In the field of distributed decision making, different agents share a common processing resource, and each agent tonic shower cap wants to minimize a cost function depending on its jobs only.These issues arise in different application contexts, including real-time systems, integrated service networks, industrial districts, and telecommunication systems.Motivated by its importance on practical applications, we consider two-agent scheduling on a single machine where the objective is to minimize the total completion time of the jobs of the first agent with the restriction that an upper bound is allowed the click here total completion time of the jobs for the second agent.
For solving the proposed problem, a branch-and-bound and three simulated annealing algorithms are developed for the optimal solution, respectively.In addition, the extensive computational experiments are also conducted to test the performance of the algorithms.