Asian Journal of Control· 2026Q2
Solving Deadlock Problem for Consensus‐Based Bundle Algorithm in Distributed Systems With Practical Fuel Consumption Evaluation
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- Q2SCImago
- 2026year
Short summary
The Deadlock-Free Consensus‐Based Bundle Algorithm (DF‐CBBA) resolves deadlock in multi-agent task allocation by incorporating fuel consumption penalties and a novel bidding strategy with adaptive conflict resolution.
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Key points
- Introduces the Deadlock-Free Consensus‐Based Bundle Algorithm (DF‐CBBA) to solve deadlock in distributed multi-agent task allocation.
- Enhances fuel consumption penalties in a time-window task allocation framework, deviating from the Diminishing Marginal Gains property.
- Task marginal benefits are derived from the actual increase in overall objective value upon task insertion, not a predefined formula.
- Features a redesigned bidding strategy with adaptive conflict-resolution mechanisms for dynamic task valuation re-evaluation.
AI-generated from the title and abstract; the full text is not read.
Abstract
ABSTRACT This paper proposes the Deadlock‐Free Consensus‐Based Bundle Algorithm (DF‐CBBA) to address deadlock in distributed multi‐agent task allocation problems with practical fuel consumption evaluation. A more practical optimization problem, which intentionally violates the Diminishing Marginal Gains (DMG) property, is formulated by enhancing the fuel consumption penalty within the time‐window task allocation framework. Under this formulation, the marginal benefit of a task is no longer computed using a pre‐defined objective function formula. Instead, it is derived from the actual increase in the overall objective value resulting from the task's insertion. The critical innovation of the proposed DF‐CBBA is a redesigned bidding strategy equipped with adaptive conflict‐resolution mechanisms, enabling agents to dynamically re‐evaluate task valuations during the bundle construction phase. Theoretical analysis and simulation results validate the performance of the algorithm.
The authors' abstract, as published at the source. Asian Journal of Control, 2026 · DOI ↗
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Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences