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The collaborative multi-level lot-sizing problem with cost synergies.

Margaretha Gansterer1, Richard F Hartl1

  • 1Department of Business Decisions and Analytics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria.

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|March 14, 2020
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Summary
This summary is machine-generated.

This study introduces a new decentralized approach for collaborative supply chain planning, enabling cost savings without revealing sensitive data. It significantly improves upon existing methods, offering near-optimal solutions with enhanced efficiency.

Keywords:
decentralised planninggenetic algorithmslot-sizing

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Industrial Engineering

Background:

  • Modern supply chains rely on collaborative operations planning.
  • Cost synergies can be achieved when producers supply multiple products.
  • Information asymmetry often hinders collaborative planning.

Purpose of the Study:

  • To address the collaborative multi-level lot-sizing problem with cost synergies.
  • To develop a decentralized mechanism for collaborative planning that preserves information privacy.
  • To benchmark the decentralized approach against a centralized Genetic Algorithms-based method.

Main Methods:

  • Development of a decentralized mechanism for lot-sizing under cost synergies.
  • Implementation of a centralized Genetic Algorithms-based approach for benchmarking.
  • Assessment of solution approaches on existing test instances.

Main Results:

  • The centralized approach closely approximates optimal central planning.
  • The decentralized mechanism achieves near-optimal results without sharing critical cost information.
  • Compared to myopic upstream planning, centralized approaches show nearly 12% improvement.
  • For large instances without cost synergies, the centralized mechanism improves solutions by an average of 10.8%.

Conclusions:

  • The proposed decentralized mechanism is effective for collaborative decision-making under incomplete information.
  • This approach offers a viable solution for supply chain optimization where data privacy is crucial.
  • The methods are applicable to various problem classes requiring collaborative planning.