Modeling and Optimization of Disassembly Systems with a High Variety of End of Life States.

Remanufacturing is a promising product recovery method that brings new life to cores that otherwise would be discarded thus losing all value. Disassembly is a sub-process of remanufacturing where components and modules are removed from the core, sorted and graded, and directly reused, refurbished, recycled, or disposed of. Disassembly is the backbone of the remanufacturing process because this is where the reuse value of components and modules is realized. Disassembly is a process that is also very difficult in most instances because it is a mostly manual process creating stochastic removal times of components. There is a high variety of EOL states a core can be in when disassembled and an economic downside due to not all components having reuse potential. This thesis focuses on addressing these difficulties of disassembly in the areas of sequence generation, line balancing, and throughput modeling. In Chapter 2, we develop a series of sequence generation models that considers the material properties, partial disassembly, and sequence dependent task times to determine the optimal order of disassembly in the presence of a high variety of EOL states. In Chapter 3, we develop a joint precedence graph method for disassembly that models all possible EOL states a core can be in that can be used with a wide variety of line balancing algorithms. We also develop a stochastic joint precedence graph method in the situation where some removal times of components are normal random variables. In Chapter 4, we further advance the analytical modeling framework to analyze transfer lines that perform routing logics that result from a high variety of EOL states, such as a restrictive split routing logic and the possibility that disassembly and split operations can be performed at the same workstation.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111570/1/robriggs_1.pd

EMOGA: a hybrid genetic algorithm with extremal optimization core for multiobjective disassembly line balancing

In a world where products get obsolescent ever more quickly, discarded devices produce million tons of electronic waste. Improving how end-of-life products are dismantled helps reduce this waste, as resources are conserved and fed back into the supply chain, thereby promoting reuse and recycling. This paper presents the Extremal MultiObjective Genetic Algorithm (EMOGA), a hybrid nature-inspired optimization technique for a multiobjective version of the Disassembly Line Balancing Problem (DLBP). The aim is to minimize the number of workstations, and to maximize profit and disassembly depth, when dismounting products in disassembly lines. EMOGA is a Pareto-based genetic algorithm (GA) hybridized with a module based on extremal optimization (EO), which uses a tailored mutation operator and a continuous relaxation-based seeding technique. The experiments involved the disassembly of a hammer drill and a microwave oven. Performance evaluation was carried out by comparing EMOGA to various efficient algorithms. The results showed that EMOGA is faster or gets closer to the Pareto front, or both, in all comparisons

Modeling, design and scheduling of computer integrated manufacturing and demanufacturing systems

This doctoral dissertation work aims to provide a discrete-event system-based methodology for design, implementation, and operation of flexible and agile manufacturing and demanufacturing systems. After a review of the current academic and industrial activities in these fields, a Virtual Production Lines (VPLs) design methodology is proposed to facilitate a Manufacturing Execution System integrated with a shop floor system. A case study on a back-end semiconductor line is performed to demonstrate that the proposed methodology is effective to increase system throughput and decrease tardiness. An adaptive algorithm is proposed to deal with the machine failure and maintenance. To minimize the environmental impacts caused by end-of-life or faulty products, this research addresses the fundamental design and implementation issues of an integrated flexible demanufacturing system (IFDS). In virtue of the success of the VPL design and differences between disassembly and assembly, a systematic approach is developed for disassembly line design. This thesis presents a novel disassembly planning and demanufacturing scheduling method for such a system. Case studies on the disassembly of personal computers are performed illustrating how the proposed approaches work

Leveraging Virtual Reality Experiences With Mixed-Integer Nonlinear Programming Visualization of Disassembly Sequence Planning Under Uncertainty

Disassembly sequence planning at the early conceptual stage of design leads to enormous benefits including simplification of products, lower assembly and disassembly costs, and design modifications which result in increased potential profitability of end-of-life salvaging operations. However, in the early design stage, determining the best disassembly sequence is challenging. First, the required information is not readily available and very time-consuming to gather. In addition, the best solution is sometimes counterintuitive, even to those with experience and expertise in disassembly procedures. Integrating analytical models with immersive computing technology (ICT) can help designers overcome these issues. A two-stage procedure for doing so is introduced in this paper. In the first stage, a stochastic programming model together with the information obtained through immersive simulation is applied to determine the optimal disassembly sequence, while considering uncertain outcomes, such as time, cost, and the probability of causing damage. In the second stage, ICT is applied as a tool to explore alternative disassembly sequence solutions in an intuitive way. The benefit of using this procedure is to determine the best disassembly sequence, not only by solving the analytic model but also by capturing human expertise. The designer can apply the obtained results from these two stages to analyze and modify the product design. An example of a Burr puzzle is used to illustrate the application of the method

Recommended techniques for effective maintainability. A continuous improvement initiative of the NASA Reliability and Maintainability Steering Committee

This manual presents a series of recommended techniques that can increase overall operational effectiveness of both flight and ground based NASA systems. It provides a set of tools that minimizes risk associated with: (1) restoring failed functions (both ground and flight based); (2) conducting complex and highly visible maintenance operations; and (3) sustaining a technical capability to support the NASA mission using aging equipment or facilities. It considers (1) program management - key elements of an effective maintainability effort; (2) design and development - techniques that have benefited previous programs; (3) analysis and test - quantitative and qualitative analysis processes and testing techniques; and (4) operations and operational design techniques that address NASA field experience. This document is a valuable resource for continuous improvement ideas in executing the systems development process in accordance with the NASA 'better, faster, smaller, and cheaper' goal without compromising safety

A Scatter Search Approach for Multiobjective Selective Disassembly Sequence Problem

Disassembly sequence has received much attention in recent years. This work proposes a multiobjective optimization of model for selective disassembly sequences and maximizing disassembly profit and minimizing disassembly time. An improved scatter search (ISS) is adapted to solve proposed multiobjective optimization model, which embodies diversification generation of initial solutions, crossover combination operator, the local search strategy to improve the quality of new solutions, and reference set update method. To analyze the effect on the performance of ISS, simulation experiments are conducted on different products. The validity of ISS is verified by comparing the optimization effects of ISS and nondominated sorting genetic algorithm (NSGA-II)