Planning of manufacturing and maintenance activities together, creating a balance between maintenance and production parameters and developments on maintenance will prevent…
Abstract
Purpose
Planning of manufacturing and maintenance activities together, creating a balance between maintenance and production parameters and developments on maintenance will prevent technical and economic losses and increase production efficiency. Optimizing production and maintenance scheduling enable us to see how maintenance parameters (β, η, tp, tr, a[o]) will affect production performance, completion time (Ec.) and maximum machine availability, and shows which maintenance parameters minimum completion time (Ecmin) will be provided. Difference between Ecmin and maximum completion time (Ecmak) effect to the production costs will be calculated. The purpose of this paper is to show how a genetic algorithm (GA) procedure is successfully applied to the integrated optimization model to determine optimum production policies based maintenance parameters.
Design/methodology/approach
GA is used for optimization and a computer program is prepared to make optimization for integrated preventive maintenance and production planning (IPMPP). Using the program, experimental studies are carried out with different number of jobs be done, to optimize production policy taking maintenance parameters into account.
Findings
Numerous experiments have been conducted with developed GA computer program and see maintenance parameters (β, η, tp, tr, a[o]) effect to the production performance, Ec and maximum machine availability and at which maintenance parameters Ecmin will be provided, and also operating cost saving and maintenance parameters how affect Ec subjects are examined. Due to optimal preventive maintenance (PM) and production sequence arrangement and application of PM provided by GA, Ecmin is greatly decreased.
Originality/value
In this paper, GA procedure is successfully applied to the integrated optimization model to determine optimum production policies based maintenance parameters.
Details
Keywords
This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and…
Abstract
Purpose
This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices.
Design/methodology/approach
This study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10.
Findings
The real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices.
Originality/value
This study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.