Use of a Markov decision process model for treatment selection in an asymptomatic disease with consideration of risk sensitivity

Some potentially dangerous diseases are completely asymptomatic. Their diagnosis as incidental findings of ever-more-sensitive medical imaging can leave patients and physicians in something of a quandary. The patient feels well, and potential interventions to stave off long-term deterioration or death bring with them immediate risks. We discuss the use of a Markov Decision Process (MDP) model (rather than Monte Carlo simulation of a Markov Model) to create a tool for analyzing individual treatment decisions for asymptomatic chronic diseases where a patient’s condition cannot improve. We formulate a finite-horizon MDP model to determine optimal treatment plans and discuss three distinct optimality criteria: (a) maximizing expected quality-adjusted-life years with and without discounting, (b) maximizing the expected number of life years in good health, and (c) maximizing the expected utility for number of years in good health. In (c) we assume exponential utility and consider different risk aversion factors reported in the medical literature. We illustrate the model’s use by considering asymptomatic intracranial aneurysm. Our model builds on a simulation model [19] created to examine treatment recommendations based on cost-effectiveness. We demonstrate that incorporating risk aversion leads to “no treatment” recommendations for some types of aneurysm. Furthermore, the use of alternate patient-selected criteria leads to recommendations that vary from [19] in several scenarios. We also discuss the use of the software as a decision support tool to help make individualized treatment recommendations and demonstrate that the computational performance of the algorithm makes its use feasible during a short office visit.     

A multi-criteria expert decision system for investment decisions: The case of commercial real estate investments in China

Consistent with the rapid growth of the Chinese economy and its commercial real estate (CRE) market, evaluating CRE investments in China is becoming an important application area where a multi-criteria expert decision system can make a significant contribution. We used a multi-criteria expert decision system to evaluate ten actual CRE investments in China. Based on thirty-one industry experts, we identified key decision factors to consider when evaluating different CRE investment projects and determined their importance weights. We then evaluated whether or not the ten CRE investment projects were considered to be successful, taking into account the unique characteristics of each CRE investment project. We finally compared the evaluations for the ten cases with their actual performance using four different performance indicators. The results showed that the evaluations had at least 87.5% accuracy across four different performance indicators. As probably one of the first systematic and in-depth scholarly studies to evaluate CRE investment projects in China, this study contributes to a better understanding of such evaluations and helps CRE investors evaluate CRE investment projects in a more informed fashion.     

Assessing supply chain risks with the analytic hierarchy process: Providing decision support for the offshoring decision by a US manufacturing company

This paper reports the process used by a US manufacturing company to assess supply chain risks within the context of an offshore sourcing decision. The case study company was faced with the objective of finding a new supplier for two of its major product lines. Five alternatives were considered: (1) sourcing finished goods from Mexico; (2) sourcing finished goods from China; (3) sourcing parts from China and assembly in the US; (4) sourcing parts from China, assembly in a Maquiladora in Mexico with investment; and (5) sourcing parts from China, assembly in a Maquiladora in Mexico with no investment in the venture. To find the best solution, action research methodology was combined with the application of the analytic hierarchy process (AHP). Through iterative and structured discussions, 17 risk factors were identified, which were subsequently grouped into main and sub objectives. AHP was then used to evaluate the importance of each risk factor, and to determine the best alternative. This study makes several contributions to the field of purchasing and supply management. First, it provides a comprehensive framework of empirically derived risk factors to be considered in an international sourcing context. Second, it shows how AHP can be used to assess these risk factors and alternatives as part of the framework to facilitate and support the final offshoring decision. And third, it illustrates the successful application of the approach by a US manufacturing company. As such, this paper contributes to the research streams of offshoring and risk management in purchasing and supply, as well as to decision-making under uncertainty and AHP. In addition, it serves as a practical methodology for firms in similar situations.