Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management

  • Authors:

  • R. A. Aliev;B. Fazlollahi;B. G. Guirimov;R. R. Aliev

  • Affiliations:

  • Azerbaijan State Oil Academy, 20 Azadlyg Avenue, Baku, Azerbaijan;Georgia State University, USA;Azerbaijan State Oil Academy, 20 Azadlyg Avenue, Baku, Azerbaijan;Eastern Mediterranean University, Turkey

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2007

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Abstract

Aggregate production-distribution planning (APDP) is one of the most important activities in supply chain management (SCM). When solving the problem of APDP, we are usually faced with uncertain market demands and capacities in production environment, imprecise process times, and other factors introducing inherent uncertainty to the solution. Using deterministic and stochastic models in such conditions may not lead to fully satisfactory results. Using fuzzy models allows us to remove this drawback. It also facilitates the inclusion of expert knowledge. However, the majority of existing fuzzy models deal only with separate aggregate production planning without taking into account the interrelated nature of production and distribution systems. This limited approach often leads to inadequate results. An integration of the two interconnected processes within a single production-distribution model would allow better planning and management. Due to the need for a joint general strategic plan for production and distribution and vague planning data, in this paper we develop a fuzzy integrated multi-period and multi-product production and distribution model in supply chain. The model is formulated in terms of fuzzy programming and the solution is provided by genetic optimization (genetic algorithm). The use of the interactive aggregate production-distribution planning procedure developed on the basis of the proposed fuzzy integrated model with fuzzy objective function and soft constraints allows sound trade-off between the maximization of profit and fillrate. The experimental results demonstrate high efficiency of the proposed method.