Forward recursion for markov decision processes with skip-free-to-the-right transitions part ii: non-standard applications

This paper is the sequel to Wijngaard and Stidham (1986). The topic is a countable state average reward semi-Markov decision process with a transition mechanism that is skip-free to the right. The applications are controlled GI /M/1 queues. Skip-free to the right means that state n cannot be reached from the states i (< n ) without reaching first state n −1. In such decision processes the reversed optimality equation can be used to estimate the optimal average reward by bi-section. Wijngaard and Stidham (1986) show that it is possible to use upper and lower bounds on the value function for this bi-section. This paper considers queueing problems where this bi-section can not be used in a standard way. Instead of an upper bound on the value function is is possible now to use the character of the optimal strategy.     

Application of an integrated decision support process for supplier selection

In many firms the value of purchased materials and components accounts for 50–80% of total cost of goods sold. For those firms, purchasing decisions cause enterprise wide side effects, and, therefore, should be treated as an integration matter. Supplier selection is a problem that deals with both qualitative and quantitative issues that must be measured using information that is normally spread within the enterprise. To see how this problem was addressed in a small construction company in Brazil, an integrated procurement process, which is a simplification of a more complex methodology, is presented. The case study shows the importance of simpler, yet efficient, solutions to address the decision making problems of smaller enterprises in development countries—a research and commercial niche not well developed yet.     

Sequential optimization three-way decision model with information gain for credit default risk evaluation

To categorize credit applications into defaulters or non-defaulters, most credit evaluation models have employed binary classification methods based on default probabilities. However, while some loan applications can be directly accepted or rejected, there are others on which immediate accurate credit status decisions cannot be made using existing information. To resolve these issues, this study developed an optimized sequential three-way decision model. First, an information gain objective function was built for the three-way decision, after which a genetic algorithm (GA) was applied to determine the optimal decision thresholds. Then, appropriate accept or reject decisions for some applicants were made using basic credit information, with the remaining applicants, whose credit status was difficult to determine, being divided into a boundary region (BND). Supplementary information was then added to reevaluate the credit applicants in the BND, and a sequential optimization process was employed to ensure more accurate predictions. Therefore, the model’s predictive abilities were improved and the information acquisition costs controlled. The empirical results demonstrated that the proposed model was able to outperform other benchmarking credit models based on performance indicators.     

Decision making framework for tender evaluation and contractor selection in public organizations with risk considerations

Selecting the best construction contractor plays a critical role in the success of any construction project. Moreover, in the public sector, the traditional lowest bid method is still broadly used and contracts are often awarded based on the lowest price. However, this method has been criticized by many researchers because even if it might guarantee the lowest cost for a project, it does not guarantee the maximum value in terms of time and quality. More particularly, the risk exposure during the tendering process is usually very high and the success of the construction project is strongly related to managing this risk in an appropriate way. Therefore, the selection of the most appropriate contractor should be based on a set of criteria such as technical capability, financial stability, risk, safety, etc., in addition to cost in order to avoid problems that may happen after the contract is awarded. This study aims therefore to develop a decision making framework (DMF) to assist the public organizations in selecting the most appropriate construction contractor(s). The proposed DMF uses a combination of multi-criteria decision making (MCDM) tools and fuzzy logic theory and consists of three stages. In the first stage, data envelopment analysis (DEA) is used to reduce the number of contractors, based on multiple criteria, such that only efficient contractors are considered. In the second stage, the risk factors that are related to each efficient contractor are identified and assessed using a fuzzy logic based approach. Finally, in the third stage, a bi-objective mixed integer linear programming (MILP) model is developed to select the best contractor(s) based on cost and risk and to determine the optimal quantity of work to be assigned to each selected contractor. Indeed, the proposed approach considers the multiple award contract case in which more than one contractor can be selected to share the amount of work to be performed. We assume that prior to using the proposed DMF, a pre-qualification study is conducted using multiple criteria chosen by the public organization, and only pre-qualified contractors are included in the first stage of the proposed DMF. Moreover, a case study inspired from the contractor selection process in a public organization in the United Arab Emirates (UAE) is used to show the effectiveness of the proposed approach.     

Nonparametric model validations for hidden Markov models with applications in financial econometrics

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous-time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.     

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.     

Conditions and benefits of applying decision technological solutions as a tool to curb corruption within the procurement process: The case of Hungary

Unethically influencing a decision maker is as old as civilization. Corruption is an especially damaging phenomenon in the field of public procurement. Experiences in curbing procurement corruption in Hungary, a former Eastern-block country which recently joined the EU, strengthens the view that by raising the level of evaluation practices through the use of decision support methodologies that can serve to narrow the possible damaging effects of corruption. This outcome may be achieved if legal regulations provide a supportive frame and organizational culture can be shaped to accommodate new decision practices.     

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.     

Assessment of alternative railway systems for sustainable transportation using an integrated IRN SWARA and IRN CoCoSo model

The long-term sustainability of the railway transportation system is determined by a set of criteria that must be considered. For this reason, multi-criteria decision-making (MCDM) techniques have been used because of their high possibility of providing complementary instruments with successful flexibility. Keeping this in mind, we developed a multi-criteria method to conduct an assessment of currently operational railway systems in West Africa for sustainable transportation based on the examination of key challenges affecting the railway system. This study presents the interval rough step-wise weight assessment ratio analysis-combined compromise solution (IR-SWARA-CoCoSo) model. We have adapted the model to group decision-making by incorporating a new technique for dealing with ambiguity and the utilization of interval rough numbers (IRNs). To evaluate the importance coefficients of criteria in the group decision-making procedure, a new interval rough SWARA method has been introduced as an improvement to handle the issues related to MCDM. For the evaluation of the railway transportation system for sustainable transportation, a novel interval rough CoCoSo method was used. According to the findings, information systems is the most critical challenge to the railway transportation system, while the railway system in Nigeria is the best among others. The results were validated in two stages of sensitivity analysis: a comparison with different interval rough approaches, and the calculation of Spearman's correlation coefficient and WS coefficient for all ranks in the comparative analysis.     

Application of an Analytic Hierarchy Process (AHP) in the GIS interface for suitable fire site selection: A case study from Kathmandu Metropolitan City,Nepal

Kathmandu Metropolitan City, the capital city of Nepal, is prone to different types of disasters. Fire disaster is one of the most recurring in the city. Due to haphazard urbanization, poor fire services, few and old fire engines, insufficient skilled human resources combined with narrow road lanes, clustered households increase the fire vulnerability in Kathmandu Metropolitan City. This paper documents the fire station suitability zonation mapping in Kathmandu Metropolitan City using Group Decision Making Process (GDMP) in the GIS interface. Four different selection criteria factors such as distance from roads, land cover, distance from rivers and population density are considered for analysis and the Analytic Hierarchy Process (AHP) is used for Group Decision Making Process (GDMP). The results reveal that only 13.46% of the study area is highly suitable for fire station location. Hence, the fire station suitability zonation map is trustworthy and can be used for the construction of new fire stations in Kathmandu Metropolitan City.     

Stochastic search variable selection in vector error correction models with an application to a model of the UK macroeconomy

This paper develops methods for stochastic search variable selection (currently popular with regression and vector autoregressive models) for vector error correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic model. Copyright © 2011 John Wiley & Sons, Ltd.     

A note on the central limit theorem for the idleness process in a one‐sided reflected Ornstein–Uhlenbeck model

In this short communication, we present a (functional) central limit theorem for the idleness process of a one‐sided reflected Ornstein–Uhlenbeck proces.     

Taxing capital is a good idea: The role of idiosyncratic risk in an OLG model

We investigate an overlapping generations (OLG) model in which agents who live for two periods receive idiosyncratic productivity shocks when they are old. We show that, around zero tax equilibria, we can always construct a combination of a small capital tax and a lump-sum transfer that are Pareto-improving. As Dávila et al. (Econometrica (2012)) show, a capital reduction in one period raises the welfare level of agents who are old in that period, but lowers that of the young agents, because it reduces their wages. We show that the government can compensate for these wage losses by additionally taxing the old agents, such that their welfare gains remain positive. Our result is unchanged when earnings are uncertain at young age.     

A method for estimating the timing interval in a linear econometric model, with an application to Taylor's model of staggered contracts

This paper describes and implements a procedure for estimating the timing interval in any linear econometric model. The procedure is applied to Taylor's model of staggered contracts using annual averaged price and output data. The fit of the version of Taylor's model with serially uncorrelated disturbances improves as the timing interval of the model is reduced.     

The development and application of a process model for R&D project management in a high tech firm: A field study

In R&;D organizations of high tech firms, multiple R&;D projects are executed concurrently and timeliness of project completion - i.e., developing the right products at the right times - is a matter of serious concern. Given that the priority of R&;D projects and the interdependencies between the projects in a high tech firm change dynamically, high tech R&;D project management is a complex and challenging endeavor. To improve the understanding and management of high tech R&;D projects, this paper reports the findings of a field study where we, first, develop and empirically estimate a model that relates project priority over time with the generative mechanisms of market pull and technical challenge associated with R&;D projects. Next, we develop and demonstrate the application of a process model within which the time-varying project priority model is embedded. The process model makes it possible to allocate fixed resources among competing projects with time-varying interdependencies, thereby improving the timeliness of project completion. This research was conducted in collaboration with a major U.S. high tech firm. The corporate R&;D center of the firm served as the research setting for the field study. We present an application of the process model to delineate the evolution of the R&;D organization with the merger of its (technology driven) parent firm with another (market driven) high tech manufacturing firm. The application of the process model generates theoretical insights that are used to develop testable propositions. Implications of the study findings and directions for future research are discussed.