Senlin Guan

Migration Effects of Parallel Genetic Algorithms on Line Topologies of Heterogeneous Computing Resources

This paper investigates migration effects of parallel genetic algorithms (GAs) on the line topology of heterogeneous computing resources. Evolution process of parallel GAs is evaluated experimentally on two types of arrangements of heterogeneous computing resources: the ascending and descending order arrangements. Migration effects are evaluated from the viewpoints of scalability, chromosome diversity, migration frequency and solution quality. The results reveal that the performance of parallel GAs strongly depends on the design of the chromosome migration in which we need to consider the arrangement of heterogeneous computing resources, the migration frequency and so on. The results contribute to provide referential scheme of implementation of parallel GAs on heterogeneous computing resources.

Mathematical Model and Solution for Land-Use Crop Planning with Cooperative Work

Most farm work planning for land-use crops such as sugarcane belongs to flexible flow shop scheduling if neglecting cooperative work and other specific constraints. Because the conventional approaches to the flexible flow shop scheduling cannot formulate these specific constraints, we require a new approach for solving land-use crop planning problems that considers cooperative work. This paper describes a detailed mathematical model and a hybrid algorithm for solving the model, in which many practical constraints are taken into account, including cooperative work, optimum time windows, waiting time between operations, and moving time. The hybrid algorithm uses meta-heuristic simulated annealing and a mixed integer programming solver in Gurobi. In order to obtain good schedules in a reasonable time, we adopt a strategy of fixing partial work sequences in the simulated annealing procedure and optimizing the partial schedule using the solver. The results of the evaluation computation show that the proposed model is operative for the practical constraints, and that the hybrid algorithm is adaptable to scheduling computation. The strategy of fixing partial work sequences is applicable to reducing computation times for large-sized land-use crop planning problems.

Hybrid Petri Nets and Metaheuristic Approach to Farm Work Scheduling

Scheduling problems for general cases are characterized as NP hard, and the computation time required to obtain the optimal schedule will grow exponentially with the problem size. The scheduling problems that consider the limited or shared resources, alterable constraints or environmental changes become very complex in both formulation and solution. Since the solution for these problems has great serviceability and reliability against environmental changes, much research has been devoted in optimization strategy in the presence of a wide range of uncertainties (Li & Ierapetritou, 2008). Such research with application is applicable to not only the manufacturing in industry, but also production in agriculture. Modeling and scheduling in the agricultural domain may be more promising because of the requirement of new approaches to handling the uncertainties in the nature environment. In agriculture, a system that aims to produce maximum amount of profit from available land by high inputs of capital, labour, or efficient usage of machinery, is defined as intensive farming (or intensive agriculture). Like common businesses, many intensive farming units are operating their businesses by the ways to improving profits in farming while reducing costs. In Japan, there are over 190,000 intensive farming units such as farmers’ cooperatives/ agricultural corporations that aim at efficient and large-scale farm management (The Ministry of Agriculture, Forestry and Fisheries of Japan, 2006). These corporations lease and consolidate agricultural lands in vicinal regions, manage large-scale farmland with full mechanization, and carry out farm works entrusted by vicinal farmers. The farmlands managed by these corporations sometimes number over thousands and are scattered within a wide area. In order to gain substantial economic increase and further development, these corporations need to improve the daily work management, extend the contracts of leasing farmland, lease more farmlands, and carry out more extra works. As a consequence, they considerably require wise management decisions such as timeliness in all operations, equipment adjustments, crop rotations, land rent, taxes and so on. The best decision certainly conduces to the increase of yield, profitability, and work efficiency. Solving the farm work scheduling problem requires appropriate approaches to modeling and optimization. There are plenty of mathematical models and approaches have addressed

A Two-Phase Metaheuristic for Farm Workscheduling

This paper proposes a two-phase metaheuristic approach to planning daily farm work for agriculture production corporations. The two-phase metaheuristic contains the optimization of resources assignment and searching schedule based on Genetic Algorithm and hybrid Petri nets model. In the experiment, the effect on optimizing the resource assignment and priority list, initializing population of GA with sorted chromosomes by waiting time, inheriting priority list from tasks in the previous resources assignment enhanced the evolutionary speed and solution quality. The computational experiment revealed high effectiveness for constructing farm work schedule with high ratio of resource utilization. The proposed approach also contributes a referential scheme for combining metaheuristic to solve scheduling problem under constraints.