تجزیه و تحلیل از مدل موقعیت مکانی تسهیلات با استفاده از شبکه های بیزی

In this study, we propose an integrated approach that combines Bayesian Networks and Total Cost of Ownership (TCO) to address complexities involved in selecting an international facility for a manufacturing plant. Our goal is to efficiently represent uncertain data and ambiguous information, and to unite them to improve the quality of the decisions. Bayesian Networks provide a framework to elicit information from experts, and provide a structure guide to efficient reasoning, even with incomplete knowledge. Our method is presented in a hierarchical structure so that it can be decomposed into the forms of more manageable units. We consider many tangible and intangible facility location criteria, then these criteria are grouped into few numbers of factors. These factors are then combined to form a cost perspective using the essentials of TCO.

The location decision describes, given a set of alternatives, the problem of where to locate an organization’s facility (Melo, Nickel, & Saldanha-da-Gama, 2009). Since this decision is strategically important, it has a significant impact: the company’s overall success is directly related to the sound decisions on facility location. Over the long-term, location decisions affect every aspect across the organization, so that any poor location decision will result in excessive costs, poor customer service, disappointed workforce, and failure of the organization strategy as well as loss of competitive advantage (Canbolat et al., 2007 and Stevenson, 2009). In our study, we focus on the global facility location decision for a manufacturing firm. The numbers of companies that adopt globalization as an organizational strategy have increased in last two decades. Organizations are enthusiastic to work overseas for several reasons: expanding current market, spreading foreign exchange risks, attracting talented workforce, working with capable suppliers, and learning from foreign customers, suppliers, competitors and foreign research centers, as well as savings due to reduced taxes, labor, transportation, and labor costs, incentives and capital subsidies (Ferdows, 1997). In consequence, the question of identifying a best geographic location for facilities has gained attention from both researchers and businesses. In this study, we propose a novel and integrated approach that combines a Total Cost of Ownership (TCO) approach with a Bayesian Network for selecting an international facility for a manufacturing firm. We analyze the facility location problem from a holistic view that addresses numerous qualitative and quantitative criteria and their effects on different cost items in probabilistic cause-effect relations. Bayesian Networks facilitate modeling of causality in an efficient, intuitive, and transparent way, and the available graphical probabilistic structure allows the user to account for uncertainty and ambiguous domain knowledge within the problem context. Cost elements that are selected as final measures for comparing different alternatives are not limited to investment costs unique to the location; included are all the costs related to the life cycle of the facility that can vary based on the selected facility. This Total Cost of Ownership approach (TCO) aims to evaluate true cost of the location decision and provide a better estimate of total costs while comparing different alternatives. This approach provides us an opportunity to combine numerous qualitative and quantitative factors by considering the effects on different cost elements that are critical to the location decision. The proposed combined technique will simultaneously tackle both monetary and non-monetary criteria and combine them to create a thorough and accurate measure that takes into consideration all of the costs that are relevant to these selection criteria. Significant amount of facility location criteria are uncertain and can easily fluctuate before and after decisions have been made (Snyder, 2006). Therefore, it is critical to clearly model the uncertainty in each facility location criteria and their effects on different cost elements. A key problem has been addressing the influence of each criterion on the long horizon, so an explicit representation of uncertainty has gained significant importance. Failure to precisely represent and understand these uncertainties results in strategic mistakes that are difficult to overcome. Human subjective judgment is critical in this task, especially if the decision makers need to make an assessment when there is limited and incomplete data. Therefore, there is a requirement for a complete and efficient method to encode human belief to analyze different alternatives under this uncertain and complex environment. The proposed method allows exploring and directing judgment and managers’ opinions by following a systematic approach while globally considering all the relations among factors, as well as between factors and cost measures. Bayesian Networks have a high potential to efficiently facilitate incorporating, representing, and propagating the uncertain and ambiguous information. A Bayesian Networks (BN) approach offers several distinctive features that are useful in our facility location decision analysis. First, BN builds on causality that provides useful insight and intuitive understanding. This feature is particularly important in our case since it will assist to model the relationships between facility selection criteria and their effects on cost elements. Second, BN allows us to incorporate subjective information of the decision makers. This domain information is considerably useful when the data is limited, and human experience is significant. Third, BN can combine historical data with human knowledge while drawing conclusions for the problem at hand. In a facility location decision, some of the information and degree of uncertainty can be learned from historical data such as currency and inflation rates. However, human inputs are required for a significant part of the selection criteria and their effects on the cost elements. BN can unite these two different sources of information. Fourth, BN allows us to model uncertainty by defining probability distributions over the random variables. Modeling uncertainty by plain probability assessments provides compactness and explanations for variability and risks involved in decisions. Finally, BN facilitates reasoning by providing efficient inference algorithms. Even when the information is incomplete and the knowledge of an expert is limited, BN allows us to draw conclusions even in poorly defined environments. This feature also allows the sensitivity analysis to understand the degree of variability that caused by the different selection criteria. This provides explanations for the responsibility of each factor for the company’s overall success. The proposed approach consists of mainly four steps: one, identify the selection criteria, factors and cost elements that are significant for global location decision; two, determine the structure by building relations among the selection criteria and factors, and among factors and cost elements; three, construct conditional and unconditional probabilities and collect alternatives; four, make decisions. Fig. 1 shows the model elements in a hierarchical view. The uncertainties related to facility location are conceptualized by the data and human judgment. For example, the uncertainty involved in current and future inflation rate and demand may be addressed by analyzing the historic data. The overall mean and variability can be parameterized by the existing data. On the other hand, for some factors the decision maker may have inadequate information due to a lack of historical data. However, the incomplete knowledge of the decision maker due to vagueness in the selection criteria is still critical and needs to be included in the analysis. BN has the potential to combine existing data with incomplete expert knowledge. Full-size image (6 K) Fig. 1. Hierarchical view of model. Figure options The remainder of this paper is structured as follows. In Section 2, the relevant research is presented concerning the global facility location. Section 3 provides a short overview of BN. The proposed model and its details are explained in Section 4. The model is then tested and a manufacturing sector illustrative example is presented in Section 5. The last section presents a summary and conclusion of the proposed method.

In this study, we analyze the problem of selecting a global manufacturing facility location under conditions of uncertainty and information ambiguity. Using a Bayesian Networks approach offers a high potential for representing this ambiguous knowledge and for performing reasoning under uncertainty. We evaluate alternative facility locations from a cost perspective, taking into account a number of quantitative and qualitative criteria. Using a combination approach of Bayesian Network with Total Cost of Ownership (TCO) provides a realistic measure for comparing facility location alternatives. The proposed method is structured as a hierarchy. The top level consists of 36 supplier selection criteria. Those criteria then explain and measure 12 factors in the second level, with a final level that evaluates 11 cost items based on the state of each factor in the second level. This hierarchical modeling assists decision makers in guiding and structuring the problem while selecting the best location. The problem is decomposed into smaller and modular parts that can be solved more efficiently. The insights obtained using this approach are also more intuitive. One of the key features of the proposed method is that it combines data with domain knowledge. Decision makers can then incorporate their soft beliefs regarding the status of the each alternative in an efficient way. The Bayesian Network is constructed by using expert knowledge with additional inputs from extensive literature in facility location decisions. The conditional probabilities and their parameters are identified using both expert knowledge and the data. With an increasing number of alternatives and factors, the involvement of a large amount of uncertainty around facility location becomes more complex. Human judgment obviously plays a key role in the decision making, particularly around problems that involve a number of qualitative factors. Our goal was to create a tool that provides a complete and efficient method for accurately encoding human beliefs into a model that can analyze different alternatives within the complex environment. The proposed method allows exploring and directing judgment, opinions and belief of managers by following a systematic approach while globally considering all the relations among factors, and between factors and objectives. Another advantage of the Bayesian Networks approach is its casual representation of various facility location factors. Bayesian Networks relate factors to each other by parental representation in a graph so that transparency in reasoning is possible. The decision makers have a better understanding and interpretation of factors by this graphical representation. The proposed method identifies the significant cost elements along with the status of each location in terms of different factors. This study provides opportunities for decision makers to identify the strengths and weakness of the each facility location place.