Pre-defined Boundaries Research Articles

Sequential process planning: A hybrid optimal macro-level approach

Increased global competition and unpredictable market changes are challenges facing manufacturing enterprises. Changes of part design and engineering specifications trigger frequent and costly changes in process plans, setups, and machinery. The paradigm shift in manufacturing systems and their increased changeability also require corresponding responsiveness in support functions; process planning is a key logical enabler that should be further developed to cope with changes encountered at the system level and to support new manufacturing paradigms and continuously evolving products. Retrieval-based planning, predicated on rigid predefined boundaries of part families, does not satisfactorily support this changeable manufacturing environment. On the other hand, pure generative planning is not yet a reality. Therefore, a sequential hybrid approach at the macro level is proposed where, initially, the part family’s master plan is retrieved, followed by application of modeling tools and solution algorithms to arrive at the plans of the new parts, whose features could exceed its respective original family boundaries. Two distinct generative methods, namely reconfigurable process planning and process replanning, are presented and compared. A genuine reconfiguration of process plans to optimize the scope, extent, and cost of reconfiguration is achieved using a 0–1 integer programming model. Also, because the problem is combinatorial in nature, a random-based evolutionary simulated annealing algorithm has been tailored for replanning. The developed methods are, conceptually and computationally, analyzed and validated using an industrial case study.

Spatial Autocorrelations and Urban Housing Market Segmentation

This paper seeks to let data define urban housing market segments, replacing the conventional administrative or any pre-defined boundaries used in the previous housing submarket literature. We model housing transaction data using a conventional hedonic function. The hedonic residuals are used to estimate an isotropic semi-variogram, from which residual variance–covariance matrix is constructed. The correlations between hedonic residuals are used as identifier to assign housing units into clusters. Standard submarket identification tests are applied to each cluster to examine the segmentation of housing market. The results are compared with the prevailing structure of market segments. Weighted mean square test shows that the defined submarket structure can improve the precision of price prediction by 17.5%. This paper is experimental in the sense that it represents one of the first attempts at investigating market segmentation through house price spatial autocorrelations.

Incorporation of physical bounds on rate parameters for reaction mechanism optimization using genetic algorithms

In this study a genetic algorithm (GA) approach for determining new reaction rate parameters ( A , g , and E a in the non-Arrhenius expressions) for the combustion of a hydrogen/air mixture in a perfectly stirred reactor (PSR) is assessed. A new floating-point coded GA and fitness function have been developed that dramatically increase both the rate of convergence and the predictive accuracy of the algorithm, thus promising the extension of the method to more detailed reaction schemes. Output profiles of species for 20 sets of PSR conditions, obtained from an original set of rate constants, are reproduced following a GA optimization inversion process. The new sets of rate constants following each iteration are constrained to lie between predefined boundaries that represent the uncertainty associated with the experimental findings listed in the National Institute of Standards and Technology (NIST) database. Comparisons with previous optimization work have demonstrated that those mechanisms generated using the NIST constraints can be applied to combustion scenarios outside those used in the mechanism's construction. In addition, the flexibility of the GA has been demonstrated by its success in generating reaction rate coefficients that reproduce a set of randomly perturbed species profiles.

The Jobs-Housing Balance and Urban Commuting

This paper applies geographical information system (GIS) techniques and a piece-wise, non-linear model-spline functions—to analyse empirically the relationship between the jobs-housing ratio and urban commuting patterns in terms of vehicle miles travelled (VMT) and trip length. A dynamic buffering process in GIS is developed to measure the jobs-housing ratio within floating catchment areas of a 5-7 mile (8.05-11.27 km) radius as opposed to pre-defined and arbitrary jurisdictional boundaries. This study found a non-linear relationship between the jobs-housing ratio and VMT and trip length in the Portland, Oregon, metropolitan area. Only when the jobs-housing ratio is less than 1.2 or larger than 2.8 do VMT vary noticeably as the jobs-housing ratio changes. Marginal changes in VMT are small when the jobs-housing ratio is between 1.2 and 2.8. Since most areas in the study region have a job-household ratio between 1.2 and 2.8, any changes in the jobs-housing ratio will have little impact on VMT. Consequently, hardly any land use policy changes could affect the jobs-housing ratio enough to produce a significant change in VMT per capita, and jobs-housing policy will have limited impact on VMT at the regional level.