Genetic algorithm for short-term scheduling of make-and-pack batch production process

This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.

A Hybrid Multi-Objective Evolutionary Algorithm for Optimal Groundwater Management under Variable Density Conditions

In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search （TS） based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm （GA） with a TS based MOEA, the niched Pareto tabu search （NPTS）, which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources.

A Case Study of 3D Protein Structure Prediction with Genetic Algorithm and Tabu Search

This paper describes a case study of 3D protein structure prediction of six sequences from protein data bank （PDB） by genetic algorithm and tabu search （GATS）, where off-lattice AB model is considered as a simplified model of protein structure. The lowest-energy values required for forming the native conformation of proteins are searched by GATS, and then the coarse structures （i.e., simplified structure） of the proteins are obtained according to the multiple angle parameters corresponding to the lowest energies. All the coarse structures form single hydrophobic cores surrounded by hydrophilic residues, which stay on the right side of the actual characteristic of protein structure. It demonstrates that this approach can predict the 3D protein structure effectively.

Application of Interval Algorithm in Rural Power Network Planning

Rural power network planning is a complicated nonlinear optimized combination problem which based on load forecasting results, and its actual load is affected by many uncertain factors, which influenced optimization results of rural power network planning. To solve the problems, the interval algorithm was used to modify the initial search method of uncertainty load mathematics model in rural network planning. Meanwhile, the genetic/tabu search combination algorithm was adopted to optimize the initialized network. The sample analysis results showed that compared with the certainty planning, the improved method was suitable for urban medium-voltage distribution network planning with consideration of uncertainty load and the planning results conformed to the reality.

RTS-PGATS based approach for data-intensive scheduling in data grids

Grid computing is the combination of com- puter resources in a loosely coupled, heterogeneous, and geographically dispersed environment. Grid data are the data used in grid computing, which consists of large-scale data-intensive applications, producing and consuming huge amounts of data, distributed across a large number of machines. Data grid computing composes sets of independent tasks each of which require massive distributed data sets that may each be replicated on different resources. To reduce the completion time of the application and improve the performance of the grid, appropriate computing resources should be selected to execute the tasks and appropriate storage resources selected to serve the files required by the tasks. So the problem can be broken into two sub-problems： selection of storage resources and assignment of tasks to computing resources. This paper proposes a scheduler, which is broken into three parts that can run in parallel and uses both parallel tabu search and a parallel genetic algorithm. Finally, the proposed algorithm is evaluated by comparing it with other related algorithms, which target minimizing makespan. Simulation results show that the proposed approach can be a good choice for scheduling large data grid applications.