The Study of Algorithms
The study of algorithm has helped me learn a lot about decision making in this age of technology. Algorithms can be used in arguably all sectors of the economy to develop well-informed decisions and to ensure different sectors in an economy operate as planned. Businesses in particular have become major beneficiaries of algorithms in this age of technology where competition has increased and business strive to obtain relevant and reliable information to survive.
A Dynamic Programming Approach to Integrated Assembly Planning and Supplier Assignment with Lead Time Constraints
Kuang et al, in their article, A Dynamic Programming Approach to Integrated Assembly Planning and Supplier Assignment with Lead Time Constraints, state that responsiveness has become an important competitive advantage just like cost and quality in this age of competition in the global market. They say that one essential responsiveness strategy is the reduction of lead times and product development through the integration of supplier assignment with assembly planning. The authors attempt to address the problem of supply chain and integrated assembly design by developing and solving a problem of optimization with the minimum supply chain costs. For the problem to be properly modified, a novel Hyper graph is developed and applied for the integration of assembly and supply chain decisions (Kuang, et al.). To solve the integrated optimization problem, we used a dynamic programming model.
Algorithms in Mining
Lin et al., in their article also talk of how algorithms can be used in finding solutions to a problem in mining. They state that frequent pattern mining has become popular with technological advances. Through the use of frequent pattern mining, useful information can be obtained that can help in applying judgment and making decisions. For instance, managers in the marketplace conduct data mining on transaction to retrieve information that helps them in understanding customers, improve services, determine an appropriate strategy for the placement of goods and services and for biotechnology and medical applications (Lin, et al.). The data generation rate is however very rapid and this leads to problems due to the large amounts of data processed. Researchers have studied parallel, cloud and distributed computing technology with an aim of selecting the most appropriate. Data mining utilizes several computing nodes that require the transfer of a large amounts of data in a grid. In most cases, the bandwidth of the network available becomes limited because diverse tasks are being transferred simultaneously and different servers are working at the same time (Lin, et al.). The result is often poor transmission which causes severe delay in transfer in either the internal or external network.
Distributed Mining Algorithm for Recurrent Designs
A distributed mining algorithm that is also fast is proposed in the discovery of recurrent designs in congested networks algorithm. The major objective is to lessen FP-tree transmission in a way that a fraction of the information is needed for mining through computing nodes according to Lin et al,. On the other hand, Kuang et al. proposes that the development of a dynamic algorithm to find a solution to the integrated optimization problem. Quantitative case studies concur that the methods that have been developed are able to find a solution to the decision-making problem efficiently and optimally (Kuang, et al.).
Works Cited
Kuang, Heng, et al. "A Dynamic Programming Approach to Integrated Assembly Planning and Supplier Assignment with Lead Time Constraints." International Journal of Production Research, vol. 54, no. 9, May 2016, pp. 2691-2708. EBSCOhost, doi:10.1080/00207543.2015.1118575.
Lin, Kawuu, et al. "A Fast and Distributed Algorithm for Mining Frequent Patterns in Congested Networks." Computing, vol. 98, no. 3, Mar. 2016, pp. 235-256. EBSCOhost, doi:10.1007/s00607-015-0457-6.
