行銷硏究 : Marketing research

Despite the importance of decision-making (DM) techniques for construction of effective decision models for supplier selection, there is a lack of a systematic literature review for it. This paper provides a systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection. By using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches, we finally selected and reviewed 123 journal articles. To examine the research trend on uncertain supplier selection, these articles are roughly classified into seven categories according to different uncertainties. Under such classification framework, 26 DM techniques are identified from three perspectives: (1) Multicriteria decision making (MCDM) techniques, (2) Mathematical programming (MP) techniques, and (3) Artificial intelligence (AI) techniques. We reviewed each of the 26 techniques and analyzed the means of integrating these techniques for supplier selection. Our survey provides the recommendation for future research and facilitates knowledge accumulation and creation concerning the application of DM techniques in supplier selection.

Application of decision-making techniques in supplier selection: A systematic review of literature

This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended Exercises

Nonlinear System Identification

Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques

A literature study for DEA applied to energy and environment

The success of a supply chain is highly dependent on selection of best suppliers. These decisions are an important component of production and logistics management for many firms. Little attention is given in the literature to the simultaneous consideration of cardinal and ordinal data in supplier selection process. This paper proposes a new integrated data envelopment analysis (DEA) model which is able to identify most efficient supplier in presence of both cardinal and ordinal data. Then, utilizing this model, an innovative method for prioritizing suppliers by considering multiple criteria is proposed. As an advantage, our method identifies best supplier by solving only one mixed integer linear programming (MILP). Applicability of proposed method is indicated by using data set includes specifications of 18 suppliers.

http://www.cs.toronto.edu/~soroosh/papers/ESWA_Supplier_Selection.pdf

A new DEA method for supplier selection in presence of both cardinal and ordinal data

Finding the most efficient DMUs in DEA: An improved integrated model

A new integrated dea model for finding most bcc-efficient dmu

A modified slacks-based measure of efficiency in data envelopment analysis

Data mining techniques, extracting patterns from large databases have become widespread in business. Using these techniques, various rules may be obtained and only a small number of these rules may be selected for implementation due, at least in part, to limitations of budget and resources. Evaluating and ranking the interestingness or usefulness of association rules is important in data mining. This paper proposes a new integrated data envelopment analysis (DEA) model which is able to find most efficient association rule by solving only one mixed integer linear programming (MILP). Then, utilizing this model, a new method for prioritizing association rules by considering multiple criteria is proposed. As an advantage, the proposed method is computationally more efficient than previous works. Using an example of market basket analysis, applicability of our DEA based method for measuring the efficiency of association rules with multiple criteria is illustrated.

https://www.cs.utoronto.ca/~soroosh/papers/ESWA_Raking.pdf

Mehdi Toloo

Papers

A new DEA method for supplier selection in presence of both cardinal and ordinal data

Finding the most efficient DMUs in DEA: An improved integrated model

A new integrated dea model for finding most bcc-efficient dmu

A modified slacks-based measure of efficiency in data envelopment analysis

A new method for ranking discovered rules from data mining by DEA