Applied Mechanics and Materials Vol. 835

Abstract: Permutation flowshop scheduling problem (PFSP) is a classical NP-hard combinatorial optimization problem, which provides a challenge for evolutionary algorithms.Since it has been shown that simple evolutionary algorithms cannot solve the PFSP efficiently, local search methods are often adopted to improve the exploitation ability of evolutionary algorithms. In this paper, a hybrid differential evolution algorithm is developed to solve this problem. This hybrid algorithm is designed by incorporating a dynamic variable neighborhood search (DVNS) into the differential evolution. In the DVNS, the neighborhood is based on multiple moves and its size can be dynamically changed from small to large so as to obtain a balance between exploitation and exploration. In addition, a population monitoring and adjusting mechanism is also incorporated to enhance the search diversity and avoid being trapped in local optimum.Experimental results on benchmark problems illustrated the efficiency of the proposed algorithm.

Abstract: The real world engineering problems are complex associated with lot of factors. The objective of mathematic models in simulated manufacturing problems are to minimize cost or maximize profits while satisfying the constraints. The purpose of this article was to study two algorithms for testing their efficiency in solving non-linear optimization problems and simulated manufacturing problems. A well-known meta-heuristic approach called Differential Evolution (DE) was compared with Shuffled Frog-leaping Algorithm (SFLA) in term of mean, maximum, minimum, and standard deviation of the solution. SFLA was better than DE in terms of the performance to finding optimal solutions because of the unique process of memeplex, which can increase speed of convergence and find turning parameters.

Abstract: The aim of this research is to study the problem and efficiency improvement of the instrument factory in Thailand. The methods of production schedule are performed by four heuristic techniques which are Shortest Processing Time (SPT), Earliest Due Date (EDD), First Come First Serve (FCFS) and Bee algorithm (BA). All four methods aim to reduce the cost of delay shipment and time loss in manufacturing process. We apply these methods in the production process in an instrument factory. The results show that BA method is powerful in term of minimize make span and average completion time. Whereas, SPT method is the best method for finding minimum value of total late job.