IMPROVABILITY OF THE FABRICATION LINE IN A SHIPYARD

Neven Hadžić,Viktor Ložar,Hrvoje Cajner,Tihomir Opetuk

doi:10.21278/brod72302

Abstract

The ship production process is a complex manufacturing system involving numerous working stations mutually interconnected by transport devices and buffers. Such a production system can be efficiently modeled using the stochastic system approach and Markov chains. Once formulated, the mathematical model enables analysis of the governing production system properties like the production rate, work-in-process, and probabilities of machine blockage and starvation that govern the production system bottleneck identification and its continuous improvement. Although the continuous improvement of the production system is a well-known issue, it is usually based on managerial intuition or more complex discrete event simulation yielding sub-optimal results. Therefore, a semi-analytical procedure for the improvability analysis using the Markov chain framework is presented in this paper in the case of the shipyard’s fabrication lines. Potential benefits for the shipyards are pointed out as the main gain of the improvability analysis.

Highlights

The ship production process is known as a perplexed, demanding, and long-lasting process composed of numerous and complex space-time interactions [1, 2]
The performance measures have a significant role in the improvability of the production systems, [21]
To illustrate the performance measures evaluation consider a serial production line composed of M machines mi, i=1, 2, ..., M of the Bernoulli(pi) reliability model, where pi is the probability of machine being in state {up}, Figure 1

Summary

The ship production process is a complex manufacturing system involving numerous working stations mutually interconnected by transport devices and buffers. Such a production system can be efficiently modeled using the stochastic system approach and Markov chains. Once formulated, the mathematical model enables analysis of the governing production system properties like the production rate, work-in-process, and probabilities of machine blockage and starvation that govern the production system bottleneck identification and its continuous improvement. The continuous improvement of the production system is a well-known issue, it is usually based on managerial intuition or more complex discrete event simulation yielding sub-optimal results.

Introduction

Performance measures

Improvability concepts

Application case

Findings

Conclusion