Multiple criteria decision making in distribution system planning

The paper presents the methodological background of Multiple Criteria Decision Making/Aiding (MCDM/A) and its practical application in logistics systems. It explains why MCDM/A methodology is important while dealing with different categories of decision problems that arise in those systems. Major features and basic notions of MCDM/A methodology are presented. Different categories of MCDM/A methods are characterized and classified. Two case studies demonstrate possible applications of MCDM/A methodology in logistics. In the first case study multiple objective optimization of the distribution system is carried out and compared with the single objective optimization. The decision problem is formulated as multiple criteria mathematical programming problem and solved by an extended version of MS Excel Solver – Premium Solver Plus. The second case study focuses on the multiple criteria evaluation and ranking of the logistics infrastructure objects, i.e. a set of warehouses – distribution centers. The decision problem is formulated as a multiple criteria ranking problem and solved with an application of ELECTRE III/IV method.

Download Full-text

Artificial Intelligence in Stochastic Multiple-Criteria Decision Making

Natural Language Processing ◽

10.4018/978-1-7998-0951-7.ch046 ◽

2020 ◽

pp. 957-982

Author(s):

Hanna Sawicka

Keyword(s):

Decision Making ◽

Probability Theory ◽

Distribution System ◽

Multiple Criteria Decision Making ◽

Pharmaceutical Products ◽

Multiple Criteria ◽

Distribution Centers ◽

Promethee Ii ◽

The Core ◽

Mcdm Method

This chapter presents the concept of stochastic multiple criteria decision making (MCDM) method to solve complex ranking decision problems. This approach is composed of three main areas of research, i.e. classical MCDM, probability theory and classification method. The most important steps of the idea are characterized and specific features of the applied methods are briefly presented. The application of Electre III combined with probability theory, and Promethee II combined with Bayes classifier are described in details. Two case studies of stochastic multiple criteria decision making are presented. The first one shows the distribution system of electrotechnical products, composed of 24 distribution centers (DC), while the core business of the second one is the production and warehousing of pharmaceutical products. Based on the application of presented stochastic MCDM method, different ways of improvements of these complex systems are proposed and the final i.e. the best paths of changes are recommended.

Download Full-text

Artificial Intelligence in Stochastic Multiple Criteria Decision Making

Advances in Business Information Systems and Analytics - Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence ◽

10.4018/978-1-5225-2031-3.ch019 ◽

2017 ◽

pp. 315-340

Author(s):

Hanna Sawicka

Keyword(s):

Decision Making ◽

Probability Theory ◽

Distribution System ◽

Multiple Criteria Decision Making ◽

Pharmaceutical Products ◽

Multiple Criteria ◽

Distribution Centers ◽

Promethee Ii ◽

The Core ◽

Mcdm Method

This chapter presents the concept of stochastic multiple criteria decision making (MCDM) method to solve complex ranking decision problems. This approach is composed of three main areas of research, i.e. classical MCDM, probability theory and classification method. The most important steps of the idea are characterized and specific features of the applied methods are briefly presented. The application of Electre III combined with probability theory, and Promethee II combined with Bayes classifier are described in details. Two case studies of stochastic multiple criteria decision making are presented. The first one shows the distribution system of electrotechnical products, composed of 24 distribution centers (DC), while the core business of the second one is the production and warehousing of pharmaceutical products. Based on the application of presented stochastic MCDM method, different ways of improvements of these complex systems are proposed and the final i.e. the best paths of changes are recommended.

Download Full-text