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Genetic Algorithms in Supply Chain Scheduling of Ready-Mixed Concrete

Author

Listed:
  • Naso, D.
  • Surico, M.
  • Turchiano, B.
  • Kaymak, U.

Abstract

The coordination of just-in-time production and transportation in a network of partially independent facilities to guarantee timely delivery to distributed customers is one of the most challenging aspects of supply chain management. From the theoretical perspective, the timely production/distribution can be viewed as a hybrid combination of planning, scheduling and routing problem, each notoriously affected by nearly prohibitive combinatorial complexity. From a practical viewpoint, the problem calls for a trade-off between risks and profits. This paper focuses on the ready-made concrete delivery: in addition to the mentioned complexity, strict time-constraints forbid both earliness and lateness of the supply. After developing a detailed model of the considered problem, we propose a novel meta-heuristic approach based on a hybrid genetic algorithm combined with constructive heuristics. A detailed case study derived from industrial data is used to illustrate the potential of the proposed approach.

Suggested Citation

  • Naso, D. & Surico, M. & Turchiano, B. & Kaymak, U., 2004. "Genetic Algorithms in Supply Chain Scheduling of Ready-Mixed Concrete," ERIM Report Series Research in Management ERS-2004-096-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:1802
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    Citations

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    Cited by:

    1. Asbach, Lasse & Dorndorf, Ulrich & Pesch, Erwin, 2009. "Analysis, modeling and solution of the concrete delivery problem," European Journal of Operational Research, Elsevier, vol. 193(3), pages 820-835, March.
    2. Chi, Hoi-Ming & Ersoy, Okan K. & Moskowitz, Herbert & Ward, Jim, 2007. "Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms," European Journal of Operational Research, Elsevier, vol. 180(1), pages 174-193, July.
    3. Surico, M. & Kaymak, U. & Naso, D. & Dekker, R., 2006. "Hybrid Meta-Heuristics for Robust Scheduling," ERIM Report Series Research in Management ERS-2006-018-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    More about this item

    Keywords

    concrete delivery; genetic algorithms; meta-heuristics; supply chain management;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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