具機台合不兼容系列時間窗口限制與適度決定之平行批次處理問題

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：20

、訪客IP：18.219.189.247

姓名

阮恆江英(Nguyen Hang Giang Anh) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

具機台合不兼容系列時間窗口限制與適度決定之平行批次處理問題
(Scheduling Parallel Batch Processing Machines with Incompatible Families, Time Window Con-straints and Machine Eligibility Determination)

相關論文

★ 以類神經網路探討晶圓測試良率預測與重測指標值之建立	★ 六標準突破性策略—企業管理議題
★ 限制驅導式在製罐產業生產管理之應用研究	★ 應用倒傳遞類神經網路於TFT-LCD G4.5代Cell廠不良問題與解決方法之研究
★ 限制驅導式生產排程在PCBA製程的運用	★ 平衡計分卡規劃與設計之研究-以海軍後勤支援指揮部修護工廠為例
★ 木製框式車身銷售數量之組合預測研究	★ 導入符合綠色產品RoHS之供應商管理-以光通訊產業L公司為例
★ 不同產品及供應商屬性對採購要求之相關性探討－以平面式觸控面板產業為例	★ 中長期產銷規劃之個案探討 -以抽絲產業為例
★ 消耗性部品存貨管理改善研究-以某邏輯測試公司之Socket Pin為例	★ 封裝廠之機台當機修復順序即時判別機制探討
★ 客戶危害限用物質規範研究-以TFT-LCD產業個案公司為例	★ PCB壓合代工業導入ISO/TS16949品質管理系統之研究-以K公司為例
★ 報價流程與價格議價之研究–以機殼產業為例	★ 產品量產前工程變更的分類機制與其可控制性探討-以某一手機產品家族為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2027-1-1以後開放)

摘要(中)

本研究考慮了一個並行批量處理機器的問題，以在任意批量、不兼容系列、開始時間窗口的約束和適度決定下達到最小化製造時間。本研究首先通過混合整數程序化模型來格式化問題，並且也提供了所研究問題的下界。但因爲研究問題為NP-Hard且因應實務上須能解決大的問題，本研究亦將發展了一種基於分解的啟發式算法和進化算法，以便在計算時間作為一個側重點時獲得大規模問題的近似最優解。二維節約函數被引入來量化時間和容量空間被浪費的值。對於遺傳算法，我們提出了用於編碼的二維矩陣和一維表示，以及適當的二維交叉和突變以產生後代。此外，此遺傳算法旨在改善基於已開發分解的啟發式算法的解決方案品質，該啟發式被用作已開發遺傳算法的初始解。計算實驗表明，所提出的啟發式算法對於小規模問題的執行表現良好，並且可以在合理的計算時間內有效地處理大規模問題。此外，計算結果還表明，本研究提出的啟發式算法在答案品質 (Solution quality) 方面優於文獻中現有的啟發式算法。

摘要(英)

This study considers a parallel batch processing machines problem to minimize the makespan under constraints of arbitrary lot sizes, incompatible families, start time windows, and machine eligibility determination. We first formulate the problem by a mixed-integer programming model and a lower bound for the studied problem is also provided. Due to the NP-hardness of the problem, we then develop a decomposition-based heuristic and an evolutionary algorithm to obtain a near-optimal solution for large-scale problems when computational time is a concern. A two-dimensional saving function is introduced to quantify the value of time and capacity space wasted. For the genetic algorithm, we propose a two-dimensional matrix and one-dimensional representation for encoding, and appropriate two-dimensional crossovers as well as mutations to generate offspring. In addition, the genetic algorithm aims to improve the quality of the solution found by the developed decomposition-based heuristic which is used as an initial solution for the developed genetic algorithm. Computational experiments show that the proposed heuristic algorithms perform well for small-size problems and can deal with large-scale problems efficiently within a reasonable computational time. Moreover, computational results also indicate that our proposed heuristics outperform an existing heuristic from the literature in terms of solution quality.

關鍵字(中)

★ 平行批次處理
★ 時間窗口限制
★ 機台合適度決定
★ 分解法
★ 儲蓄法
★ 遺傳算法

關鍵字(英)

★ Parallel batch processing machines
★ Time window constraints
★ Machine eligibility determination
★ Decomposition approach
★ Savings method
★ Genetic algorithm

論文目次

摘要 i
Abstract ii
Acknowledgments iii
Table of Contents iv
List of Figures vii
List of Tables viii
Chapter 1. Introduction 1
1.1. Background and Motivation 1
1.2. Problem definition 4
1.3. Research objectives 5
1.4. Research methodology and framework 6
1.4.1. Research methodology 6
1.4.2. Research framework 6
Chapter 2. Literature Review 8
2.1. Parallel batch processing machines with incompatible families 8
2.2. Machine eligibility 10
2.3. Time window constraints 11
2.4. Decomposition-based method 13
2.5. Savings method 13
2.6. Genetic algorithm approach to scheduling problems 14
Chapter 3. MIP model and Decomposition-based Heuristic Algorithm 16
3.1. Mixed-integer programming model 16
3.1.1. Notations 16
3.1.2. MIP model formulation 17
3.1.3. Analysis of the size of the MIP model 19
3.2. Lower bound 19
3.3. Decomposition-based heuristic algorithm 20
3.3.1. Batch formation phase 20
3.3.2. Batch scheduling phase 24
Chapter 4. Genetic Algorithm 31
4.1. Chromosome representation 33
4.2. Encoding scheme 33
4.2.1. Batch sub-chromosome 33
4.2.2. Machine sub-chromosome 36
4.3. Decoding scheme 37
4.4. Initial population and Parent selection strategy
38
4.5. Crossover Operator 39
4.5.1. Batch sub-chromosome crossover 39
4.5.2. Machine sub-chromosome crossover 44
4.6. Mutation Operator 46
4.6.1. Batch sub-chromosome mutation 46
4.6.2. Machine sub-chromosome mutation 47
4.7. Feasibility test 49
Chapter 5. Computational Experiment 50
5.1. Experiment design 50
5.2. Parameter setting 51
5.3. Experimental results 51
5.3.1. Small-size instances 51
5.3.2. Large-size instances 56
Chapter 6. Conclusions 59
Bibliographies 61
Appendix 69
Appendix A. Additional notations used in DH and GA algorithms. 69
Appendix B. An illustrative example to demonstrate how a chromosome is generated. 70
Appendix C. Decoding scheme demonstration 76
Appendix D. Batching vector determination 78
Appendix E. Demonstration of batch sub-chromosome mutation
79

參考文獻

1. Adianto, H., Riawan, A.I., Susanto, E., 2018. Determination of liquid product distribution route using clark and wright saving and tabu seacrh algorithm for a milk industry in indonesia. Int. J. Eng. Technol. 7, 102–105. https://doi.org/10.14419/ijet.v7i2.29.13138
2. Afzalirad, M., Rezaeian, J., 2016. Resource-constrained unrelated parallel machine scheduling problem with sequence dependent setup times, precedence constraints and machine eligibility restrictions. Comput. Ind. Eng. 98, 40–52. https://doi.org/10.1016/j.cie.2016.05.020
3. Almeder, C., Mönch, L., 2011. Metaheuristics for scheduling jobs with incompatible families on parallel batching machines. J. Oper. Res. Soc. 62, 2083–2096. https://doi.org/10.1057/jors.2010.186
4. Arroyo, J.E.C., Leung, J.Y.T., 2017a. An effective iterated greedy algorithm for scheduling unrelated parallel batch machines with non-identical capacities and unequal ready times. Comput. Ind. Eng. 105, 84–100. https://doi.org/10.1016/j.cie.2016.12.038
5. Arroyo, J.E.C., Leung, J.Y.T., 2017b. Scheduling unrelated parallel batch processing machines with non-identical job sizes and unequal ready times. Comput. Oper. Res. 78, 117–128. https://doi.org/10.1016/j.cor.2016.08.015
6. Arroyo, J.E.C., Leung, J.Y.T., Tavares, R.G., 2019. An iterated greedy algorithm for total flow time minimization in unrelated parallel batch machines with unequal job release times. Eng. Appl. Artif. Intell. 77, 239–254. https://doi.org/10.1016/j.engappai.2018.10.012
7. Aytug, H., Khouja, M., Vergara, F.E., 2003. Use of genetic algorithms to solve production and operations management problems: A review. Int. J. Prod. Res. 41, 3955–4009. https://doi.org/10.1080/00207540310001626319
8. Balasubramanian, H., Mönch, L., Fowler, J., Pfund, M., 2004. Genetic algorithm based scheduling of parallel batch machines with incompatible job families to minimize total weighted tardiness. Int. J. Prod. Res. 42, 1621–1638. https://doi.org/10.1080/00207540310001636994
9. Bard, J.F., Rojanasoonthon, S., 2006. A branch-and-price algorithm for parallel machine scheduling with time windows and job priorities. Nav. Res. Logist. 53, 24–44. https://doi.org/10.1002/nav.20118
10. Bean, J.C., 1994. Genetic Algorithms and Random Keys for Sequencing and Optimization. ORSA J. Comput. 6, 154–160. https://doi.org/10.1287/ijoc.6.2.154
11. Bektur, G., Saraç, T., 2019. A mathematical model and heuristic algorithms for an unrelated parallel machine scheduling problem with sequence-dependent setup times, machine eligibility restrictions and a common server. Comput. Oper. Res. 103, 46–63. https://doi.org/10.1016/j.cor.2018.10.010
12. Bilyk, A., Mönch, L., Almeder, C., 2014. Scheduling jobs with ready times and precedence constraints on parallel batch machines using metaheuristics. Comput. Ind. Eng. 78, 175–185. https://doi.org/10.1016/j.cie.2014.10.008
13. Brucker, P., Gladky, A., Hoogeveen, H., Kovalyov, M.Y., Potts, C.N., Tautenhahn, T., Van De Velde, S.L., 1998. Scheduling a batching machine. J. Sched. 1, 31–54. https://doi.org/10.1002/(SICI)1099-1425(199806)1:1<31::AID-JOS4>3.0.CO;2-R
14. Brucker, P., Kravchenko, S.A., 2008. Scheduling jobs with equal processing times and time windows on identical parallel machines. J. Sched. 11, 229–237. https://doi.org/10.1007/s10951-008-0063-y
15. Çatay, B., 2010. A new saving-based ant algorithm for the Vehicle Routing Problem with simultaneous Pickup and Delivery. Expert Syst. Appl. 37, 6809–6817. https://doi.org/10.1016/j.eswa.2010.03.045
16. Centeno, G., Armacost, R.L., 2004. Minimizing makespan on parallel machines with release time and machine eligibility restrictions. Int. J. Prod. Res. 42, 1243–1256. https://doi.org/10.1080/00207540310001631584
17. Centeno, G., Armacost, R.L., 1997. Parallel machine scheduling with release time and machine eligibility restrictions. Comput. Ind. Eng. 33, 273–276. https://doi.org/10.1016/s0360-8352(97)00091-0
18. Chan, F.T.S., Wong, T.C., Chan, L.Y., 2006. Flexible job-shop scheduling problem under resource constraints. Int. J. Prod. Res. 44, 2071–2089. https://doi.org/10.1080/00207540500386012
19. Chang, P.Y., Damodaran, P., Melouk, S., 2004. Minimizing makespan on parallel batch processing machines. Int. J. Prod. Res. 42, 4211–4220. https://doi.org/10.1080/00207540410001711863
20. Chen, H., Du, B., Huang, G.Q., 2011. Scheduling a batch processing machine with non-identical job sizes: A clustering perspective. Int. J. Prod. Res. 49, 5755–5778. https://doi.org/10.1080/00207543.2010.512620
21. Chen, H., Zhou, S., Li, X., Xu, R., 2014. A hybrid differential evolution algorithm for a two-stage flow shop on batch processing machines with arbitrary release times and blocking. Int. J. Prod. Res. 52, 5714–5734. https://doi.org/10.1080/00207543.2014.910625
22. Cheng, B., Yang, S., Hu, X., Chen, B., 2012. Minimizing makespan and total completion time for parallel batch processing machines with non-identical job sizes. Appl. Math. Model. 36, 3161–3167. https://doi.org/10.1016/j.apm.2011.09.061
23. Chiang, T.C., Cheng, H.C., Fu, L.C., 2010. A memetic algorithm for minimizing total weighted tardiness on parallel batch machines with incompatible job families and dynamic job arrival. Comput. Oper. Res. 37, 2257–2269. https://doi.org/10.1016/j.cor.2010.03.017
24. Chou, F. Der, 2007. A joint GA+DP approach for single burn-in oven scheduling problems with makespan criterion. Int. J. Adv. Manuf. Technol. 35, 587–595. https://doi.org/10.1007/s00170-006-0738-5
25. Chung, S.H., Tai, Y.T., Pearn, W.L., 2009. Minimising makespan on parallel batch processing machines with non-identical ready time and arbitrary job sizes. Int. J. Prod. Res. 47, 5109–5128. https://doi.org/10.1080/00207540802010807
26. Clarke, G., Wright, J.W., 1964. Scheduling of Vehicles from a Central Depot to a Number of Delivery Points. Oper. Res. 12, 568–581. https://doi.org/10.1287/opre.12.4.568
27. Dai, Z., Gao, K., Giri, B.C., 2020. A hybrid heuristic algorithm for cyclic inventory-routing problem with perishable products in VMI supply chain. Expert Syst. Appl. 153, 113322. https://doi.org/10.1016/j.eswa.2020.113322
28. Damodaran, P., Chang, P.Y., 2008. Heuristics to minimize makespan of parallel batch processing machines. Int. J. Adv. Manuf. Technol. 37, 1005–1013. https://doi.org/10.1007/s00170-007-1042-8
29. Damodaran, P., Kumar Manjeshwar, P., Srihari, K., 2006. Minimizing makespan on a batch-processing machine with non-identical job sizes using genetic algorithms. Int. J. Prod. Econ. 103, 882–891. https://doi.org/10.1016/j.ijpe.2006.02.010
30. Damodaran, P., Vélez-Gallego, M.C., 2012. A simulated annealing algorithm to minimize makespan of parallel batch processing machines with unequal job ready times. Expert Syst. Appl. 39, 1451–1458. https://doi.org/10.1016/j.eswa.2011.08.029
31. Damodaran, P., Vélez-Gallego, M.C., Maya, J., 2011. A GRASP approach for makespan minimization on parallel batch processing machines. J. Intell. Manuf. 22, 767–777. https://doi.org/10.1007/s10845-009-0272-z
32. Gabrel, V., 1995. Scheduling jobs within time windows on identical parallel machines: New model and algorithms. Eur. J. Oper. Res. 83, 320–329. https://doi.org/10.1016/0377-2217(95)00010-N
33. Garcia, J.M., Lozano, S., 2005. Production and delivery scheduling problem with time windows. Comput. Ind. Eng. 48, 733–742. https://doi.org/10.1016/j.cie.2004.12.004
34. Gokhale, R., Mathirajan, M., 2012. Scheduling identical parallel machines with machine eligibility restrictions to minimize total weighted flowtime in automobile gear manufacturing. Int. J. Adv. Manuf. Technol. 60, 1099–1110. https://doi.org/10.1007/s00170-011-3653-3
35. Graham, R.L., Lawler, E.L., Lenstra, J.K., Kan, A.H.G.R., 1979. Optimization and approximation in deterministic sequencing and scheduling: A survey. Ann. Discret. Math. https://doi.org/10.1016/S0167-5060(08)70356-X
36. Holland., J.H., 1975. Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
37. Huang, R.H., Yang, C.L., Huang, H.T., 2010. Parallel machine scheduling with common due windows. J. Oper. Res. Soc. 61, 640–646. https://doi.org/10.1057/jors.2008.184
38. Hungerländer, P., Truden, C., 2018. Efficient and Easy-to-Implement Mixed-Integer Linear Programs for the Traveling Salesperson Problem with Time Windows. Transp. Res. Procedia 30, 157–166. https://doi.org/10.1016/j.trpro.2018.09.018
39. Hwang, H.C., Chang, S.Y., Lee, K., 2004. Parallel machine scheduling under a grade of service provision. Comput. Oper. Res. 31, 2055–2061. https://doi.org/10.1016/S0305-0548(03)00164-3
40. Ikura, Y., Gimple, M., 1986. Efficient scheduling algorithms for a single batch processing machine. Oper. Res. Lett. 5, 61–65. https://doi.org/10.1016/0167-6377(86)90104-5
41. Jabbarizadeh, F., Zandieh, M., Talebi, D., 2009. Hybrid flexible flowshops with sequence-dependent setup times and machine availability constraints. Comput. Ind. Eng. 57, 949–957. https://doi.org/10.1016/j.cie.2009.03.012
42. Jeřábek, K., Majercak, P., Kliestik, T., Valaskova, K., 2016. Clark Wright algoritam modela uštede koji se koristi kod rješavanja problema usmjeravanja u logistici opskrbe. Nase More 63, 115–119. https://doi.org/10.17818/NM/2016/SI7
43. Ji, P., Sze, M.T., Lee, W.B., 2001. Genetic algorithm of determining cycle time for printed circuit board assembly lines. Eur. J. Oper. Res. 128, 175–184. https://doi.org/10.1016/S0377-2217(99)00348-3
44. Jia, Z.H., Leung, J.Y.T., 2014. An improved meta-heuristic for makespan minimization of a single batch machine with non-identical job sizes. Comput. Oper. Res. 46, 49–58. https://doi.org/10.1016/j.cor.2014.01.001
45. Jia, Z.H., Li, K., Leung, J.Y.T., 2015. Effective heuristic for makespan minimization in parallel batch machines with non-identical capacities. Int. J. Prod. Econ. 169, 1–10. https://doi.org/10.1016/j.ijpe.2015.07.021
46. Jia, Z.H., Wang, C., Leung, J.Y.T., 2016. An ACO algorithm for makespan minimization in parallel batch machines with non-identical job sizes and incompatible job families. Appl. Soft Comput. J. 38, 395–404. https://doi.org/10.1016/j.asoc.2015.09.056
47. Kashan, A.H., Karimi, B., Jenabi, M., 2008. A hybrid genetic heuristic for scheduling parallel batch processing machines with arbitrary job sizes. Comput. Oper. Res. 35, 1084–1098. https://doi.org/10.1016/j.cor.2006.07.005
48. Khouja, M., Michalewicz, Z., Satoskar, S.S., 2000. A comparison between genetic algorithms and the rand method for solving the joint replenishment problem. Prod. Plan. Control 11, 556–564. https://doi.org/10.1080/095372800414115
49. Kimms, A., 1999. A genetic algorithm for multi-level, multi-machine lot sizing and scheduling. Comput. Oper. Res. 26, 829–848. https://doi.org/10.1016/S0305-0548(98)00089-6
50. Klemmt, A., Weigert, G., Almeder, C., Mönch, L., 2009. A comparison of MIP-based decomposition techniques and VNS approaches for batch scheduling problems. Proc. - Winter Simul. Conf. 1686–1694. https://doi.org/10.1109/WSC.2009.5429173
51. Koh, S.G., Koo, P.H., Ha, J.W., Lee, W.S., 2004. Scheduling parallel batch processing machines with arbitrary job sizes and incompatible job families. Int. J. Prod. Res. 42, 4091–4107. https://doi.org/10.1080/00207540410001704041
52. Lee, H., Maravelias, C.T., 2017. Discrete-time mixed-integer programming models for short-term scheduling in multipurpose environments. Comput. Chem. Eng. 107, 171–183. https://doi.org/10.1016/j.compchemeng.2017.06.013
53. Li, Y., Chen, J., Cai, X., 2007. Heuristic genetic algorithm for capacitated production planning problems with batch processing and remanufacturing. Int. J. Prod. Econ. 105, 301–317. https://doi.org/10.1016/j.ijpe.2004.11.017
54. Li, Y., Lim, A., Rodrigues, B., 2004. Crossdocking - JIT scheduling with time windows. J. Oper. Res. Soc. 55, 1342–1351. https://doi.org/10.1057/palgrave.jors.2601812
55. Li, Z., Chen, H., Xu, R., Li, X., 2015. Earliness-tardiness minimization on scheduling a batch processing machine with non-identical job sizes. Comput. Ind. Eng. 87, 590–599. https://doi.org/10.1016/j.cie.2015.06.008
56. Liao, L.W., Sheen, G.J., 2008. Parallel machine scheduling with machine availability and eligibility constraints. Eur. J. Oper. Res. 184, 458–467. https://doi.org/10.1007/s00170-006-0810-1
57. Lin, Y., Li, W., 2004. Parallel machine scheduling of machine-dependent jobs with unit-length. Eur. J. Oper. Res. 156, 261–266. https://doi.org/10.1016/S0377-2217(02)00914-1
58. Malve, S., Uzsoy, R., 2007. A genetic algorithm for minimizing maximum lateness on parallel identical batch processing machines with dynamic job arrivals and incompatible job families. Comput. Oper. Res. 34, 3016–3028. https://doi.org/10.1016/j.cor.2005.11.011
59. Mathirajan, M., Sivakumar, A.I., 2006. A literature review, classification and simple meta-analysis on scheduling of batch processors in semiconductor. Int. J. Adv. Manuf. Technol. 29, 990–1001. https://doi.org/10.1007/s00170-005-2585-1
60. Mehta, S. V., Uzsoy, R., 1998. Minimizing total tardiness on a batch processing machine with incompatible job families. IIE Trans. (Institute Ind. Eng. 30, 165–178. https://doi.org/10.1080/07408179808966448
61. Mönch, L., Balasubramanian, H., Fowler, J.W., Pfund, M.E., 2005. Heuristic scheduling of jobs on parallel batch machines with incompatible job families and unequal ready times. Comput. Oper. Res. 32, 2731–2750. https://doi.org/10.1016/j.cor.2004.04.001
62. Mönch, L., Fowler, J.W., Mason, S.J., 2012. Production planning and control for semiconductor wafer fabrication facilities: modeling, analysis, and systems. Springer Science & Business Media.
63. Montero, A., Méndez-Díaz, I., Miranda-Bront, J.J., 2017. An integer programming approach for the time-dependent traveling salesman problem with time windows. Comput. Oper. Res. 88, 280–289. https://doi.org/10.1016/j.cor.2017.06.026
64. Norman, B.A., Bean, J.C., 1999. A Genetic Algorithm Methodology for Complex Scheduling Problems. Nav. Res. Logist. 46, 199–211. https://doi.org/10.1002/(SICI)1520-6750(199903)46:2<199::AID-NAV5>3.0.CO;2-L
65. Örnek, A., Özpeynirci, S., Öztürk, C., 2010. A note on “A mixed integer programming model for advanced planning and scheduling (APS).” Eur. J. Oper. Res. 203, 784–785. https://doi.org/10.1016/j.ejor.2009.09.025
66. Ozturk, O., Begen, M.A., Zaric, G.S., 2014. A branch and bound based heuristic for makespan minimization of washing operations in hospital sterilization services. Eur. J. Oper. Res. 239, 214–226. https://doi.org/10.1016/j.ejor.2014.05.014
67. Pamosoaji, A.K., Dewa, P.K., Krisnanta, J.V., 2019. Proposed Modified Clarke-Wright Saving Algorithm for Capacitated Vehicle Routing Problem. Int. J. Ind. Eng. Eng. Manag. 1, 9. https://doi.org/10.24002/ijieem.v1i1.2292
68. Pan, Q.K., Tasgetiren, M.F., Liang, Y.C., 2007. A discrete differential evolution algorithm for the permutation flowshop scheduling problem. Proc. GECCO 2007 Genet. Evol. Comput. Conf. 126–133. https://doi.org/10.1145/1276958.1276976
69. Pearn, W.L., Hong, J.S., Tai, Y.T., 2013. The burn-in test scheduling problem with batch dependent processing time and sequence dependent setup time. Int. J. Prod. Res. 51, 1694–1706. https://doi.org/10.1080/00207543.2012.694488
70. Perez-Gonzalez, P., Fernandez-Viagas, V., Zamora García, M., Framinan, J.M., 2019. Constructive heuristics for the unrelated parallel machines scheduling problem with machine eligibility and setup times. Comput. Ind. Eng. 131, 131–145. https://doi.org/10.1016/j.cie.2019.03.034
71. Pinedo, M., Hadavi, K., 1992. Scheduling: Theory, Algorithms and Systems Development. Oper. Res. Proc. 1991 35–42. https://doi.org/10.1007/978-3-642-46773-8_5
72. Pinedo, M.L., 1995. Scheduling: Theory, algorithms, and systems, Scheduling: Theory, Algorithms, and Systems. https://doi.org/10.1007/978-0-387-78935-4
73. Potts, C.N., Kovalyov, M.Y., 2000. Scheduling with batching: a review. Eur. J. Oper. Res. 120, 228–249. https://doi.org/10.1016/S0377-2217(99)00153-8
74. Reichelt, D., Mönch, L., 2006. Multiobjective scheduling of jobs with incompatible families on parallel batch machines. Eur. Conf. Evol. Comput. Comb. Optim. 209–221. https://doi.org/10.1007/11730095_18
75. Segerstedt, A., 2014. A simple heuristic for vehicle routing-A variant of Clarke and Wright’s saving method. Int. J. Prod. Econ. 157, 74–79. https://doi.org/10.1016/j.ijpe.2013.09.017
76. Sheen, G.J., Liao, L.W., 2007. A branch and bound algorithm for the one-machine scheduling problem with minimum and maximum time lags. Eur. J. Oper. Res. 181, 102–116. https://doi.org/10.1016/j.ejor.2006.06.003
77. Sheremetov, L., Martínez-Muñoz, J., Chi-Chim, M., 2018. Two-stage genetic algorithm for parallel machines scheduling problem: Cyclic steam stimulation of high viscosity oil reservoirs. Appl. Soft Comput. J. 64, 317–330. https://doi.org/10.1016/j.asoc.2017.12.021
78. Shirvani, N., Ruiz, R., Shadrokh, S., 2014. Cyclic scheduling of perishable products in parallel machine with release dates, due dates and deadlines. Int. J. Prod. Econ. 156, 1–12. https://doi.org/10.1016/j.ijpe.2014.04.013
79. Shubin, X., Bean, J.C., 2007. A genetic algorithm for scheduling parallel non-identical batch processing machines. Proc. 2007 IEEE Symp. Comput. Intell. Sched. CI-Sched 2007 143–150. https://doi.org/10.1109/SCIS.2007.367682
80. Snyder, L. V., Daskin, M.S., 2006. A random-key genetic algorithm for the generalized traveling salesman problem. Eur. J. Oper. Res. 174, 38–53. https://doi.org/10.1016/j.ejor.2004.09.057
81. Tarhini, A., Danach, K., Harfouche, A., 2020. Swarm intelligence-based hyper-heuristic for the vehicle routing problem with prioritized customers. Ann. Oper. Res. 1–22. https://doi.org/10.1007/s10479-020-03625-5
82. Uzsoy, R., 1995. Scheduling batch processing machines with incompatible job families. Int. J. Prod. Res. 33, 2685–2708. https://doi.org/10.1080/00207549508904839
83. Uzsoy, R., 1994. Scheduling a single batch processing machine with non-identical job sizes. Int. J. Prod. Res. 32, 1615–1635. https://doi.org/10.1080/00207549408957026
84. Vallada, E., Ruiz, R., 2011. A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times. Eur. J. Oper. Res. 211, 612–622. https://doi.org/10.1016/j.ejor.2011.01.011
85. Wang, I.L., Wang, Y.C., Chen, C.W., 2013. Scheduling unrelated parallel machines in semiconductor manufacturing by problem reduction and local search heuristics. Flex. Serv. Manuf. J. 25, 343–366. https://doi.org/10.1007/s10696-012-9150-7
86. Wang, S.Y., Sheen, G.J., Yeh, Y., 2015. Pricing and shelf space decisions with non-symmetric market demand. Int. J. Prod. Econ. 169, 233–239. https://doi.org/10.1016/j.ijpe.2015.08.012
87. Yu, C., Semeraro, Q., Matta, A., 2018. A genetic algorithm for the hybrid flow shop scheduling with unrelated machines and machine eligibility. Comput. Oper. Res. 100, 211–229. https://doi.org/10.1016/j.cor.2018.07.025
88. Zhang, G., Hu, Y., Sun, J., Zhang, W., 2020. An improved genetic algorithm for the flexible job shop scheduling problem with multiple time constraints. Swarm Evol. Comput. 54, 100664. https://doi.org/10.1016/j.swevo.2020.100664
89. Zhou, S., Chen, H., Xu, R., Li, X., 2014. Minimising makespan on a single batch processing machine with dynamic job arrivals and non-identical job sizes. Int. J. Prod. Res. 52, 2258–2274. https://doi.org/10.1080/00207543.2013.854937
90. Zhou, S., Liu, M., Chen, H., Li, X., 2016. An effective discrete differential evolution algorithm for scheduling uniform parallel batch processing machines with non-identical capacities and arbitrary job sizes. Int. J. Prod. Econ. 179, 1–11. https://doi.org/10.1016/j.ijpe.2016.05.014
91. Zhou, S., Xie, J., Du, N., Pang, Y., 2018. A random-keys genetic algorithm for scheduling unrelated parallel batch processing machines with different capacities and arbitrary job sizes. Appl. Math. Comput. 334, 254–268. https://doi.org/10.1016/j.amc.2018.04.024
92. Zhou, Z., Che, A., Yan, P., 2012. A mixed integer programming approach for multi-cyclic robotic flowshop scheduling with time window constraints. Appl. Math. Model. 36, 3621–3629. https://doi.org/10.1016/j.apm.2011.10.032

指導教授

沈國基(Gwo-Ji Sheen)

審核日期

2022-5-10

推文