Manufacturing Learning and Forgetting: Steady State Optimal Batch Size for Constant Demand Case
Sunantha Prime (Teyarachakul)
Dr. Sunantha Prime (Teyarachakul), Department of Information Systems and Decision Sciences (ISDS) at California State University, Fresno, California, USA.
Manuscript received on 05 September 2019 | Revised Manuscript received on 29 September 2019 | Manuscript Published on 29 June 2020 | PP: 404-409 | Volume-8 Issue-10S2 August 2019 | Retrieval Number: J107508810S19//2019©BEIESP | DOI: 10.35940/ijitee.J1075.08810S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Assuming learning and forgetting in processing units, constant demand rate, and infinite horizon, we analyze costs and properties related to lot sizes in the steady state. Steady State characteristics are described by a convergence in worker experience level or skills. The average per period cost as a function of lot size is found to be non-convex in the steady state. Thus, a simple approach such as first-order condition is not guaranteed to give an optimal solution. We develop sufficient conditions for existence of a uniqueoptimal solution, which are found in some problems. Our study shows that EOQ-type policies that use fixed batch size and produce when inventory reaches zero are not necessarily optimal.
Keywords: manufacturing Learning and Forgetting, Steady State Optimal Batch Size, Cost Minimization, Forms of Optimal Policy
Scope of the Article: Advanced Manufacturing Technologies