This paper deals with the integration problem between production scheduling and maintenance plan-
ning in a single machine, where the impact of failure uncertainty is considered. The objective is to minimize the
weighted sum of quality robustness and solution robustness, which is determined by the jobs' sequence, preventive
maintenances' position and bu?er time in the schedule. Then, a three-stage algorithm is devised to solve the
problem, where the gradient descent algorithm based on an effective surrogate measure is developed in the second
stage. The numerical experiments show that the deviation of the approximate approach is very small, as compared
with the exact solution obtained by CPLEX. The balance between quality robustness and solution robustness and
the distribution of buffer time in different scenarios are shown in a case study. It validates the necessity and
e?ectiveness of the consideration of robustness in the industrial practice.
CUI Weiwei (崔维伟)
. Approximate Approach to Deal with the Uncertainty in Integrated Production Scheduling and Maintenance Planning[J]. Journal of Shanghai Jiaotong University(Science), 2020
, 25(1)
: 106
-117
.
DOI: 10.1007/s12204-019-2086-2
[1] LI L, SUN Z Y, XU X W, et al. Multi-zone proportional hazard model for a multi-stage degradation process [C]//ASME 2013 International Manu-facturing Science and Engineering Conference Collocated with the North American Manufacturing Research Conference. Madison, Wisconsin, USA: ASME,2013: V002T02A013.
[2] YAO X F, SUN Z Y, WEI D, et al. Joint maintenance and energy management in manufacturing systems: Prospect discussion, challenge analysis, and a case study [C]//ASME 2016 International Manufacturing Science and Engineering Conference. Blacksburg, Virginia, USA: ASME, 2016: V002T04A041.
[3] XIA T B, TAO X Y, XI L F. Operation process rebuilding (OPR)-oriented maintenance policy for changeable system structures [J]. IEEE Transactions on Automation Science and Engineering, 2017, 14(1): 139-148.
[4] WANG S J, LIU M. Two-stage hybrid flow shop scheduling with preventive maintenance using multi-objective tabu search method [J]. International Journal of Production Research, 2014, 52(5): 1495-1508.
[5] JIA W Y, JIANG Z B, LI Y. Scheduling to minimize the makespan in large-piece one-of-a-kind production with machine availability constraints [J]. Expert Systems with Applications, 2015, 42(23): 9174-9182.
[6] ZHENG X L, WANG L. A two-stage adaptive fruit fly optimization algorithm for unrelated parallel machine scheduling problem with additional resource constraints [J]. Expert Systems with Applications, 2016,65: 28-39.
[7] SHAOWS, PI D C. A self-guided di?erential evolution with neighborhood search for permutation flow shop scheduling [J]. Expert Systems with Applications, 2016,51: 161-176.
[8] CASSADY C R, KUTANOGLU E. Integrating preventive maintenance planning and production scheduling for a single machine [J]. IEEE Transactions on Reliability, 2005, 54(2): 304-309.
[9] WANG S J, LIU M. A branch and bound algorithm for single-machine production scheduling integrated with preventive maintenance planning [J]. International Journal of Production Research, 2013, 51(3):847-868.
[10] ABDELRAHIM E H, VIZV?ARI B. Simultaneous scheduling of production and preventive maintenance on a single machine [J]. Arabian Journal for Science &Engineering, 2016, 42(7): 2867-2883.
[11] SORTRAKUL N, NACHTMANN H L, CASSADY C R. Genetic algorithms for integrated preventive maintenance planning and production scheduling for a single machine [J]. Computers in Industry, 2005, 56(2):161-168.
[12] DING P W, JIANG Z H, HU J W, et al. Integrating production scheduling and preventive maintenance for a single machine with due window [J]. Journal of Shanghai Jiao Tong University, 2015, 49(4): 524-530(in Chinese).
[13] PEI H Y, JIANG Z H, HU J W, et al. Integrating rescheduling with preventive maintenance in the flow-shop problem under rush orders [J]. Industrial Engineering and Management, 2017, 22(1): 50-57 (in Chinese).
[14] RUIZ R, GARC?IA-D?IAZ J C, MAROTO C. Considering scheduling and preventive maintenance in the flow-shop sequencing problem [J]. Computers & Operations Research, 2007, 34(11): 3314-3330.
[15] JABBARIZADEH F, ZANDIEH M, TALEBI D. Hybrid flexible flowshops with sequence-dependent setup time and machine availability constraints [J]. Computers & Industrial Engineering, 2009, 57(3): 949-957.
[16] NADERI B, ZANDIEH M, AMINNAYERI M. Incorporating periodic preventive maintenance into flexible flowshop scheduling problems [J]. Applied Soft Computing, 2011, 11(2): 2094-2101.
[17] SEIDGAR H, ZANDIEH M, MAHDAVI I. Biobjective optimization for integrating production and preventive maintenance scheduling in two-stage assembly flow shop problem [J]. Journal of Industrial and Production Engineering, 2016, 33(6): 404-425.
[18] TAO X Y, XIA T B, XI L F. Health-index-based joint optimization of preventive maintenance and multiattribute production scheduling [J]. Journal of Shanghai Jiao Tong University, 2014, 48(8): 1170-1174 (in Chinese).
[19] ZHOU B H, LIU Z L. Integrated scheduling method of production and preventive maintenance in flow shops with degradations [J]. Computer Integrated Manufacturing Systems, 2016, 22(5): 1272-1278 (in Chinese).
[20] YANG H B, SHEN L, CHENG M, et al. Integrated optimization of scheduling and maintenance in multi-state production systems with deterioration effects [J].Computer Integrated Manufacturing Systems, 2018,24(1): 80-88 (in Chinese).
[21] XIAO L, SONG S L, CHEN X H, et al. Joint optimization of production scheduling and machine group preventive maintenance [J]. Reliability Engineering and System Safety, 2016, 146: 68-78.
