This paper presents a novel, two-level mixed-integer programming model of scheduling N jobs on M parallel machines that minimizes bi-objectives, namely the number of tardy jobs and the total completion time of all the jobs.
Gems, along with the number of eggs collected, count to the total completion percentage of the game.
The intervention consists of three modules requiring an estimated total completion time of 90 minutes.
This problem is formulated as a mixed integer programming (MIP) model, whose two objectives minimize the total completion delay of all task groups and the total energy consumption of all YCs.
Three criteria are involved: the total completion time of the accepted jobs in the adjusted schedule, the degree of disruption measured by the maximum completion time disruption to any accepted job between the original and adjusted schedules, and the total rejection cost.
In both models, the goal is to minimize the makespan and the total completion time of agent A respectively, subject to an upper bound on the makespan of agent B. We prove that these two problems are NP-hard and can be solved in pseudo-polynomial time.
Tasks are then allocated to actors according to their measured capabilities in such a way to reduce the total completion times of all tasks in the network.
We formalize this situation as a game in which the players are job owners, the strategies are machines, and a player's utility is inversely proportional to the total completion time of the machine selected by the agent.
Minimization of total completion times, tardiness costs, number of tardi jobs, makespan, etc. are instances of these objectives.
Therefore, it is crucial to design a system that satisfies the total completion time requirement of mobile users while maximizes channel utilization.
