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

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.