A Label Correcting Algorithm for Partial Disassembly Sequences in the Production Planning for End-of-Life Products

Remanufacturing of used products has become a strategic issue for cost-sensitive businesses. Due to the nature of uncertain supply of end-of-life EoL products, the reverse logistic can only be sustainable with a dynamic production planning for disassembly process. This research investigates the sequencing of disassembly operations as a single-period partial disassembly optimization SPPDO problem to minimize total disassembly cost. AND/OR graph representation is used to include all disassembly sequences of a returned product. A label correcting algorithm is proposed to find an optimal partial disassembly plan if a specific reusable subpart is retrieved from the original return. Then, a heuristic procedure that utilizes this polynomial-time algorithm is presented to solve the SPPDO problem. Numerical examples are used to demonstrate the effectiveness of this solution procedure

AO* and penalty based algorithms for the Canadian traveler problem

Tezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.The Canadian Traveler Problem (CTP) is a challenging path planning problem on stochastic graphs where some edges are blocked with certain probabilities and status of edges can be disambiguated only upon reaching an end vertex. The goal is to devise a traversal policy that results in the shortest expected traversal length between a given starting vertex and a termination vertex. The organization of this thesis is as follows: In the first chapter we define CTP and its variant SOSP and present an extensive literature review related to these problems. In the second chapter, we introduce an optimal algorithm for the problem, based on an MDP formulation which is a new improvement on AO* search that takes advantage of the special problem structure in CTP. The new algorithm is called CAO*, which stands for AO* with Caching. CAO* uses a caching mechanism and makes use of admissible upper bounds for dynamic state-space pruning. CAO* is not polynomial-time, but it can dramatically shorten the execution time needed to find an exact solution for moderately sized instances. We present computational experiments on a realistic variant of the problem involving an actual maritime minefield data set. In the third chapter, we introduce a simple, yet fast and effective penalty-based heuristic for CTP that can be used in an online fashion. We present computational experiments involving real-world and synthetic data that suggest our algorithm finds near-optimal policies in very short execution times. Another efficient method for sub-optimally solving CTP, rollout-based algorithms, have also been shown to provide high quality policies for CTP. In the final chapter, we com- pare the two algorithmic frameworks via computational experiments involving Delaunay and grid graphs using one specific penalty-based algorithm and four rollout-based algo- rithms. Our results indicate that the penalty-based algorithm executes several orders of magnitude faster than rollout-based ones while also providing better policies, suggest- ing that penalty-based algorithms stand as a prominent candidate for fast and efficient sub-optimal solution of CTP.Declaration of Authorship ii Abstract iii Öz iv Acknowledgments v List of Figures viii List of Tables ix Abbreviations x 1 Introduction 1 1.1 Overview .................................... 1 1.2 The Canadian Traveler Problem ........................ 1 1.2.1 The Discrete Stochastic Obstacle Scene Problem .......... 2 1.3 Literature Review ................................ 3 1.4 Organization of the Thesis ........................... 4 2 An AO* Based Exact Algorithm for the Canadian Traveler Problem 5 2.1 Introduction ................................... 5 2.2 MDP and POMDP Formulations ....................... 6 2.2.1 MDP Formulation and The Bellman Equation ............ 7 2.2.2 Deterministic POMDP Formulation ................. 9 2.3 The CAO* Algorithm ............................. 11 2.3.1 AO Trees ................................ 11 2.3.2 The AO* Algorithm .......................... 14 2.3.3 The CAO* Algorithm ......................... 16 2.4 Computational Experiments .......................... 19 2.4.1 The BAO* and PAO* Algorithms ................... 19 2.4.2 Experimental Setup .......................... 21 2.4.3 Simulation Environment A ...................... 21 2.4.4 Simulation Environment B ....................... 22 2.4.5 Simulation Environment C....................... 24 2.4.6 Simulation Environment D ...................... 25 2.5 Summary and Conclusions ........................... 26 3 A Fast and Effective Online Algorithm for the Canadian Traveler Prob- lem 29 3.1 Introduction ................................... 29 3.2 The DT Algorithm ............................... 30 3.3 Computational Experiments .......................... 32 3.3.1 Environment 1 ............................. 32 3.3.2 Environment 2 ............................. 34 3.4 Conclusions and Future Research ....................... 34 3.4.1 Conclusions ............................... 34 3.4.2 Limitations and Future Research ................... 35 4 A Comparison of Penalty and Rollout-Based Policies for the Canadian Traveler Problem 36 4.1 Introduction ................................... 36 4.2 Algorithms for CTP .............................. 37 4.2.1 Optimism (OMT) ........................... 37 4.2.2 Hindsight Optimization (HOP) .................... 38 4.2.3 Optimistic Rollout (ORO) ....................... 39 4.2.4 Blind UCT (UCTB) .......................... 39 4.2.5 Optimistic UCT (UCTO) ....................... 40 4.3 Computational Experiments .......................... 41 4.3.1 Delaunay Graph Results ........................ 43 4.3.2 Grid Graph Results .......................... 45 4.4 Conclusions and Future Research ....................... 46 4.4.1 Conclusions ............................... 46 4.4.2 Limitations and Future Research ................... 46 A Problem Instances in Simulation Environments C and D 48 Bibliography 5

A forecast of space technology, 1980 - 2000

The future of space technology in the United States during the period 1980-2000 was presented, in relation to its overall role within the space program. Conclusions were drawn and certain critical areas were identified. Three different methods to support this work were discussed: (1) by industry, largely without NASA or other government support, (2) partially by industry, but requiring a fraction of NASA or similar government support, (3) currently unique to space requirements and therefore relying almost totally on NASA support. The proposed work was divided into the following areas: (1) management of information (acquisition, transfer, processing, storing) (2) management of energy (earth-to-orbit operations, space power and propulsion), (3) management of matter (animate, inanimate, transfer, storage), (4) basic scientific resources for technological advancement (cryogenics, superconductivity, microstructures, coherent radiation and integrated optics technology)

Human Motion Modelling for Simulation Testing of GNSS Equipment

Pedestrian motion-induced dynamics along the line-of-sight (LOS) between a GNSS receiver and a satellite, may disrupt the nominal operation of GNSS carrier-tracking loops, by introducing cycle slips and/or false frequency locks. In combination with other factors, e.g. multipath interference, weak signal conditions or limited availability of GNSS signals, the receiver could provide a degraded navigation solution or even lose signal lock. This thesis researches firstly how pedestrian motion affects the operation of carrier phase lock loops (PLLs), used by some GNSS receivers, and frequency lock loops (FLLs), used by all GNSS receivers; and secondly, what is the best way to model pedestrian motion in order to simulate the error effects of pedestrian motion-induced dynamics on a GNSS antenna, via a simulated GNSS carrier phase lock loop (PLL). The thesis reviews the relevant literature on human biomechanical modelling, path-finding and inertial/GNSS navigation, to design a custom pedestrian motion model (PMM). The PMM validation is supported by motion capture (MoCap) experiments using an inertial/GNSS sensor held by, or attached, on a pedestrian. The thesis also describes an implementation of simulated GNSS carrier-tracking loops (SGCTLs) in Matlab, to assess the effect of human MoCap profiles and synthetic human motion profiles (from the PMM) on the performance of the SGCTLs. The testing results suggest that GNSS antenna motion dynamics due to typical pedestrian motion can induce excessive cycle slips due to dynamics stress on the simulated PLL and FLL. Therefore, antenna dynamics should be considered when designing GNSS tracking loops and navigation algorithms for pedestrian applications to allow the GNSS receiver track human motion-induced dynamics effectively. The thesis concludes with carrier-tracking bandwidth recommendations for GNSS receiver design, based on the presented evidence. Under good signal conditions (above 40dB-Hz), the minimum recommended bandwidths for PLLs and FLLs are 15Hz and 5Hz, respectively, in order to respond effectively to the dynamic stress induced by typical pedestrian movements. Finally, the results indicate that the PMM can represent the LOS dynamics stress on the SGPLL within an acceptable tolerance. Future work encompasses the analysis of the pedestrian motion effects on real GNSS receivers