Model-Driven Framework for Automatic Generation of Auction-Based Coordination Strategies for Crisis Response

Document Type : Research Article

Authors

University of Isfahan

Abstract

Coordination strategies for crisis response mainly deal with task allocation. Auction-based coordination strategies are one of the most common strategies for task allocation in Emergency Response Environments (ERE), i.e., the environments in which the crisis occurs. Due to the emergency in EREs, coordination must be performed efficiently. To this end, before a crisis occurs, crisis managers try to benefit from modeling and simulation of coordination strategies. However, programming for the simulation of different coordination strategies is a difficult and time-consuming task for domain experts who usually do not have enough programming skills. To overcome this issue, in this paper, we propose a model-driven framework for modeling and automatic generation of auction-based coordination strategies for crisis response. The proposed framework consists of two main parts: (1) a Domain-Specific Modeling Language (DSML), named Auction-ML, with tool support for designing auction-based coordination strategies at a high level of abstraction, and (2) a transformation engine for automatic code generation from designed models. The generated code can be executed on ERE models to ‎generate the output model that shows ‎team formations, ‎task allocations, resource ‎allocation, and ‎strategy performance. To show the applicability of the proposed framework, two inter- and intra-organizational auction-based coordination strategies are modeled using AuctionML. ‎Then, the performance of the strategies is measured ‎by the automatic ‎generation of coordination strategies and‎ execution ‎of the ‎strategies on the ERE models.‎ The results confirm the applicability of the framework for enabling non-programmers to model, simulate, and evaluate auction-based strategies.

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References

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  • Receive Date: 12 December 2025
  • Revise Date: 24 February 2026
  • Accept Date: 03 May 2026

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