Page 12 - Data Science Algorithms in a Week
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Preface                                  ix


                       Optimization for a Manufacturing Supply Chain.” The methodology uses particle swarm
                       optimization (PSO) in order to find stability in the supply chain using a system dynamics
                       model of an actual situation. This is a classical problem where asymptotic stability has
                       been listed as one of the problems to solve. The authors show there are many factors that
                       affect supply chain dynamics including: shorter product life cycles, timing of inventory
                       decisions,  and  environmental  regulations.  Supply  chains  evolve  with  these  changing
                       dynamics  which  causes  the  systems  to  behave  non-linearly.  The  impacts  of  these
                       irregular  behaviors  can  be  minimized  when  the  methodology  solves  an  optimization
                       problem to find a stabilizing policy using PSO (that outperformed GAs in the same task).
                       To obtain a convergence, a hybrid algorithm must be used. By incorporating a theorem
                       that allows finding ideal equilibrium levels, enables a broader search to find stabilizing
                       policies.
                          • Cutting Forces: Accurate prediction of cutting forces has a significant impact on
                       quality of product in manufacturing. The chapter “Estimation of Cutting Forces in turning
                       of Inconel 718 Assisted with High Pressure Coolant using Bio-Inspired Artificial Neural
                       Networks”  aims  at  utilizing  neural  networks  to  predict  cutting  forces  in  turning  of  a
                       nickel-based  alloy  Inconel  718  assisted  with  high  pressure  coolant.  Djordje  Cica  and
                       Davorin Kramar discuss a study that employs two bio-inspired algorithms; namely GAs
                       and  PSO,  as  training  methods  of  neural  networks.  Further,  they  compare  the  results
                       obtained  from  the  GA-based  and  PSO-based  neural  network  models  with  the  most
                       commonly used back propagation-based neural networks for performance.
                          •  Predictive  Analytics  using  Genetic  Programming:  The  chapter  “Predictive
                       Analytics using Genetic Programming” by Luis Rabelo,  Edgar Gutierrez, Sayli Bhide,
                       and  Mario  Marin  focus  on  predictive  analytics  using  genetic  programming  (GP).  The
                       authors describe with detail the methodology of GP and demonstrate its advantages. It is
                       important to highlight the use of the decile table to classify better predictors and guide the
                       evolutionary process. An actual application to the Reinforced Carbon-Carbon structures
                       of the NASA Space Shuttle is used. This example demonstrates how GP has the potential
                       to be a better option than regression/classification trees due to the fact that GP has more
                       operators which include the ones from regression/classification trees. In addition, GP can
                       help create synthetic variables to be used as input to other AI paradigms.
                          •  Managing  Overcrowding  in  Healthcare  using  Fuzzy  Logic:  The  chapter
                       “Managing  Overcrowding  in  Healthcare  using  Fuzzy  Logic”  focuses  on  the
                       overcrowding  problem  frequently  observed  in  the  emergency  departments  (EDs)  of
                       healthcare  systems.  The  hierarchical  fuzzy  logic  approach  is  utilized  by  Abdulrahman
                       Albar, Ahmad Elshennawy, Mohammed Basingab, and Haitham Bahaitham to develop a
                       framework for quantifying overcrowding. The purpose of this research was to develop a
                       quantitative  measurement  tool  for  evaluating  ED  crowding  which  captures  healthcare
                       experts’  opinions  and  other  ED  stakeholder’s  perspectives.  This  framework  has  the
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