4 Presentations

• 
  10:30 AM - 10:55 AM

  Application of simulation and lean techniques for the improvement of glulam production: a case study

  • Marzieh Ghiyasinasab, presenter,
  • Nadia Lehoux, université laval
  • Sylvain Menard, Université du Québec à Chicoutim
  • Caroline Cloutier, Université Laval

  Modular construction manufacturing helps to improve standardization and increase productivity in the construction industry. Glued laminated timber (glulam) is a type of engineered wood which can be used in modular construction as an environment-friendly product. This article considers improvements in the production of curved glulam by investigating the sources of waste and non-value adding activities in the production processes. A simulation model is built for glulam production and validated by a case-study for a gridshell project. Sources of waste are identified, and lean methods are suggested for improvement. The lean solutions are tested in the simulation model and results demonstrate 27% improvement in cycle time and 77% improvement in wait time. Additionally, the impact of applying only 50% elimination of non-value adding activities is compared with 100% elimination.

• 
  10:55 AM - 11:20 AM

  Predicting the behavior of dynamic systems using reduced-order modeling and interval computations

  • Martine Ceberio, presenter, University of Texas at El Paso
  • Angel Garcia Contreras, University of Texas at El Paso
  • Leobardo Valera, University of Texas at El Paso

  The ability to conduct fast and reliable simulations of dynamic systems is of special interest to many fields of operations. Such simulations can be very complex and, to be thorough, involve millions of variables, making it prohibitive in CPU time to run repeatedly for many different configurations. Reduced-Order Modeling (ROM) provides a concrete way to handle such complex simulations using a realistic amount of resources. However, uncertainty is hardly taken into account. Changes in the definition of a model, for instance, could have dramatic effects on the outcome of simulations. Therefore, neither reduced models nor initial conclusions could be 100% relied upon. In this research, Interval Constraint Solving Techniques (ICST) are employed to handle and quantify uncertainty. The goal is to identify key features of a given dynamical phenomenon in order to be able to propagate the characteristics of the model forward and predict its future behavior to obtain 100% guaranteed results. This is specifically important in applications, as a reliable understanding of a developing situation could allow for preventative or palliative measures before a situation aggravates.

• 
  11:20 AM - 11:45 AM

  Pre-optimizing tools positions in a CNC machine when facing arbitrary sequences of production

  • Marc-André Ménard, presenter, Université Laval
  • Claude-Guy Quimper, Université Laval
  • Jonathan Gaudreault, FORAC Research Consortium, Université Laval

  The manufacturing sector uses numerically controlled machines that require a setup time to change tools to move from manufacturing one product to another. We use a MIP to position the tools in the machine while minimizing the average setup time on a random sequence of products.

• 
  11:45 AM - 12:10 PM

  A multi-objective optimization approach for the blending problem in the tea industry

  • Djeumou Fomeni Franklin, presenter, Lancaster University

  The blending problem is one of the oldest and well-known optimisation problems. It is generally
  formulated as a linear program and has been applied in many industries. However, the blending problem
  encountered in the tea industry requires a lot more than a straight forward linear programming formulation. Indeed, the classical blending model would almost always be infeasible for the blending problem
  in the tea industry. This is because it is often not possible to match the characteristics of the blends
  as desired, which prompts the decision makers to search for solutions that are the closest possible to
  the targeted ones. In this talk, I will present a multi-objective optimisation model for the tea
  blending problem that we developed and solved. The model minimises the total cost of the raw materials as well as the violations of the desired characteristic scores of the final blends. I will also present some computational results conducted with real data from a UK-based tea company who brought the problem to us. These results show that our model can provide useful decision support tools to select the best solution option from a set of acceptable ones.