由於自動化技術及資訊科技之發展與普及，過去以人工作業為主之傳統物流產業亦隨科技與技術之發展，開始導入自動化（如分類機）或半自動化（如輸送帶系統）之輔助揀貨設備，以期能節省龐大的人工成本支出，進而提高物流中心競爭力。然對於利用輸送帶作為輔助揀貨工具之半自動化揀貨系統而言，如何決定輸送帶中各工作站所應負責之揀貨品項，以達到輸送帶中各工作站之作業負荷平衡，進而提升物流中心整體資源利用率，乃為揀貨作業規劃人員所關注之議題。傳統物流中心針對此類議題往往僅以經驗法則或決策者主觀認定方式為之，但由於人工決策過程耗時且主觀認定之決策結果未必為正確結果，因此可能造成揀貨效率不佳，甚至必須重新進行揀貨作業指派，而徒增整體物流作業成本。故本研究即針對輸送帶輔助揀貨系統之作業指派問題，提出一套「揀貨作業平衡方法論」，以透過電腦系統快速產生一符合專家期望之最適指派方案建議，作為揀貨規劃人員進行作業指派之參考。
於揀貨作業平衡方法論中，本研究乃先以揀貨日程總表中之待揀貨品品項為基礎，利用窮舉法或隨機方式產生候選作業指派方案。之後，透過與資深揀貨作業規劃人員訪談，以釐清其進行揀貨作業指派時所使用之經驗法則與精神意涵；其後，再行將經驗法則量化為經驗指標；並利用歷史作業指派資料，推論符合專家期望之經驗指標權重，以作為後續評選作業之參考。此後，透過前述步驟所得之經驗指標與其對應權重，由眾多候選方案中評選一最適指派方案，以作為揀貨作業規劃人員進行作業指派之參考。本研究除發展揀貨作業平衡模式與方法論外，乃依此方法建構一套「輸送帶作業指派支援系統」並進行案例驗證，進而確認方法論與技術之可行性。
整體而言，本研究所提出之揀貨作業平衡方法論將可有效降低揀貨作業規劃人員於作業指派所耗費之時間，並提升其決策結果之正確性，進而降低物流中心整體營運成本，達到提升物流中心競爭力之目的。

In the conveyor-aided picking system, the shop-floor supervisor has to assign the picking tasks to workstations on conveyor based on loading balance between the workstations. In the past, the traditional distribution center that utilizes the conveyor-aided picking system rely on expertise of the supervisor to balance the tasks of workstations. The traditional decision process for workload balance is usually time consuming and inefficiency.
In order to assist the supervisor to efficiently and accurately search a workload balance solution for workstations of the conveyer-aided picking system. This research proposed a quantitative methodology for workload balance by extracting the expertise of domain experts. Based on the proposed methodology, a computer-aided decision support system can be developed to automatically generate the workload balance solutions of the conveyer-aided picking system. The main idea of this research is to extract the empirical principles by interview with the domain experts and to establish quantitative indicators for workload balance. In addition, weightings of indicators can be derived from historical information. By utilizing the indicators for workload balance, the supervisor can objectively and efficiently determine the picking tasks of workstations.
As a whole, this research provides a quantitative methodology and technique for the supervisor to efficiently and accurately determine the picking tasks of workstations in the conveyor aided picking system.

1. 王建鈞，2004，「整合同步工程與剛好及時哲理下的生產線平衡技術」，碩士論文（指導教授：藍俊雄），南華大學管理科學研究所。
2. 周秀玲（2004），「物流中心隨機性揀貨線平衡之研究-以台灣雅芳公司為例」，碩士論文（指導教授：邱煥能），國立台灣科技大學工業管理學系
3. 林嘉慶，2004，「運用關聯法則技術於倉儲系統儲位指派與揀貨策略配置模型之研究」，碩士論文（指導教授：謝玲芬），中華大學科技管理研究所。
4. 張啟鑌，2001，「物流中心訂單揀取與分類批次分割方法之研究」，碩士論文（指導教授：黃承傳），國立交通大學交通運輸研究所。
5. 郭宏明，2006，「運用頻繁模式樹改善小型品項儲位配置與揀貨效率之應用」，碩士論文（指導教授：許玟斌），東海大學資訊工程與科學研究所。
6. 黃耀生，2005，「生產線平衡原型系統應用於多部取置機台之表面黏著PCB組裝線」，碩士論文（指導教授：施武榮），南台科技大學工業管理研究所。
7. 葉若春，1979，「生產計劃與管制」，中興管理顧問公司。
8. 董福慶與陳明德，1995，「物流中心揀貨作業」，經濟部商業司。
9. 劉彩霈，2000，「運用模糊專家系統修正EIQ分析之揀貨決策」，碩士論文（指導教授：巫沛倉），義守大學管理科學研究所。
10. 劉慧芬，2002，「雙機分工模式下多樣PCB類型製造時間最佳化研究」，碩士論文（指導教授：徐旭昇），元智大學工業工程與管理學系。
11. 蔣治平，1995，「裝配線合理化之應用研究」，正修學報，第8期，第109-115
12. 蔡崇榮（2000），「自動倉儲揀貨作業之研究」，碩士論文（指導教授：張舜德），國立台北大學企業管理學系。
13. 鄭莒，1997，「智慧型電子標籤輔助揀貨系統在物流中心揀貨作業之實作分析」，碩士論文（指導教授：張靖），中華大學工業工程與管理研究所。
14. 蕭文綺，2003，「不同訂單型態下，揀貨環境配置對揀貨效率之影響」，碩士論文（指導教授：林燦煌），國立高雄第一科技大學運輸與倉儲營運學系。
15. 蘇騰昇，2002，「物流中心之最佳化揀貨策略」，碩士論文（指導教授：何應欽），國立中央大學工業管理學系。
16. Amen, M., 2000, “An exact method for cost-oriented assembly line balancing,” International Journal of Production Economics, Vol. 64, No. 1-3, pp. 187-195.
17. Arcus, A. L., 1966, “COMSOAL: A computer method of sequencing operations for assembly line,” International Journal of Production Research, Vol. 4, No. 4, pp. 259-277.
18. Bakchin, Y. and Rabinnowitch, I., 2005, “A branch-and-bound based solution approach for the mixed-model assembly line-balancing problem for minimizing stations and task duplication costs,” European Journal of Operational Research, Vol. 174, No. 1, pp. 492-508.
19. Bhattacharyya, S. K., Roy, R., Allchurch, M. and Low, M. J., 1992, “A knowledge based line balance system for a multi-product environment,” The First International Conference on Intelligent Systems Engineering, pp. 92-97.
20. Bozer, Y. A. and White, J. A., 1996, “A generalized design and performance analysis model for end-of-aisle order-picking systems,” IIE Transactions, Vol. 28, No. 4, pp. 271-280.
21. Brynzer, H. and Johansson, M. I.，1996，”Storage location assignment: Using the product structure to reduce order picking times,” International Journal of Production Economics, Vol. 46-47, No. 1, pp. 595-603.
22. Chen, M. C. and Wu, H. P., 2005, “An association-based cluster approach to order batching considering customer demand patterns,” Omega, Vol. 33, No. 4, pp. 333-343.
23. Chen, M. C., Cheng, L. H., Chen, K. Y. and Wu, H. P., 2005, “Aggregation of orders in distribution centers using data mining,” Expert System with Applications, Vol. 28, No. 3, pp. 453-460.
24. Chen, R. S., Lu, K. Y. and Yu, S. C., 2002, “A hybrid genetic algorithm approach on multi-objective of assembly planning problem,” Engineering Application of Artificial Intelligence, Vol. 15, No. 5, pp. 447-457.
25. Chen, R. S., Lu, K. Y., Yu, S. C., Tzeng, H. W. and Chang, C. C., 2003, “A case study in the design of BTO/CTO shop floor control system,” Information and Management, Vol. 41, No. 1, pp. 25-37.
26. Chen, W. H. and Lin, C. S., 2000, “A hybrid heuristic to solve a task allocation problem,” Computers and Operations Research, Vol. 27, No. 3, pp. 287-303.
27. Chow, H. K. H., Choy, K. L., Lee, W. B. and Lau, K. C., 2006, “Design of a RFID case-based resource management system for warehouse operation,” Expert System with Application, Vol. 30, No. 40, pp. 561-576.
28. Falkenauer, E. and Delchambre, A., 1992, “A genetic algorithm for bin packing and line balancing,” Proceedings of IEEE International Conference on Robotics and Automation , pp. 1186-1192.
29. Geng, Y., Li, Y. and Lim, A., 2005, “A very large-scale neighborhood search approach to capacitated warehouse routing problem,” Proceedings of The 17th IEEE International Conference on Tool with Artificial Intelligence, pp. 58-65.
30. Gibson, D. R. and Sharp, R. G., 1992，”Order batching procedures,” European Journal of Operational Research, Vol. 58, No. 11, pp. 57-67.
31. Gohen, Y., Vitner, G. and Sarin, S. C., 2004, “Optimal allocation of work in assembly lines for lots with homogenous learning,” European Journal of Operation Research, Vol. 168, No. 3, pp. 922-931.
32. Haq, A. N., Jayaprakash, J. and Rengarajan, K., 2005, “A hybrid genetic algorithm approach to mixed-model assembly line balancing,” International Journal of Advanced Manufacturing Technology, Vol. 28, No. 3-4, pp. 337-341.
33. Heragu, S. S. and Mazacioglu, B., 1992, “Solving order picking problem using simulated annealing based algorithms,” Proceedings of the Third International Conference on Computer Integrated Manufacturing, pp. 323-330.
34. Hsu, C. M., Chen, K. Y. and Chen, M. C., 2005, “Batching order in warehouses by minimizing travel distance with genetic algorithms,” Computers in Industry, Vol. 56, No. 2 pp.169-178.
35. Jane, C. C. and Laih, Y. W., 2005, “A clustering algorithm for item assignment in a synchronized zone order picking system,” European Journal of Operational Research, Vol. 166, No. 2, pp. 489-496.
36. Jane, C. C., 2000, “Storage location assignment in a distribution center,” International Journal of Physical Distribution and Logistics Management, Vol. 30, No. 1, pp. 55-71.
37. Jarvis, J. M. and McDowell, E. D., 1991, “Optimal product layout in an order picking warehouse,” IIE Transactions, Vol. 23, No. 1, pp. 93-102.
38. Johtela, T., Smed, J., Lehtinen, R. and Nevalainen, O., 1997, “Supporting production planning by production process simulation,” Computer Integrated Manufacturing System, Vol. 10, No. 3, pp.193-203.
39. Kim, Y. J., Kim, Y. K. and Cho, Y. K., 1998, “A heuristic-based genetic algorithm for workload smoothing in assembly lines,” Computers and Operations Research, Vol. 25, No. 2, pp. 99-111.
40. Korcyl, A., Lebkowski, P. and Sawik, T., 1995, “Selection of assembly sequences and balancing workloads in a flexible assembly line,” Proceedings of INRIA/IEEE Symposium on Emerging Technologies and Factory Automation, Vol. 3, pp. 349-359.
41. Lapierre, S. D., Ruiz, A. and Soriano, P., 2006, “Balancing assembly lines with Tabu search,” European Journal of Operational Research, Vol. 168, No. 3, pp. 826-837.
42. Levitin, G., Rubinnovitz, J. and Shnits, B., 2006, “A genetic algorithm for robotic assembly line balancing,” European Journal of Operational Research, Vol. 168, No. 3, pp. 811-825.
43. Lin, C.-H. and Lu, I.-Y.，1999，“The procedure of determining the order picking strategies in distribution center,” International Journal of Production Economics Vol. 66-67, No. 1, pp. 301-307.
44. Liu, C. M. and Chen, C. H., 2002, “Multi-section electronic assembly line balancing problem: A case study,” Production Planning and Control, Vol. 13, No. 5, pp. 451-461.
45. Liu, C. M., 1999, “Clustering techniques for stock location and order-picking in a distribution center,” Computers and Operations Research, Vol. 26, No.10-11, pp. 989-1002.
46. Liu, S. B., Ong, H. L. and Huang, H. C., 2005, “A bi-directional heuristic for stochastic assembly line balancing Type II problem,” International Journal of Advanced Manufacturing Technology, Vol. 25, No. 1-2, pp.71-77.
47. Mendes, A. R., Ramos, A. L., Simaria A. S. and Vilarinho, P. M., 2005, “Combining heuristic procedures and simulation models for balancing a PC camera assembly line,” Computers and Industrial Engineering, Vol. 49, No. 3, pp. 413-431.
48. Molnar, B. and Lipovszki, G., 2005, “Multi-objective routing and scheduling of order pickers in a warehouse,” International Journal of Simulation, Vol. 6, No. 5, pp. 22-32.
49. Molnar, B., 2004, “Multi-criteria scheduling of order picking processes with simulation optimization,” Periodical Polytechnic, Transportation Engineering, Vol. 33, No. 1-2, pp. 59-68.
50. Nicosia, G., Pacciarelli, D. and Pacifici, A., 2002, “Optimally balancing assembly lines with different workstations,” Discrete Applied Mathematics, Vol. 118, No. 1-2, pp. 99-113.
51. Ozmehmet, S. and Tunali, S., 2005, “A hybrid Meta-heuristic approach for solving SALBP,” The 35th International Conference on Computers and Industrial Engineering, pp.1475-1480.
52. Ponnambalam, S. G., Aravindan, P. and Naidu, G. M., 2000, “A multi-objective genetic algorithm for solving assembly line balancing problem,” International Journal of Advanced Manufacturing Technology, Vol. 16, No. 5, pp. 341-352.
53. Postor, R. and Corominas, A., 1999, “Assembly line balancing with work zone and station load time windows constraints,” Proceedings of IEEE International Symposium on Assembly and Task Planning, pp. 448-453.
54. Ratliff, H. D. and Rosenthal, A. S., 1983, “Order-picking in a rectangular warehouse: A solvable case of the traveling salesman problem,” Operations Research, Vol. 31, No. 3, pp. 507-521.
55. Rosenblatt, M. J., and Eynan, A., 1989, “Deriving the optimal boundaries for class-based automatic storage/retrieval systems,” Management Science, Vol. 35, No. 12, pp. 1519-1524.
56. Sabuncuoglu, I., Erel, E. and Tanyer, M., 1998, “Assembly line balancing using genetic algorithms,” Journal of Intelligent Manufacturing, Vol. 11, No. 3, pp.295-310.
57. Sarin, S. C., Erel, E. and Dar-Ei, E. M., 1999, “A methodology for solving single-model, stochastic assembly line balancing problem,” Omega, Vol. 27, No. 5, pp. 525-535.
58. Sawik, T. J., 1997, “An interactive approach to bi-criterion loading of flexible assembly system,” Mathematical and Computer Modeling, Vol. 25, No. 6, pp. 71-83.
59. Sawik, T., 1997, “Flexible assembly line balancing with alternate assembly plans and duplicate task assignments,” Proceedings of the 6th International Conference on Emerging Technologies and Factory Automation, pp. 171-176.
60. Sawik, T., 2002, “Monolithic vs. hierarchical balancing and scheduling of a flexible assembly line,” European Journal of Operational Research, Vol. 143, No. 1, pp. 115-124.
61. Scott, M. S. and Ricardo, E., 1993, “Applying group technology principles to warehousing operations,” International Journal of Purchasing and Materials Management, Vol. 29, No. 2, pp. 39-42.
62. Shashi, S. M. and Viswanadham, N., 1996, “Simulated annealing approach to machine loading problem in flexible assembly system,” Proceedings of IEEE International Conference on Robotics and Automation, pp. 3349-3354.
63. Simaria, A. S. and Vilarinho, P. M., 2004, “A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II,” Computers and Industrial Engineering, Vol. 47, No. 4, pp. 391-407.
64. Swarnkar, R. and Tiwari, M. K., 2004, “Modeling machine loading problem of FMSs and its solution methodology using a hybrid Tabu search and simulated annealing-based heuristic approach,” Robotics and Computer-Integrated Manufacturing, Vol. 20, No. 3, pp. 199-209.
65. Tiwari, M. K. and Vidyarthi, N. K., 2000, “Solving machine loading problems in a flexible manufacturing system using a genetic algorithm based heuristic approach,” International Journal of Production Research, Vol. 38, No. 14, pp. 3357-3384.
66. Trappey, A. and Ho, P. S., 2002, “Human resource assignment system for distribution centers,” Industrial Management and Data Systems, Vol. 102, No. 2, pp. 64-72.
67. Ugurdag, H. F., Rachamadugu, R. and Papachristou, C. A., 1997, “Designing paced assembly lines with fixed number of stations,” European Journal of Operational Research, Vol. 102, No. 3, pp. 488-501.