A parallel machine schedule updating game with compensations and clients averse to uncertain loss. (March 2019) - Kovalyov, Mikhail Y.; Kress, Dominik; Meiswinkel, Sebastian; Pesch, Erwin
Highlights: We consider the problem of scheduling selfish jobs on parallel machines. Job weights and due dates are private information of the jobs. The jobs (resp. their owners) are averse to uncertain loss. We present a mechanism that incentivizes the job owners to claim true parameters. The mechanism proves to perform well in computational tests. Abstract: There is a finite number of non-cooperating clients, who are averse to uncertain loss and compete for execution of their jobs not later than by their respective due dates in a parallel service environment. For each client, a due date violation implies a cost. In order to address the minimization of the total scheduling cost of all clients as a social criterion, a game mechanism is suggested. It is designed such that no client has an incentive to claim a false due date or cost. The game mechanism allows the clients to move their jobs to complete earlier in a given schedule. However, they must compensate costs of those clients whose jobs miss their due dates because of these moves. Algorithmic aspects are analyzed. Furthermore, a polynomial time algorithm that determines an equilibrium of the considered game is suggested and embedded into the game mechanism. Computational tests analyze the performance and practical suitability of the resulting game mechanism.