Article ID: | iaor1994579 |
Country: | United States |
Volume: | 1 |
Issue: | 3 |
Start Page Number: | 320 |
End Page Number: | 321 |
Publication Date: | Jun 1992 |
Journal: | Production and Operations Management |
Authors: | Aggarwal A.K., Vemuganti R.R., Fetner W. |
Keywords: | manufacturing industries, artificial intelligence: decision support |
Raw lumber must be dried to a specified level of moisture content before it can be used to make furniture. This paper deals with a model-based decision support system (DSS) for a local furniture manufacturing company to assist its management in scheduling lumber drying operations. In addition to buying ready-to-use dried lumber from vendors at a premium, the company processes raw lumber in house using two production processes that require various lengths of processing time in predryers and dry-kilns. Given the demand for various types of dried lumber over a specified planning horizon, the processing times and costs for each production process, technological restrictions, and management policies, the problem of interest is to satisfy the demand at a minimum cost. The DSS incorporates the mathematical formulation of this problem, is user friendly, maintains model and data independence, and generates the necessary reports, including loading and unloading schedules for the equipment.