Cost Factor Focused Scheduling and Sequencing: A Neoteric Literature Review
Abstract
The hastily emergent concern from researchers in the application of scheduling and sequencing has urged the necessity for analysis of the latest research growth to construct a new outline. This paper focuses on the literature on cost minimization as a primary aim in scheduling problems represented with less significance as a whole in the past literature reviews. The purpose of this paper is to have an intensive study to clarify the development of cost-based scheduling and sequencing (CSS) by reviewing the work published over several parameters for improving the understanding in this field. Various parameters, such as scheduling models, algorithms, industries, journals, publishers, publication year, authors, countries, constraints, objectives, uncertainties, computational time, and programming languages and optimization software packages are considered. In this research, the literature review of CSS is done for thirteen years (2010-2022). Although CSS research originated in manufacturing, it has been observed that CSS research publications also addressed case studies based on health, transportation, railway, airport, steel, textile, education, ship, petrochemical, inspection, and construction projects. A detailed evaluation of the literature is followed by significant information found in the study, literature analysis, gaps identification, constraints of work done, and opportunities in future research for the researchers and experts from the industries in CSS.
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Abadi, S. T. S., Tokmehdash, N. M., Hosny, A., Nik‐bakht, M. (2021). Bim‐based co‐simulation of fire and occupants’ behavior for safe construction rehabilitation planning. Fire, 4(4). https://doi.org/10.3390/fire4040067
Abdullah, S., Shamayleh, A., Ndiaye, M. (2019). Three stage dynamic heuristic for multiple plants capacitated lot sizing with sequence-dependent transient costs. Computers and Industrial Engineering, 127, 1024–1036. https://doi.org/10.1016/j.cie.2018.11.035
Abotaleb, I. S., El-adaway, I. H., Ibrahim, M. W., Hanna, A. S., Russell, J. S. (2020). Developing a Rating Score for Out-of-Sequence Construction. Journal of Management in Engineering, 36(3), 04020013. https://doi.org/10.1061/(asce)me.1943-5479.0000765
Agnetis, A., Nicosia, G., Pacifici, A., Pferschy, U. (2015). Scheduling two agent task chains with a central selection mechanism. Journal of Scheduling, 18(3), 243–261. https://doi.org/10.1007/s10951-014-0414-9 DOI: https://doi.org/10.1007/s10951-014-0414-9
Ait-Alla, A., Teucke, M., Lütjen, M., Beheshti-Kashi, S., Karimi, H. R. (2014). Robust production planning in fashion apparel industry under demand uncertainty via conditional value at risk. Mathematical Problems in Engineering, https://doi.org/10.1155/2014/901861 DOI: https://doi.org/10.1155/2014/901861
Al-Refaie, A., Abedalqader, H. (2022). Optimal berth scheduling and sequencing under unexpected events. Journal of the Operational Research Society, 73(2), 430–444. https://doi.org/10.1080/01605682.2020.1843981
Al-Refaie, A., Judeh, M., Chen, T. (2018a). Optimal multiple-period scheduling and sequencing of operating room and intensive care unit. Operational Research, 18(3), 645–670. https://doi.org/10.1007/s12351-016-0287-0 DOI: https://doi.org/10.1007/s12351-016-0287-0
Al-Refaie, A., Judeh, M., Li, M. H. (2018b). Optimal fuzzy scheduling and sequencing of multiple-period operating room. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, 32(1), 108–121. https://doi.org/10.1017/S0890060417000269 DOI: https://doi.org/10.1017/S0890060417000269
Alaghebandha, M., Naderi, B., Mohammadi, M. (2019). Economic lot sizing and scheduling in distributed permutation flow shops. Journal of Optimization in Industrial Engineering, 12(1), 103–117. https://doi.org/10.22094/JOIE.2018.542997.1510
Alfieri, A., Cantamessa, M. (2010). Optimising recipes in hybrid continuous-batch manufacturing systems: Application in the multi-layer plywood industry. International Journal of Production Research, 48(6), 1539–1556. https://doi.org/10.1080/00207540802616835 DOI: https://doi.org/10.1080/00207540802616835
Ali, A., Iqbal, M. M. (2022). A cost and energy efficient task scheduling technique to offload microservices based applications in mobile cloud computing. IEEE Access, 10, 46633–46651. https://doi.org/10.1109/ACCESS.2022.3170918
Álvarez-Gil, N., Álvarez García, S., Rosillo, R., de la Fuente, D. (2022). Sequencing jobs with asymmetric costs and transition constraints in a finishing line: A real case study. Computers and Industrial Engineering, 165. https://doi.org/10.1016/j.cie.2021.107908
Ardakani, A., Fei, J., Beldar, P. (2020). Truck-to-door sequencing in multi-door cross-docking system with dock repeat truck holding pattern. International Journal of Industrial Engineering Computations, 11(2), 201–220. https://doi.org/10.5267/j.ijiec.2019.10.001
Areal, J. J., Martín, R. M., Campos, J. G. (2011). Simulated annealing vs. genetic algorithms applied using a new cost function for the car sequencing problem. International Journal of Manufacturing Technology and Management, 23(1–2), 113–136. https://doi.org/10.1504/IJMTM.2011.042111 DOI: https://doi.org/10.1504/IJMTM.2011.042111
Armstrong, M., Galli, A. (2012). New approach to flexible open pit optimisation and scheduling. Transactions of the Institutions of Mining and Metallurgy, Section A: Mining Technology, 121(3), 132–138. https://doi.org/10.1179/1743286312Y.0000000008 DOI: https://doi.org/10.1179/1743286312Y.0000000008
Asad, M. W. A. (2011). A heuristic approach to long-range production planning of cement quarry operations. Production Planning and Control, 22(4), 353–364. https://doi.org/10.1080/09537287.2010.484819 DOI: https://doi.org/10.1080/09537287.2010.484819
Azari-Rad, S., Yontef, A., Aleman, D. M., Urbach, D. R. (2014). A simulation model for perioperative process improvement. Operations Research for Health Care, 3(1), 22–30. https://doi.org/10.1016/j.orhc.2013.12.003 DOI: https://doi.org/10.1016/j.orhc.2013.12.003
Baker, K. R. (2014). Minimizing earliness and tardiness costs in stochastic scheduling. European Journal of Operational Research, 236(2), 445–452. https://doi.org/10.1016/j.ejor.2013.12.011 DOI: https://doi.org/10.1016/j.ejor.2013.12.011
Baker, K. R., Scudder, G. D. (1990). Sequencing with earliness and tardiness penalties. A review. Operations Research, 38(1), 22–36. https://doi.org/10.1287/opre.38.1.22 DOI: https://doi.org/10.1287/opre.38.1.22
Baker, K. R., Trietsch, D. (2009). Principles of Sequencing and Scheduling. In Principles of Sequencing and Scheduling. https://doi.org/10.1002/9780470451793 DOI: https://doi.org/10.1002/9780470451793
Ballester N., Parikh P. J., Kong N., Peck J. (2022). Sequencing daily patient workload for an ancillary service provider. IEEE Transactions on Automation Science and Engineering, 19(1), 178-190. doi: 10.1109/TASE.2019.2896317.
Bari, P., Karande, P. (2021). Application of PROMETHEE-GAIA method to priority sequencing rules in a dynamic job shop for single machine. Materials Today: Proceedings, 46(17), 7258-7264 https://doi.org/10.1016/j.matpr.2020.12.854
Barlatt, A. Y., Cohn, A. M., Gusikhin, O. (2010). A hybridization of mathematical programming and dominance-driven enumeration for solving shift-selection and task-sequencing problems. Computers and Operations Research, 37(7), 1298–1307. https://doi.org/10.1016/j.cor.2009.09.013 DOI: https://doi.org/10.1016/j.cor.2009.09.013
Bayu, F., Panda, D., Shaik, M. A., Ramteke, M. (2020). Scheduling of gasoline blending and distribution using graphical genetic algorithm. Computers and Chemical Engineering, 133, 106636. https://doi.org/10.1016/j.compchemeng.2019.106636
Bhosale, K. C., Pawar, P. J. (2020). Production planning and scheduling problem of continuous parallel lines with demand uncertainty and different production capacities. Journal of Computational Design and Engineering, 7(6), 761–774. https://doi.org/10.1093/jcde/qwaa055
Biele, A., Mönch, L. (2019). Decomposition methods for cost and tardiness reduction in aircraft manufacturing flow lines. Computers and Operations Research, 103, 134–147. https://doi.org/10.1016/j.cor.2018.10.001
Boysen, N., Fliedner, M., Scholl, A. (2009). Sequencing mixed-model assembly lines: Survey, classification and model critique. European Journal of Operational Research, 192(2), 349–373. https://doi.org/10.1016/j.ejor.2007.09.013 DOI: https://doi.org/10.1016/j.ejor.2007.09.013
Braat, J., Hamers, H., Klijn, F., Slikker, M. (2019). A selfish allocation heuristic in scheduling: Equilibrium and inefficiency bound analysis. European Journal of Operational Research, 273(2), 634–645. https://doi.org/10.1016/j.ejor.2018.08.024
Bueno, L., Magatão, L., Arruda, L. V. R., Neves, F., Monteiro, A., Vaqueiro, J. P. (2020). Assigning and sequencing batches and blends of oil derivatives in a mesh-like pipeline network. Computers and Chemical Engineering, 139, 106894. https://doi.org/10.1016/j.compchemeng.2020.106894
Burger, A. P., Jacobs, C. G., van Vuuren, J. H., Visagie, S. E. (2015). Scheduling multi-colour print jobs with sequence-dependent setup times. Journal of Scheduling, 18(2), 131–145. https://doi.org/10.1007/s10951-014-0400-2 DOI: https://doi.org/10.1007/s10951-014-0400-2
Cafaro, D. C., Cerdá, J. (2010). Operational scheduling of refined products pipeline networks with simultaneous batch injections. Computers and Chemical Engineering, 34(10), 1687–1704. https://doi.org/10.1016/j.compchemeng.2010.03.005 DOI: https://doi.org/10.1016/j.compchemeng.2010.03.005
Campos, P. H. A., Cabral, I. E., Ortiz, C. E. A., Morales, N. (2018). Comparison between the application of the conventional mine planning and of the direct block scheduling on an open pit mine project. Revista Escola de Minas, 71(2), 269–274. https://doi.org/10.1590/0370-44672017710037 DOI: https://doi.org/10.1590/0370-44672017710037
Canca, D., De-Los-Santos, A., Laporte, G., Mesa, J. A. (2019). The railway rapid transit network construction scheduling problem. Computers and Industrial Engineering, 138, 106075. https://doi.org/10.1016/j.cie.2019.106075
Carvalho, D. M., Nascimento, M. C. V. (2022). Hybrid metaheuristics to solve the integrated lot sizing and scheduling problem on parallel machines with sequence-dependent and non-triangular setup. European Journal of Operational Research, 296(1), 158–173. https://doi.org/10.1016/j.ejor.2021.03.050
Cayo, P., Onal, S. (2020). A shifting bottleneck procedure with multiple objectives in a complex manufacturing environment. Production Engineering, 14(2), 177–190. https://doi.org/10.1007/s11740-019-00947-7
Cerdá, J., Pautasso, P. C., Cafaro, D. C. (2015). Efficient approach for scheduling crude oil operations in marine- Access refineries. Industrial and Engineering Chemistry Research, 54(33), 8219–8238. https://doi.org/10.1021/acs.iecr.5b01461 DOI: https://doi.org/10.1021/acs.iecr.5b01461
Çetinkaya, F. C., Duman, M. (2021). Scheduling with lot streaming in a two-machine re-entrant flow shop. Operational Research in Engineering Sciences: Theory and Applications, 4(3), 142–175. https://doi.org/10.31181/ORESTA111221142C DOI: https://doi.org/10.31181/oresta111221142c
Chaieb Memmi, I., Hammani Laaroussi, S. (2013). A new approach for solving capacitated lot sizing and scheduling problem with sequence and period-dependent setup costs. Journal of Industrial Engineering and Management, 6(4), 1027–1054. https://doi.org/10.3926/jiem.707 DOI: https://doi.org/10.3926/jiem.707
Chan, F. T. S., Choy, K. L., Bibhushan. (2011). A genetic algorithm-based scheduler for multiproduct parallel machine sheet metal job shop. Expert Systems with Applications, 38(7), 8703–8715. https://doi.org/10.1016/j.eswa.2011.01.078 DOI: https://doi.org/10.1016/j.eswa.2011.01.078
Chen, R. R., Robinson, L. W. (2014). Sequencing and scheduling appointments with potential call-in patients. Production and Operations Management, 23(9), 1522–1538. https://doi.org/10.1111/poms.12168 DOI: https://doi.org/10.1111/poms.12168
Cheng, T. C. E. (1991). Optimal assignment of slack due-dates and sequencing of jobs with random processing times on a single machine. European Journal of Operational Research, 51(3), 348–353. https://doi.org/10.1016/0377-2217(91)90310-R DOI: https://doi.org/10.1016/0377-2217(91)90310-R
Choi, S., Wilhelm, W. E. (2020). Sequencing in an appointment system with deterministic arrivals and non-identical exponential service times. Computers and Operations Research, 117, 104901. https://doi.org/10.1016/j.cor.2020.104901
Choi, S., Wilhelm, W. E. (2012). An analysis of sequencing surgeries with durations that follow the lognormal, gamma, or normal distribution. IIE Transactions on Healthcare Systems Engineering, 2(2), 156–171. https://doi.org/10.1080/19488300.2012.684272 DOI: https://doi.org/10.1080/19488300.2012.684272
Chou, F. N. F., Lee, H. C., Yeh, W. W. G. (2013). Effectiveness and Efficiency of Scheduling Regional Water Resources Projects. Water Resources Management, 27(3), 665–693. https://doi.org/10.1007/s11269-012-0208-9 DOI: https://doi.org/10.1007/s11269-012-0208-9
Corry, P., Bierwirth, C. (2019). The berth allocation problem with channel restrictions. Transportation Science, 53(3), 708–727. https://doi.org/10.1287/trsc.2018.0865
Costa, A., Fichera, S., Cappadonna, F. A. (2013). A genetic algorithm for scheduling both job families and skilled workforce. International Journal of Operations and Quantitative Management, 19(4), 221–247.
Cui, Y., Cao, K., Li, L., Zhou, J., Wei, T., Hu, S. (2020). Augmented Cross-Entropy-Based Joint Temperature Optimization of Real-Time 3-D MPSoC Systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(10), 1987–1999. https://doi.org/10.1109/TCAD.2019.2939328
Czibula, O. G., Gu, H., Hwang, F. J., Kovalyov, M. Y., Zinder, Y. (2016). Bi-criteria sequencing of courses and formation of classes for a bottleneck classroom. Computers and Operations Research, 65, 53–63. https://doi.org/10.1016/j.cor.2015.06.010 DOI: https://doi.org/10.1016/j.cor.2015.06.010
Dang, Q. V., Singh, N., Adan, I., Martagan, T., van de Sande, D. (2021). Scheduling heterogeneous multi-load AGVs with battery constraints. Computers and Operations Research, 136, 105517. https://doi.org/10.1016/j.cor.2021.105517
De Maere, G., Atkin, J. A. D., Burke, E. K. (2018). Pruning rules for optimal runway sequencing. Transportation Science, 52(4), 898–916. https://doi.org/10.1287/trsc.2016.0733 DOI: https://doi.org/10.1287/trsc.2016.0733
Deceuninck, M., Fiems, D., De Vuyst, S. (2018). Outpatient scheduling with unpunctual patients and no-shows. European Journal of Operational Research, 265(1), 195–207. https://doi.org/10.1016/j.ejor.2017.07.006 DOI: https://doi.org/10.1016/j.ejor.2017.07.006
Djassemi, M., Seifoddini, H. (2019). Analysis of critical machine reliability in manufacturing cells. Journal of Industrial Engineering and Management, 12(1), 70–82. https://doi.org/10.3926/jiem.2757
Domínguez-Martín, B., Rodríguez-Martín, I., Salazar-González, J. J. (2017). An exact algorithm for a Vehicle-and-Driver Scheduling Problem. Computers and Operations Research, 81, 247–256. https://doi.org/10.1016/j.cor.2016.12.022 DOI: https://doi.org/10.1016/j.cor.2016.12.022
Dou, J., Su, C., Zhao, X. (2020). Mixed integer programming models for concurrent configuration design and scheduling in a reconfigurable manufacturing system. Concurrent Engineering Research and Applications, 28(1), 32–46. https://doi.org/10.1177/1063293X19898727
Durazo-Cardenas, I., Starr, A., Turner, C. J., Tiwari, A., Kirkwood, L., Bevilacqua, M., Tsourdos, A., Shehab, E., Baguley, P., Xu, Y., Emmanouilidis, C. (2018). An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition, planning and cost. Transportation Research Part C: Emerging Technologies, 89, 234–253. https://doi.org/10.1016/j.trc.2018.02.010 DOI: https://doi.org/10.1016/j.trc.2018.02.010
Eguia, I., Lozano, S., Racero, J., Guerrero, F. (2011). A methodological approach for designing and sequencing product families in Reconfigurable Disassembly Systems. Journal of Industrial Engineering and Management, 4(3), 418–435. https://doi.org/10.3926/jiem.2011.v4n3.p418-435 DOI: https://doi.org/10.3926/jiem.2011.v4n3.p418-435
Elyasi, A., Salmasi, N. (2013). Due date assignment in single machine with stochastic processing times. International Journal of Production Research, 51(8), 2352–2362. https://doi.org/10.1080/00207543.2012.737945 DOI: https://doi.org/10.1080/00207543.2012.737945
Eun, Y., Hwang, I., Bang, H. (2010). Optimal arrival flight sequencing and scheduling using discrete airborne delays. IEEE Transactions on Intelligent Transportation Systems, 11(2), 359–373. https://doi.org/10.1109/TITS.2010.2044791 DOI: https://doi.org/10.1109/TITS.2010.2044791
Faghihi, V., Reinschmidt, K. F., Kang, J. H. (2014). Construction scheduling using Genetic Algorithm based on Building Information Model. Expert Systems with Applications, 41(16), 7565–7578. https://doi.org/10.1016/j.eswa.2014.05.047 DOI: https://doi.org/10.1016/j.eswa.2014.05.047
Fang, K. T., Lin, B. M. T. (2013). Parallel-machine scheduling to minimize tardiness penalty and power cost. Computers and Industrial Engineering, 64(1), 224–234. https://doi.org/10.1016/j.cie.2012.10.002 DOI: https://doi.org/10.1016/j.cie.2012.10.002
Farhadi, F., Ghoniem, A., Al-Salem, M. (2014). Runway capacity management - An empirical study with application to Doha International Airport. Transportation Research Part E: Logistics and Transportation Review, 68, 53–63. https://doi.org/10.1016/j.tre.2014.05.004 DOI: https://doi.org/10.1016/j.tre.2014.05.004
Farmand, N., Zarei, H., Rasti-Barzoki, M. (2021). Two meta-heuristic algorithms for optimizing a multi-objective supply chain scheduling problem in an identical parallel machines environment. International Journal of Industrial Engineering Computations, 12(3), 249–272. https://doi.org/10.5267/j.ijiec.2021.3.002
Feng, C. W., Chen, Y. J., Huang, J. R. (2010). Using the MD CAD model to develop the time-cost integrated schedule for construction projects. Automation in Construction, 19(3), 347–356. https://doi.org/10.1016/j.autcon.2009.12.009 DOI: https://doi.org/10.1016/j.autcon.2009.12.009
Fernandez, S., Alvarez, S., Díaz, D., Iglesias, M., Ena, B. (2014). Scheduling a galvanizing line by ant colony optimization. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8667, 146–157. https://doi.org/10.1007/978-3-319-09952-1_13 DOI: https://doi.org/10.1007/978-3-319-09952-1_13
Ferro, G., Laureri, F., Minciardi, R., Robba, M. (2019). A predictive discrete event approach for the optimal charging of electric vehicles in microgrids. Control Engineering Practice, 86, 11–23. https://doi.org/10.1016/j.conengprac.2019.02.004
Fumero, Y., Corsano, G., Montagna, J. M. (2013). A Mixed Integer Linear Programming model for simultaneous design and scheduling of flowshop plants. Applied Mathematical Modelling, 37(4), 1652–1664. https://doi.org/10.1016/j.apm.2012.04.043 DOI: https://doi.org/10.1016/j.apm.2012.04.043
Fumero, Y., Montagna, J. M., Corsano, G. (2012). Simultaneous design and scheduling of a semicontinuous/batch plant for ethanol and derivatives production. Computers and Chemical Engineering, 36(1), 342–357. https://doi.org/10.1016/j.compchemeng.2011.08.004 DOI: https://doi.org/10.1016/j.compchemeng.2011.08.004
Gao, C., Qu, D. (2018). A modelling and a new hybrid MILP/CP decomposition method for parallel continuous galvanizing line scheduling problem. ISIJ International, 58(10), 1820–1827. https://doi.org/10.2355/isijinternational.ISIJINT-2018-305
Gao, Z., Bard, J. F., Chacon, R., Stuber, J. (2015). An assignment-sequencing methodology for scheduling assembly and test operations with multi-pass requirements. IIE Transactions (Institute of Industrial Engineers), 47(2), 153–172. https://doi.org/10.1080/0740817X.2014.917778 DOI: https://doi.org/10.1080/0740817X.2014.917778
Gao, Z., Sun, D., Zhao, R., Dong, Y. (2021). Ship-unloading scheduling optimization for a steel plant. Information Sciences, 544, 214–226. https://doi.org/10.1016/j.ins.2020.07.029
Gholipour-Kanani, Y., Tavakkoli-Moghaddam, R., Khorrami, A. (2011). Solving a multi-criteria group scheduling problem for a cellular manufacturing system by scatter search. Journal of the Chinese Institute of Industrial Engineers, 28(3), 192–205. https://doi.org/10.1080/10170669.2010.549663 DOI: https://doi.org/10.1080/10170669.2010.549663
Gifford, T., Opicka, T., Sinha, A., Brink, D. Vanden, Gifford, A., Randall, R. (2018). Dispatch optimization in bulk tanker transport operations. Interfaces, 48(5), 403–421. https://doi.org/10.1287/inte.2018.0956
Glazer, A., Hassin, R., Ravner, L. (2018). A strategic model of job arrivals to a single machine with earliness and tardiness penalties. IISE Transactions, 50(4), 265–278. https://doi.org/10.1080/24725854.2017.1395098 DOI: https://doi.org/10.1080/24725854.2017.1395098
Golmakani, H. R., Namazi, A. (2012). Multiple-route job shop scheduling with fixed periodic and age-dependent preventive maintenance to minimize makespan. Journal of Quality in Maintenance Engineering, 18(1), 60–78. https://doi.org/10.1108/13552511211226193 DOI: https://doi.org/10.1108/13552511211226193
Golmohammadi, D. (2013). A neural network decision-making model for job-shop scheduling. International Journal of Production Research, 51(17), 5142–5157. https://doi.org/10.1080/00207543.2013.793476 DOI: https://doi.org/10.1080/00207543.2013.793476
Gordon, V., Proth, J. M., Chu, C. (2002). A survey of the state-of-the-art of common due date assignment and scheduling research. European Journal of Operational Research, 139(1), 1–25. https://doi.org/10.1016/S0377-2217(01)00181-3 DOI: https://doi.org/10.1016/S0377-2217(01)00181-3
Grabenstetter, D. H., Usher, J. M. (2015). Sequencing jobs in an engineer-to-order engineering environment. Production and Manufacturing Research, 3(1), 201–217. https://doi.org/10.1080/21693277.2015.1035461 DOI: https://doi.org/10.1080/21693277.2015.1035461
Grigoriev, A., Kreuzen, V. J., Oosterwijk, T. (2021). Cyclic lot-sizing problems with sequencing costs. Journal of Scheduling, 24(2), 123–135. https://doi.org/10.1007/s10951-020-00645-8
Gu, H., Li, X., Lu, Z. (2021). Scheduling Spark Tasks with Data Skew and Deadline Constraints. IEEE Access, 9, 2793–2804. https://doi.org/10.1109/ACCESS.2020.3040719
Gul, S., Denton, B. T., Fowler, J. W., Huschka, T. (2011). Bi-criteria scheduling of surgical services for an outpatient procedure center. Production and Operations Management, 20(3), 406–417. https://doi.org/10.1111/j.1937-5956.2011.01232.x DOI: https://doi.org/10.1111/j.1937-5956.2011.01232.x
Gürel, S., Körpeoǧlu, E., Aktürk, M. S. (2010). An anticipative scheduling approach with controllable processing times. Computers and Operations Research, 37(6), 1002–1013. https://doi.org/10.1016/j.cor.2009.09.001 DOI: https://doi.org/10.1016/j.cor.2009.09.001
Guzman, E., Andres, B., Poler, R. (2022). Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model. Computers, 11(1), 1–16. https://doi.org/10.3390/computers11010001
Haddad, A., Taajobian, M., Jahanian, A. (2018). Massive parallel digital microuidic biochip architecture for automating large-scale biochemistry assays. Scientia Iranica, 25(6D), 3461–3474. https://doi.org/10.24200/sci.2018.20797
Haoran, Z., Yongtu, L., Qi, L., Yun, S., Xiaohan, Y. (2018). A self-learning approach for optimal detailed scheduling of multi-product pipeline. Journal of Computational and Applied Mathematics, 327, 41–63. https://doi.org/10.1016/j.cam.2017.05.040 DOI: https://doi.org/10.1016/j.cam.2017.05.040
Heath, S. K., Bard, J. F., Morrice, D. J. (2013). A GRASP for simultaneously assigning and sequencing product families on flexible assembly lines. Annals of Operations Research, 203(1), 295–323. https://doi.org/10.1007/s10479-012-1167-5 DOI: https://doi.org/10.1007/s10479-012-1167-5
Ho, T. Y., Zengt, J., Chakrabarty, K. (2010). Digital microfluidic biochips: A vision for functional diversity and more than Moore. IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD, 578–585. https://doi.org/10.1109/ICCAD.2010.5654199 DOI: https://doi.org/10.1109/ICCAD.2010.5654199
Hu, X., Wang, J., Leng, K. (2019). The Interaction between Critical Chain Sequencing, Buffer Sizing, and Reactive Actions in a CC/BM Framework. Asia-Pacific Journal of Operational Research, 36(3), 1–22. https://doi.org/10.1142/S0217595919500106
Huang, J. Y., Yao, M. J. (2013). On the optimal lot-sizing and scheduling problem in serial-type supply chain system using a time-varying lot-sizing policy. International Journal of Production Research, 51(3), 735–750. https://doi.org/10.1080/00207543.2012.662604 DOI: https://doi.org/10.1080/00207543.2012.662604
Hussain, M., Wei, L. F., Lakhan, A., Wali, S., Ali, S., Hussain, A. (2021). Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing. Sustainable Computing: Informatics and Systems, 30, 100517. https://doi.org/10.1016/j.suscom.2021.100517
Hussain, M., Wei, L.-F., Rehman, A., Abbas, F., Hussain, A., Ali, M. (2022). Deadline-constrained energy-aware workflow scheduling in geographically distributed cloud data centers. Future Generation Computer Systems, 132, 211-222, https://doi.org/10.1016/j.future.2022.02.018
Jafarnia-Jahromi, M., Jain, R. (2020). Non-indexability of the stochastic appointment scheduling problem. Automatica, 118, 109016. https://doi.org/10.1016/j.automatica.2020.109016
Kim, B., Youn, C. H., Park, Y. S., Lee, Y., Choi, W. (2012). An adaptive workflow scheduling scheme based on an estimated data processing rate for next generation sequencing in cloud computing. Journal of Information Processing Systems, 8(4), 555–566. https://doi.org/10.3745/JIPS.2012.8.4.555 DOI: https://doi.org/10.3745/JIPS.2012.8.4.555
Kobayashi, M. (2021). Application of the surrogate gradient method for a multi-item single-machine dynamic lot size scheduling problem. SN Applied Sciences, 3(7). https://doi.org/10.1007/s42452-021-04669-3
Kong, W., Lei, Y., Ma, J. (2016). Virtual machine resource scheduling algorithm for cloud computing based on auction mechanism. Optik, 127(12), 5099–5104. https://doi.org/10.1016/j.ijleo.2016.02.061 DOI: https://doi.org/10.1016/j.ijleo.2016.02.061
Kopanos, G. M., Puigjaner, L., Georgiadis, M. C. (2011). Production Scheduling in Multiproduct Multistage Semicontinuous Food Processes. Industrial and Engineering Chemistry Research, 50(10), 6316–6324. https://doi.org/10.1021/ie2001617 DOI: https://doi.org/10.1021/ie2001617
Kurniawan, B., Chandramitasari, W., Gozali, A. A., Weng, W., Fujimura, S. (2020a). Triple-chromosome genetic algorithm for unrelated parallel machine scheduling under time-of-use tariffs. IEEJ Transactions on Electrical and Electronic Engineering, 15(2), 208–217. https://doi.org/10.1002/tee.23047
Kurniawan, B., Song, W., Weng, W., Fujimura, S. (2020b). Distributed-elite local search based on a genetic algorithm for bi-objective job-shop scheduling under time-of-use tariffs. Evolutionary Intelligence, 0123456789. https://doi.org/10.1007/s12065-020-00426-4
Laili, Y., Peng, C., Chen, Z., Ye, F., Zhang, L. (2022). Concurrent local search for process planning and scheduling in the industrial Internet-of-Things environment. Journal of Industrial Information Integration, 28, https://doi.org/10.1016/j.jii.2022.100364.
Lakhan, A., Mastoi, Q. U. A., Elhoseny, M., Memon, M. S., Mohammed, M. A. (2021a). Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud. Enterprise Information Systems, 16(7), 1–23. https://doi.org/10.1080/17517575.2021.1883122
Lakhan, A., Memon, M. S., Mastoi, Q., Elhoseny, M., Mohammed, M. A., Qabulio, M., Abdel-Basset, M. (2021b). Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network. Cluster Computing, 26,6429-6442. https://doi.org/10.1007/s10586-021-03333-0
Lakhan, A., Mohammed, M. A., Elhoseny, M., Alshehri, M. D., Abdulkareem, K. H., (2022a). Blockchain multi-objective optimization approach-enabled secure and cost-efficient scheduling for the Internet of Medical Things (IoMT) in fog-cloud system. Soft Computing. 26, 6429–6442. https://doi.org/10.1007/s00500-022-07167-9
Lakhan A., Mohammed M.A., Rashid A.N., Kadry S., Hameed A. K., Nedoma J., Martinek R., Razzak I. (2022b). Restricted Boltzmann machine assisted secure serverless edge system for internet of medical things. IEEE Journal of Biomedical and Health Informatics. doi: 10.1109/JBHI.2022.3178660.
Lauff, V., Werner, F. (2004). Scheduling with common due date, earliness and tardiness penalties for multimachine problems: A survey. Mathematical and Computer Modelling, 40(5–6), 637–655. https://doi.org/10.1016/j.mcm.2003.05.019 DOI: https://doi.org/10.1016/j.mcm.2003.05.019
Le, C. V., Pang, C. K. (2013). Fast reactive scheduling to minimize tardiness penalty and energy cost under power consumption uncertainties. Computers and Industrial Engineering, 66(2), 406–417. https://doi.org/10.1016/j.cie.2013.07.006 DOI: https://doi.org/10.1016/j.cie.2013.07.006
Lemos, R. F., Ronconi, D. P. (2015). Heuristics for the stochastic single-machine problem with E/T costs. International Journal of Production Economics, 168, 131–142. https://doi.org/10.1016/j.ijpe.2015.06.014 DOI: https://doi.org/10.1016/j.ijpe.2015.06.014
Leyvand, Y., Shabtay, D., Steiner, G. (2010). Optimal delivery time quotation to minimize total tardiness penalties with controllable processing times. IIE Transactions (Institute of Industrial Engineers), 42(3), 221–231. https://doi.org/10.1080/07408170903394322 DOI: https://doi.org/10.1080/07408170903394322
Li, D., Li, M., Meng, X., Tian, Y. (2015). A hyperheuristic approach for intercell scheduling with single processing machines and batch processing machines. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(2), 315–325. https://doi.org/10.1109/TSMC.2014.2332443 DOI: https://doi.org/10.1109/TSMC.2014.2332443
Liang, F., Guo, Y., Fung, R. Y. K. (2015). Simulation-Based Optimization for Surgery Scheduling in Operation Theatre Management Using Response Surface Method. Journal of Medical Systems, 39(11). https://doi.org/10.1007/s10916-015-0349-5 DOI: https://doi.org/10.1007/s10916-015-0349-5
Lin, D. Y., Chu, Y. M. (2013). The mixed-product assembly line sequencing problem of a door-lock company in Taiwan. Computers and Industrial Engineering, 64(1), 492–499. https://doi.org/10.1016/j.cie.2012.08.010 DOI: https://doi.org/10.1016/j.cie.2012.08.010
Liu, X., Chen, J., Huang, X., Guo, S., Zhang, S., Chen, M. (2021). A new job shop scheduling method for remanufacturing systems using extended artificial bee colony algorithm. IEEE Access, 9, 132429–132441. https://doi.org/10.1109/ACCESS.2021.3114712
Liu, Z., Lu, L., Qi, X. (2018). Cost allocation in rescheduling with machine unavailable period. European Journal of Operational Research, 266(1), 16–28. https://doi.org/10.1016/j.ejor.2017.09.015 DOI: https://doi.org/10.1016/j.ejor.2017.09.015
Lopes, T. C., Michels, A. S., Sikora, C. G. S., Molina, R. G., Magatão, L. (2018). Balancing and cyclically sequencing synchronous, asynchronous, and hybrid unpaced assembly lines. International Journal of Production Economics, 203, 216–224. https://doi.org/10.1016/j.ijpe.2018.06.012
Lopes, T. C., Sikora, C. G. S., Michels, A. S., Magatão, L. (2020). An iterative decomposition for asynchronous mixed-model assembly lines: combining balancing, sequencing, and buffer allocation. International Journal of Production Research, 58(2), 615–630. https://doi.org/10.1080/00207543.2019.1598597
Lu, L., Liu, Z., Qi, X. (2013). Coordinated price quotation and production scheduling for uncertain order inquiries. IIE Transactions (Institute of Industrial Engineers), 45(12), 1293–1308. https://doi.org/10.1080/0740817X.2012.748993 DOI: https://doi.org/10.1080/0740817X.2012.748993
Mak, H.-Y., Rong, Y., Zhang, J. (2013). Appointment scheduling with limited distributional information. Management Science, 61, 316-334. https://doi.org/10.1287/mnsc.2013.1881 DOI: https://doi.org/10.1287/mnsc.2013.1881
Mancilla, C., Storer, R. (2012). A sample average approximation approach to stochastic appointment sequencing and scheduling. IIE Transactions (Institute of Industrial Engineers), 44(8), 655–670. https://doi.org/10.1080/0740817X.2011.635174 DOI: https://doi.org/10.1080/0740817X.2011.635174
Mancilla, C., Storer, R. H. (2013). Stochastic sequencing of surgeries for a single surgeon operating in parallel operating rooms. IIE Transactions on Healthcare Systems Engineering, 3(2), 127–138. https://doi.org/10.1080/19488300.2013.787563 DOI: https://doi.org/10.1080/19488300.2013.787563
Mandelbaum, A., Momčilović, P., Trichakis, N., Kadish, S., Leib, R., Bunnell, C. A. (2020). Data-driven appointment-scheduling under uncertainty: The case of an infusion unit in a cancer center. Management Science, 66(1), 243–270. https://doi.org/10.1287/mnsc.2018.3218
Martínez, K. P., Adulyasak, Y., Jans, R., Morabito, R., Toso, E. A. V. (2019). An exact optimization approach for an integrated process configuration, lot-sizing, and scheduling problem. Computers and Operations Research, 103, 310–323. https://doi.org/10.1016/j.cor.2018.10.005
Martínez, K. P., Morabito, R., Toso, E. A. V. (2018). A coupled process configuration, lot-sizing and scheduling model for production planning in the molded pulp industry. International Journal of Production Economics, 204, 227–243. https://doi.org/10.1016/j.ijpe.2018.07.018
Mathur, K., Süer, G. A. (2013). Math modeling and GA approach to simultaneously make overtime decisions, load cells and sequence products. Computers and Industrial Engineering, 66(3), 614–624. https://doi.org/10.1016/j.cie.2013.08.012 DOI: https://doi.org/10.1016/j.cie.2013.08.012
Mazdeh, M. M., Zaerpour, F., Jahantigh, F. F. (2010a). A fuzzy modeling for single machine scheduling problem with deteriorating jobs. International Journal of Industrial Engineering Computations, 1(2), 147–156. https://doi.org/10.5267/j.ijiec.2010.02.004 DOI: https://doi.org/10.5267/j.ijiec.2010.02.004
Mazdeh, M. M., Zaerpour, F., Zareei, A., Hajinezhad, A. (2010b). Parallel machines scheduling to minimize job tardiness and machine deteriorating cost with deteriorating jobs. Applied Mathematical Modelling, 34(6), 1498–1510. https://doi.org/10.1016/j.apm.2009.08.023 DOI: https://doi.org/10.1016/j.apm.2009.08.023
Mendonça, N. D. P., Leite, I., Gomes, V. D. S., Andrade, M. F., Cruz, B. R., Silva, C. S. J., Gomide, L. R. (2022). Silvicultural tasks scheduling optimization: a case study of functions and methods. Revista Árvore. 46. https://doi.org/10.1590/1806-908820220000002"
Mobaieen, S., Rabii, A., Mohamady, B. (2012). Optimal robot arm movement using tabu search algorithm. Research Journal of Applied Sciences, Engineering and Technology, 4(4), 383–386.
Mohan, S., Kumar, K. P. (2016). Waste Load Allocation Using Machine Scheduling: Model Application. Environmental Processes, 3(1), 139–151. https://doi.org/10.1007/s40710-016-0122-x DOI: https://doi.org/10.1007/s40710-016-0122-x
Mohtashami, A. (2015). A novel dynamic genetic algorithm-based method for vehicle scheduling in cross docking systems with frequent unloading operation. Computers and Industrial Engineering, 90, 221–240. https://doi.org/10.1016/j.cie.2015.09.008 DOI: https://doi.org/10.1016/j.cie.2015.09.008
Mokhtari, H., Abadi, I. N. K., Cheraghalikhani, A. (2011). A multi-objective flow shop scheduling with resource-dependent processing times: Trade-off between makespan and cost of resources. International Journal of Production Research, 49(19), 5851–5875. https://doi.org/10.1080/00207543.2010.523724 DOI: https://doi.org/10.1080/00207543.2010.523724
Molina-Sánchez, L. P., González-Neira, E. M. (2016). GRASP to minimize total weighted tardiness in a permutation flow shop environment. International Journal of Industrial Engineering Computations, 7(1), 161–176. https://doi.org/10.5267/j.ijiec.2015.6.004 DOI: https://doi.org/10.5267/j.ijiec.2015.6.004
Mostafaei, H., Castro, P. M., Ghaffari-Hadigheh, A. (2015). A Novel monolithic MILP framework for lot-sizing and scheduling of multiproduct treelike pipeline networks. Industrial and Engineering Chemistry Research, 54(37), 9202–9221. https://doi.org/10.1021/acs.iecr.5b01440 DOI: https://doi.org/10.1021/acs.iecr.5b01440
Murça, M. C. R. (2017). A robust optimization approach for airport departure metering under uncertain taxi-out time predictions. Aerospace Science and Technology, 68, 269–277. https://doi.org/10.1016/j.ast.2017.05.020 DOI: https://doi.org/10.1016/j.ast.2017.05.020
Murugesan, G., Chellappan, C. (2012). Fuzzy based optimal allocation of resources for grid scheduling. Research Journal of Applied Sciences, 7(2), 119–125. https://doi.org/10.3923/rjasci.2012.119.125 DOI: https://doi.org/10.3923/rjasci.2012.119.125
Musavi, M. M., Bozorgi-Amiri, A. (2017). A multi-objective sustainable hub location-scheduling problem for perishable food supply chain. Computers and Industrial Engineering, 113, 766–778. https://doi.org/10.1016/j.cie.2017.07.039 DOI: https://doi.org/10.1016/j.cie.2017.07.039
Nazif, H. (2018). Operating room surgery scheduling with fuzzy surgery durations using a metaheuristic approach. advances in operations research, 1-8. https://doi.org/10.1155/2018/8637598
Nguyen, S., Zhang, M., Johnston, M., Tan, K. C. (2015). Automatic programming via iterated local search for dynamic job shop scheduling. IEEE Transactions on Cybernetics, 45(1), 1–14. https://doi.org/10.1109/TCYB.2014.2317488 DOI: https://doi.org/10.1109/TCYB.2014.2317488
Nonsiri, S., Christophe, F., Coataneéa, E., Mokammel, F. (2014). A combined design structure matrix (DSM) and discrete differential evolution (DDE) approach for scheduling and organizing system development tasks modelled using SysML. Journal of Integrated Design and Process Science, 18(3), 19–40. https://doi.org/10.3233/jid-2014-0013
Oyetunji, E. O. (2009). Some common performance measures in scheduling problems: Review article. Research Journal of Applied Sciences, Engineering and Technology, 1(2), 6–9.
Paik, S., Lee, S., Shin, Y. (2011). Retiming pulsed-latch circuits with regulating pulse width. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 30(8), 1114–1127. https://doi.org/10.1109/TCAD.2011.2126932 DOI: https://doi.org/10.1109/TCAD.2011.2126932
Palaniappan, P. K., Jawahar, N. (2010). Integration of procurement and production scheduling in flexible flow-line assembly. International Journal of Integrated Supply Management, 5(4), 344–364. https://doi.org/10.1504/IJISM.2010.035642 DOI: https://doi.org/10.1504/IJISM.2010.035642
Pan, X., Geng, N., Xie, X. (2021). European Journal of Operational Research, 295(1), 246–260. https://doi.org/10.1016/j.ejor.2021.02.055
Pang, B., Xie, X., Ju, F., Pipe, J. (2022). A dynamic sequential decision-making model on MRI real-time scheduling with simulation-based optimization. Health Care Management Science, 25, 426-440. https://doi.org/10.1007/s10729-022-09592-6.
Paul, S. K., Azeem, A. (2010). Minimization of work-in-process inventory in hybrid flow shop scheduling using fuzzy logic. International Journal of Industrial Engineering: Theory Applications and Practice, 17(2), 115–127.
Pautasso, P. C., Cafaro, D. C., Cerdá, J. (2019). Scheduling upstream operations at inland petroleum refineries using a precedence-based formulation. Industrial and Engineering Chemistry Research, 58(12), 4906–4924. https://doi.org/10.1021/acs.iecr.8b05671
Pei, J., Wang, X., Fan, W., Pardalos, P. M., Liu, X. (2019). Scheduling step-deteriorating jobs on bounded parallel-batching machines to maximise the total net revenue. Journal of the Operational Research Society, 70(10), 1830–1847. https://doi.org/10.1080/01605682.2018.1464428 DOI: https://doi.org/10.1080/01605682.2018.1464428
Pinedo, M. L. (2004). Planning and Scheduling in Manufacturing and Services. Springer Series in Operations Research and Financial Engineering.
Purohit, B. S., Lad, B. K. (2016). Production and maintenance planning: an integrated approach under uncertainties. International Journal of Advanced Manufacturing Technology, 86(9–12), 3179–3191. https://doi.org/10.1007/s00170-016-8415-9 DOI: https://doi.org/10.1007/s00170-016-8415-9
Quinteros, M., Guignard, M., Weintraub, A., Llambias, M., Tapia, C. (2019). Optimizing the pipeline planning system at the national oil company. European Journal of Operational Research, 277(2), 727–739. https://doi.org/10.1016/j.ejor.2019.03.007
Ramezanian, R., Saidi-Mehrabad, M. (2012). Capacitated production planning problem considering the detailed scheduling constraints in a flow shop environment. International Journal of Management Science and Engineering Management, 7(4), 293–302. https://doi.org/10.1080/17509653.2012.10671235
Reddy, N. S., Lalitha, M. P., Pandey, S. P., Venkatesh, G. S. (2021). Simultaneous scheduling of machines and tools in a multi machine FMS with alternative routing using symbiotic organisms search algorithm. Journal of Engineering Research, 10,274-297, https://doi.org/10.36909/jer.10653
Reddy, N. S., Ramamurthy V. D., Rao P. K. and Lalitha M. P. (2019). Integrated scheduling of machines, AGVs and tools in multi-machine FMS using crow search algorithm, International Journal of Computer Integrated Manufacturing, 32(11), 1117–1133. https://doi.org/10.1080/0951192X.2019.1686171
Rezaeiahari, M., Khasawneh, M. T. (2020). Simulation optimization approach for patient scheduling at destination medical centers. Expert Systems with Applications, 140, 112881. https://doi.org/10.1016/j.eswa.2019.112881
Rijal A., Bijvank M., Goel A., Koster R. (2021). Workforce scheduling with order-picking assignments in distribution facilities. Transportation Science, 55(3), 725-746. https://doi.org/10.1287/trsc.2020.1029
Rodríguez-Sanz, Á., Cózar, P. L., Pérez-Castán, J. A., Comendador, F. G. (2021). Tactical runway scheduling for demand and delay management. IOP Conference Series: Materials Science and Engineering, 1024(1). https://doi.org/10.1088/1757-899X/1024/1/012108
Rohaninejad, Mohamad, Kheirkhah, A., Fattahi, P., Vahedi-Nouri, B. (2015). A hybrid multi-objective genetic algorithm based on the ELECTRE method for a capacitated flexible job shop scheduling problem. International Journal of Advanced Manufacturing Technology, 77(1–4), 51–66. https://doi.org/10.1007/s00170-014-6415-1 DOI: https://doi.org/10.1007/s00170-014-6415-1
Rohaninejad, Mohammad, Sahraeian, R., Nouri, B. V. (2016). Multi-objective optimization of integrated lot-sizing and scheduling problem in flexible job shops. RAIRO - Operations Research, 50(3), 587–609. https://doi.org/10.1051/ro/2015049 DOI: https://doi.org/10.1051/ro/2015049
Roshanaei, V., Luong, C., Aleman, D. M., Urbach, D. R. (2017). Collaborative operating room planning and scheduling. INFORMS Journal on Computing, 29(3), 558–580. https://doi.org/10.1287/ijoc.2017.0745 DOI: https://doi.org/10.1287/ijoc.2017.0745
Rudek, R. (2016). Computational complexity and solution algorithms for a vector sequencing problem. Computers and Industrial Engineering, 98, 384–400. https://doi.org/10.1016/j.cie.2016.06.009 DOI: https://doi.org/10.1016/j.cie.2016.06.009
Saadouli, H., Jerbi, B., Dammak, A., Masmoudi, L., Bouaziz, A. (2015). A stochastic optimization and simulation approach for scheduling operating rooms and recovery beds in an orthopedic surgery department. Computers and Industrial Engineering, 80, 72–79. https://doi.org/10.1016/j.cie.2014.11.021 DOI: https://doi.org/10.1016/j.cie.2014.11.021
Sadhasivam, N., Balamurugan, R., Pandi, M. (2018). Cancer diagnosis epigenomics scientific workflow scheduling in the cloud computing environment using an improved PSO algorithm. Asian Pacific Journal of Cancer Prevention, 19(1), 243–246. https://doi.org/10.22034/APJCP.2018.19.1.243
Said, R., Elarbi, M., Bechikh, S., Said, L.B. (2021). Solving combinatorial bi-level optimization problems using multiple populations and migration schemes. Operational Research, 22, 1697–1735. https://doi.org/10.1007/s12351-020-00616-z
Samorani, M., Ganguly, S. (2016). Optimal sequencing of unpunctual patients in high-service-level clinics. Production and Operations Management, 25(2), 330–346. https://doi.org/10.1111/poms.12426 DOI: https://doi.org/10.1111/poms.12426
Santos, M. O., Almada-Lobo, B. (2012). Integrated pulp and paper mill planning and scheduling. Computers and Industrial Engineering, 63(1), 1–12. https://doi.org/10.1016/j.cie.2012.01.008 DOI: https://doi.org/10.1016/j.cie.2012.01.008
Savino, M. M., Meoli, E., Luo, M., Wong, M. M. (2010). Dynamic batch scheduling in a continuous cycle-constrained production system. International Journal of Services Operations and Informatics, 5(4), 313–329. https://doi.org/10.1504/IJSOI.2010.037001 DOI: https://doi.org/10.1504/IJSOI.2010.037001
Seif, J., Yu, A. J., Rahmanniyay, F. (2018). Modelling and optimization of a bi-objective flow shop scheduling with diverse maintenance requirements. International Journal of Production Research, 56(9), 3204–3225. https://doi.org/10.1080/00207543.2017.1403660 DOI: https://doi.org/10.1080/00207543.2017.1403660
Senturk, I. F., Balakrishnan, P., Abu-Doleh, A., Kaya, K., Malluhi, Q., Çatalyürek, Ü. V. (2018). A resource provisioning framework for bioinformatics applications in multi-cloud environments. Future Generation Computer Systems, 78, 379–391. https://doi.org/10.1016/j.future.2016.06.008 DOI: https://doi.org/10.1016/j.future.2016.06.008
Shahram F. S., Vahdani, B. (2019). Assignment and scheduling trucks in cross-docking system with energy consumption consideration and trucks queuing. Journal of Cleaner Production, 213, 21–41. https://doi.org/10.1016/j.jclepro.2018.12.106
Shehadeh, K. S., Padman, R. (2022). Stochastic optimization approaches for elective surgery scheduling with downstream capacity constraints: Models, challenges, and opportunities. Computers and Operations Research, 137, 105523. https://doi.org/10.1016/j.cor.2021.105523
Shen, K., De Pessemier, T., Martens, L., Joseph, W. (2021). A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing. Computers and Industrial Engineering, 161, 107659. https://doi.org/10.1016/j.cie.2021.107659
Shobaki, G., Gordon, V. S., McHugh, P., Dubois, T., Kerbow, A. (2022). Register-Pressure-Aware instruction scheduling using ant colony optimization. ACM Transactions on Architecture and Code Optimization, 19(2), 1–23. https://doi.org/10.1145/3505558
Sidney, J. B. (1977). Optimal Single-Machine Scheduling with Earliness and Tardiness Penalties. Operations Research, 25(1), 62–69. DOI: https://doi.org/10.1287/opre.25.1.62
Singh, J., Cheng Jack C. P., Anumba Chimay J. (2021). BIM-Based approach for automatic pipe systems installation coordination and schedule optimization. Journal of Construction Engineering and Management, 147. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002077
Sinisterra, W. Q., Cavalcante, C. A. V. (2020). An integrated model of production scheduling and inspection planning for resumable jobs. International Journal of Production Economics, 227. https://doi.org/10.1016/j.ijpe.2020.107668
Stanković, A., Petrović, G., Ćojbašić, Ž., Marković, D. (2020). An application of metaheuristic optimization algorithms for solving the flexible job-shop scheduling problem. Operational Research in Engineering Sciences: Theory and Applications, 3(3), 13–28. https://doi.org/10.31181/oresta20303013s DOI: https://doi.org/10.31181/oresta20303013s
Stiverson, P., Rathinam, S. (2011). Heuristics for a runway-queue management problem. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 225(5), 481–499. https://doi.org/10.1177/09544100JAERO871 DOI: https://doi.org/10.1177/09544100JAERO871
Su, Y., Chu, X., Zhang, Z., Chen, D. (2015). Process planning optimization on turning machine tool using a hybrid genetic algorithm with local search approach. Advances in Mechanical Engineering, 7(4), 1–14. https://doi.org/10.1177/1687814015581241 DOI: https://doi.org/10.1177/1687814015581241
Subbiah, S., Schoppmeyer, C., Engell, S. (2011). An intuitive and efficient approach to process scheduling with sequence-dependent changeovers using timed automata models. Industrial and Engineering Chemistry Research, 50(9), 5131–5152. https://doi.org/10.1021/ie101652d DOI: https://doi.org/10.1021/ie101652d
Sun, X., Garg, M., Balaporia, Z., Bailey, K., Gifford, T. (2014). Optimizing transportation by inventory routing and workload balancing: Optimizing daily dray operations across an intermodal freight network. Interfaces, 44(6), 579–590. https://doi.org/10.1287/inte.2014.0746 DOI: https://doi.org/10.1287/inte.2014.0746
Sun, Y., Raghavan, U. N., Vaze, V., Hall, C. S., Doyle, P., Richard, S. S., Wald, C. (2021). Stochastic programming for outpatient scheduling with flexible inpatient exam accommodation. Health Care Management Science, 24(3), 460–481. https://doi.org/10.1007/s10729-020-09527-z
Supithak, W., Liman, S. D., Montes, E. J. (2010). Lot-sizing and scheduling problem with earliness tardiness and setup penalties. Computers and Industrial Engineering, 58(3), 363–372. https://doi.org/10.1016/j.cie.2008.10.005 DOI: https://doi.org/10.1016/j.cie.2008.10.005
T’Kindt, V., Billaut, J.-C. (2005). Multicriteria Scheduling - Theory, Models and Algorithms. Springer-Verlag.
Tan, X. (2012). A mathematical quadratic integer model based on ant colony optimization for air traffic control. International Journal on Advances in Information Sciences and Service Sciences, 4(1), 185-191.
Tang, Q., Li, J., Floudas, C. A., Deng, M., Yan, Y., Xi, Z., Chen, P., Kong, J. (2012). Optimization framework for process scheduling of operation-dependent automobile assembly lines. Optimization Letters, 6(4), 797–824. https://doi.org/10.1007/s11590-011-0303-5 DOI: https://doi.org/10.1007/s11590-011-0303-5
Tavakkoli-Moghaddam, R., Yaghoubi-Panah, M., Radmehr, F. (2012). Scheduling the sequence of aircraft landings for a single runway using a fuzzy programming approach. Journal of Air Transport Management, 25, 15–18. https://doi.org/10.1016/j.jairtraman.2012.03.004 DOI: https://doi.org/10.1016/j.jairtraman.2012.03.004
Toledo, M.C.F., De Oliveira, L., Pereira, R.D.F., França, P.M., Morabito, R. (2014). A genetic algorithm/mathematical programming approach to solve a two-level soft drink production problem. Computers and Operations Research, 48, 40–52. https://doi.org/10.1016/j.cor.2014.02.012 DOI: https://doi.org/10.1016/j.cor.2014.02.012
Transchel, S., Minner, S., Kallrath, J., Lohndorf, N., Eberhard, U. (2011). A hybrid general lot-sizing and scheduling formulation for a production process with a two-stage product structure. International Journal of Production Research, 49(9), 2463–2480. https://doi.org/10.1080/00207543.2010.532910 DOI: https://doi.org/10.1080/00207543.2010.532910
Tsai, S. C., Yeh, Y., Kuo, C. Y. (2021). Efficient optimization algorithms for surgical scheduling under uncertainty. European Journal of Operational Research, 293(2), 579–593. https://doi.org/10.1016/j.ejor.2020.12.048
Udhayakumar, P., Kumanan, S. (2010). Sequencing and scheduling of job and tool in a flexible manufacturing system using ant colony optimization algorithm. International Journal of Advanced Manufacturing Technology, 50(9–12), 1075–1084. https://doi.org/10.1007/s00170-010-2583-9 DOI: https://doi.org/10.1007/s00170-010-2583-9
Vandenberghe, M., Vuyst, S. De, Aghezzaf, E. H., Bruneel, H. (2020). Stochastic surgery selection and sequencing under dynamic emergency break-ins. Journal of the Operational Research Society, 72(6), 1–21. https://doi.org/10.1080/01605682.2020.1718559
Wah-Peng L., See-Seng N., Tien-Choon T., Cheng-Sim L., Jeffrey Y.B.-H. (2017). Critical factors affecting productivity for table form system in the Malaysian construction industry. Advanced Science Letters, 23(7). https://doi.org/10.1166/asl.2017.9203 DOI: https://doi.org/10.1166/asl.2017.9203
Wang, C., Gong, L., Li, X., Zhou, X. (2020). A ubiquitous machine learning accelerator with automatic parallelization on FPGA. IEEE Transactions on Parallel and Distributed Systems, 31(10), 2346–2359. https://doi.org/10.1109/TPDS.2020.2990924
Wang, C., Mao, Y. sheng, Hu, B., Deng, Z., Shin, J. G. (2016). Ship block transportation scheduling problem based on greedy algorithm. Journal of Engineering Science and Technology Review, 9(2), 93–98. https://doi.org/10.25103/jestr.092.15
Wang, C. N., Hsu, H. P., Fu, H. P., Phan, N. K. P., Nguyen, V. T. (2022). Scheduling flexible flow shop in labeling companies to minimize the makespan. Computer Systems Science and Engineering, 40(1), 17–36. https://doi.org/10.32604/CSSE.2022.016992
Wang, D. J., Liu, F., Jin, Y. (2019). A proactive scheduling approach to steel rolling process with stochastic machine breakdown. Natural Computing, 18(4), 679–694. https://doi.org/10.1007/s11047-016-9599-5 DOI: https://doi.org/10.1007/s11047-016-9599-5
Wang, J., Cabrera, J., Tsui, K. L., Guo, H., Bakker, M., Kostis, J. B. (2020). Clinical and nonclinical effects on operative duration: Evidence from a database on thoracic surgery. Journal of Healthcare Engineering, 2020. https://doi.org/10.1155/2020/3582796
Wang, J. B., Zhang, B., Li, L., Bai, D., Feng, Y. B. (2020). Due-window assignment scheduling problems with position-dependent weights on a single machine. Engineering Optimization, 52(2), 185–193. https://doi.org/10.1080/0305215X.2019.1577411
Wang, M. Z., Zhang, L. L., Choi, T. M. (2020). Bi-objective optimal scheduling with raw material’s shelf-life constraints in unrelated parallel machines production. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(11), 4598–4610. https://doi.org/10.1109/TSMC.2018.2855700
Wang, S., Li, X., Sheng, Q. Z., Beheshti, A. (2022). Performance analysis and optimization on scheduling stochastic cloud service requests: A survey. IEEE Transactions on Network and Service Management, https://doi.org/10.1109/TNSM.2022.3181145
Wang, Y., Wang, S. (2021). Deploying, scheduling, and sequencing heterogeneous vessels in a liner container shipping route. Transportation Research Part E: Logistics and Transportation Review, 151, 102365. https://doi.org/10.1016/j.tre.2021.102365
Wei, W., Li, H., Leus, R. (2017). Test sequencing for sequential system diagnosis with precedence constraints and imperfect tests. Decision Support Systems, 103, 104–116. https://doi.org/10.1016/j.dss.2017.09.009 DOI: https://doi.org/10.1016/j.dss.2017.09.009
Weiss C. M., Foxx, H., Ben A. S. (2019). A decision support flexible scheduling system for continuous galvanization lines using genetic algorithm. Production Engineering, 13(1), 43–52. https://doi.org/10.1007/s11740-018-0856-6
Werner, F., Burtseva, L., Sotskov, Y. N. (2018). Special issue on algorithms for scheduling problems. Algorithms, 11(6), 1–4. https://doi.org/10.3390/a11060087
Wisittipanich, W., Hengmeechai, P. (2017). Truck scheduling in multi-door cross docking terminal by modified particle swarm optimization. Computers and Industrial Engineering, 113, 793–802. https://doi.org/10.1016/j.cie.2017.01.004 DOI: https://doi.org/10.1016/j.cie.2017.01.004
Wu, X, Shen, X., Zhang, L. (2019). Solving the planning and scheduling problem simultaneously in a hospital with a bi-layer discrete particle swarm optimization. Mathematical Biosciences and Engineering, 16(2), 831–861. https://doi.org/10.3934/mbe.2019039
Wu, J., Ding, Y., Shi, L. (2021). Mathematical modeling and heuristic approaches for a multi-stage car sequencing problem. Computers and Industrial Engineering, 152, 107008. https://doi.org/10.1016/j.cie.2020.107008
Wu, X, Zhou, S. (2022). Sequencing and scheduling appointments on multiple servers with stochastic service durations and customer arrivals. Omega, 106, 102523. https://doi.org/10.1016/j.omega.2021.102523
Xian-ru, T. (2012). A Mathematical quadratic integer model based on ant colony optimization for air traffic control. International Journal on Advances in Information Sciences and Service Sciences, 4, 185–191. DOI: https://doi.org/10.4156/aiss.vol4.issue1.24
Xu, W., Hu, Y., Luo, W., Wang, L., Wu, R. (2021). A multi-objective scheduling method for distributed and flexible job shop based on hybrid genetic algorithm and tabu search considering operation outsourcing and carbon emission. Computers and Industrial Engineering, 157, https://doi.org/10.1016/j.cie.2021.107318
Xu, W., Wu, R., Wang, L., Zhao, X., Li, X. (2022). Solving a multi-objective distributed scheduling problem for building material equipment group enterprises by measuring quality indicator with a product gene evaluation approach. Computers and Industrial Engineering, 168, https://doi.org/10.1016/j.cie.2022.108142
Yadav, G., Desai, T. N. (2016). Lean Six Sigma: A categorized review of the literature. International Journal of Lean Six Sigma, 7(1), 2–24. https://doi.org/10.1108/IJLSS-05-2015-0015 DOI: https://doi.org/10.1108/IJLSS-05-2015-0015
Yamada, T. T., Nagano, M. S., Miyata, H. H. (2021). Minimization of total tardiness in no-wait flowshop production systems with preventive maintenance. International Journal of Industrial Engineering Computations, 12(4), 415–426. https://doi.org/10.5267/j.ijiec.2021.5.002
Yan, H. Sen, Wan, X. Q., Xiong, F. L. (2014). A hybrid electromagnetism-like algorithm for two-stage assembly flow shop scheduling problem. International Journal of Production Research, 52(19), 5626–5639. https://doi.org/10.1080/00207543.2014.894257 DOI: https://doi.org/10.1080/00207543.2014.894257
Yang, Y., Shen, H. (2022). Deep reinforcement learning enhanced greedy optimization for online scheduling of batched tasks in cloud HPC systems. IEEE Transactions on Parallel and Distributed Systems, 33, 3003-3014, https://doi.org/10.1109/TPDS.2021.3138459
Yeung, W. K., Choi, T. M., Cheng, T. C. E. (2011). Supply chain scheduling and coordination with dual delivery modes and inventory storage cost. International Journal of Production Economics, 132(2), 223–229. https://doi.org/10.1016/j.ijpe.2011.04.012 DOI: https://doi.org/10.1016/j.ijpe.2011.04.012
Yin, Y., Cheng, T. C. E., Xu, D., Wu, C. C. (2012). Common due date assignment and scheduling with a rate-modifying activity to minimize the due date, earliness, tardiness, holding, and batch delivery cost. Computers and Industrial Engineering, 63(1), 223–234. https://doi.org/10.1016/j.cie.2012.02.015 DOI: https://doi.org/10.1016/j.cie.2012.02.015
Yuan, Y., Ye, S., Lin, L., Gen, M. (2021). Multi-objective multi-mode resource-constrained project scheduling with fuzzy activity durations in prefabricated building construction. Computers and Industrial Engineering, 158, 107316. https://doi.org/10.1016/j.cie.2021.107316
Yue, L., Guan, Z., Saif, U., Zhang, F., Wang, H. (2016). Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with sequence-dependent setup times and learning effects. SpringerPlus, 5(1). https://doi.org/10.1186/s40064-016-3265-3 DOI: https://doi.org/10.1186/s40064-016-3265-3
Zhang, F., Mei, Y., Nguyen, S., Zhang, M. (2021). Collaborative multifidelity-based surrogate models for genetic programming in dynamic flexible job shop scheduling. IEEE Transactions on Cybernetics, 1–15. https://doi.org/10.1109/TCYB.2021.3050141
Zhang, G., Xing, K., Cao, F. (2018). Discrete differential evolution algorithm for distributed blocking flowshop scheduling with makespan criterion. Engineering Applications of Artificial Intelligence, 76(28), 96–107. https://doi.org/10.1016/j.engappai.2018.09.005
Zhang, X., Liu, X., Tang, S., Królczyk, G., Li, Z. (2019). Solving scheduling problem in a distributed manufacturing system using a discrete fruit fly optimization algorithm. Energies, 12(17). https://doi.org/10.3390/en12173260
Zhang, Y., D’Ariano, A., He, B., Peng, Q. (2019). Microscopic optimization model and algorithm for integrating train timetabling and track maintenance task scheduling. Transportation Research Part B: Methodological, 127, 237–278. https://doi.org/10.1016/j.trb.2019.07.010
Zhao, H., Xiang, Y., Shen, Y., Guo, Y., Xue, P., Sun, W., Cai, H., Gu, C., Liu, J. (2022). Resilience assessment of hydrogen-integrated energy system for airport electrification. IEEE Transactions on Industry Applications, 58(2), 2812-2824, https://doi.org/10.1109/TIA.2021.3127481
Zhao, X., Huang, C. (2020). Microservice based computational offloading framework and cost efficient task scheduling algorithm in heterogeneous fog cloud network. IEEE Access, 8, 56680–56694. https://doi.org/10.1109/ACCESS.2020.2981860
Zhao, Y., Xu, T., Chakrabarty, K. (2011). Broadcast electrode-addressing and scheduling methods for pin-constrained digital microfluidic biochips. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 30(7), 986–999. https://doi.org/10.1109/TCAD.2011.2116250 DOI: https://doi.org/10.1109/TCAD.2011.2116250
Zheng, F., Wang, Z., Zhang, E., Liu, M. (2022). K-adaptability in robust container vessel sequencing problem with week-dependent demands of a service route. International Journal of Production Research, 60(9), 2787–2801. https://doi.org/10.1080/00207543.2021.1902014
Zhou, S., Yue, Q. (2021a). Appointment scheduling for multi-stage sequential service systems with limited distributional information. Computers and Operations Research, 132, 105287. https://doi.org/10.1016/j.cor.2021.105287
Zhou, S., Yue, Q. (2021b). Sequencing and scheduling appointments for multi-stage service systems with stochastic service durations and no-shows. International Journal of Production Research, 60(5), 1500-1519. https://doi.org/10.1080/00207543.2020.1862431
Zhou, Y., Yang, J. J., Zheng, L. Y. (2019). Hyper-heuristic coevolution of machine assignment and job sequencing rules for multi-objective dynamic flexible job shop scheduling. IEEE Access, 7, 68–88. https://doi.org/10.1109/ACCESS.2018.2883802
Zhu, X., Ruiz, R., Li, S., Li, X. (2017). An effective heuristic for project scheduling with resource availability cost. European Journal of Operational Research, 257(3), 746–762. https://doi.org/10.1016/j.ejor.2016.08.049 DOI: https://doi.org/10.1016/j.ejor.2016.08.049
