Simulation-based optimization approach with scenario-based product sequence in a Reconfigurable Manufacturing System (RMS): A case study

In this study, we consider a production planning and resource allocation problem of a Reconfigurable Manufacturing System (RMS). Four general scenarios are considered for the product arrival sequence. The objective function aims to minimize total completion time of jobs. For a given set of input parameters defined by the market, we want to find the best configuration for the production line with respect to the number of resources and their allocation on workstations. In order to solve the problem, a hybridization approach based on simulation and optimization (Sim-Opt) is proposed. In the simulation phase, a Discrete Event Simulation (DES) model is developed. On the other hand, a simulated annealing (SA) algorithm is developed in Python to optimize the solution. In this approach, the results of the optimization feed the simulation model. On the other side, performance of these solutions are copied from simulation model to the optimization model. The best solution with the best performance can be achieved by this manually cyclic approach. The proposed approach is applied on a real case study from the automotive industry

Economic impact failure mode and effects analysis

Failure mode and effects analysis (FMEA) is a method for reducing or eliminating failure modes in a system. A failure mode occurs when a system does not meet its specification. While FMEA is widely used in different industries, its multiple limitations can cause the method to be ineffective. One major limitation is the ambiguity of the risk priority number (RPN), which is used for risk prioritization and is the product of three ordinal variables: severity of effect, probability of occurrence, and likelihood of detection. There have been multiple attempts to address the RPN's ambiguity, but more work is still needed. Any new risk prioritization method needs to have a decision-support system to determine when to implement a corrective action or improvement.This research addresses some of the shortcomings of traditional FMEA through the creation of a new method called Economic Impact FMEA (EI-FMEA). EI-FMEA replaces the three ordinal values used in the RPN calculation with a new set of variables focusing on the expected cost of a failure occurring. A detailed decision-support system allows for the evaluation of corrective actions based on implementation cost, recurring cost, and adjusted failure cost. The RPN risk prioritization metric is replaced by the economic impact value (EIV) risk prioritization metric which ranks risks based on the impact of the corrective action through the largest reduction in potential failure cost. To help with resource allocation, the EIV only ranks risks where the corrective actions are economically sustainable.A comparison of three FMEA methods is performed on a product, and the risk prioritization metrics for each method are used to determine corrective action implementation. An evaluation of the FMEA methods are shown, based on the expected failure cost reduction, using the decision-support criteria of each method.The EI-FMEA method contributes to the body of knowledge by addressing the ambiguity of the RPN in FMEA by creating the EIV risk prioritization metric. This allows the EI-FMEA method to reduce failure cost by providing a decision-support system to determine when to implement a corrective action when both finite and infinite resources are available