Quatitative Analysis for Management 12th Edition I would like to get solution fo
ID: 457756 • Letter: Q
Question
Quatitative Analysis for Management 12th Edition
I would like to get solution for 3.29
Thank you
Mick Karra is the manager of MCZ Drilling Products, which produces a variety of specialty valves for oil field equipment. Recent activity in the oil fields has caused demand to increase drastically, and a decision has been made to open a new manufacturing facility. Three locations are being considered, and the size of the facility would not be the same in each location. Thus, overtime might be necessary at times. The following table gives the total monthly cost (in $1,000s) for each possible location under each demand possibility. The probabilities for the demand levels have been determined to be 20% for low demand, 30% for medium demand and 50% for high demand. DEMAND DEMAND DEMAND IS LOW IS MEDIUM IS HIGH 110 100 120 85 90 110 150 120 130 Ardmore, OK Sweetwater, TX Lake Charles, LA (a) Which location would be selected based on the optimistic criterion? (b) Which location would be selected based on the pessimistic criterion? (c) Which location would be selected based on the minimax regret criterion? (d) Which location should be selected to minimize the expected cost of operation? (e) How much is a perfect forecast of the demand worth? (f) Which location would minimize the expected opportunity loss? (g) What is the expected value of perfect information in this situation?Explanation / Answer
MULTI-CRITERIA EVALUATION Multi-criteria evaluation is a decision support methodology, which is based on the idea that humans use multiple decision criteria to determine the best solution. Multi-criteria decision rules have been implemented in GIS since the 1990s including the Simple Additive Weighting, Analytic Hierarchy Process, Ideal Point Analysis, Concordance, and OWA methods (Janssen & Rietveld 1990, Carver 1991, Church et al. 1992, Banai 1993, Pereira & Duckstein 1993, Jankowski 1995, Eastman 1997, Malczewski 1999, Thill 1999, Jankowski et al. 2001). Some GIS-based spatial decision support systems allow testing different standardization and aggregation procedures to explore differences in the results (Heywood et al. 1995, Rinner & Malczewski 2002). Multi-criteria evaluation has been implemented in conjunction with online GIS (Rinner 2003a, 2003b) but to the authors’ knowledge, it has not yet been suggested to integrate multi-criteria evaluation with LBS. The first part of the task described in the background scenario above, i.e. determining a set of nearby hotels, is solved by selecting hotels within a certain radius of the user’s current location. This selection uses a decision rule that is non-compensatory. Non-compensatory operators do not allow for a tradeoff between good and poor criteria values (Jankowski 1995). In other words, the distance from the current position is a “hard” selection criterion. This type of criterion is typically applied in present LBS. Solving the second part of the task requires the integration of compensatory decision rules, which allow users to control the trade-off between good and poor characteristics of alternative locations. Compensatory rules require standardization to make criterion values comparable. These values are then aggregated to a single evaluation score per alternative according to the rule. The user typically selects the highest scoring alternative. In this paper we will aggregate multiple criteria into a single evaluation score for each decision alternative according to the OWA rule. We suggest an interactive approach, which lets the user (1) select decision criteria, (2) express his/her preferred criteria values on a qualitative scale, (3) define the importance of each criterion, and (4) define a personal decision strategy through the settings of the OWA method. Steps (1) to (3) are described in the following paragraphs while step (4) is discussed in more detail in section 4. Selection of decision criteria In a vector-based GIS context, attributes of geographic features may serve as decision criteria while in a raster-based system, different raster datasets (maps) would represent the decision criteria (Heywood et al. 2002). In a location problem such as the hotel selection, the decision alternatives would typically be modeled as features. Thus we will allow users to select the attributes of hotel features on which to base their decision, and our approach performs calculations in the feature attribute table. A second concern regarding decision criteria relates to the levels of measurement (Chrisman 1997) that can be handled in the decision analysis. We will allow users to work with numerical, ordinal, as well as nominal criteria. However, multi-criteria evaluation requires commensurate, numerical criteria so that all selected criteria have to be transformed to a common, numerical scale as described in the following paragraph. Standardization of criteria Standardization of criteria is required to allow for trade-off between criteria in the calculation of the final evaluation score. In order to improve the system’s usability we work with a qualitative “Good – Fair – Poor” scale. According to the rank-order rule, the qualitative values can be transformed to numerical values of 3, 2, and 1, respectively, for further processing. Table 1 shows an example of standardized criterion values for a business traveler. Table 1: Example of standardized criterion values for a business traveler. Standardization occurs on a qualitative scale to facilitate the user’s preference statements. Criterion Original values Standardized values Room price 80-120€ 50-80€ >120€ Good Fair Poor Private bath Yes No Good Poor Check-out time >11:00 11:00 <11:00 Good Fair Poor This approach can be described as a value/utility function (Russell & Norvig 1995) in which the user transforms ranges of attribute values to a single utility score according to his/her preferences. In our approach, the value/utility function allows for a transformation of attribute intervals (e.g. price ranges) or attribute categories (e.g. no private bath) into utility scores. Another common method of deriving commensurate decision criteria is linear scale transformation, which is limited to numerical attribute data. Importance weights for criteria The OWA decision rule allows the user to specify a set of weights representing the relative importance of criteria according to the user’s preferences. The weight of a criterion defines its impact in the compensatory aggregation. For example, if price is considered twice as important as having a private bath, then the drawback of a high price cannot be fully compensated by the benefit of a private bath. By default, criterion weights are set to 1/n to represent n equally important criteria.