As part of the development of any computer simulation of procedures, equipment, or airspace, an appropriate model of
aircraft must be selected. Due to the complexity and aviation safety-critical nature of these development efforts, a
detailed survey of the current state-of-the-art in aircraft flight dynamic models is desired. Options include basic 3-D
performance envelopes of various aircraft (e.g., acceleration, deceleration, turn rate, and climb rate), high-fidelity models
(e.g., proprietary aircraft manufacturer models), commercial-off-the-shelf models (e.g., Laminar Research's X-Plane and
Microsoft's Flight Simulator), Government models (e.g., NASA or FAA), and originally developed six degree-of-freedom
mathematical models. Here, the simple kinematics model (using basic kinematic relationships without
considering the impact of aerodynamics), the small perturbation theory model (which uses only the known, non-dimensional
aerodynamic properties of the aircraft), the total forces and moments method (which solves the complete set
of nonlinear differential equations and requiring large tables describing aircraft parameters in different flight regimes),
and blade element theory (which makes use of the aircraft's physical structure to calculate the aerodynamic forces and
moments on thin strips of the aircraft) are reviewed.
Disassembly has recently gained attention in the literature due to its role in product recovery. Disassembly activities
take place in various recovery operations including remanufacturing, recycling, and disposal. The disassembly line is
the best choice for automated disassembly of returned products. It is therefore important that the disassembly line be
designed and balanced so that it works as efficiently as possible. However, finding the optimal balance is
computationally intensive with exhaustive search quickly becoming prohibitively large, even for relatively small
products, due to exponential growth. In this paper, complexity theory is reviewed and used to prove that the
DISASSEMBLY LINE BALANCING PROBLEM is NP-complete, unary NP-complete, and NP-hard, necessitating
specialized solution methodologies, including those from the field of combinatorial optimization.
Disassembly takes place in remanufacturing, recycling, and disposal, with a line being the best choice for automation. The disassembly line balancing problem seeks a sequence which: is feasible, minimizes workstations, and ensures similar idle times, as well as other
end-of-life specific concerns. Finding the optimal balance is computationally intensive due to exponential growth. Combinatorial optimization methods hold promise for providing solutions to the disassembly line balancing problem, which is proven here to belong to the class of unary NP-complete problems. Probabilistic (ant colony optimization) and uninformed (H-K) search methods are presented and compared. Numerical results are obtained using a recent case study to illustrate the search implementations and compare their performance. Conclusions drawn include the consistent generation of near-optimal solutions, the ability to preserve precedence, the speed of the techniques, and their practicality due to ease of implementation.
Disassembly takes place in remanufacturing, recycling, and disposal with a line being the best choice for automation. The disassembly line balancing problem seeks a sequence which: minimizes workstations, ensures similar idle times, and is feasible. Finding the optimal balance is computationally intensive due to factorial growth. Combinatorial optimization methods hold promise for providing solutions to the disassembly line balancing problem, which is proven to belong to the class of NP-complete problems. Ant colony optimization, genetic algorithm, and H-K metaheuristics are presented and compared along with a greedy/hill-climbing heuristic hybrid. A numerical study is performed to illustrate the implementation and compare performance. Conclusions drawn include the consistent generation of optimal or near-optimal solutions, the ability to preserve precedence, the speed of the techniques, and their practicality due to ease of implementation.
Selection of an optimal disassembly sequence is essential for the efficient processing of a product at the end of its life. Disassembly sequences are listings of disassembly actions (such as the separation of an assembly into two or more subassemblies, or removing one or more connections between components). Disassembly takes place in remanufacturing, recycling, and disposal with a disassembly line being the best choice for automation. In this paper, the disassembly sequencing problem is solved for a cell phone case on a disassembly line, seeking a sequence which is feasible, minimizes the number of workstations (and hence idle times), provides for early removal of high demand/value parts, provides the removal of parts that lead to the access of greatest number of still-installed parts, and early removal of hazardous parts as well as for the grouping of parts for removal having identical part removal directions. Since finding the optimal sequence is computationally intensive due to factorial growth, a heuristic method is used taking into account various disassembly-specific matters. Using the experimentally determined precedence relationships and task times of a real-world cell phone, a MATLAB program is designed and a sequencing solution is generated. Finally, Design for Disassembly (DFD) improvements are recommended with respect to environmentally conscious manufacturing.
The latest enhancements in industrial technologies, especially the ones in electronics industry, have provided organizations with the ability to manufacture faster and more economical products. This fact, coupled with the growing interest and demand for the latest technology, have led electronic equipment manufacturers to start producing “hightech” and “personalized” products at an increasing rate. This has led to a high rate of obsolescence for electronic
products worldwide, even though the majority of these “obsolete” products still function. In this paper, we investigate a product recovery facility where the end-of-life (EOL) products are taken back from the last users and are brought into the facility for processing. We assume that there are multiple types of EOL products and that a combination of these can be disassembled to provide for a sufficient number of demanded components and materials. We then present a data
envelopment analysis (DEA) algorithm to determine the number and types of the EOL products that will be required to fulfill the demand. A numerical example is presented to demonstrate the functionality of the methodology.
Disassembly activities are an important part of product recovery operations. The disassembly line is the best choice for automated disassembly of returned products. However, finding the optimal balance for a disassembly line is computationally intensive with exhaustive search quickly becoming prohibitively large. In this paper, a greedy algorithm is presented for obtaining optimal or near-optimal solutions to the disassembly line-balancing problem. The greedy algorithm is a first-fit decreasing algorithm further enhanced to preserve precedence relationships. The algorithm seeks to minimize the number of workstations while addressing hazardous and high demand components. A two optimal algorithm is then developed to balance the part removal sequence and attempt to further reduce the total number of workstations. Examples are considered to illustrate the methodology. The conclusions drawn from the study include the
consistent generation of optimal or near-optimal solutions, the ability to preserve precedence, the speed of the algorithms and their practicality due to the ease of implementation.