Reverse Auction-Based Job Scheduling among Contract Manufacturers

• Main Authors: Ming-Ming Hsieh, 謝旻旻
• Other Authors: Shi-Chung Chang
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/08565253229566596060

碩士 === 國立臺灣大學 === 電機工程學研究所 === 93 === Pressed by market globalization and concomitant competition, more and more manufacturers are relying on their suppliers to provide raw materials and component parts so as to focus on their core competence. Outsourcing becomes an increasing trend due to the competitiveness of the IT (Information Technology) industry. In this study, we focus on the job scheduling problem among contract manufacturers. In almost all cases, however, scheduling methods are developed under the assumption of centrally available information, or distributed information with cooperative behavior. Nevertheless, centralized methods are not directly applicable in the problem where each entity has its own individual objective and privately held information about the requirements for and values of possible uses. We choose reverse auction among several kinds of auctions to develop our algorithm in solving the problem. Implementing the reverse auction mechanism, the job provider only has to know the price and the beginning time/delivering time of each job. The auction is processed through iterations. The auction is a non-cooperative game so we model auctioneer’s and bidder’s problems separately and consider their interactions. We formulate the auctioneer behavior and the assignment is a simple greedy assignment problem. For auctioneer, it only considers the expected profit from job assigned and the cost from job unassigned. The not or late delivering job compensation is all compensated by bidder. The scheduling problem of the bidder is a NP hard scheduling problem itself. We apply the Lagrangian Relaxation to decompose the problem. Several experiments are made to prove its efficiency. Besides, in order to be applied in the real condition, we will consider a situation where there are uncertainties and the cycle time of each job is a random variable. We simulate the stochastic cycle time and get its mean and variance. By re-formulating our mathematic formulation, we can make the algorithm be closer to the real problem. Experiment results show that the stochastic problem formulation perform better than the deterministic problem formulation when the cycle time is a random variable. Because there are several independent decision makers in the auction, the concept of an optimal solution is not suitable to use. We then use the concept of an equilibrium solution to evaluate the result. We define our equilibrium solution as that no entity can benefit from changing its strategy given the other entities’ strategies. We show that the auction will converge to an equilibrium solution in finite iterations. We also observe that iterations will increases while the number of bidders or jobs increases and the auctioneer will benefit if bidders use deterministic algorithm instead of deterministic algorithm.