This article studies logical reconfiguration of reconfigurable manufacturing systems (RMS) to minimise production lead times and buffer inventory level when the process variations and worker utilisation are considered. Since the RMS must be flexible for different job orders, the design of RMS requires diagnostic methodology and stream of variations (SoV) theory for rapid ramp-up in order to control the process variations that might occur as time goes on. The flexibility of the manufacturing systems is represented by logical elements of RMS in terms of changeable production batch size. The three phases solution is proposed by (1) utilising SoV modelling to find the allowable production lead times, (2) finding the optimum buffer stock level and production capacity at changeable production batch size and (3) finding worker routings at optimum worker utilisation. Monte carlo simulation is employed at Phase 1 to get the optimum production lead times, Phase 2 decision is formulated as a stochastic two-stages programming where buffer inventory level (first stage decison) has to be established prior to changeable production batching at future period and shortest path problems (SPP) algorithm is used to find an optimum worker routing at Phase 3. A serial inventory production (SIP) is used as an example to answer the following research questions: (1) What is the impact of SoV on both buffer inventory allocation and worker routings? (2) When is logical reconfiguration most beneficial in improving SIP profitability? (3) What is the impact of logical reconfiguration on both cost and lead time reduction? Three instances are used to investigate the effect of logical reconfiguration on the different structure of SIP systems. The results and analysis indicate that consideration of SoV is capable of increasing the profit, reducing operation lead times and maximising worker utilisation. Finally, management decision-making is discussed among other concluding remarks.
All Science Journal Classification (ASJC) codes
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering