Region Foreign Economic Relations as a Factor of Reindustiralization

At present, the strengthening vector of the developing economies, in particular, of the countries of Eurasian Economic Union and BRICS, in the geographic structure of Russian foreign economic activity is observed. The subject matter of the article is to see, how the development of this direction can be economically favorable. For this purpose, the forms and degree of the development of economic partnership including collaboration with the Russian regions are investigated. The agreements on the economic partnership with the regions of partner countries of Russia, their production relations are considered. The analysis of foreign trade shows that in the case of the partner countries within Eurasian integration and the BRICS group — Russian economy gets the high possibility to become the supplier of products of relatively high degree of processing which is the key factor of development of its processing industry, i.e. the factor of reindustrialization and export-oriented import substitution.The article has been prepared within the project of fundamental research of the Ural Branch of the Russian Academy of Sciences (20152017) No. 15-14-7-13 “Scenario approaches to realization of Ural vector of management and development of the Russian Arctic in terms of world instability”

Region Foreign Economic Relations as a Factor of Reindustiralization

At present, the strengthening vector of the developing economies, in particular, of the countries of Eurasian Economic Union and BRICS, in the geographic structure of Russian foreign economic activity is observed. The subject matter of the article is to see, how the development of this direction can be economically favorable. For this purpose, the forms and degree of the development of economic partnership including collaboration with the Russian regions are investigated. The agreements on the economic partnership with the regions of partner countries of Russia, their production relations are considered. The analysis of foreign trade shows that in the case of the partner countries within Eurasian integration and the BRICS group — Russian economy gets the high possibility to become the supplier of products of relatively high degree of processing which is the key factor of development of its processing industry, i.e. the factor of reindustrialization and export-oriented import substitution

Formulating and Solving Sustainable Stochastic Dynamic Facility Layout Problem: A Key to Sustainable Operations

Facility layout design, a NP Hard problem, is associated with the arrangement of facilities in a manufacturing shop floor, which impacts the performance, and cost of system. Efficient design of facility layout is a key to the sustainable operations in a manufacturing shop floor. An efficient layout design not only optimizes the cost and energy due to proficient handling but also increase flexibility and easy accessibility. Traditionally, it is solved using meta-heuristic techniques. But these algorithmic or procedural methodologies do not generate effective and efficient layout design from sustainable point of view, where design should consider multiple criteria such as demand fluctuations, material handling cost, accessibility, maintenance, waste and more. In this paper, to capture the sustainability in the layout design these parameters are considered, and a new Sustainable Stochastic Dynamic Facility Layout Problem (SDFLP) is formulated and solved. SDFLP is optimized for material handling cost and rearrangement cost using various meta-heuristic techniques. The pool of layouts thus generated is then analyzed by Data Envelopment Analysis (DEA) to identify efficient layouts. A novel hierarchical methodology of consensus ranking of layouts is proposed which combines the multiple attributes/criteria. Multi Attribute decision-making (MADM) Techniques such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Interpretive Ranking Process (IRP) and Analytic hierarchy process (AHP), Borda-Kendall and Integer Linear Programming based rank aggregation techniques are applied. To validate the proposed methodology data sets for facility size N=12 for time period T=5 having Gaussian demand are considered