Son Duy Dao

Optimisation of partner selection and collaborative transportation scheduling in Virtual Enterprises using GA

Partner selection and transportation scheduling are critical to the success of a Virtual Enterprise. Collaborative transportation is a promising strategy that can help many enterprises survive and thrive in todays highly competitive market. To help decision makers establish and operate Virtual Enterprises more effectively, an innovative decision support system is proposed in this paper. First, new model for integration of partner selection and collaborative transportation scheduling in Virtual Enterprises is developed. This integrated optimisation problem is very dynamic in nature and it is required to optimise a number of interlinked sub-problems at the same time. Then, a novel Genetic Algorithm with a unique dynamic chromosome representation and genetic operations is developed to find an optimal solution to the integrated problem. The effectiveness of the proposed approach is demonstrated in a representative case study.

Genetic Algorithms for Integrated Optimisation of Precedence-Constrained Production Sequencing and Scheduling

This chapter presents the development of genetic algorithms (GA) for integrated optimisation of precedence-constrained production sequencing and scheduling in a multi-production line environment. This class of problem is NP-hard, combinatorial problem, requiring a triple optimisation at the same time: allocation of resources to each production line, production line sequencing and production line scheduling. Due to nature of constraints, the length of solution for the problem is variable. To cope with this variability and search for a global optimum, new strategies for resource allocation, encoding chromosome, crossover and mutation are developed herein. Robustness of the proposed GA is demonstrated by a complex and realistic case study.

Hybrid Genetic Algorithm Optimisation of Distribution Networks—A Comparative Study

This chapter focuses on the second of a three-stage, integrated methodology for modeling and optimising distribution networks (DN) based on hybrid genetic algorithms (HGA). The methodology permits any combination of transportation and warehousing costs for deterministic/stochastic demand. This chapter analyses and compares the fluctuation of overall costs when the number of facilities varies and indicates how to minimize them. The chapter concentrates on capacitated location allocation of distribution centers, a large scale, highly constrained, NP-hard, combinatorial problem. The HGA used has a classical structure, but incorporates a special encoding of solutions as chromosomes and integrates linear programming/mixed integer programming modules in the genetic operators (GO). A complex and extensive case study is described, demonstrating the robustness of the HGA and the optimization approach.

An innovative model for resource scheduling in VCIM systems

Abstract Virtual computer-integrated manufacturing (VCIM) is a promising manufacturing strategy in today’s global market because it is capable of exploiting distributed resources effectively, both locally as well as globally. Resource scheduling is critical to the operation of VCIM systems. In this article, a novel model for resource scheduling in VCIM systems is developed. By integrating collaborative transportation scheduling into manufacturing resource scheduling, the proposed model can provide a better solution because of reduction in the fixed transportation cost. Performance of the developed model is verified by computer simulation for a case study producing computer hard disks. The proposed model serves as a fundamental step towards global optimisation of VCIM systems.

A bibliometric analysis of Genetic Algorithms throughout the history

Abstract In this article, a bibliometric analysis of Genetic Algorithms (GA) throughout the history is conducted. A big picture of publications associated with GA is created. A number of dominant statistics of GA publications by years, research fields, document types, source titles, countries, institutions and authors are provided herein. In addition, some insights as well as future perspectives of publications associated with GA are discussed.

An innovative framework for designing genetic algorithm structures

Abstract Genetic Algorithms are popular optimization algorithms, often used to solve complex large scale optimization problems in many fields. Like other meta-heuristic algorithms, Genetic Algorithms can only provide a probabilistic guarantee of the global optimal solution. Having a Genetic Algorithm (GA) capable of finding the global optimal solution with high success probability is always desirable. In this article, an innovative framework for designing an effective GA structure that can enhance the GAs success probability of finding the global optimal solution is proposed. The GA designed with the proposed framework has three innovations. First, the GA is capable of restarting its search process, based on adaptive condition, to jump out of local optima, if being trapped, to enhance the GAs exploration. Second, the GA has a local solution generation module which is integrated in the GA loop to enhance the GAs exploitation. Third, a systematic method based on Taguchi Experimental Design is proposed to tune the GA parameter set to balance the exploration and exploitation to enhance the GA capability of finding the global optimal solution. Effectiveness of the proposed framework is validated in 20 large-scale case study problems in which the GA designed by the proposed framework always outperforms five other algorithms available in the global optimization literature.

An improved structure of genetic algorithms for global optimisation

Genetic Algorithm (GA) is one of the most general global optimisation solution methods used in countless number of works. However, like other search techniques, GA has weak theoretical guarantee of global optimal solution and can only offer a probabilistic guarantee. Having a GA capable of searching for the global optimal solution with very high success probability is always desirable. In this paper, an innovative structure of GA, in which adaptive restarting and chromosome elite transferring strategies are harmoniously integrated together, is proposed to improve the success rate of achieving global optimal solution of the algorithm. The robustness of the proposed GA structure is demonstrated through a number of case studies.

An Innovative GA for VCIM Production Scheduling Optimisation

As indicated in Chap. 2, the VCIM production scheduling, an important issue to operation of any VCIM system, is a dynamic constrained optimisation problem.

An Innovative Production Scheduling Model for VCIM Systems

As indicated in Chap. 2, the VCIM production scheduling, an important issue in operation of any VCIM system, is a dynamic constrained optimisation problem.

Determination of the Significance of Tolerance Parameters on Robot Performance Using Taguchi’s Tolerance Design Experiment

Tolerance parameters have different effects on robot accuracy. Therefore, it is better to tighten the tolerances of the factors that have statistically significant effect on robot accuracy and widen the tolerances of insignificant ones. By doing so, one not only achieves the given robot accuracy but also reduces manufacturing costs. Objective of this paper is to present an approach used to determine statistical significance of each tolerance parameter of robot manipulator on robot accuracy which can assist robot designers in making decisions regarding tolerance design. In this paper, a comprehensive model of industrial robot manipulator capable of carrying out various applications is developed and computer simulated. Then Taguchi’s Tolerance Design Experiment is applied to determine the statistical significances of the tolerances on robot accuracy. The approach is illustrated by a case study dealing with 6-DOF PUMA 560 robot manipulator.