Task allocation problem is of critical importance in manufacturing industry, and determines the effectiveness and efficiency of advanced manufacturing systems. In order to be adaptable to the needs and future challenges that industry are facing, like rapid changes of demands and requirements, the cost of the entire production system have to be minimized. This performance start by optimizing the sequences of tasks and resource allocations through the system, and this can be handled by using computers optimization algorithms.
This is essentially the same problem addressed in mu paper "Adaptive submodel selection in hybrid models" doi: 10.3389/fenvs.201500058
The paper describes a strategy for changing the mix of submodels used to represent a complex system in response to the efficiencies and needs of the current components.
This extends the work described in the paper "Increasing model efficiency by changing model representations", which is availabe on researchgate. A mathemarical structure is developed in the supplementary data which might help (also on RG).
Dear ZAIDI,
The algorithm used depends on the nature of the configuration of the multi-agent system and the structure of its communication network. The dynamics of the system can be modeled by the Petri nets.
Best Regards
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