In this paper, an invasive weed optimization (IWO) scheduling algorithm is presented for optimization no-idle flow-shop scheduling problem (NFSP) with the criterion to minimize the maximum completion time (makespan). Firstly, a simple approach is put forward to calculate the makespan of job sequence. Secondly, the most position value (MPV) method is used to code the weed individuals so that fit...

An improved Invasive weed optimization (IWO) based on hybrid genetic (HGIWO) is presented. In the new arithmetic, the inertial weight is adaptively adjusted to improve the convergence speed. The weeds are multiples by the selection and hybridization of genetic arithmetic. The import of hybrid genes improves excellent performance of weeds and reduces likelihood on getting into local optimization...

Martin M. Williams II Corresponding author. U.S. Department of Agriculture—Agricultural Research Service, Invasive Weed Management Research, University of Illinois, 1102 S. Goodwin Ave., Urbana, IL 61801; [email protected] The critical period for weed control (CPWC) identifies the phase of the crop growth cycle when weed interference results in unacceptable yield losses; however, the effect of ...

Manufacturing plants are commonly organized in cells containing machines that process different parts of a given product. The Manufacturing Cell Design Problem (MCDP) aims at efficiently organizing the machines into cells in order to increase productivity by minimizing the inter-cell moves of parts. In this paper, we present a new approach based on Invasive Weed Optimization (IWO) for solving s...

In this paper, instead of the classical approach to the multi-criteria location selection problem, a new approach was presented based on selecting a portfolio of locations. First, the indices affecting the selection of maintenance stations were collected. The K-means model was used for clustering the maintenance stations. The optimal number of clusters was calculated through the Silhou...

With the rapid development of the internet, the amount of information and data which are produced, are extremely massive. Hence, client will be confused with huge amount of data, and it is difficult to understand which ones are useful. Data mining can overcome this problem. While data mining is using on cloud computing, it is reducing time of processing, energy usage and costs. As the speed of ...