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Chinese vegetable production cooperatives supply their members, mostly smallholder farmers, with a rotation schedule for the year. Since vegetable prices are not stable throughout the year, designing a rotation schedule that maximizes expected profits, distributes farmers' profits more equitably, maintains the diversity of produce in the market, and reduces the risk of pests and diseases, requires adaptive, price-contingent rotation schedules (here, called “self-adaptive adjustment”). This study uses an agent-based simulation (ABS) to design self-adaptive rotation schedules that deliver these aims. The self-adaptive adjustment strategy was more profitable for farmers when faced with price volatility, and more equitable as well. This work provides a decision-support tool for managers of Chinese vegetable production cooperatives to provide farmers with more profitable and equitable rotation schedules.

Self-evolution problem of an aircraft-engine assembly workshop with uncertain number of assembly times in knowledgeable manufacturing environment is studied in this article. The whole dynamic self-evolution process is decomposed into a series of static ones by using the rolling horizon procedure. A rolling rule, which is employed to select operations into rolling windows, is designed on the basis of the level priority of each operation. An algorithm for the self-evolution algorithm is then proposed. Due to the deterministic production information, a static decision sub-problem is needed to be solved at each decision point. A general mathematical model of sub-problems is built to minimize the production cost. For solving it, a bi-level genetic algorithm is proposed. In the lower level genetic algorithm, an improved decoding method is also designed by considering production features. Finally, simulation results demonstrate the feasibility and validity of the model and algorithms. Then, computational data indicate that the system with self-evolution capability pays lower costs for production.

This paper proposes a new method for vector quantization by minimizing the Divergence of Kullback-Leibler between the class label distributions over the quantization inputs, which are original vectors, and the output, which is the quantization subsets of the vector set. In this way, the vector quantization output can keep as much information of the class label as possible. An objective function is constructed and we developed an iterative algorithm to minimize it as well. The novel method is evaluated on bag-of-features based image classification problems.

The way to realize the automatic focusing rapidly and efficiently is a focus in some automatic optical inspection case. The key problem is how to select a suitable focusing evaluation function and design an effective auto-focusing algorithm. In this paper, based on comparing some well-known focusing evaluation functions, a new automatic focusing algorithm was presented. It used two steps to do the search. At the first step, it searched a large region quickly just to get the approximate focusing position, the next step was to do precise search in a small area in order to get the exact focusing position. This new algorithm performed well on the vision measuring machine and could focus rapidly and exactly.