Kurt Palmer

An insertion heuristic for scheduling Mobility Allowance Shuttle Transit (MAST) services

In this paper, we develop an insertion heuristic for scheduling Mobility Allowance Shuttle Transit (MAST) services, an innovative concept that merges the flexibility of Demand Responsive Transit (DRT) systems with the low cost operability of fixed-route systems. A MAST system allows vehicles to deviate from the fixed path so that customers within a service area may be picked up or dropped off at their desired locations. Such a service already exists in Los Angeles County, where MTA Line 646 is a MAST nighttime service, transporting passengers between a business area and a nearby bus terminal. Since the current demand is very low, the service is entirely manageable by the bus operator, but a higher demand would certainly require the development of a scheduling algorithm. The proposed insertion heuristic makes use of control parameters, which properly regulate the consumption of the slack time. A set of simulations performed in the service area covered by the existing MTA Line 646 at different demand levels attests the effectiveness of the algorithm by comparing its performance versus a first-come/first-serve (FCFS) policy and optimal solutions generated by a commercial integer program solver. The results show that our approach can be used as an effective method to automate scheduling of this line and other services similar to it.

A minimum bias Latin hypercube design

Deterministic engineering design simulators can be too complex to be amenable to direct optimization. An indirect route involves data collection from the simulator and fitting of less complex surrogates: metamodels, which are more readily optimized. However, common statistical experiment plans are not appropriate for data collection from deterministic simulators due to their poor projection properties. Data collection plans based upon number-theoretic methods are also inappropriate because they tend to require large sample sizes in order to achieve their desirable properties. We develop a new class of data collection plan, the Minimum Bias Latin Hypercube Design (MBLHD), for sampling from deterministic process simulators. The class represents a compromise between empirical model bias reduction and dispersion of the points within the input variable space. We compare the MBLHD class to previously known classes by several model independent measures selected from three general families: discrepancies, maximin distance measures, and minimax distance measures. In each case, the MBLHD class is at least competitive with the other classes; and, in several cases the MBLHD class demonstrates superior performance. We also make a comparison of the empirical squared bias of fitted metamodels. We approximate a mechanistic model for water flow through a borehole, using both kriging and polynomial metamodels. Here again, the performance of the MBLHD class is encouraging.

An experimental study of surface quality and dimensional accuracy for selective inhibition of sintering

Selective inhibition of sintering (SIS) is a new rapid prototyping method that builds parts in a layer‐by‐layer fabrication basis. SIS works by joining powder particles through sintering in the parts body, and by sintering inhibition of some selected powder areas. In this research, statistical tools were applied to improve some important properties of the parts fabricated by the SIS process. An investigation of surface quality and dimensional accuracy was conducted using response surface methodology and through analysis of the experimental results, the impact of the factors on them was modeled. After developing a desirability function model, process operating conditions for maximum desirability are identified. Finally, the desirability model is validated.

SIS – a new SFF method based on powder sintering

Selective inhibition of sintering (SIS) is a layered fabrication process which is capable of rapidly producing accurate functional parts out of polymers and metals using a relatively inexpensive machine. This article presents a brief overview of the research and development aimed at establishing the feasibility and the potential of the process.

Development of a Rapid Prototyping System using Response Surface Methodology

This case study presents an investigation of the relationships between eight process operating variables (factors) and five part performance measures (responses) in a rapid prototyping system. The use of fractional factorial, single-factor foldover, and central composite designs is demonstrated. Polynomial regression models are constructed for each response, followed by a desirability function model. Canonical and ridge analyses are used to identify a group of factor settings that simultaneously produce improved performance for all responses. Copyright

Advancements in the selective inhibition sintering process development

Selective inhibition sintering (SIS) is a new layer-based rapid prototyping process that fabricates parts by joining polymer powder particles in the parts body to form a coherent solid and by preventing particles from joining at the parts boundary to form edges. This paper presents the fundamentals of the SIS process, its dedicated path generator, material selection research and the application of response surface methodology which has led to optimum performance, given various factors affecting part strength, surface quality and dimensional accuracy.