chosen to derive the priority vectors, and preserves most of the original information in matrix A since only the most inconsistent element(s) need(s) to be modified. Our method is much easier to implement than any of the existing models, and the values it recommends for replacement outperform those derived from the literature. It significantly enhances matrix consistency and improves the reliability of PCM decision making.

A unified framework for multicriteria evaluation of transportation projects

Transportation project selection is one of the most important planning activities encountered by a government, especially in a developing city. In this paper, we explore the potential of applying the analytic network process (ANP) to evaluate transportation projects in Ningbo, China. ANP differs from traditional hierarchical analysis tools in that it allows feedback and interdependence among various decision levels and criteria. Compared with the conventional transportation evaluation methods, our model has incorporated a much wider range of long-term and short-term factors, which are classified into benefits, opportunities, costs, and risks. Tactical and operational issues are taken into consideration. The evaluation framework is comprehensive and flexible, and shows great potential for helping decision-makers and others concerned with the transportation decision-making process.

An innovative orders-of-magnitude approach to AHP-based mutli-criteria decision making: Prioritizing divergent intangible humane acts

An innovative Analytic Hierarchy Process-based structure is developed to capture the relationship between various levels of activities contributed by people to society. Physical objects have widespread extension and degrees of importance that often differ by many orders of magnitude. Similarly, mental thoughts and criteria occur in widely heterogeneous entities that have to be sorted and arranged into homogeneous groups of few elements in each group so that one can evaluate the relationships among them accurately, from the smallest to the largest. It is through such a framework for organizing factors with smooth transition that it is possible to derive reliable priorities from expert judgments. The proposed model enables one to make decisions and allocate resources in as detailed and fine a way as possible. In addition to the traditional approach of structuring criteria into multiple clusters, the alternatives of a decision are also organized into the lowest multiple levels of that hierarchy. This arrangement and evaluation of alternatives differs from one criterion to another, which adds to the complexity of the undertaking when the alternatives are heterogeneous. The coherent approach to structuring complex decisions with the Analytic Hierarchy Process enables one to transcend the complexity of dealing in a scientific way with the problem of widespread orders of magnitude of criteria and alternatives in a complex decision. When the magnitudes are actually very small or very large, the accuracy of rating alternatives one at a time instead of comparing them in pairs involves much guessing, and can lead to a questionable outcome. Alternatively, comparisons, which are necessary for the measurement of intangibles, have greater and better justified accuracy.

Maximizing customer satisfaction through an online recommendation system: A novel associative classification model

Offering online personalized recommendation services helps improve customer satisfaction. Conventionally, a recommendation system is considered as a success if clients purchase the recommended products. However, the act of purchasing itself does not guarantee satisfaction and a truly successful recommendation system should be one that maximizes the customers after-use gratification. By employing an innovative associative classification method, we are able to predict a customers ultimate pleasure. Based on customers characteristics, a product will be recommended to the potential buyer if our model predicts his/her satisfaction level will be high. The feasibility of the proposed recommendation system is validated through laptop Inspiron 1525.

Operational design of a supply chain system using the Taguchi method, response surface methodology, simulation, and optimization

Managing a supply chain to meet an organizations objectives is a challenge to many firms. It involves collaboration in multiple dimensions, such as cooperation, information sharing, and capacity planning. In this research, we focus on identifying the ‘best’ operating conditions for a supply chain. We propose a hybrid approach that incorporates simulation, Taguchi techniques, and response surface methodology to examine the interactions among the factors, and to search for the combination of factor levels throughout the supply chain to achieve the ‘optimal’ performance. This study makes it possible for firms to understand the dynamic relations among various factors, and provides guidelines for management to minimize the impact of demand uncertainty on the performance of the supply chain. The results help the manufacturer determine the proper plant capacity and adopt the right level of delayed differentiation strategy for its products. We also quantify the potential gains of cooperation among different members of the supply chain. Using such knowledge, a manufacturer can develop an appropriate incentive plan to motivate the retailers and suppliers to collaborate, and to realize the potential of the entire supply chain.

Learning from class-imbalanced data

527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

Decision making in academia: A case of the dean selection process

This study describes the use of the analytical hierarchy process (AHP) in the dean selection process. A subcommittee of the dean search task force created an order-ranking based upon group discussions as well as through the application of the AHP. The results of the two processes were compared against the Provosts final decision. Discrepancies were analyzed and explained. The results suggest that root cause of the differences was a variation in emphasis on certain criteria. The authors conclude that AHP is a valuable tool and should be incorporated into personnel selection processes in academia. The AHP provides a convenient and effective tool for evaluating personnel. Fears that it might prove overly complex or difficult for non-technical people to use proved to be unfounded.

The analytic hierarchy process and human resource allocation: Half the story

The Analytic Hierarchy Process (AHP) provides a way to rank the alternatives of a problem by deriving priorities. A question that occurs in practice is: what is the best combination of alternatives that has the largest sum of priorities and satisfies given constraints? This leads one to consider the interface between the AHP and the combinatorial approach inherent in Linear Programming (LP). The priorities of the alternatives often serve as coefficients of the objective function of an LP problem. The constraints are determined from existing measurements, such as the range for the number of employees needed and the salaries required for various jobs. Another way to use the AHP might be to determine the coefficients of the constraints. This paper addresses the first half of the problem. Through various examples, we show how to apply the absolute measurement mode of the AHP together with LP to optimize human resource allocation problems. For example, one can determine which positions to fill, or which mix of candidates to hire. We also give an example of how to allocate resources to maximize the returns to a corporation of its training programs. Finally, we show that the combined AHP and LP model is capable of solving hiring problems involving synergy, such as when two persons with different complementary skills work as a team.

A decision support framework for internal audit prioritization in a rental car company: A combined use between DEA and AHP

Corporate scandals such as those at Tyco, Enron, and WorldCom have caused the decline of public trust in accounting and reporting practices. In response, US passed the Sarbanes Oxley Law (Sarbanes) in 2002, the most important corporate governance law since securities laws in 1930s. Section 302 of Sarbanes mandates corporations to ensure accurate financial disclosure and to take greater financial reporting and control responsibilities. Management is required to make an annual assertion regarding the design and effectiveness of companys internal controls. Consequently, many internal auditing resources are stretched. The objective of this case study is to develop a multi-criteria decision making aid that can identify the most critical businesses units within a corporation. Using the aid, we can use efficiently and effectively the internal auditing resources. Internal audits determine if the accounting processes and systems are working as intended. It focuses on the reliability of the accounting data and evaluates business through financial, operational, and compliance review. It assesses the risk of asset loss, studies business processes, and identifies opportunities to improve efficiency and effectiveness. This study explores the potential of applying data envelopment analysis (DEA) and analytic hierarchy process (AHP) to determine the business units that need audit. Compared with conventional methods, the proposed combined model incorporates a much wider range of quantitative and qualitative criteria, and provides a more detailed and thorough study. The proposed evaluation framework is comprehensive and flexible and it shows great potential for internal audit prioritization and resource allocation.

Output maximization of a CIM system: simulation and statistical approach

Abstract Computer simulation is one of the most popular and frequently used techniques among the various management science problem-solving tools. Simulation has often been used either to describe the behaviour of a manufacturing system or to compare several alternate configurations of the system. In this paper, we use simulation and statistical experimental design methods for providing normative solutions We investigated a computer-integrated manufacturing system of an automated printed circuit board manufacturing plant. Owing to the complexity of the manufacturing system, simulation is employed for analysis. The goal of this study is to determine what impact each individual input variable has on the output rate, what settings of the system factors will yield maximum output, and how to make the simulation practicable when multiple variables and complex manufacturing environment are involved. In solving this problem, we adopt the fractional factorial design method to reduce the number of experiments and t...