Formulation and Solution of Nonlinear Integer Production Planning Problems for Flexible Manufacturing Systems
Формулировка и решение нелинейных целочисленных задач планирования производства для гибких производственных систем
1983-03-01
SCID: 54.1/79pd66ah
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flexible manufacturing systemsgrouping and loading problemslinearization methodsnonlinear integer programmingproduction planning
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Abstract (AI)
A flexible manufacturing system (FMS) is an integrated, computer-controlled complex of automated material handling devices and numerically controlled machine tools that can simultaneously process medium-sized volumes of a variety of part types. FMSs are becoming an attractive substitute for the conventional means of batch manufacturing, especially in the metal-cutting industry. This new production technology has been designed to attain the efficiency of well-balanced, machine-paced transfer lines, while utilizing the flexibility that job shops have to simultaneously machine multiple part types. Some properties and constraints of these systems are similar to those of flow and job shops, while others are different. This technology creates the need to develop new and appropriate planning and control procedures that take advantage of the system's capabilities for higher production rates. This paper defines a set of five production planning problems that must be solved for efficient use of an FMS, and addresses specifically the grouping and loading problems. These two problems are first formulated in detail as nonlinear 0-1 mixed integer programs. In an effort to develop solution methodologies for these two planning problems, several linearization methods are examined and applied to data from an existing FMS. To decrease computational time, the constraint size of the linearized integer problems is reduced according to various methods. Several real world problems are solved in very reasonable time using the linearization that results in the fewest additional constraints and/or variables. The problem characteristics that determine which linearization to use, and the application of the linearized models in the solution of actual planning problems, are also discussed.
Key Findings
1
Constraint-reduction techniques decrease the size and computational burden of the linearized integer programs.
2
It formulates the grouping and loading problems as nonlinear 0–1 mixed-integer programming models.
3
Several linearization methods are examined and applied to data from an existing flexible manufacturing system.
4
Several real-world planning problems are solved in reasonable time using linearizations that add the fewest constraints and/or variables.
5
The paper identifies five production-planning problems required for efficient operation of flexible manufacturing systems.
6
The study discusses how problem characteristics determine the most appropriate linearization and how the models support actual planning decisions.
Research Object
flexible manufacturing system (FMS) production planning
Research Subject
grouping and loading problems, including their nonlinear 0-1 mixed-integer formulations and linearized solution methods
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1983-03-01
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