在經濟起飛的六十年代初期，台灣出口貿易管制放鬆，在中小企業的帶動下整體經濟快速成長，對於工業製程中需大量使用的石化產品需求日漸增加，特殊氣體越來越被業界所重視。特殊氣體雖無法直接從自然界取得，但其原料來源為取之不盡用之不竭的空氣，只要藉由化學加工方式將特殊工業用氣體從空氣中分離出來，即可生產顧客所需純度的氣體，是一種無原料成本的產業，而專職於此的行業則被稱作氣體分離工業。氣體的配送方式有直接以管線配送、設立小型分離廠與先將氣態氣體壓製成液態氣體再由低溫高壓氣體配送槽車（High Pressure Cylinder）運送至客戶貯槽，以供工業製成的使用。
由於產品的特殊性，氣體分離業者就是氣體配送業者，必須自行負責液態氣體槽車的配送規劃工作，找出如何在最節省人力成本及運輸成本的前提下使物流配送有最高的效益，有別於一般物流的運輸模式，屬於特殊運輸方式的一種。在不違反氣體配送槽車容量限制下，滿足顧客需求的最佳氣體配送路線的規劃被稱為車輛途程問題，隨問題的複雜度增減問題內限制式的建立，因此對於限制式較多或複雜的問題而言，求解過程出現過度限制乃十分常見，故本研究將運用限制規劃替此類複雜的限制滿足問題進行求解。
本研究以氣體分離業者的氣體配送路線為例，探討每一台槽車從氣體分離廠出發後，如何將顧客之貯槽填充完畢的路徑規劃，因此本研究會先把氣體配送槽車的路線問題行成一個限制滿足問題後，採用ILOG公司所開發的ILOG Solver軟體來當作研究輔助工具，藉由ILOG Solver探討氣體配送路線限制式的最佳化選擇，找出最有效率的配車方式並將此方案提供給氣體配送業者參考，希望能讓氣體配送業者之派車達成最大效率的目標。

In the 60s in Taiwan, the export restruction was relaxed by the government and the Small and Medium Enterprises of Taiwan were flourishing to develop industrial operations, so the requirement of industrial gas production in industrial operation was becoming more and more huge. The industrial gas production can’t be obtain from the nature but can be refindment from the air we breath evertday, and the business of refinding the air production is call the Industrial Air Separation Industiry.
This study develops a routing and scheduling decision support system that considers both hard and soft constraints of the problem for industrial gas deliveries. Hard constraints are the constraints should be satisfied while soft constraints are the constraints satisfied the more and the better, safer and more efficient distribution solution would be achieving. This study also treated the problem as a Constraint Satisfactory Problem and formulated it into an optimization problem which could help operations management to decide the composition of soft constraints and provide routes and schedule solution in real time.
This problem is treating as a constraint satisfaction problem (CSP) and use ILOG Dispatcher, ILOG Solver and ILOG Scheduler to simplify the process and capture the specificities of problem representation. The remaining of this study is arranged as follows. Chapter 2 reviews related literatures on routing industrial-gas deliveries, CSP, VRP, and ILOG. Chapter 3 models an industrial-gas routing and scheduling problem as a Vehicle Routing Problem with Time Windows (VRPTW) and capacity constraints. The solution approach is presented in Chapter 4, and the conclusion and suggestion of future research come in Chapter 5.

一、中文部分

1. 王國琛，結合限制規劃與數學規劃求解大型後勤空勤組員排班問題，國立交通大學運輸科技與管理學系碩士論文，中華民國九十一年六月。
2. 施國銓，應用限制規劃於營建專案有限資排程與重排程最佳化問題之研究，國立雲林科技大學營建工程系碩士班碩士論文，中華民國九十三年六月。
3. 唐依伶，以限制規劃求解公平性空服組員派遣問題－以座艙長為例，國立交通大學運輸科技與管理學系碩士論文，中華民國九十二年六月。
4. 楊智凱，以門檻接受法改善TSP 及VRP 路網成本之研究，國立交通大學土木工程系碩士論文，中華民國八十四年六月。
5. 蔡佳吟，應用CSP規劃大學排課系統，國立高雄第一科技大學資訊管理系碩士論文，中華民國九十二年六月。
6. 廖祐笙，結合基因演算法與限制條件滿足之研究，東海大學工業工程研究所碩士論文，中華民國八十八年六月。
7. 盧建元，結合限制滿足條件計術的物件導向斐式圖現場排程方法，東海大學工業工程研究所碩士論文，中華民國八十六年六月。交通部公路總局高雄區監理所，http://www.komv.gov.tw/index.php
8. 活地圖Map，http://maps.yam.com/
9. 行政院勞工委員會勞工安全衛生研究所，勞工作業場所爆炸防止對策之探討－槽車液化石油氣安全，中華民國八十八年。
10. 行政院勞工委員會化學品調和制度GHS介紹網站，http://ghs.cla.gov.tw/

二、英文部分

1. Backer B. D., Furnon V., Shaw P., Kilby P. and Prosse P.. Solving Vehicle Routing Problems Using Constraint Programming and Metaheuristics, Journal of Heuristics, 6, 501–523. 2000.
2. Bartk R.. Constraint Programming: In Pursuit of the Holy Grail, Charles University, Faculty of Mathematics and Physics, Department of Theoretical Computer Science. 1-10. 1999.
3. Bell W. J., Dalberto M.L., Fisher M. L., Greenfield A. J., Jaikumar R., Kedia P., Mack R. G., and Prutzman P.J.. Improving the distribution of industrial gases with an on-line computerized routing and scheduling optimizer, Interfaces, 13 (6), 4–23. 1983.
4. Bistarelli S., Montanari U., and Rossi F.. Semiring-based Constraint Satisfaction and Optimization. Journal of ACM, 44:2, 201-236. 1997.
5. Battiti R., and Tecchiolli G.. The reactive tabu search. ORSA Journal on Computing, 6 (2), 126-140. 1994
6. Borning A., Freeman-Benson B., and Wilson M.. Constraint Hierarchies. Lisp and Symbolic Computation: An International Journal, 5. 223-270. 1992.
7. Brailsford S. C., Potts C. N., and Smith B. M.. Constraint satisfaction problems: Algorithms and applications, European Journal of Operational Research, 119, 557-581. 1999.
8. Cerny V.. Thermodynamical Approach to the traveling Salesman Problem : An efficient simulation algorithm. Journal of Optimization Theory and Application. 45, 41-51. 1985.
9. Chiang W. and Russell R. A.. Integrating purchasing and routing in propane gas supply chain, European Journal of Operational Research, 154, 710–729. 2004.
10. CoisCordeau J., Laporte G., Savelsbergh M. W.P., and Vigo D... Vehicle Routing. In: Barnhart, C. and Laporte, G.(Eds.), Transportation, Handbooks in Operations Research and Management Science. North-Holland, Amsterdam. forthcoming. 1-72. 2006.
11. Cooper T.B. and Kingston J.H.. The Complexity of Timetable Construction Problems, Interfaces 17, 183-195. 1996.
12. Dantzig, G.B. and J.M. Ramser. The truck dispatching problem. Management Science, 6, 81–91. 1959.
13. Deris S., Omatu S., and Ohta H.. Timetable planning using the constraint-based reasoning, Computers & Operations Research, 27, 819-840. 2000.
14. Deris, S., Omatu, S. and Ohta, H. Timetable planning using the constraint-based reasoning, Computers & Operations Research, 27, 819-840. 2000.
15. Desrochers M., Desrosiers J., and Solomon M.. A New Optimization Algorithm for the Vehicle Routing Problem with Time Windows. Operations Research, 40 (2), 342-354. 1992
16. Dincbas, M., Simonis, H., Van Hentenryck, P.. Solving a cutting-stock problem in constraint logic programming. In: Kowalski, R.A., Bowen, K.A. (Eds.), Logic Programming. MIT Press, Cambridge, MA, 42-58. 1988.

17. Dincbas, M., Simonis, H., Van Hentenryck, P.. Solving the car-sequencing problem in constraint logic programming. In: Kodrato, Y. (Ed.), Proceedings of European Conference on Artificial Intelligence, Pitman, London, 290-295. 1988.
18. Dougherty M.. A review of meural networks applied to ransport. Transportation Research C, 3 (4), 247-260. 1995.
19. Dueck G. and T. Scheuer. Threshold Accepting : a General Purpose Optimization Algorithm Appearing Superior to Simulated Annealing, Journal of Computational Physics, 90, 161-175. 1990.
20. Fisher, H., Thompson, G.L... Probabilistic learning combinations of local job-shop scheduling rules. In: Muth, J.F., Thompson, G.L. (Eds.), Industrial Scheduling. Prentice-Hall, Englewood Cliffs, NJ, 225-251. 1963.
21. Freuder E. C. and Wallance R.J.. Partial Constraint Satisfaction. Artificial Intelligence, 58. 21-70. 1992.
22. Garey, M.R. and D.S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman. 1979.
23. Gendreau, M., Laporte, G., Potvin, J.-Y.. Vehicle routing: modern heuristics. In: Aarts, E.H.L., Lenstra, J.K. (Eds.), Local Search in Combinatorial Optimization. Wiley, Chichester, UK, 311-336. 1997.
24. Golden, B.L., Assad A.A., Wasil E.A. Routing Vehicles in Real World. In The Vehicle Routing Problem, Edited by Toth P. and Vigo D., SIAM Monographs on Discrete Mathematics and Applications. 2002.
25. Glover F.. Tabu Search: A Tutorial, The Institute of Management Sciences, July-August, 74-94. 1990.
26. Haralick R.M. and Elliott G. L.. Increasing Tree Search Efficiency for Constraint Satisfaction Problems, Artificial Intelligence, 14, 263-313. 1980.
27. Holland J. H.. Genetic Algorithms. Scientific American 267(1), 66−72. 1992.
28. ILOG Inc., ILOG User’s Manual, 2005.
29. ILOG Inc. Homepage, http://www.ilog.com.sg/
30. Kirkpatrick, S., C. D. Gelatt and M. P. Vecchi. Optimization by simulated annealing, Science 220, May, 671-680. 1983.
31. Langevin A. and Riopel D. (Eds.). Logistics Systems: Design and Optimization, Springer 1 edition. 2005.
32. Laporte, G. and Osman, I.H. Routing Problems: A Bibliography, Annals of Operations Research, 61, 227–262. 1995.
33. Larrosa J. and Schiex T.. Solving Weighted CSP by Maintaining Arc Consistency”, Artificial Intelligence, 159 (1-2), 1-26. 2004.
34. Mackworth, A.K.. Consistency in networks of relations. Artificial Intelligence, 8, 99-118. 1977.
35. Modares A., Somhom S., and Enkawa T.. A self-organizing neural network approach for multiple traveling salesman and vehicle routing problems. International Transactions in Operational Research, 6 (6), 591-606. 1999.
36. Montanari, U. Networks of constraints: fundamental properties and applications to picture processing. Information Science, 7, 95-132. 1974.
37. Paker RG, Rardin RL. Discrete Optimization. San Diego: Academic Press Inc, USA. 1988.
38. Plebe A. A Neural-Network-Based Approach to the Double Traveling Salesman Problem. Neural Computation, 14, 437-471. 2002.
39. Quimper C., Golynski A., Alejandro Lo Pez-ortiz, and Beek P. V.. An Efficient Bounds Consistency Algorithm for the Global Cardinality Constraint, Constraints, 10, 115-135. 2002.
40. Sabin D. and Freuder E. C.. Contradicting Conventional Wisdom in Constraint Satisfaction, Proceedings of the Second International Workshop on Principles and Practice of Constraint Programming, 10-20. 1994.
41. Shue L., Lin P. and Tsai C.. Constraint Programming Approach for a University Timetabling Decision Support System with Hard and Soft Constraints”, Department of Information Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan. 1-37. 2005.
42. Tsang, E. Foundations of Constraint Satisfaction, in Academic Press, London . 1993.
43. Waltz, D.. Generating semantic descriptions from drawings of scenes with shadows. Technical Report AI271. MIT, MA. 1972.
44. Willard, J. A. G.. Vehicle Routing Using r-Optimal Tabu Search. M.Sc. Dissertation. The Management School, Imperial College, London.1989.
45. Whitley D., Starkweather T., and Fuquay D.. Scheduling problems and traveling salesman: the genetic edge recombination. Proceedings of the third international conference on Genetic algorithms. George Mason University, United States. 133-140. 1989.