A hierarchical system for effective coordination of available-to-promise logic mechanisms

International audienceThis paper aims to provide a combinatory approach towards addressing the advanced ATP problem, consisting of three deterministic optimization models that operate on both sides of the Customer Order Decoupling Point. The proposed approach is based on long term aggregate capacity reservation for periods when increased volatility is expected, while still obtaining production plans that meet the predefined and agreed customer service levels. The three optimization models together guide a system that helps manufacturers to optimally decide on ATP quantity and due date quoting on the basis of available manufacturing resources. To support this system, a prototype software module was designed and implemented in Java that loosely integrates with the popular Open Source ERP system Compiere2's databases and uses the Linear Programming solver QS-Opt to solve the models developed in this research. The system response times as evidenced in the experiments described in this paper are quite acceptable for real-world operations. The proposed solution of the ATP problem is of great value for all competitive and proactive organizations that need a practical tool to support, in the best possible way and in an almost real time fashion, their decision on whether to accept or decline an incoming customer order request. It is our belief that an integration of the proposed models into existing ERP systems will enhance their limited ATP functionality and provide management with a powerful decision support tool

Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization

We present high-level, decomposition-based algorithms for large-scale block-angular optimization problems containing integer variables, and demonstrate their effectiveness in the solution of large-scale graph partitioning problems. These algorithms combine the subproblemcoordination paradigm (and lower bounds) of price-directive decomposition methods with knapsack and genetic approaches to the utilization of "building blocks" of partial solutions. Even for graph partitioning problems requiring billions of variables in a standard 0-1 formulation, this approach produces high-quality solutions (as measured by deviations from an easily computed lower bound), and substantially outperforms widely-used graph partitioning techniques based on heuristics and spectral methods. 1 Introduction Most large-scale mathematical programming problems are constructed by concatenating and linking together blocks of data corresponding to major components of the problem. In the case of network optimization, ..