الفهرس | Only 14 pages are availabe for public view |
Abstract The learning curve concept has widely been applied to many industrialwith different setup to holding cost ratios. .apaciry levels and learning parameters. The results obtained are analyzed to study the setup learnmg effects on production schedules and production costs. The results obtained for both single and multi product cases are compared together to illustrate cost savings that can be aclueved applications as a control tool to predict costs and time requirements . The problem of lot size determinanon IS one of the most important industrial applications that the learning concept was applied. In this thesis. the work is focused on the study of the effects of setup learning on capacity constraints lot sizing problem with variable demand and tnne varying capacity for both single and multi product cases. The present work is divided into four parts: The first part is concerned with the learning concept and Its applications. Some of the researches dealing with lot Sizing problem under process and setup learning are also mentioned. The main topics discussed in this part are’ I. Types of learning and remission. 2. Learning curve concept and its applications 3. Effect of learning on lot size dererminanou The econd and tlurd pans are concerned with the capacity constrained lot sizing problem under setup learning for both ”Ingle and multi product case. The work is devoted to study the learmng effects on producuon schedules and production costs. A mathematical approach to find the optimal lor size under learning effects is used. A nonlinear integer programming model is developed to find the opnrnal lot sizes for the capacuy constrained single product problem under setup learning. The model developed consists of. Minimized objective function : that IS the total costs l inventory costs. setup costs and constant costs) . to be minimized. 2. Decision variables. those are the production volume. inventory level and number of setups . .:;. Constraints. those are the capacity available and the units produced must be greater or equal [0 the demands An efficient heuristic ( Planning with Setup Learning PS L) algorithm is used to solve the mathematical model with comparable computation time, especially . when the number of variables is large. A computer program for the PSL heuristic is developed to find the production schedules and production costs. The mathematical model developed for the single product case is modified to express the more complex and most probable case for multi product capacity constrained lot sizing problem with learning . The planning with setup learning ( PSL ) heuristic algorithm is modified to be used as nn efficient tool LO solve the problem In comparable computation time as the number of variables becomes large compared with the mgle product one. The computer program esrabhshed for the single product is also adapted to suit the modified ( PSL ) heunsnc algorithm for the multi product case. Experimental works are carried out in the forth part to validate the models de eloped for both single and multi product cases using products |