Priority rules for job shops with weighted tardiness costs
Management Science
The single machine early/tardy problem
Management Science
Minimizing total tardiness on one machine is NP-hard
Mathematics of Operations Research
Ordering and production decisions with supply quality and demand uncertainty
Management Science
A Simple and Effective Component Procurement Policy for Stochastic Assembly Systems
Queueing Systems: Theory and Applications
Supply Contract Competition and Sourcing Policies
Manufacturing & Service Operations Management
Capacity Acquisition, Subcontracting, and Lot Sizing
Management Science
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
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Pressure to increase agility and reduce costs is pushing enterprises to dynamically select among offers from a broader range of suppliers. This process is facilitated by the adoption of web services standards. An important requirement in this context is the ability to move away from unidimensional price-based e-procurement models and develop richer solutions that are capable of capturing other important attributes in the selection of supplier bids. Research on the evaluation and selection of supplier bids (''winner determination'') has traditionally ignored the temporal and finite capacity constraints under which manufacturers and service providers often operate. We consider the problem faced by a firm that procures multiple key components or services from a number of possible suppliers. Bids submitted by suppliers include both a price and a delivery date. The firm has to select a combination of supplier bids that will maximize its overall profit. Profit is determined by the revenue generated by the products (or services) sold by the firm, the costs of the components (or services) it acquires as well as late delivery penalties it incurs if it fails to deliver its products/services in time to its own customers. We provide a formal model of this important class of problems, discuss its complexity and introduce rules that can be used to efficiently prune the resulting search space. We proceed to show that our model can be characterized as a pseudo-early/tardy scheduling problem and use this observation to build an efficient heuristic search procedure. Computational results show that our heuristic procedure typically yields solutions that are within a few percent from the optimum. They further indicate that taking into account the manufacturer/service provider's capacity can significantly improve its bottom line.