No abstract

No abstract

This  article  discusses  the  analysis  of  customer  loyalty  using  three  data  mining  methods:  C4.5,Naive Bayes, and Nearest Neighbor Algorithms and real-world  empirical  data.  The  data  contain  ten  attributes related to the customer loyalty and are obtained from a national  multimedia  company  in  Indonesia.  The  dataset contains 2269 records. The study also evaluates the effects of  the  size  of  the  training  data  to  the  accuracy  of  the classification.  The  results  suggest  that  C4.5  algorithm produces   highest classification   accuracy   at   the   order of  81%  followed  by  the  methods  of  Naive  Bayes  76% and  Nearest  Neighbor  55%.  In  addition,  the  numerical evaluation  also  suggests  that  the  proportion  of  80%  is optimal  for  the  training  set.

No abstract

No abstract

The permutation flow shop scheduling problem (PFSP) is a renowned problem in the scheduling research community. It is an NP-hard combinatorial optimization problem that has useful real-world applications. In this problem, finding a useful algorithm to handle the massive amounts of jobs required to retrieve an actionable permutation order in a reasonable amount of time is important. The recently developed crow search algorithm (CSA) is a novel swarm-based metaheuristic algorithm originally proposed to solve mathematical optimization problems. In this paper, a hybrid CSA (HCSA) is proposed to minimize the makespans of PFSPs. First, to make the CSA suitable for solving the PFSP, the smallest position value rule is applied to convert continuous numbers into job sequences. Then, the HCSA uses a Nawaz–Enscore–Ham (NEH) technique to create a population with the required levels of quality and diversity. We apply a local search to enhance the quality of the solutions and avoid premature convergence; simulated annealing enhances the local search of a method based on a variable neighborhood search. Computational tests are used to evaluate the algorithm using PFSP benchmarks with job sizes between 20 and 500. The tests indicate that the performance of the proposed HCSA is significantly superior to that of other algorithms.

No abstract

No abstract