Analytical Models in Rail Transportation: An Annotated Bibliography

Not AvailableThis research has been supported, in part, by the U.S. Department of Transportation under contract DOT-TSC-1058, Transportation Advanced Research Program (TARP)

A Lower Bounding Result for the Optimal Policy in an Adaptive Staffing Problem

We derive a lower bound for the staffing levels required to meet a projected load in a retail service facility. We model the queueing system as a Markovian process with non-homogeneous Poisson arrivals. Motivated by an application from the postal services, we assume that the arrival rate is piecewise constant over the time horizon and retain such transient effects as build- up in the system. The optimal staffing decision is formulated as a multiperiod dynamic programming problem where staff is allocated to each time period to minimize the total costs over the horizon. The main result is the derivation of a lower bound on the staffing requirements that is computed by decoupling successive time periods

Modelling of rail networks: Toward a routing/makeup model

Freight flow management in rail systems involves multicommodity flows on a network complicated by node activities (queueing and classification of cars at marshalling yards). Routing in these systems should account for technology requirements of motive power and traction as well as resource allocation (cars to blocks, blocks to trains). In this paper, we propose a hierarchial taxonomy of modelling issues and describe a class of models dealing with car routing and train makeup from the viewpoint of network flows and combinatorial optimization. We compare our model with two previous rail network models and discuss possibilities for algorithmic development.

Alternating Priority Versus FCFS Scheduling in a Two-Class Queueing System

For the single-server two-class queueing system studied in the classical text of Conway et al. (1967), we compare the mean flow times for First-Come, First-Served (FCFS) and Alternating Priority (AP) scheduling rules assuming zero setup costs for switching between classes. We show that the condition for the superiority of AP over FCFS stated in that text is incorrect, provide the correct conditions, and establish a lower bound on the difference between the mean flow times under the two rules

A Lower Bounding Result for the Optimal Policy in an Adaptive Staffing Problem

We derive a lower bound for the staffing levels required to meet a projected load in a retail service facility. We model the queueing system as a Markovian process with non-homogeneous Poisson arrivals. Motivated by an application from the postal services, we assume that the arrival rate is piecewise constant over the time horizon and retain such transient effects as build-up in the system. The optimal staffing decision is formulated as a multiperiod dynamic programming problem where staff is allocated to each time period to minimize the total costs over the horizon. The main result is the derivation of a lower bound on the staffing requirements that is computed by decoupling successive time periods. Keywords: dynamic programming, staffing, service operations 1 M.C. Fu is supported in part by the National Science Foundation under Grant No. NSF EEC 94-02384. 1 Introduction Queueing theory is frequently used to determine the staffing required to meet a desired level of service. Stan..

Comparing Functional and Cellular Layouts: A Simulation Study Based on Standardization

In the last decade, over two dozen simulation studies have focused on comparing cellular and functional layouts. The results reported by these studies vary widely, however. This remains true even when the key performance measure is flow time. These variations reflect the disparate manufacturing and operating environments, as well as differences in parts demands, set-up economies, overall loads and other factors. This work attempts to reduce the sources of variation due to different operating assumptions while retaining the variability associated with differences in part mix and demand characteristics. Instead of focusing on a single data source, this study uses a test bed of six problems extracted from the literature and ensures they share the same operational rules. The simulation results show that conversion to CMS can reduce flow times (relative to the job shop configuration) consistently across all data sets, provided the same operating rules and ranges for key parameter are used. We investigate the reduction in flow time while controlling for the key factors of set-up reduction, overall load on the system and batch size. We also assess the benefits of using transfer batches as a further factor in reducing flow time. Our overall conclusion is that set-up reductions in cells can overcome pooling losses, even under the conservative assumptions where batch size remain unchanged and the material transport times in the job shop are assumed to be negligible