A tabu search approach to fuzzy goal programs and an application to aggregate production planning

In this article, a multiple-objective tabu-search-based solution method is developed to solve fuzzy goal programs. The application of the method is shown on a fuzzy aggregate production-planning problem. Extensive computational work is carried out to present the performance of the algorithm and the effect of its parameters on the solution quality

Predicting maize phenology: intercomparison of functions for developmental response to temperature

Accurate prediction of phenological development in maize (Zea mays L.) is fundamental to determining crop adaptation and yield potential. A number of thermal functions are used in crop models, but their relative precision in predicting maize development has not been quantified. The objectives of this study were (i) to evaluate the precision of eight thermal functions, (ii) to assess the effects of source data on the ability to differentiate among thermal functions, and (iii) to attribute the precision of thermal functions to their response across various temperature ranges. Data sets used in this study represent >1000 distinct maize hybrids, >50 geographic locations, and multiple planting dates and years. Thermal functions and calendar days were evaluated and grouped based on their temperature response and derivation as empirical linear, empirical nonlinear, and process-based functions. Precision in predicting phase durations from planting to anthesis or silking and from silking to physiological maturity was evaluated. Large data sets enabled increased differentiation of thermal functions, even when smaller data sets contained orthogonal, multi-location and -year data. At the highest level of differentiation, precision of thermal functions was in the order calendar days < empirical linear < process based < empirical nonlinear. Precision was associated with relatively low temperature sensitivity across the 10 to 26 degrees C range. In contrast to other thermal functions, process-based functions were derived using supra-optimal temperatures, and consequently, they may better represent the developmental response of maize to supra-optimal temperatures. Supra-optimal temperatures could be more prevalent under future climate-change scenarios, but data sets in this study contained few data in that range