Cooperative Bargaining Game-Based Scheduling Model With Variable Multiobjective Weights in a UWB and 5G Embedded Workshop

The Internet-of-Things (IoT) technology realizes deep integration of the physical process and production information in a manufacturing workshop through the real-time acquisition of data and seamless interaction of equipment, showing valuable academic and applicable potential in upgrading the workshop environment and eliminating the effect of dynamics. This brings a new opportunity to increase workshop productivity. However, how to realize active perception, dynamic optimization, and real-time regulation of the manufacturing process based on IoT technology is a research hotspot facing technical and theoretical challenges. To address these issues, this study constructs a multiagent-based dynamic scheduling (MADS) workshop IoT architecture by embedding advanced ultrawide band (UWB) and 5th-generation (5G) communication technologies. Differing from conventional scheduling models, the proposed model optimally assigns processes to machines by designing a resource scheduling agency (RS Agency). The
$NP$
-complete of the multiobjective optimization dynamic job-shop scheduling problem is proved and reduced to a cooperative bargaining game negotiation model (CBGNM) to rationalize the allocation of workshop resources. Then, a multiobjective weights tuning scheme enables effective and efficient responses to workshop exceptions. The performance comparisons with public data of the Kacem
$8\times 8$
benchmark verify that the CBGNM has better results than the conventional scheduling methods and reaches maximum optimization of 41.7%, 39.3%, 47.2%, 32.9%, 54.5%, and 38.4% regarding makespan, critical machine workload, total processing time, fairness index, total energy consumption, and comprehensive index, respectively. The numerical experiments show that the game players have no significant coupling with their joint payoff and validate that their bargaining power varies with abnormal events.