Call number: LD2668 .T4 1978 P63 / Master of Science

published_or_final_version / Mathematics / Doctoral / Doctor of Philosophy

published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / abstract / Civil Engineering / Master / Master of Philosophy

Many companies are confronted with the problem of creating xed master routes for a period of more than a day either for geographically dispersed sales representatives or for eets of delivery vehicles which operate from a single depot. This involves the assignment of the company's customers to the sales reps/vehicles as well as visit pro les. For the problems de ned herein, these allocations of customers to a service group must remain xed for the duration of the planning period. A pro le represents a valid combination of visit days for a customer as well as a proportion of distributable workload (time for sales reps or mass for delivery vehicles) for each visit. For the sales rep problem, there is the option to solve for the optimal number of salesmen and their home locations if they are not known. Also, routes for the salesmen may include a new feature, sleep-outs, which are governed by rules indicating possible combinations of nights spent away from home as well as sleep-out locations. These combinatorial optimization problems are solved using exact and heuristic branch-and-bound algorithms which also assists in de ning the problem complexity. A genetic algorithm hybridised with problem speci c heuristics (i.e. a memetic algorithm) is also applied to problems which cannot be solved exactly in a reasonable amount of time. This evolutionary programming metaheuristic technique uses natural multi- level data structures and problem-sensitive genetic operators.

by Lau Wai-tong. / Bibliography: leaves 45-47 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1985

Huang Liren. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 96). / Abstract --- p.1 / Introduction --- p.2 / References --- p.5 / Chapter Chapter 1. --- Some elementary results in nonsmooth analysis and optimization --- p.6 / Chapter 1. --- "Some properties for ""lim sup"" and ""lim inf""" --- p.6 / Chapter 2. --- The directional derivative of the sup-type function --- p.8 / Chapter 3. --- Some results in nonsmooth analysis and optimization --- p.12 / References --- p.19 / Chapter Chapter 2. --- On generalized second-order derivatives and Taylor expansions in nonsmooth optimization --- p.20 / Chapter 1. --- Introduction --- p.20 / Chapter 2. --- "Dini-directional derivatives, Clark's directional derivatives and generalized second-order directional derivatives" --- p.20 / Chapter 3. --- On Cominetti and Correa's conjecture --- p.28 / Chapter 4. --- Generalized second-order Taylor expansion --- p.36 / Chapter 5. --- Detailed proof of Theorem 2.4.2 --- p.40 / Chapter 6. --- Corollaries of Theorem 2.4.2 and Theorem 2.4.3 --- p.43 / Chapter 7. --- Some applications in optimization --- p.46 / Ref erences --- p.51 / Chapter Chapter 3. --- Second-order necessary and sufficient conditions in nonsmooth optimization --- p.53 / Chapter 1. --- Introduction --- p.53 / Chapter 2. --- Second-order necessary and sufficient conditions without constraint --- p.56 / Chapter 3. --- Second-order necessary conditions with constrains --- p.66 / Chapter 4. --- Sufficient conditions theorem with constraints --- p.77 / References --- p.87 / Appendix --- p.89 / References --- p.96

Tai Chi-hung. / Thesis (M.Phil.)--Chinese University of Hong Kong. / Bibliography: leaves 40-41.

Chan Ka Wo. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 114-118). / Abstracts in English and Chinese. / Introduction --- p.iii / Conventions of This Thesis --- p.vi / Prerequisites --- p.xiii / Chapter 1 --- Cones in Real Vector Spaces --- p.1 / Chapter 1.1 --- The Fundamentals of Cones --- p.2 / Chapter 1.2 --- Enlargements of a Cone --- p.22 / Chapter 1.3 --- Special Cones in Real Vector Spaces --- p.29 / Chapter 1.3.1 --- Positive Cones --- p.29 / Chapter 1.3.2 --- Bishop-Phelps Cones --- p.36 / Chapter 1.3.3 --- Quasi-Bishop-Phelps Cones --- p.42 / Chapter 1.3.4 --- Quasi*-Bishop-Phelps Cones --- p.45 / Chapter 1.3.5 --- Gallagher-Saleh D-cones --- p.47 / Chapter 2 --- Generalizations in Topological Vector Spaces --- p.52 / Chapter 2.1 --- Efficiency and Positive Proper Efficiency --- p.54 / Chapter 2.2 --- Type I Generalizations --- p.71 / Chapter 2.3 --- Type II Generalizations --- p.82 / Chapter 2.4 --- Type III Generalizations --- p.92 / Chapter 3 --- Generalizations in Dual Spaces --- p.97 / Chapter 3.1 --- Weak*-Support Points of a Set --- p.98 / Chapter 3.2 --- Generalizations in the Dual Space of a General Normed Space --- p.100 / Chapter 3.3 --- Generalizations in the Dual Space of a Banach Space --- p.104 / Epilogue: Glimpses Beyond --- p.112 / Bibliography --- p.114

Tsan-Ming Choi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 117-118). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Literature Review --- p.3 / Chapter 1.3 --- Summary of Classic Results --- p.6 / Chapter Chapter 2 --- Two-Stage Single Ordering 一 Unknown Mean and Variance --- p.12 / Chapter 2.1 --- Mathematical Model --- p.12 / Chapter 2.2 --- "Order Quantities, Expected Profits and Expected Quantities of Goods Sold" --- p.16 / Chapter 2.3 --- Benefits from Forecast Update --- p.21 / Chapter 2.4 --- Applying the Model --- p.25 / Chapter 2.5 --- Application Example --- p.25 / Chapter Chapter 3 --- Two-Stage Single Ordering with Ordering Cost Difference --- p.28 / Chapter 3.1 --- Mathematical Model --- p.28 / Chapter 3.2 --- Order Quantity and Expected Profit --- p.31 / Chapter 3.3 --- Two-Stage Dynamic Programming Formulation --- p.33 / Chapter 3.4 --- Application Example --- p.37 / Chapter 3.5 --- Sensitivity Studies --- p.39 / Chapter Chapter 4 --- Two-Stage Two-Ordering with Ordering Cost Difference --- p.44 / Chapter 4.1 --- Mathematical Model --- p.44 / Chapter 4.2 --- Dynamic Programming Formulation --- p.47 / Chapter 4.3 --- Optimal Order Quantities --- p.51 / Chapter 4.4 --- Application Example --- p.52 / Chapter 4.5 --- Sensitivity Studies --- p.54 / Chapter Chapter 5 --- Multiple-Stage Single Ordering with Ordering Cost Difference --- p.60 / Chapter 5.1 --- Mathematical Model --- p.61 / Chapter 5.2 --- Order Quantity and Expected Profit --- p.64 / Chapter 5.3 --- Dynamic Programming Formulation --- p.64 / Chapter 5.4 --- Approximation by Taylor Series Expansion --- p.72 / Chapter 5.5 --- Approximation by Polynomial --- p.76 / Chapter 5.6 --- Comparison of Taylor Series and Polynomial Approximations --- p.84 / Chapter 5.7 --- Application Examples --- p.85 / Chapter 5.8 --- Sensitivity Studies --- p.91 / Chapter 5.9 --- Extension from Two Stages to Multiple Stages --- p.98 / Chapter 5.10 --- Non-Monotonicity of Cutting Points --- p.99 / Chapter Chapter 6 --- Real World Applications --- p.102 / Chapter 6.1 --- Background --- p.102 / Chapter 6.2 --- Two-Stage Cases --- p.105 / Chapter 6.3 --- Multiple-Stage Cases --- p.109 / Chapter Chapter 7 --- Conclusion and Further Studies --- p.115 / References --- p.117 / Appendix --- p.119