SEARCH THEORY RISK PREFERENCE AND FARMLAND PRESERVATION

This paper uses search theory to examine the role that risk preference (RP) plays in farmland preservation. Assuming that the distribution of the offer price is fixed, the analysis indicates that risk-averse agents have lower reservation prices than risk-neutral agents, and that agricultural land held by the former exits farming at a faster rate. The results also show that farmland preservation policies which increase reservation prices have a greater capitalization effect if agents are risk-loving, and that such policies, while effectively protecting the interest of land speculators, may be less effective in serving the needs of farming and farm-held open space.Risk and Uncertainty,

THE ROLE OF FARM OWNERSHIP IN OFF-FARM WORK PARTICIPATION

A labor supply model is used to examine the relationship between farm ownership and operators' participation in the off-farm labor market for the Northeast region. The results indicate that ownership significantly influences operators' off-farm employment participation. In particular, part-owners significantly allocate labor services to off-farm activities. The results also show that the participation rate among part-owner operators is high partly because the availability of other income sources accelerates the process of acquiring assets to become full-owner operators.Labor and Human Capital,

A Component Based Heuristic Search Method with AdaptivePerturbations for Hospital Personnel Scheduling

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with adaptive perturbations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then mimic a natural evolutionary process on these components to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs a dynamic evaluation function which evaluates how well each component contributes towards the final objective. Two perturbation steps are then applied: the first perturbation eliminates a number of components that are deemed not worthy to stay in the current schedule; the second perturbation may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems

Remaining service life prediction based on gray model and empirical Bayesian with applications to compressors and pumps

open access articleIn this study, a three-step remaining service life (RSL) prediction method, which involves feature extraction, feature selection, and fusion and prognostics, is proposed for large-scale rotatingmachinery in the presence of scarce failure data. In the feature extraction step, eight time-domain degradation features are extracted from the faulty variables. A fitness function as a weighted linear combination of the monotonicity, robustness, correlation, and trendabilitymetrics is defined and used to evaluate the suitability of the features for RSL prediction. The selected features are merged using a canonical variate residuals-based method. In the prognostic step, graymodel is used in combinationwith empirical Bayesian algorithm for RSL prediction in the presence of scarce failure data. The proposed approach is validated on failure data collected froman operational industrial centrifugal pump and a compressor