Considering manufacturing cost and scheduling performance on a CNC turning machine

• Published in: European journal of operational research Vol. 177; no. 1; pp. 325 - 343
• Main Authors: Gurel, Sinan, Akturk, M. Selim
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.02.2007; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Applied sciences; Bicriteria optimization; CNC machines; Controllable processing times; Decision theory. Utility theory; Exact sciences and technology; Heuristic; Manufacturing; Manufacturing cost; Numerical controls; Operational research and scientific management; Operational research. Management science; Processing speed; Production costs; Scheduling; Scheduling algorithms; Studies; Total weighted completion time; Total weighted completion time; Scheduling; Bicriteria optimization; Controllable processing times; Manufacturing cost; CNC machines; Computer digital control; Decision support system; Decision making; Machining; Processing time; Turning; Optimization; Time average; Heuristic method; Controllability; Sequencing; Single machine; Completion time
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2005.11.029

A well known industry application that allows controllable processing times is the manufacturing operations on CNC machines. For each turning operation as an example, there is a nonlinear relationship between the manufacturing cost and its required processing time on a CNC turning machine. If we consider total manufacturing cost ( F 1) and total weighted completion time ( F 2) objectives simultaneously on a single CNC machine, making appropriate processing time decisions is as critical as making job sequencing decisions. We first give an effective model for the problem of minimizing F 1 subject to a given F 2 level. We deduce some optimality properties for this problem. Based on these properties, we propose a heuristic algorithm to generate an approximate set of efficient solutions. Our computational results indicate that the proposed algorithm performs better than the GAMS/MINOS commercial solver both in terms of solution quality and computational requirements such that the average CPU time is only 8% of the time required by the GAMS/MINOS.