On the allocation of new lines in a competitive transit network with uncertain demand and scale economies

2011 ◽  
Vol 45 (4) ◽  
pp. 233-251 ◽  
Author(s):  
Zhi-Chun Li ◽  
William H. K. Lam ◽  
S. C. Wong
Keyword(s):  
Scale Economies ◽  
Uncertain Demand ◽  
Transit Network

The Optimum Size of a Producer Service Firm Facing Uncertain Demand

1998 ◽  
Vol 30 (1) ◽  
pp. 129-141 ◽  
Author(s):  
D Phillips ◽  
A D MacPherson ◽  
B Lentnek
Keyword(s):  
General Model ◽  
Manufacturing Sector ◽  
Scale Economies ◽  
Comparative Statics ◽  
Uncertain Demand ◽  
Optimum Size ◽  
Maintenance And Repair ◽  
Service Firm ◽  
Producer Service ◽  
Service Location

In this paper we present a theory of optimum size and number of clients for a producer service firm performing maintenance and repair services for clients in the manufacturing sector. The theory holds that scale economies vary directly with the level of contact requirements for service delivery. This is illustrated by a model of a monopoly repair specialist in which frequency of breakdown (and therefore client demand for service) is stochastic. Comparative statics are used to draw testable hypotheses from the model which, if extended to a multisite case, may serve as a portion of a general model of producer service location.

scholarly journals Optimizing Bus Frequencies under Uncertain Demand: Case Study of the Transit Network in a Developing City

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Zhengfeng Huang ◽  
Gang Ren ◽  
Haixu Liu
Keyword(s):  
Travel Time ◽  
Programming Model ◽  
Uncertain Demand ◽  
Transit Assignment ◽  
Transit System ◽  
Transit Network ◽  
Passenger Demand ◽  
Level Model ◽  
Upper Level ◽  
Bilevel Programming Model

Various factors can make predicting bus passenger demand uncertain. In this study, a bilevel programming model for optimizing bus frequencies based on uncertain bus passenger demand is formulated. There are two terms constituting the upper-level objective. The first is transit network cost, consisting of the passengers’ expected travel time and operating costs, and the second is transit network robustness performance, indicated by the variance in passenger travel time. The second term reflects the risk aversion of decision maker, and it can make the most uncertain demand be met by the bus operation with the optimal transit frequency. With transit link’s proportional flow eigenvalues (mean and covariance) obtained from the lower-level model, the upper-level objective is formulated by the analytical method. In the lower-level model, the above two eigenvalues are calculated by analyzing the propagation of mean transit trips and their variation in the optimal strategy transit assignment process. The genetic algorithm (GA) used to solve the model is tested in an example network. Finally, the model is applied to determining optimal bus frequencies in the city of Liupanshui, China. The total cost of the transit system in Liupanshui can be reduced by about 6% via this method.

Industry Specialization by Auditors

1999 ◽  
Vol 18 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Chris E. Hogan ◽  
Debra C. Jeter
Keyword(s):  
High Risk ◽  
Market Share ◽  
Scale Economies ◽  
The Other ◽  
Industry Specialization ◽  
Audit Market ◽  
Audit Firms ◽  
Regulated Industries ◽  
Concentration Levels ◽  
Over Time

Dramatic changes in recent years in the audit market suggest the timeliness of an investigation of trends in auditor concentration and an extension of prior research (e.g., Danos and Eichenseher 1982). In recent press, large audit firms have claimed that specialization is a goal of increasing importance. Peat Marwick, for example, has restructured along industry lines, claiming to be recruiting professionals for national teams of multidisciplinary experts organized to “focus on the same industry to serve clients optimally.” On the other hand, litigation concerns might prompt auditors to diversify their risks by diversifying their clientele. In this study, we examine trends in industry specialization from 1976 to 1993 and the industry factors which may affect specialization; whether market share increases are greater for audit firms classified as specialists; and whether the nation's largest audit firms have increased their market share in the industries which they have identified as their focus industries. We find evidence that concentration levels have increased over this period, consistent with the claims of the large audit firms. We find that auditor concentration levels are higher in regulated industries, in more concentrated industries and in industries experiencing rapid growth, but lower in industries with a high risk of litigation. Levels of concentration have increased over time in nonregulated industries providing evidence that scale economies or superior efficiencies of heavy-involvement auditors are not limited to regulated industries but extend to nonregulated industries as well. We also find that for the audit firms classified as market leaders at the beginning of the year, market share has increased over time, whereas market share has declined for firms with a smaller share at the beginning of the year. This suggests that there are returns to investing in specialization.

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