Article ID: | iaor201113125 |
Volume: | 20 |
Issue: | 5 |
Start Page Number: | 737 |
End Page Number: | 753 |
Publication Date: | Sep 2011 |
Journal: | Production and Operations Management |
Authors: | Sriskandarajah Chelliah, Dawande Milind, Geismar H Neil |
Keywords: | production: JIT, manufacturing industries, supply & supply chains, allocation: resources, networks, transportation: general, management, optimization, combinatorial optimization, demand |
We study zero-inventory production-distribution systems under pool-point delivery. The zero-inventory production and distribution paradigm is supported in a variety of industries in which a product cannot be inventoried because of its short shelf life. The advantages of pool-point (or hub-and-spoke) distribution, explored extensively in the literature, include the efficient use of transportation resources and effective day-to-day management of operations. The setting of our analysis is as follows: A production facility (plant) with a finite production rate distributes its single product, which cannot be inventoried, to several pool points. Each pool point may require multiple truckloads to satisfy its customers' demand. A third-party logistics provider then transports the product to individual customers surrounding each pool point. The production rate can be increased up to a certain limit by incurring additional cost. The delivery of the product is done by identical trucks, each having limited capacity and non-negligible traveling time between the plant and the pool points. Our objective is to coordinate the production and transportation operations so that the total cost of production and distribution is minimized, while respecting the product lifetime and the delivery capacity constraints. This study attempts to develop intuition into zero-inventory production-distribution systems under pool-point delivery by considering several variants of the above setting. These include multiple trucks, a modifiable production rate, and alternative objectives. Using a combination of theoretical analysis and computational experiments, we gain insights into optimizing the total cost of a production-delivery plan by understanding the trade-off between production and transportation.