Selection of partners in virtual enterprise paradigm

Agility is the competitive advantage in the global manufacturing environment. It is believed that the agility can be realized by dynamically reconfigurable virtual enterprise. However, the configuration of virtual enterprises is a challenge to us. In selecting the partners for the reconfiguration of virtual enterprise, many factors should be taken into consideration. However, the manufacturing cost and time to market are the most important factors. In this paper, in considering the completion time as a constraint we model the partner selection problem by an integer programming formulation to minimize the manufacturing cost. The formulation is then transformed into a graph-theoretical formulation and a 2-phase algorithm is developed to solve the problem. In the first phase, a polynomial bounded algorithm is proposed to find the earliest completion time, so we obtained a feasible solution. In phase 2, we improve the solution by exchanging the candidate partners in keeping the solution in the feasible region. Although we may not find the optimal solution for the problem by the improvement algorithm, the objective is reduced iteration by iteration. Thus, the algorithm is efficient and can be applicable to practical problems. An illustrative example is presented to show the application of the proposed algorithm.     

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 today’s 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.     

基于AHP/动态规划的虚拟企业伙伴选择决策方法

伙伴选择作为虚拟企业组建过程中的重要环节，是目前研究的热点。层次分析法(AHP)是解决该问题的一种常见方法，可以较好地解决备选企业的综合评价问题，但该方法的应用只局限于选择单个伙伴企业的情况。本文引入动态规划的思想，将伙伴企业的选择从一个扩充到多个，成功解决了同一类型的多个伙伴企业问的任务分配问题，并给出了应用实例。     

虚拟企业伙伴选择的双种群自适应遗传算法

针对虚拟企业伙伴选择这一多目标优化问题,采用理想点法将其转换为多个单目标问题,并应用双种群自适应遗传算法进行问题求解。该算法涉及两个种群和自适应交叉、变异概率。在遗传过程中,每个种群的个体都根据适应度自动选择其交叉和变异概率,使个体对环境变化具有自适应调节能力;在一代遗传完成后,种群间交换优秀个体携带的遗传信息,以增加种群的多样性,避免陷入局部极值。通过算例,证实了该算法能很好地解决虚拟企业伙伴选择这一多目标优化问题。     

一种面向电子商务的虚拟企业盟友选择方法

采用对数最小二乘法对AHP中的判断矩阵进行计算,由此获得的盟友选择数学模型规范、简便,在计算机上容易实现,在企业中初实可行,结合具体实例,在Matlab环境下对该数学模型在服装企业的盟友选择和评价展开了应用研究。     

Decision supporting functionality in a virtual enterprise network

Enterprises are now facing growing global competition and the continual success in the marketplace depends very much on how efficient and effective the companies are able to respond to customer demands. The Internet has provided a powerful tool to link up manufacturers, suppliers and consumers to facilitate the bi-directional interchange of useful information. The formation of virtual enterprise network is gathering momentum to meet this challenge. The idea of virtual enterprise network is meant to establish a dynamic organization by the synergetic combination of dissimilar companies with different core competencies, thereby forming a “best of everything” consortium to perform a given business project to achieve maximum degree of customer satisfaction. In this emerging business model of virtual enterprise network, the decision support functionality, which addresses the issues such as selection of business partners, coordination in the distribution of production processes and the prediction of production problems, is an important domain to be studied. This paper attempts to introduce a Neural On-Line Analytical Processing System (NOLAPS), which is able to contribute to the creation of decision support functionality in a virtual enterprise network. NOLAPS is equipped with two main technologies for achieving various objectives, including neural network for extrapolating probable outcomes based on available pattern of events and data mining for converting complex data into useful corporate information. A case example is also covered to validate the feasibility of the adoption of NOLAPS in real industrial situations.     

动态联盟支撑系统实施策略

分析了动态联盟支撑系统中加盟企业的信息需求和相互关系以及两种不同结构类型的支撑系统在获取加盟企业信息和支持动态联盟运行上的方式和特点。从技术和管理两个方面提出了实施动态联盟支撑系统的一些策略。     

Analyzing the behaviors of virtual cells (VCs) and traditional manufacturing systems: Ant colony optimization (ACO)-based metamodels

The aim of this paper is two fold. First we investigate the three different types of systems, namely cellular layout (CL), process layout (PL) and virtual cells (VCs). VCs are addressed by using family-based scheduling rule, developed by a part allocation algorithm in a PL. Simulation is used to compare three types of systems under the performance metrics such as mean flow time and mean tardiness. Results indicate that VCs have better responsiveness in terms of the performance metrics. Second we develop a new ant colony optimization-based metamodels fed by existing simulation runs to represent the prospective simulation runs, which require a lot of time and effort. Regression metamodels, which allow us to obtain much faster results, are seen to be promising to estimate the systems behaviors.     

Countering the negative search bias of ant colony optimization in subset selection problems

In their quest to find a good solution to a given optimization problem, metaheuristic search algorithms intend to explore the search space in a useful and efficient manner. Starting from an initial state or solution(s), they are supposed to evolve towards high-quality solutions. For some types of genetic algorithms (GAs), it has been shown that the population of chromosomes can converge to very bad solutions, even for trivial problems. These so-called deceptive effects have been studied intensively in the field of GAs and several solutions to these problems have been proposed. Recently, similar problems have been noticed for ant colony optimization (ACO) as well. As for GAs, ACO's search can get biased towards low-quality regions in the search space, probably resulting in bad solutions. Some methods have been proposed to investigate the presence and strength of this negative bias in ACO. We present a framework that is capable of eliminating the negative bias in subset selection problems. The basic Ant System algorithm is modified to make it more robust to the presence of negative bias. A profound simulation study indicates that the modified Ant System outperforms the original version in problems that are susceptible to bias. Additionally, the proposed methodology is incorporated in the Max–Min AS and applied to a real-life subset selection problem.