Development of Intelligent ATP System Using Genetic Algorithm

유전 알고리듬을 적용한 지능형 ATP 시스템 개발

  • 김태영 (동양미래대학 경영학부)
  • Received : 2010.11.24
  • Accepted : 2010.12.08
  • Published : 2010.12.31


The framework for making a coordinated decision for large-scale facilities has become an important issue in supply chain(SC) management research. The competitive business environment requires companies to continuously search for the ways to achieve high efficiency and lower operational costs. In the areas of production/distribution planning, many researchers and practitioners have developedand evaluated the deterministic models to coordinate important and interrelated logistic decisions such as capacity management, inventory allocation, and vehicle routing. They initially have investigated the various process of SC separately and later become more interested in such problems encompassing the whole SC system. The accurate quotation of ATP(Available-To-Promise) plays a very important role in enhancing customer satisfaction and fill rate maximization. The complexity for intelligent manufacturing system, which includes all the linkages among procurement, production, and distribution, makes the accurate quotation of ATP be a quite difficult job. In addition to, many researchers assumed ATP model with integer time. However, in industry practices, integer times are very rare and the model developed using integer times is therefore approximating the real system. Various alternative models for an ATP system with time lags have been developed and evaluated. In most cases, these models have assumed that the time lags are integer multiples of a unit time grid. However, integer time lags are very rare in practices, and therefore models developed using integer time lags only approximate real systems. The differences occurring by this approximation frequently result in significant accuracy degradations. To introduce the ATP model with time lags, we first introduce the dynamic production function. Hackman and Leachman's dynamic production function in initiated research directly related to the topic of this paper. They propose a modeling framework for a system with non-integer time lags and show how to apply the framework to a variety of systems including continues time series, manufacturing resource planning and critical path method. Their formulation requires no additional variables or constraints and is capable of representing real world systems more accurately. Previously, to cope with non-integer time lags, they usually model a concerned system either by rounding lags to the nearest integers or by subdividing the time grid to make the lags become integer multiples of the grid. But each approach has a critical weakness: the first approach underestimates, potentially leading to infeasibilities or overestimates lead times, potentially resulting in excessive work-inprocesses. The second approach drastically inflates the problem size. We consider an optimized ATP system with non-integer time lag in supply chain management. We focus on a worldwide headquarter, distribution centers, and manufacturing facilities are globally networked. We develop a mixed integer programming(MIP) model for ATP process, which has the definition of required data flow. The illustrative ATP module shows the proposed system is largely affected inSCM. The system we are concerned is composed of a multiple production facility with multiple products, multiple distribution centers and multiple customers. For the system, we consider an ATP scheduling and capacity allocationproblem. In this study, we proposed the model for the ATP system in SCM using the dynamic production function considering the non-integer time lags. The model is developed under the framework suitable for the non-integer lags and, therefore, is more accurate than the models we usually encounter. We developed intelligent ATP System for this model using genetic algorithm. We focus on a capacitated production planning and capacity allocation problem, develop a mixed integer programming model, and propose an efficient heuristic procedure using an evolutionary system to solve it efficiently. This method makes it possible for the population to reach the approximate solution easily. Moreover, we designed and utilized a representation scheme that allows the proposed models to represent real variables. The proposed regeneration procedures, which evaluate each infeasible chromosome, makes the solutions converge to the optimum quickly.

ERP, SCM 등과 같은 기업용 정보 시스템을 활용함에 있어, 고객의 문의에 따라 제품 판매 가능 유무와 가능일자를 계산하여 통보해 주는 지능형 ATP 시스템은 전산 정보를 활용하여 고객 만족도를 최대화할 수 있는 유용한 기능이라고 할 수 있다. 그렇지만 공급 사슬 환경에서 ATP 시스템을 적용하려고 할 경우, 고객이 문의해 온 Retailer에게 납품 가능한 모든 분배센터(Distribution Center)와 공장(Plant)의 미래 시점의 재고량 변화와 운송 능력 등을 모두 고려하여야 하므로 계산량이 방대한 NP-Complete 문제가 된다. 따라서 시스템 사용자가 빠른 시간 내에 해를 구하여 고객에게 결과를 알려 줄 수 있는 ATP 시스템의 개발은 공급 사슬 관리를 효과적으로 활용하기 위하여 반드시 필요한 일이라고 할 수 있다. 본 논문에서는 동적 생산 함수의 개념을 이용하여 비 정수 타임 랙을 고려하여 ATP 시스템을 모델링하고, 해당 수리 모형으로부터 효율적으로 해를 얻기 위하여 유전 알고리듬을 개발하였다. 비 정수 타임 랙을 활용한 ATP 시스템은 비 정수 타임 랙을 올림이나 내림을 통하여 정수화 시킨 후 모형 수립하는 기존의 방법보다 정교하게 현실을 반영할 수 있고, ATP 시스템을 위한 유전 알고리듬의 진화 시스템은 문제크기가 작은 것에서부터 큰 것까지 최적해에 매우 근사한 값을 매우 빠른 시간 내에 풀 수 있음을 알 수 있었다.



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