Auction based scheduling for distributed systems

Businesses deal with huge databases over a geographically distributed supply network. When this is combined with scheduling and planning needs, it becomes too difficult to handle. Recently, Fast Consumer Goods sector tends to consolidate their manufacturing facilities on a single supplier serving to a distributed customer network. This decentralized structure causes imperfect information sharing between customers and the supplier. We model this problem as a single machine distributed scheduling problem with job agents representing the customers and the machine agent representing the supplier. For benchmarking purpose, we analyzed the problem under three different scenarios: decentralized utility case (realistic case), centralized utility case, centralized cost case (classical single machine early/tardy problem). We developed Auction Based Algorithm by exploiting the opportunity to use game theoretic approach to solve the problem in the decentralized utility case. We used optimization techniques (Lagrangean Relaxation and Branch-and-Bound) for the centralized cases. Results of our extensive computational experiments indicate that Auction Based Algorithm converges to the upper bound found for the total utility measure.