In this study, we address the long-term project pricing problem for a company that operates in the defense industry. The pricing problem is a bid project pricing problem which includes various technical and financial uncertainties, such as estimations of workhour content of the project and exchange &amp / inflation rates. We propose a Robust Optimization (RO) approach that can deal with the uncertainties during the project lifecycle through the identification of several discrete scenarios. The bid project&rsquo / s performance measures, other than the monetary measures, for R&amp / D projects are identified and the problem is formulated as a multi-attribute utility project pricing problem. In our RO approach, the bid pricing problem is decomposed into two parts which are v solved sequentially: the Penalty-Model, and the RO model. In the Penalty-Model, penalty costs for the possible violations in the company&rsquo / s workforce level due to the bid project&rsquo / s workhour requirements are determined. Then the RO model searches for the optimum bid price by considering the penalty cost from the Penalty-Model, the bid project&rsquo / s performance measures, the probability of winning the bid for a given bid price and the deviations in the bid project&rsquo / s cost. Especially for the R&amp / D type projects, the model tends to place lower bid prices in the expected value solutions in order to win the bid. Thus, due to the possible deviations in the project cost, R&amp / D projects have a high probability of suffering from a financial loss in the expected value solutions. However, the robust solutions provide results which are more aware of the deviations in the bid project&rsquo / s cost and thus eliminate the financial risks by making a tradeoff between the bid project&rsquo / s benefits, probability of winning the bid and the financial loss risk. Results for the probability of winning in the robust solutions are observed to be lower than the expected value solutions, whereas expected value solutions have higher probabilities of suffering from a financial loss.

Conventionally, the focus during supplier evaluation has been to assess cost, quality and delivery effectiveness due to their impact on profitability. In recent years, there has been increased emphasis on promoting more sustainable business practices that focus on reducing environmental impact and improve societal well-being, in addition to economic benefits. However, most of the existing supplier evaluation methods in literature as well as those used by leading companies fall short of comprehensively assessing suppliers from a Triple Bottom Line (TBL) perspective. TBL defined as holistically looking at the economic, environmental, and societal aspects of an entity. This paper presents a review and selection of metrics for economic, environmental and societal sustainability evaluation. In addition, this work proposes a methodology for combining the scores into a comprehensive score that can be used to compare two entities performance relative to the TBL.

The purpose of this study was to develop a decision model for early-stage breast cancer patients. This model provides an opportunity for comparing two main treatment options, mastectomy and lumpectomy, with respect to quality of life by making use of Decision Theoretic Techniques. A Markov chain was constructed to project the clinical history of breast carcinoma following surgery. Then, health states used in the model were characterized by transition probabilities and utilities for quality of life. A Multi Attribute Utility Model was developed for outcome evaluation. This study was performed on the sample population of female university students, and utilities were elicited from these healthy volunteers. The results yielded by Multi Attribute Utility Model were validated by using Von Neumann-Morgenstern Standard Gamble technique. Finally, Monte Carlo Simulation was utilized in Treeage-Pro 2006 Suit software program in order to solve model and calculate expected utility value generated by each treatment option. The results showed that lumpectomy is more favorable for people who participated in this study. Sensitivity analysis on transition probabilities to local recurrence and salvaged states was performed and two threshold values were observed. Additionally, sensitivity analysis on utilities showed that the model was more sensitive to no evidence of disease state / however, was not sensitive to utilities of local recurrence and salvaged states.