Design of dedicated, shared and remainder cells in a probabilistic demand environment

Abstract

In this paper, a new layered cellular manufacturing system is proposed to form dedicated, shared and remainder cells to deal with the probabilistic demand, and later its performance is compared with the classical cellular manufacturing system. In the layered cellular design, each family may need more than one cell to cover capacity requirements. The proposed approach for layered cellular design involves five stages: (1) product clustering, (2) identifying number of cells and demand coverage probabilities, (3) determining cell types using the proposed heuristic procedure, (4) performing simulation to determine operating conditions and (5) statistical analysis to pick the best design configuration among layered cellular designs. Simulation and statistical analysis are performed to help identify the best design within and among both layered cellular design and classical cellular design. It was observed that as the number of part families increased, the number of machines needed to process the parts decreased first. Then the number of machines started to increase once again as the number of part families continued to increase. Another observation was that the average flow time and total WIP were not always the lowest when additional machines were used by the system. The last and the most important observation was that the layered cellular system provided much better results than the classical cellular system when high demand fluctuation was observed.

A Decision Investment Model to Design Manufacturing Systems based on a genetic algorithm and Monte-Carlo simulation

“…Ceryan and Koren (2009) formulated the optimal capacity selection problem and perform numerical studies to provide insights on how these decisions are affected by the investment costs, product revenues, demand forecast scenarios and volatilities over the planning period. Süer, Huang, and Maddisetty (2010) proposed a new layered cellular manufacturing system to form dedicated, shared and remainder cells to deal with the probabilistic demand, and later its performance is compared with the classical cellular manufacturing system. Through a variety of numerical examples, we showed how a range of investment cost parameters, product revenues and demand uncertainties influence the optimal strategy to whether invest in pure flexible, pure dedicated or a portfolio of both types of systems.…”

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

“…A few years later, Egilmez and Süer (2014) evaluated the impact of risk level in an integrated cell forming and scheduling problem using Monte Carlo Simulation. Süer et al (2010) proposed a new model which could determine the dedicated, shared and reminder cells in D-CMS. One important conclusion of their research is that in the average flow time and total WIP are not always the lowest when additional machines are used.…”

Mathematical model for dynamic cell formation in fast fashion apparel manufacturing stage

“…These variations require reconfiguration of the dynamic cells (Niakan 2015;Houshyar et al 2014). Cell reconfigurations are minimized by considering all the possible demands in corresponding planning horizon and optimizing the selected performance measures for considered planning horizon and defined planning periods (Niakan et al 2016;Süer et al 2010). As stated by Niakan (2015) and Houshyar et al (2014), layout reconfiguration of dynamic cells is done by switching of existing machines between cells, adding new machines to the cells and removing existing machines from cells.…”