Appropriate organizational resources allocation becomes a major challenge for companies to address the rapid demands for resources from different operational aspects while resource utilization is keeping low. Differentiate exiting customers with common features into smaller groups can serve as a piece of useful reference for decision-making. So far, k-means algorithm is the most commonly used clustering technique for conducting customer grouping. However, k-means limits the grouping consideration to a fixed number of dimensions among each group and the grouping results are significantly influenced by the initial clusters means. In this research, a robust genetic algorithm (GA) based k-means clustering algorithm is proposed in attempt to classify existing customers of the enterprise into groups with consideration of relevant attributes for the sake of obtaining desirable grouping results in an efficient manner. Different from k-means, the proposed GA-based k-means algorithm is able to select which and how many dimensions are better to be considered for each customer group when developing approximate optimal solutions. A case study is conducted on a window curtain manufacturer with the application of software Generator associated with MS Excel.

Customer grouping for better resources allocation using GA based clustering technique

Network reliability evaluation for flow networks is an important issue in quality management. Many real-life systems can be modeled as stochastic-flow networks, in which each branch is multistate due to complete failure, partial failure, maintenance, etc. That is, each branch has several capacities with a probability distribution, and may fail. Hence, network reliability is the probability that a specified flow can be transmitted through the network successfully. Although there are many researches related to the evaluation of network reliability for a stochastic-flow network, how to assign a set of multistate components to the network so that the network reliability is maximal is never discussed. Therefore, this paper devotes to evaluating the optimal network reliability under components-assignments subject to a transmission budget, in which the transmission cost depends on each component's capacity. The network reliability under a components-assignment can be computed in terms of minimal paths, and state-space decomposition. Subsequently, we propose an optimization method based on a genetic algorithm. The experimental results show that the proposed method can be executed efficiently in a reasonable time.

Evaluation of Optimal Network Reliability Under Components-Assignments Subject to a Transmission Budget

This study proposes a genetic algorithm based method integrating the minimal paths and the recursive sum of disjoint products to find maximal network reliability with optimal transmission line assignment for a stochastic electric power network. In our problem, a set of transmission lines is ready to be assigned to branches of the electric power network. Because each transmission line combined with several physical lines has multiple states, the capacity of the electric power network associated with any transmission line assignment is stochastic. Network reliability is the probability that the network can transmit d units of electric power from an electric power generator (origin) to a specific area (destination). The discussed problem exhibits the features of network reliability and assignment problems, and thus it is non-deterministic polynomial-time hard. A simple electric power network and a real one are adopted to demonstrate the efficiency of the proposed algorithm while comparing with several approaches.

Maximal network reliability with optimal transmission line assignment for stochastic electric power networks via genetic algorithms

Using minimal cuts to optimize network reliability for a stochastic computer network subject to assignment budget

This paper investigates the continuous version of the stochastic Network Design Problem (NDP) with reliability requirements. The problem is considered as a two-stage Stackelberg game with complete information and is formulated as a stochastic bi-level programming problem, which is extended to include reliability as well as physical and budget constraints. The estimation procedure combines the use of Monte Carlo simulation for modelling the stochastic nature of the system variables with a Genetic Algorithm (GA), for treating the complexity of this new formulation. The computational experience obtained from a test road network application demonstrates the ability of the proposed methodology to address the need for incorporating reliability requirements and stochasticity into the various system components in the design process. The results can provide useful insight into the evaluation of alternative reliable network capacity improvement plans under the effect of uncertainty on the demand, supply and route choice process of travellers.

/pdf/reliable-stochastic-design-of-road-network-systems-4hyajnawxg.pdf

Reliable stochastic design of road network systems

Reliability-oriented multi-resource allocation in a stochastic-flow network

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Reliability-oriented multi-resource allocation in a stochastic-flow network