An approach to securely identifying beneficial collaboration in decentralized logistics systems

• 成果类型: Article
• 署名作者: Clifton, Chris; Iyer, Ananth; Cho, Richard; Jiang, Wei; Kantarcioglu, Murat; Vaidya, Jaideep
• 署名单位: Purdue University System; Purdue University; Purdue University System; Purdue University; University of New Brunswick; University of Texas System; University of Texas Dallas; Rutgers University System; Rutgers University Newark; Rutgers University New Brunswick
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.1070.0167
• 发表日期: 2008
• 页码: 108-125
• 关键词: Collaboration; Routing; cryptography; space-filling curve; incentive compatible; algorithm

摘要

The problem of sharing manufacturing, inventory, or capacity to improve performance is applicable in many decentralized operational contexts. However, the solution of such problems commonly requires an intermediary or a broker to manage information security concerns of individual participants. Our goal is to examine use of cryptographic techniques to attain the same result without the use of a broker. To illustrate this approach, we focus on a problem faced by independent trucking companies that have separate pick-up and delivery tasks and wish to identify potential efficiency-en-hancing task swaps while limiting the information they must reveal to identify these swaps. We present an algorithm that finds opportunities to swap loads without revealing any information except the loads swapped, along with proofs of the security of the protocol. We also show that it is incentive compatible for each company to correctly follow the protocol as well as provide their true data. We apply this algorithm to an empirical data set from a large transportation company and present results that suggest significant opportunities to improve efficiency through Pareto improving swaps. This paper thus uses cryptographic arguments in an operations management problem context to show how an algorithm can be proven incentive compatible as well as demonstrate the potential value of its use on an empirical data set.