الفهرس | Only 14 pages are availabe for public view |
Abstract Research in the field of the Job-Shop Scheduling was given little attention as compared to that given to the Flow-Shop Scheduling Problems. In addition, most of researches, which tackle the scheduling problems, aim at minimizing the total make-span and in most of the cases with no-wait allowed for the products. Little attention has been given to consider the role of cost on determining such schedules. In the present work the Common Cycle Scheduling Problem (CCSP) and the Cyclic Scheduling Problem (CSP) in the job-shop environment are considered. This production problem is concerned with determining the optimal production schedule and lot sizes for a given set of products using a number of machines. The CCSP is based on scheduling a number of products using a common (base) cycle time, so that the lot size for each product is the forecast demand of certain product over the base cycle time. The CSP is based on allocating different products to different basic cycles, so that each product is produced in equal lot sizes spaced equally apart, by a period that is an integer of the basic cycle time. The produced quantities should satisfy the expected demand in that period, until the product is reproduced. When producing the same products repetitively, it is preferred to minimize the production costs rather than the make-span. The costs considered are the setup, in-process inventory, queuing, finished products inventory holding, as well as machine delay and lost-sales costs. Genetic Algorithm is used to solve the CCSP for a multi-stage, multi-product, job-shop environment under deterministic and stationary conditions, allowing the products to wait between the different stages. The same Genetic Algorithm is used with an allocation procedure, to allocate the products to the different basic cycles, for the same problem. Applying the proposed model to minimize the make-span of benchmark instances was used as a factorial experiment to adjust the Genetic Algorithm’s parameters. The model was proved to be successful in determining the common cycle and cyclic schedules that minimize the total scheduling cost per unit time in a job-shop environment. The keywords: Common Cycle Scheduling, Cyclic Scheduling, Job-Shop Scheduling, Lot Sizing, Genetic Algorithm. |