Manufacturing problem: Optimization

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Literature Review: Optimization problem for Manufacturing

This “Ten Problems for Manufacturing in the 2020s” booklet identifies ten relevant areas from very recent contributions put forward at academic level in the form journal articles, conference proceedings and students theses. Ten freely accessible internet references have been selected for each area and direct links are provided at the end of each chapter for own consultation. Our selected references do not intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, diverse and scientifically-sound case studies for vertical dissemination aimed at non-specialist readers. They will also be able to scoop even more references through the bibliography that is reported at the end of each selected reference.

Without further ado, these are the ten problems that we are going to introduce in this booklet:

  1. sustainability, 
  2. industry 4.0,
  3. smart,
  4. responsibility,
  5. additive,
  6. digitalization,
  7. supply chain,
  8. optimization,
  9. skills gap,
  10. distributed.

Each problem has its own dedicated chapter made of an introductory section, a short presentation of the ten selected references and a conclusions section.

The final chapter of this booklet will report the conclusions from each chapter again in order to provide a complete executive summary.

8 Optimization

THE PROBLEM — Process re-engineering and optimization in manufacturing industries is a big challenge because of process interdependencies characterized by a high failure rate. Optimization is very often algorithmic these days and combinations of tailored algorithms are pursued to increase efficiency and minimize losses. Scheduling in cloud manufacturing and parts consolidation design also face numerous challenges.

CASE STUDIES — … buy this booklet from Amazon …

CONCLUSIONS — The process re-engineering technique has long been used for the optimization of manufacturing processes., Simulation data driven design approaches which combine dynamic simulation data mining and design optimization can search the optimal design solution. A new hybrid algorithm based on the Harris hawks optimization algorithm and Nelder-Mead is perfectly adapted to solving design optimization problems. A simple case study to optimize the production of couplings for pumps considers casting quality, processing quality and number of stages. Experimental data from a wiring harness manufacturing company in the automotive sector confirm the splitting to be an effective measure for the total production time reduction and process improvement even with splitting into two parts. Scheduling is one of the critical means for achieving the aim of cloud manufacturing and multi-agent technologies are a very promising approach. When increasing the number of parts that are consolidated, the production cost and time at first decrease due to reduced assembly steps, and then increase due to additional support structures needed to uphold the larger, consolidated parts. Evolutionary techniques such as Particle swarm optimization and Genetic algorithm aims to optimize the various kind of manufacturing system. The use of simulation tools provides significant benefit in energy demand modeling and prediction, making its application essential for planning and management of energy efficient industrial facilities. Various deadlock control policies for automated manufacturing systems with reliable and shared resources have been developed, based on Petri nets.

TEN FREE REFERENCES FROM THE INTERNET — … buy this booklet from Amazon …

“Ten Problems for Manufacturing in the 2020s” booklet for Amazon Kindle, 2020; click on the cover to go to the dedicated Amazon listing page

By TenProblems

Literature Reviews for Inquisitive Minds