Mathematical Models and Genetic Algorithm Approaches to Simultaneously Perform Workforce Overtime Capacity Planning and Schedule Cells
The problem studied in this thesis was observed in an actual textile company. The problem is more complex than usual scheduling problems in that we compute overtime requirements and make scheduling decisions simultaneously. Since having tardy jobs is not desirable, overtime work is allowed to minimize the number tardy jobs or total tardiness. Two different problems are considered; Problem1, to maximize the total profits by delivering jobs on or before time. The tardy jobs in this case are considered as lost sales. Problem2, to minimize the total tardiness and overtime costs. In this case tardy jobs are delivered with associated tardiness penalty costs. In problem1, various mathematical models are presented reflecting different overtime workforce hiring practices. To solve the same problem for one particular hiring policy, a Genetic Algorithm (GA) approach is also discussed. GA includes some newly proposed mutation operators, dynamic and twin. The proposed twin mutation strategy produced the best results in all problem sizes. Mathematical Model 2 was the best mathematical model with respect to both profit and execution time. This model considered partial overtime periods and also allowed different overtime periods on cells. In problem2, a mathematical model is presented to solve this complex problem. Experimentation has been carried out using three different problem types with five instances each based on the data collected from the company. For most problems, the mathematical model gave results in seconds.
Applied Mathematics, Engineering, Industrial Engineering, Information Science, Information Systems, Information Technology, Scheduling, Genetic Algorithm, Mathematical Model, decision making
Networked Digital Library of Theses and Dissertations