Independent and small scale product recovery facilities (PRFs) often struggle to achieve profits when faced with inconsistent inflows of discarded products, varying demand patterns for recovered components, and stringent environmental regulations. Inconsistent inflows coupled with the varying demand cause undue fluctuations in inventory levels and frequently affect costs involved in product recovery operations. An effective pricing strategy can stabilize the fluctuations in demand and consequently can allow PRFs to control inventory levels. This research determines the prices of reusable and recyclable components and acquisition price of discarded products that allow PRFs to simultaneously maximize their financial returns and minimize the product recovery costs. Genetic algorithms and analytic hierarchy process are employed to solve this multi-criteria decision making problem.
The main objective of a product recovery facility (PRF) is to disassemble end-of-life (EOL) products and sell the reclaimed components for reuse and recovered materials in second-hand markets. Variability in the inflow of EOL products and fluctuation in demand for reusable components contribute to the volatility in inventory levels. To stay profitable the PRFs ought to manage their inventory by regulating the price appropriately to minimize holding costs. This work presents two deterministic pricing models for a PRF bounded by environmental regulations. In the first model, the demand is price dependent and in the second, the demand is both price and time dependent. The models are valid for single component with no inventory replenishment sale during the selling horizon . Numerical examples are presented to illustrate the models.
This research investigates the advantages offered by embedded sensors for cost-effective and environmentally benign product life cycle management for desktop computers. During their use by customers as well as at the end of their lives, Sensor Embedded Computers (SECs) by virtue of sensors embedded in them generate data and information pertaining to the conditions and remaining lives of important components such as hard-drive, motherboard, and power supply unit. A computer monitoring framework is proposed to provide more customer comfort, reduce repair costs and increase the effectiveness of current disassembly practices. The framework consists of SECs, remote monitoring center (RMC), repair/service, disassembly, and disposal centers. The RMC collects dynamic data/information generated by sensors during computer usage as well as static data/information from the original equipment manufacturers (OEMs). The RMC forwards this data/information to the repair/service, disassembly, and disposal centers on need-basis. The knowledge about the condition and remaining life of computer components can be advantageously used for planning repair/service and disassembly operations as well as for building refurbished computers with known expected lives. Simulation model of the framework is built and is evaluated in terms of the following performance measures: average downtime of a computer, average repair/service cost of a computer, average disassembly cost of a computer, and average life cycle cost of a computer. Test results show that embedding sensors in computers provides a definite advantage over conventional computers in terms of the performance measures.
It is difficult to obtain information regarding compositions and remaining life periods of used products. Hence, they often undergo partial or complete disassembly for subsequent re-processing (remanufacturing and/or recycling). However, researchers are now studying sensor embedded products (SEPs), the composition and remaining life of which can be obtained at the end of their use from sensors. This paper addresses decision-making regarding the futurity of an SEP at its end of use: whether to disassemble it for subsequent recycling/remanufacturing or to repair it for subsequent sale on a second-hand market. We identify some important factors that must be considered before making a decision. Using a numerical example, we propose a simple approach that employs Bayesian updating and fuzzy set theory to aid the decision-making process.
Taking a multi-resolution approach, this research work proposes an effective algorithm for aligning a pair of scans obtained by scanning an object's surface from two adjacent views. This algorithm first encases each scan in the pair with an array of cubes of equal and fixed size. For each scan in the pair a surrogate scan is created by the centroids of the cubes that encase the scan. The Gaussian curvatures of points across the surrogate scan pair are compared to find the surrogate corresponding points. If the difference between the Gaussian curvatures of any two points on the surrogate scan pair is less than a predetermined threshold, then those two points are accepted as a pair of surrogate corresponding points. The rotation and translation values between the surrogate scan pair are determined by using a set of surrogate corresponding points. Using the same rotation and translation values the original scan pairs are aligned. The resulting registration (or alignment) error is computed to check the accuracy of the scan alignment. When the registration error becomes acceptably small, the algorithm is terminated. Otherwise the above process is continued with cubes of smaller and smaller sizes until the algorithm is terminated. However at each finer resolution the search space for finding the surrogate corresponding points is restricted to the regions in the neighborhood of the surrogate points that were at found at the preceding coarser level. The surrogate corresponding points, as the resolution becomes finer and finer, converge to the true corresponding points on the original scans. This approach offers three main benefits: it improves the chances of finding the true corresponding points on the scans, minimize the adverse effects of noise in the scans, and reduce the computational load for finding the corresponding points.
Smart sensors and their networking technology when applied in manufacturing environment for monitoring, diagnostics, and control and for data/information collection could dwarf all the advances made so far by the manufacturing community through traditional sensors. Smart sensors can significantly contribute to improving automation and reliability through high sensitivity, self-calibration and compensation of non-linearity, low-power operation, digital pre-processed output, self-checking and diagnostic modes, and compatibility with computers and other subsystem blocks. There is a huge gulf between the existing models of manufacturing systems and the computational models that are required to correctly characterize manufacturing systems integrated with smart sensor networks. This paper proposes a multi-agent model for S2IM system. The agent characteristics and the expected model behavior are presented.