【主讲】张哲美国西华盛顿大学决策科学系教授 加拿大西蒙·弗雷泽大学教师

【主题】随机需求与供应系统建模

【时间】6月20日（星期三）10:30-12:00

【地点】清华经管学院伟伦楼453室

【语言】中文

【主办】管理科学与工程系

【简介】

Zhe George Zhang

Dept. of Decision Sciences,

Western Washington University,WA,USA&

Faculty of Business Administration,

Simon FraserUniversity,BC,Canada

Part I: Service Systems:

Motivated by Flexible Staffing of the US-Canada Border Crossings

Abstract: In the first part of this research, we study waiting line

problems at the border-crossings between theU.S.andCanada. To

evaluate a practical staffing policy, we develop an analytical model to

compute the important performance measures. The policy is called

"congestion based staffing" or CBS, because the number of open

inspection booths is adjusted according to the queue length during each

planning period. Our analysis is based on the matrix-geometric solution,

the regeneration cycle, and the fluid approximations. With a certain

cost structure, we provide a numerical search approach to determine the

best CBS policy for border-crossing stations. Under certain conditions,

we can obtain the close-form solution for the optimal policy parameters

and prove the convexity of the average cost function.

Part II: Manufacturing Systems:

Apply the CBS model to Production/Inventory Systems

Abstract: In the second part of this research, we show that CBS model

can be applied to study a fixed number of production facilities

producing a specific type of items with random demand and production

time. The inventory policy is a base-stock (s, S) type with continuous

review. Some production facilities can be switched to producing other

secondary products if the inventory level is high and switched back when

the inventory level is slow. Under a cost structure which includes a

set-up cost, a linear holding cost, and a possible linear backorder

cost, an average cost function is developed. Using reasonable

approximation methods, we obtain the closed form formulas for computing

the optimal inventory and production policy. Excellent approximation

with high accuracy has been illustrated by extensive numerical analysis.

These easy-to-use formulas provide practitioners a useful tool in

determining the best inventory and production control policy under the

random demand and production time environment.