The concept of disassembly-to-order (DTO) has recently become popular. The goal of DTO is to determine the optimum number of end-of-life (EOL) products to be disassembled in order to fulfill the demand for components and materials such that some desired criteria of the system are satisfied. However, the outcome of this problem is fraught with errors. This is due to the unpredictable circumstances of the EOL products which stem from many sources such as the operating environment, different usage patterns and customers upgrades. If one could get advanced information about the status of the products, it could prove to be quite invaluable in making EOL management decisions.Advanced product information consists of two types of data, viz., static and dynamic. The static data consists of the product name, the brand name, the model type, etc. The dynamic data consists of cumulative data covering the circumstances to which the product was subjected to during its useful life. Capturing these data has become an important goal of many manufacturers. Numerous technological advances and the availability of various monitoring devices, embedded in products, offer us with many product monitoring and data collection alternatives.In this paper, an integer program is developed to model and solve the DTO problem that utilizes the captured data from EOL products. A numerical example is considered to illustrate the use of this methodology.
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