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Journal articles on the topic 'Single Machine Total Tardiness Problem'

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Published: 14 March 2023

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1

Tian, Z. J., C. T. Ng, and T. C. E. Cheng. "On the single machine total tardiness problem." European Journal of Operational Research 165, no. 3 (September 2005): 843–46. http://dx.doi.org/10.1016/j.ejor.2004.03.017.

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2

Huang, Xiaolin, Horim Ha, and Shuning Wang. "An inequality for single machine total tardiness problem." IFAC Proceedings Volumes 44, no. 1 (January 2011): 8201–4. http://dx.doi.org/10.3182/20110828-6-it-1002.00814.

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3

Szwarc, Wlodzimierz, and Samar K. Mukhopadhyay. "Decomposition of the single machine total tardiness problem." Operations Research Letters 19, no. 5 (November 1996): 243–50. http://dx.doi.org/10.1016/s0167-6377(96)00031-4.

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4

Szwarc, Wlodzimierz, Federico Della Croce, and Andrea Grosso. "Solution of the single machine total tardiness problem." Journal of Scheduling 2, no. 2 (March 1999): 55–71. http://dx.doi.org/10.1002/(sici)1099-1425(199903/04)2:2<55::aid-jos14>3.0.co;2-5.

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5

Oyetunji, Elkanah, and Ayodeji E. Oluleye. "Minimizing Total Earliness and Total Tardiness on Single Machine with Release Dates." International Journal of Engineering Research in Africa 5 (July 2011): 30–43. http://dx.doi.org/10.4028/www.scientific.net/jera.5.30.

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This paper considers the bicriteria scheduling problem of minimizing the total earliness and the total tardiness on a single machine with release dates. In view of the fact that the problem has been characterized as NP-Hard, we propose two approximation algorithms (labeled as ETA1 and ETA2) for solving the problem. The proposed algorithms were compared with the MA heuristic selected from the literature. The two criteria (the total earliness and the total tardiness) were aggregated together into a linear composite objective function (LCOF). The performances of the algorithms were evaluated based on both effectiveness and efficiency. The algorithms were tested on a set of 1200 randomly generated single machine scheduling problems. Experimental results show that both the ETA1 and ETA2 algorithms outperformed (in terms of effectiveness and efficiency) the MA heuristic under all the considered problem sizes. Also, the ETA1 algorithm outperformed the ETA2 algorithm when the number of jobs (n) ranges between 20 and 500.

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6

Tansel, B. C., and I. Sabuncuoglu. "New Insights on the Single Machine Total Tardiness Problem." Journal of the Operational Research Society 48, no. 1 (January 1997): 82. http://dx.doi.org/10.2307/3009947.

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7

Tansel, B. C., and I. Sabuncuoglu. "New insights on the single machine total tardiness problem." Journal of the Operational Research Society 48, no. 1 (January 1997): 82–89. http://dx.doi.org/10.1057/palgrave.jors.2600321.

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8

Tansel, B. C., and I. Sabuncuoglu. "New insights on the single machine total tardiness problem." Journal of the Operational Research Society 48, no. 1 (1997): 82–0089. http://dx.doi.org/10.1038/sj.jors.2600321.

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9

Szwarc, Wlodzimierz, Andrea Grosso, and Federico Della Croce. "Algorithmic paradoxes of the single-machine total tardiness problem." Journal of Scheduling 4, no. 2 (2001): 93–104. http://dx.doi.org/10.1002/jos.69.

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10

HE, CHENG, YIXUN LIN, and JINJIANG YUAN. "SOME IMPROVED ALGORITHMS ON THE SINGLE MACHINE HIERARCHICAL SCHEDULING WITH TOTAL TARDINESS AS THE PRIMARY CRITERION." Asia-Pacific Journal of Operational Research 27, no. 05 (October 2010): 577–85. http://dx.doi.org/10.1142/s0217595910002867.

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It is well-known that a single machine scheduling problem of minimizing the total tardiness is NP-hard. Recently, Liu, Ng and Cheng solved some special hierarchical minimization problems with total tardiness as the primary criterion by the Algorithm TAP (Two Assignment Problems) in O(n3) time. And in this paper we present some algorithms for these problems with running time O(n log n).

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11

Yin, Aihua, and Jing Wang. "Mixed Dispatch Rule for Single Machine Total Weighted Tardiness Problem." Journal of Applied Sciences 13, no. 21 (October 15, 2013): 4616–19. http://dx.doi.org/10.3923/jas.2013.4616.4619.

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12

TANSEL, BARBAROS Ç., BAHAR Y. KARA, and IHSAN SABUNCUOGLU. "An efficient algorithm for the single machine total tardiness problem." IIE Transactions 33, no. 8 (August 2001): 661–74. http://dx.doi.org/10.1080/07408170108936862.

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13

Ding, Junwen, Zhipeng Lü, T. C. E. Cheng, and Liping Xu. "Breakout dynasearch for the single-machine total weighted tardiness problem." Computers & Industrial Engineering 98 (August 2016): 1–10. http://dx.doi.org/10.1016/j.cie.2016.04.022.

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14

Koulamas, Christos. "The single-machine total tardiness scheduling problem: Review and extensions." European Journal of Operational Research 202, no. 1 (April 2010): 1–7. http://dx.doi.org/10.1016/j.ejor.2009.04.007.

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15

Cheng, T. C. E., A. A. Lazarev, and E. R. Gafarov. "A hybrid algorithm for the single-machine total tardiness problem." Computers & Operations Research 36, no. 2 (February 2009): 308–15. http://dx.doi.org/10.1016/j.cor.2007.10.002.

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16

Akande, Saheed. "New Solution Methods for Single Machine Bicriteria Scheduling Problems: Minimizing the Total Flowtime and Maximum Tardiness with Zero Release Dates." International Journal of Engineering Research in Africa 38 (September 2018): 144–53. http://dx.doi.org/10.4028/www.scientific.net/jera.38.144.

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This paper considers the bicriteria scheduling problem of minimizing the maximum tardiness and total flowtime on single machine with zero release dates. The problem is NP hard, though Early Due Date (EDD) and Shortest Processing Time (SPT) rules yielded optimal solutions for maximum tardiness and total flowtime, respectively if each criterion were to be considered singly. Thus, the values of each of the criteria for the two proposed heuristics; AA and SA were compared to the optimal solution of the sub problems. Results of computational experiment on job sizes varies from 5-150 jobs showed that the SA heuristic is preferred over AA. This is because the heuristic SA yielded maximum tardiness and total flowtime not significantly different from the EDD and SPT results, respectively at 99% significant level. Though AA yielded optimal for total flowtime, the maximum tardiness results is significantly different from the EDD results.

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17

Petrynski, Kacper, Iwona Pozniak-Koszalka, Leszek Koszalka, and Andrzej Kasprzak. "AIT-S for Single-Machine Weighted Tardiness Problem." Vietnam Journal of Computer Science 06, no. 03 (August 2019): 273–84. http://dx.doi.org/10.1142/s2196888819500106.

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This paper concentrates on the newly improved algorithm for solving the single-machine total weighted tardiness (SMTWT) problem, called Algorithm Inspired by Tree and Sorting (AIT-S). The algorithm is based on searching the solution space along with the tree and sorting rules. The properties of the algorithm are studied taking into account the results of experiments made using the designed and implemented experimentation system. This system allows in tuning the parameters of the algorithm as well as to compare the effects obtained by AIT-S algorithm with effects of AIT algorithm, and known meta-heuristic algorithms. The paper shows that the proposed algorithm still requires some improvements, however, it seems to be promising.

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18

Wang, Meng Lan, and Wen Bin Liu. "An Improved Tabu Search Algorithm for a Type of Single Machine Sequencing Problem." Advanced Materials Research 756-759 (September 2013): 3997–4001. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3997.

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Machine scheduling is a central task in production planning. In general it means the problem of scheduling job operations on a given number of available machines. In this paper we consider a machine scheduling problem with one machine, or the Single Machine Total Tardiness Problem. To solve this NP-hard problem, we develop an improved Tabu Search Algorithm, which is tested to have the ability to find good results by an example.

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19

Akande, Saheed, Ayodeji Emmanuel Oluleye, and Elkanah Oyetunji. "Minimization of Total Tardiness and Total Flowtime on Single Machine with Non-Zero Release Dates." International Journal of Engineering Research in Africa 29 (March 2017): 154–64. http://dx.doi.org/10.4028/www.scientific.net/jera.29.154.

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This paper considers the bicriteria scheduling problem of minimizing the composite function of total tardiness and total flow time on single machine with non-zero release dates. The problem is strongly Non Polynomial (NP). Thus, two heuristics SA7 and SA8 were proposed to solve the problem. The generalized algorithm and the branch and bound procedure were also implemented. The four solution methods were tested on a total of 800 randomly generated problems ranging from 5 to 1000 jobs. Results based on effectiveness and efficiency show that the SA7 heuristic is the most suited solution method to solve the problem.

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20

Cheng, T. C. E., Shih-Chang Tseng, Peng-Jen Lai, and Wen-Chiung Lee. "Single-Machine Scheduling with Accelerating Learning Effects." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/816235.

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Scheduling with learning effects has been widely studied. However, there are situations where the learning effect might accelerate. In this paper, we propose a new model where the learning effect accelerates as time goes by. We derive the optimal solutions for the single-machine problems to minimize the makespan, total completion time, total weighted completion time, maximum lateness, maximum tardiness, and total tardiness.

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21

Motair, Hafed Mohammed. "Solving Composite MultiobjectiveSingle Machine Scheduling ProblemUsingBranch and Bound and Local SearchAlgorithms." Al-Mustansiriyah Journal of Science 28, no. 3 (July 3, 2018): 200. http://dx.doi.org/10.23851/mjs.v28i3.122.

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This paper present algorithms for solving a single machine scheduling problem to minimize the sum of total completion times, total tardiness,maxim-um tardiness,and maximum earliness.The single machine total tardiness problem is already NP-hard, so the consider problem is strongly NP-hard, and several algorithms are used to solve it. Branch and bound algorithmwith dominance ruleand local search algorit- hms are proposed for the problem. For the Branch and bound algorithm results- show that using dominance rule improve the performance of the algorithm in both computation times and optimal values,but it need longer times.Thus we tackle the problemof large sizes with local search algorit- hms descent method, simulated annealing and tabusearch. The perfomance of these algorithms is evaluated on a large set of test problems and the results are compared.The computational results show that simulated annealing algorithm and Tabu search algorithm are better than Descent method with preference to simulated annealing algorithm,and show that the three algorithms find optimal or near optimal solutions inreasonable times.

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22

Santosa, Budi, and Umar Affandi. "Application of Viral Systems for Single-Machine Total Weighted Tardiness Problem." IOP Conference Series: Materials Science and Engineering 46 (June 6, 2013): 012010. http://dx.doi.org/10.1088/1757-899x/46/1/012010.

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23

Nearchou, Andreas C. "A HYBRID METAHEURISTIC FOR THE SINGLE-MACHINE TOTAL WEIGHTED TARDINESS PROBLEM." Cybernetics and Systems 43, no. 8 (November 2012): 651–68. http://dx.doi.org/10.1080/01969722.2012.717853.

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24

Ergun, Özlem, and James B. Orlin. "Fast neighborhood search for the single machine total weighted tardiness problem." Operations Research Letters 34, no. 1 (January 2006): 41–45. http://dx.doi.org/10.1016/j.orl.2005.01.010.

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25

França, Paulo M., Alexandre Mendes, and Pablo Moscato. "A memetic algorithm for the total tardiness single machine scheduling problem." European Journal of Operational Research 132, no. 1 (July 2001): 224–42. http://dx.doi.org/10.1016/s0377-2217(00)00140-5.

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26

Xue, Pengfei, and Yulin Zhang. "Single-Machine Scheduling with Upper Bounded Maintenance Time under the Deteriorating Effect." Discrete Dynamics in Nature and Society 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/756251.

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We consider a single-machine scheduling problem with upper bounded actual processing time and upper bounded maintenance time under deteriorating effect. The actual processing time of a job is a position-dependent power function. If the actual processing time of a job exceeds the upper bound, tardiness penalty of the job should be paid. And if the maintenance time exceeds the corresponding upper bound, tardiness penalty of the maintenance should also be paid. The maintenance duration studied in the paper is a position-dependent exponential function. The objective is to find jointly the optimal maintenance frequency and the optimal job sequence to minimize the total cost, which is a linear function of the makespan and the total tardiness. We show that the studied scheduling problem can be transformed as a classic assignment problem to solve. There is also shown that a special case of the scheduling problem can be optimally solved by a lower order algorithm.

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27

Luo, Yuncheng. "Stochastic Single Machine JIT Scheduling with Geometric Processing Times and Due Dates." Mathematical Problems in Engineering 2021 (May 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/8874445.

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In this paper, we investigate a static stochastic single machine JIT scheduling problem in which the jobs’ processing times are stochastically independent and follow geometric distributions whose mean is provided, due dates are geometrically distributed with a common mean, and both the unit penalty of earliness/tardiness and the fixed penalty of earliness/tardiness are deterministic and different. The objective is to minimize the expected total penalties for quadratic earliness, quadratic tardiness, and early and tardy jobs. We prove that the optimal schedule to minimize this problem is V-shaped with respect to the ratio of mean processing time to unit tardiness penalty under the specific condition. Also, we show a special case and two theorems related to this JIT scheduling problem under specific situations where the optimal solutions exist. Finally, based on the V-shaped characteristic, a dynamic programming algorithm is designed to achieve an optimal V-shaped schedule in pseudopolynomial time.

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28

Gafarov, Evgeny R., Alexander A. Lazarev, and Frank Werner. "Single machine total tardiness maximization problems: complexity and algorithms." Annals of Operations Research 207, no. 1 (December 28, 2012): 121–36. http://dx.doi.org/10.1007/s10479-012-1288-x.

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29

Sabamoniri, Saeed, Kayvan Asghari, and Mohammad Javad Hosseini. "Solving Single Machine Total Weighted Tardiness Problem using Variable Structure Learning Automata." International Journal of Computer Applications 56, no. 1 (October 20, 2012): 37–42. http://dx.doi.org/10.5120/8858-2816.

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30

Croce, F. Della, R. Tadei, P. Baracco, and A. Grosso. "A New Decomposition Approach for the Single Machine Total Tardiness Scheduling Problem." Journal of the Operational Research Society 49, no. 10 (October 1998): 1101. http://dx.doi.org/10.2307/3010534.

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31

Lazarev, A. A., and E. R. Gafarov. "SPECIAL CASE OF THE SINGLE MACHINE TOTAL TARDINESS PROBLEM IS NP-HARD." IFAC Proceedings Volumes 39, no. 3 (2006): 155–57. http://dx.doi.org/10.3182/20060517-3-fr-2903.00094.

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32

Chung, Tsui-Ping, Qunjie Fu, Ching-Jong Liao, and Yi-Ting Liu. "Multiple-variable neighbourhood search for the single-machine total weighted tardiness problem." Engineering Optimization 49, no. 7 (October 6, 2016): 1133–47. http://dx.doi.org/10.1080/0305215x.2016.1235707.

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33

Della Croce, F., R. Tadei, P. Baracco, and A. Grosso. "A new decomposition approach for the single machine total tardiness scheduling problem." Journal of the Operational Research Society 49, no. 10 (October 1998): 1101–6. http://dx.doi.org/10.1057/palgrave.jors.2600624.

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34

Potts, C. N., and L. N. Van Wassenhove. "Dynamic programming and decomposition approaches for the single machine total tardiness problem." European Journal of Operational Research 32, no. 3 (December 1987): 405–14. http://dx.doi.org/10.1016/s0377-2217(87)80008-5.

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35

Bożejko, Wojciech, Józef Grabowski, and Mieczysław Wodecki. "Block approach—tabu search algorithm for single machine total weighted tardiness problem." Computers & Industrial Engineering 50, no. 1-2 (May 2006): 1–14. http://dx.doi.org/10.1016/j.cie.2005.12.001.

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36

Bilge, Ümit, Müjde Kurtulan, and Furkan Kıraç. "A tabu search algorithm for the single machine total weighted tardiness problem." European Journal of Operational Research 176, no. 3 (February 2007): 1423–35. http://dx.doi.org/10.1016/j.ejor.2005.10.030.

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37

Ding, Junwen, Zhipeng Lü, T. C. E. Cheng, and Liping Xu. "A hybrid evolutionary approach for the single-machine total weighted tardiness problem." Computers & Industrial Engineering 108 (June 2017): 70–80. http://dx.doi.org/10.1016/j.cie.2017.04.006.

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38

Crauwels, H. A. J., C. N. Potts, and L. N. Van Wassenhove. "Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem." INFORMS Journal on Computing 10, no. 3 (August 1998): 341–50. http://dx.doi.org/10.1287/ijoc.10.3.341.

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39

Della Croce, F., R. Tadei, P. Baracco, and A. Grosso. "A new decomposition approach for the single machine total tardiness scheduling problem." Journal of the Operational Research Society 49, no. 10 (1998): 1101–6. http://dx.doi.org/10.1038/sj.jors.2600624.

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40

Panneerselvam, R. "Simple heuristic to minimize total tardiness in a single machine scheduling problem." International Journal of Advanced Manufacturing Technology 30, no. 7-8 (November 18, 2005): 722–26. http://dx.doi.org/10.1007/s00170-005-0102-1.

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41

Colak Altunc, Arife Burcu, and Ahmet Burak Keha. "Interval-indexed formulation based heuristics for single machine total weighted tardiness problem." Computers & Operations Research 36, no. 6 (June 2009): 2122–31. http://dx.doi.org/10.1016/j.cor.2008.08.004.

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42

Amin, Begard A., and Ayad M. Ramadan. "Novel Heuristic Approach for Solving Multi-objective Scheduling Problems." Ibn AL- Haitham Journal For Pure and Applied Sciences 34, no. 3 (August 1, 2021): 50–59. http://dx.doi.org/10.30526/34.3.2677.

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In this paper, we studied the scheduling of jobs on a single machine. Each of n jobs is to be processed without interruption and becomes available for processing at time zero. The objective is to find a processing order of the jobs, minimizing the sum of maximum earliness and maximum tardiness. This problem is to minimize the earliness and tardiness values, so this model is equivalent to the just-in-time production system. Our lower bound depended on the decomposition of the problem into two subprograms. We presented a novel heuristic approach to find a near-optimal solution for the problem. This approach depends on finding efficient solutions for two problems. The first problem is minimizing total completion time and maximum tardiness. The second is minimizing total completion time and maximum earliness. We used these efficient solutions to find a near-optimal solution for another problem which is a sum of maximum earliness and maximum tardiness. This means we eliminate the total completion time from the two problems. The algorithm was tested on a set of problems of different n. Computational results demonstrate the efficiency of the proposed method.

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43

Maheswaran, R., and S. G. Ponnambalam. "An investigation on single machine total weighted tardiness scheduling problems." International Journal of Advanced Manufacturing Technology 22, no. 3-4 (September 1, 2003): 243–48. http://dx.doi.org/10.1007/s00170-002-1466-0.

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44

Lee, Ju-Yong, and June-Young Bang. "A Two-Stage Assembly-Type Flowshop Scheduling Problem for Minimizing Total Tardiness." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6409321.

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This research considers a two-stage assembly-type flowshop scheduling problem with the objective of minimizing the total tardiness. The first stage consists of two independent machines, and the second stage consists of a single machine. Two types of components are fabricated in the first stage, and then they are assembled in the second stage. Dominance properties and lower bounds are developed, and a branch and bound algorithm is presented that uses these properties and lower bounds as well as an upper bound obtained from a heuristic algorithm. The algorithm performance is evaluated using a series of computational experiments on randomly generated instances and the results are reported.

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45

Bożejko, W., P. Rajba, and M. Wodecki. "Stable scheduling of single machine with probabilistic parameters." Bulletin of the Polish Academy of Sciences Technical Sciences 65, no. 2 (April 1, 2017): 219–31. http://dx.doi.org/10.1515/bpasts-2017-0026.

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Abstract We consider a stochastic variant of the single machine total weighted tardiness problem jobs parameters are independent random variables with normal or Erlang distributions. Since even deterministic problem is NP-hard, it is difficult to find global optimum for large instances in the reasonable run time. Therefore, we propose tabu search metaheuristics in this work. Computational experiments show that solutions obtained by the stochastic version of metaheuristics are more stable (i.e. resistant to data disturbance) than solutions generated by classic, deterministic version of the algorithm.

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46

Uchroński, Mariusz. "Parallel Algorithm with Blocks for a Single-Machine Total Weighted Tardiness Scheduling Problem." Applied Sciences 11, no. 5 (February 26, 2021): 2069. http://dx.doi.org/10.3390/app11052069.

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In this paper, the weighted tardiness single-machine scheduling problem is considered. To solve it an approximate (tabu search) algorithm, which works by improving the current solution by searching the neighborhood, is used. Methods of eliminating bad solutions from the neighborhood (the so-called block elimination properties) were also presented and implemented in the algorithm. Blocks allow a significant shortening of the process of searching the neighborhood generated by insert type moves. The designed parallel tabu search algorithm was implemented using the MPI (Message Passing Interface) library. The obtained speedups are very large (over 60,000×) and superlinear. This may be a sign that the parallel algorithm is superior to the sequential one as the sequential algorithm is not able to effectively search the solution space for the problem under consideration. Only the introduction of diversification process through parallelization can provide an adequate coverage of the entire search process. The current methods of parallelization of metaheuristics give a speedup which strongly depends on the problem’s instances, rarely greater than number of used parallel processors. The method proposed here allows the obtaining of huge speedup values (over 60,000×), but only when so-called blocks are used. The above-mentioned speedup values can be obtained on high performance computing infrastructures such as clusters with the use of MPI library.

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47

Wang, Jing, and Aihua Yin. "Differential evolution algorithm with mixed strategy for single machine total weighted tardiness problem." International Journal of Wireless and Mobile Computing 11, no. 4 (2016): 357. http://dx.doi.org/10.1504/ijwmc.2016.082283.

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48

Yin, Aihua, and Jing Wang. "Differential evolution algorithm with mixed strategy for single machine total weighted tardiness problem." International Journal of Wireless and Mobile Computing 11, no. 4 (2016): 357. http://dx.doi.org/10.1504/ijwmc.2016.10003232.

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49

Santosa, Budi, and Ade Lia Safitri. "Biogeography-based Optimization (BBO) Algorithm for Single Machine Total Weighted Tardiness Problem (SMTWTP)." Procedia Manufacturing 4 (2015): 552–57. http://dx.doi.org/10.1016/j.promfg.2015.11.075.

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50

Grosso, A., F. Della Croce, and R. Tadei. "An enhanced dynasearch neighborhood for the single-machine total weighted tardiness scheduling problem." Operations Research Letters 32, no. 1 (January 2004): 68–72. http://dx.doi.org/10.1016/s0167-6377(03)00064-6.

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