運用基因演算法求解於封閉式分揀系統之貨物分揀排程問題

簡易檢索 / 詳目顯示

回結果列表

研究生：	區庭傑 Ou, Ting-Chieh
論文名稱：	運用基因演算法求解於封閉式分揀系統之貨物分揀排程問題 Applying Genetic Based Algorithm to Solve a Parcel Hub Scheduling Problem with Shortcuts on the Closed-Loop Sortation System
指導教授：	陳建良 Chen, James C.
口試委員:	陳子立張秉宸
學位類別：	碩士 Master
系所名稱：	工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management
論文出版年：	2018
畢業學年度：	106
語文別：	英文
論文頁數：	78
中文關鍵詞：	貨物分揀中心的貨物分揀排程問題、適應性基因演算法、區域搜尋、模糊邏輯控制
外文關鍵詞：	Parcel Hub Scheduling Problem, Adaptive Genetic Algorithm, Local Search, Fuzzy Logic Control
相關次數：	點閱：2 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

由於近年來的競爭環境，許多管理者已經意識到若比其他競爭對手更快地將產品送到客戶將可以提高他們的市場競爭力，因此，公司必須尋找有效的方法來增強他們在此方面的能力，排程是提高競爭力的有效方法之一，此方法不僅可以在製造業中實施，物流業也是可行的產業之一。
本研究重點主要考量貨物分揀中心中，擁有捷徑之分揀系統的貨物分揀排程問題（PHSPwS），此問題為一較常於物流業出現之排程問題。PHSPwS被定義為在相對較少數量的卸載碼頭處理大量的進貨拖車，目的是找到一個最佳的卸載排程以最小化分揀的完工時間。在這項研究中，PHSPwS還考慮了進貨拖車的其他特徵如不同批量大小和不同到達時間，更重要的是，因封閉式分揀系統裝配有捷徑，導致包裹於系統中增加了路線選擇問題，為了清楚地呈現問題，此研究建立了一數學模型，且更進一步提出了適應性基因演算法（AGA）用以解決此問題。在所提出的AGA中，使用了區域搜尋（LS）來避免演算法陷入區域最佳解的情況，並且透過使用模糊邏輯控制（FLC）來調整交換率（Pc）及突變率（Pm），透過觀察連續兩代父母和子女平均適應值的差異而提高算法的搜索能力。經由實驗數據證實，LS和FLC可以幫助基因演算法找到更好的解決方案，而同時擁有LS和FLC的AGA則更快、更好且更穩定。

Due to the vying environment in recent years, managers have realized that getting their products to customers faster than other competitors will enhance their market competitiveness. Hence, companies must look for an effective approach to strengthen their relative capability. Scheduling is one of the efficient methods to improve competitiveness, which can not only be implemented in manufacturing but also logistics.
This paper focuses on the parcel hub scheduling problem with shortcuts (PHSPwS), which is a special scheduling problem typically found in the parcel delivery industry. The PHSPwS is defined as processing a great number of inbound trailers at a much smaller number of unload docks with the objective of finding an unload schedule to minimize the makespan of the sorting process. In this research, the PHSPwS also considers the characteristic of unequal batch size and various arrival time of inbound trailers and, most importantly, the alternative routing for the parcels since shortcuts are now considered in the closed-loop sortation system. In order to clearly present the problem, a mathematical model is formulated. This research further proposed an adaptive genetic algorithm (AGA) to solve the PHSPwS. In the proposed AGA, local search (LS) is adopted to avoid the situation of getting trapped in the local optimal solution and fuzzy logic control (FLC) is utilized to adjust the probability of crossover rate (Pc) and mutation rate (Pm), by considering the change of the average fitness value of parents and offspring in two consecutive generations, which could enhance the searching capability of the proposed algorithm. The computational results showed that LS and FLC can truly help the GA to find a better solution and that AGA with both LS and FLC can perform the solution better and stabler.

摘要......................................................I
ABSTRACT.................................................II
致謝....................................................III
Contents.................................................IV
List of Tables............................................V
List of Figures.........................................VII
Chapter 1    Introduction..............................1
1    Background and Motivation.........................1
2    Research Objective................................3
3    Research Method...................................3
4    Organization of Thesis............................5
Chapter 2    Literature Review.........................6
1    Parcel Hub Scheduling Problem.....................6
2    Fuzzy Logic Control..............................13
3    Genetic Algorithm (GA)...........................15
4    Contributions....................................18
Chapter 3    Problem Definition.......................19
1    Problem Statement................................19
2    Notations and System Description.................20
3    Problem Formulation..............................26
Chapter 4    Methodology..............................34
1    Genetic Algorithm with Fuzzy Logic Control and Local Search...................................................34
2    Steps of Genetic Algorithm.......................37
2.1    Define Problem Environment.......................37
2.2    Chromosome Representation........................37
2.3    Generate Initial Population......................38
2.4    Fitness Value Evaluation and Selection...........38
2.5    Order Preserving One-Point Crossover.............41
2.6    Pairwise Interchange Mutation....................43
2.7    Local Search Mutator.............................45
2.8    Replacement......................................46
2.9    Termination Criteria.............................47
2.10    Fuzzy Logic Control..............................47
Chapter 5    Computational Study......................53
1    Design of Experiments............................53
1.1    Factors of the DOE...............................53
1.1.1    Algorithm........................................53
1.1.2    Layout...........................................55
1.1.3    Loading..........................................57
1.1.4    Batch Size.......................................59
1.1.5    Arrival time.....................................59
1.1.6    Load Distribution................................60
1.2    Result of DOE....................................60
2    Data Analysis of Different Types of GAs..........63
2.1    Experimental implementation......................63
2.2    Result Analysis..................................64
2.2.1    Quality Ratio....................................64
2.2.2    Convergent Condition Comparison..................66
2.2.3    Run time.........................................69
2.2.4    Confidence Interval..............................70
Chapter 6    Conclusion...............................74
Reference................................................76

                                

Ahmed, T., & Toki, A. (2016). A Review on Washing Machine Using Fuzzy Logic Controller. International Journal, 4(7).
Chamnanlor, C., Sethanan, K., Chien, C.-F., & Gen, M. (2014). Re-entrant flow shop scheduling problem with time windows using hybrid genetic algorithm based on auto-tuning strategy. International Journal of Production Research, 52(9), 2612-2629.
Cheng, R., Gen, M., & Tsujimura, Y. (1999). A tutorial survey of job-shop scheduling problems using genetic algorithms, part II: hybrid genetic search strategies. Computers & Industrial Engineering, 36(2), 343-364.
Collotta, M., Bello, L. L., & Pau, G. (2015). A novel approach for dynamic traffic lights management based on Wireless Sensor Networks and multiple fuzzy logic controllers. Expert Systems with Applications, 42(13), 5403-5415.
Ghiani, G., Laporte, G., & Musmanno, R. (2004). Introduction to logistics systems planning and control: John Wiley & Sons.
Holland, J. H. (1975). Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press.
Lai, J.-P. (2006). Surrogate search: A simulation optimization methodology for large-scale systems. University of Pittsburgh.
Lee, K., Kim, B. S., & Joo, C. M. (2012). Genetic algorithms for door-assigning and sequencing of trucks at distribution centers for the improvement of operational performance. Expert Systems with Applications, 39(17), 12975-12983.
Lim, A., Ma, H., & Miao, Z. (2006). Truck dock assignment problem with time windows and capacity constraint in transshipment network through crossdocks. Paper presented at the International Conference on Computational Science and Its Applications.
Lin, L., & Gen, M. (2009). Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation. Soft Computing, 13(2), 157-168.
Masel, D. (1998). Adapting the longest processing time heuristic for output station assignment in a transfer facility. Paper presented at the Proceedings of the 1998 Industrial Engineering Research Conference.
Masel, D., & Goldsmith, D. (1997). Using a simulation model to evaluate the configuration of a sortation facility. Paper presented at the Winter simulation conference.
McWilliams, D. L. (2009a). A dynamic load-balancing scheme for the parcel hub-scheduling problem. Computers & Industrial Engineering, 57(3), 958-962.
McWilliams, D. L. (2009b). Genetic-based scheduling to solve the parcel hub scheduling problem. Computers & Industrial Engineering, 56(4), 1607-1616.
McWilliams, D. L. (2010a). A genetic based scheduling algorithm for the PHSP with unequal batch size inbound trailers. Journal of Industrial and Systems Engineering, 4(3), 167-182.
McWilliams, D. L. (2010b). Iterative improvement to solve the parcel hub scheduling problem. Computers & Industrial Engineering, 59(1), 136-144.
McWilliams, D. L., & McBride, M. E. (2012). A beam search heuristics to solve the parcel hub scheduling problem. Computers & Industrial Engineering, 62(4), 1080-1092.
McWilliams, D. L., Stanfield, P. M., & Geiger, C. D. (2005). The parcel hub scheduling problem: A simulation-based solution approach. Computers & Industrial Engineering, 49(3), 393-412.
McWilliams, D. L., Stanfield, P. M., & Geiger, C. D. (2008). Minimizing the completion time of the transfer operations in a central parcel consolidation terminal with unequal-batch-size inbound trailers. Computers & Industrial Engineering, 54(4), 709-720.
Miao, Z., Lim, A., & Ma, H. (2009). Truck dock assignment problem with operational time constraint within crossdocks. European Journal of Operational Research, 192(1), 105-115.
Ng, W., Mak, K., & Zhang, Y. (2007). Scheduling trucks in container terminals using a genetic algorithm. Engineering Optimization, 39(1), 33-47.
Ombuki, B. M., & Ventresca, M. (2004). Local search genetic algorithms for the job shop scheduling problem. Applied Intelligence, 21(1), 99-109.
Rohrer, M. (1995). Simulation and cross docking. Paper presented at the Proceedings of the 27th conference on Winter simulation.
Sabzi, H. Z., Humberson, D., Abudu, S., & King, J. P. (2016). Optimization of adaptive fuzzy logic controller using novel combined evolutionary algorithms, and its application in Diez Lagos flood controlling system, Southern New Mexico. Expert Systems with Applications, 43, 154-164.
Song, Y., Wang, G., Wang, P., & Johns, A. (1997). Environmental/economic dispatch using fuzzy logic controlled genetic algorithms. IEE Proceedings-Generation, Transmission and Distribution, 144(4), 377-382.
Tasan, S. O., & Tunali, S. (2008). A review of the current applications of genetic algorithms in assembly line balancing. Journal of Intelligent Manufacturing, 19(1), 49-69.
Werner, F. (2011). Genetic algorithms for shop scheduling problems: a survey. Preprint, 11, 31.
Yun, Y., & Gen, M. (2003). Performance analysis of adaptive genetic algorithms with fuzzy logic and heuristics. Fuzzy optimization and decision making, 2(2), 161-175.

簡易檢索 / 詳目顯示

相關論文