Multi-hierarchy methods

Pre-qualification reduces a large set of initial suppliers to a smaller set of acceptable suppliers for further assessment. De Boer et al. (2001) have cited many different techniques for pre-qualification. Some of these techniques are categorical methods, data envelopment analysis (DEA), cluster analysis, case-based reasoning (CBR) systems, and multi-criteria decision making method (MCDM). Several authors have worked on pre-qualification of suppliers. Weber and Ellram (1992) and Weber et al. (2000) have developed DEA methods for pre-qualification. Hinkel et al. (1969) and Holt (1998) used cluster analysis for pre-qualification and finally Ng and Skitmore (1995) developed CBR systems for pre-qualification. Mendoza et al. (2008) developed a three phase multi-criteria method to solve a general supplier selection problem. The paper combines analytic hierarchy process (AHP) with goal programming for both pre-qualification and final order allocation. 

Terzopoulos (1986) has shown how to speed up the computation using multi-grid relaxation methods. He uses a hierarchy of multi-resolution grids where data propagates upward as well as downward through the hierarchy. The use of a multi-resolution pyramid allows information to propagate faster over larger distances. 

Formal decision analysis methods, in particular Multi-Criteria Decision Analysis MCDA, see [960[ for an overview) such as Utility Analysis and Analytic Hierarchy Process AHP), seek to formally assess the importance of several criteria and the grade to which the criteria are respected by different alternatives, to detect inconsistencies in the assessments, and finally to recommend the best fitting alternative. Several applications in the domain of chemical engineering are reported in the literature, including the design of urban wastewater treatment plants [672] and separation systems for hydrocarbons [621]. 

Figure 13.2 Multi-scale modelling hierarchy [35], showing the approximate regimes of time and length scales over which atomistic modelling techniques (quantum and molecular mechanics) can usefully be applied and how these link with process methods via mesoscale modelling.

Most MCDA methodologies, including Multi-Attribute Utility Theory (MAUT), Analytical Hierarchy Process (AHP), and outranking, share similar steps (Steps 1 and 3), but diverge on their approach to Steps 2 and 4. A detailed analysis of the theoretical foundations of different MCDA methods and their comparative strengths and weaknesses is presented in Belton and Steward (2002). 

Generally, the use of BBNs to model the impact of MOFs aims to explicitly model their multilevel and hierarchical influences on the HEPs, as discussed in Li et al. (2012), Cai et al. (2013) and Martins Maturana (2013). Many of the influencing factors typically considered by HRA methods can be thought of having direct influence on the HEP, e.g. the quality of the human machine interface and the time available for the personnel to carry out their tasks. In contrast, many of those referred to as MOFs have indirect effects for example the management s commitment to safety influences the quality of personnel training, which then directly influences the HEP. The BBN ability to represent multi-level relations helps to model these types of hierarchies. Figure 1 shows an example of the hierarchical influences. 

This paper uses the analytic hierarchy process (AHP) to determine the index weight. Based on in-depth analysis of the influencing factors of the complex decision problems and its inherent relationship, the AHP method uses less quantitative information to decision-making process of mathematical thinking, so as to provide a decision method to the multi-level, multi factor or without structural characteristics of complex decision problem. This method is more suitable for the case when the decision-making results are difficult to directly measure. Its specific steps are as follows ... 

For the critical items in Quadrant 3, we present a three-step method for global supplier selection. In the first step, we present a GP model for country selection this step shortlists a country using various qualitative and quantitative criteria. In the second step, we assess the risks of supply using the analytic hierarchy process (AHP). In the final step, we develop a multi-objective model with price and risk as the two conflicting objectives. For every product, we assign three different suppliers—a global supplier, a domestic primary supplier, and a domestic secondary supplier. Order allocation among the suppliers is optimally decided by the model. 

Preemptive GP ranks the objective fimctions with respect to the ordered preferences of the DMs and minimizes the deviations from the target values associated with each objective in the ranked order. Several different techniques can be used to derive preemptive priorities. One convenient way is to use discrete alternative multi-criteria decision-making methods such as rating, Borda count, pairwise comparison, or the analytic hierarchy process (AHP) method (see Ravindran et al. [2010] for an application). These methods also provide a numerical strength-of-preference value that can be used in non-preemptive GP models. The preemptive GP model formulation, assuming that the preference ordering of the objectives is Zy Zy Zy Zy as follows ... 

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