# American Institute of Mathematical Sciences

April  2016, 12(2): 757-770. doi: 10.3934/jimo.2016.12.757

## The coordination of single-machine scheduling with availability constraints and delivery

 1 Department of Mathematics, School of Science, East China University of Science and Technology, Shanghai 200237, China

Received  August 2014 Revised  March 2015 Published  June 2015

Single-machine scheduling problems with production and delivery are studied in this paper. There is only one delivery vehicle with capacity $z$. Jobs are not allowed to resume. The $P \rightarrow D$ system and $D \rightarrow P$ system are considered, respectively. For the machine with an availability constraint, we present two $4/3$-approximation algorithms and show that the bounds are tight. For the machine with periodic availability constraints, we provide two polynomial time approximation algorithms which are the best possible.
Citation: Ganggang Li, Xiwen Lu, Peihai Liu. The coordination of single-machine scheduling with availability constraints and delivery. Journal of Industrial & Management Optimization, 2016, 12 (2) : 757-770. doi: 10.3934/jimo.2016.12.757
##### References:

show all references

##### References:
 [1] Leiyang Wang, Zhaohui Liu. Heuristics for parallel machine scheduling with batch delivery consideration. Journal of Industrial & Management Optimization, 2014, 10 (1) : 259-273. doi: 10.3934/jimo.2014.10.259 [2] Ganggang Li, Xiwen Lu. Two-machine scheduling with periodic availability constraints to minimize makespan. Journal of Industrial & Management Optimization, 2015, 11 (2) : 685-700. doi: 10.3934/jimo.2015.11.685 [3] Jiayu Shen, Yuanguo Zhu. An uncertain programming model for single machine scheduling problem with batch delivery. Journal of Industrial & Management Optimization, 2019, 15 (2) : 577-593. doi: 10.3934/jimo.2018058 [4] Jian Xiong, Yingwu Chen, Zhongbao Zhou. Resilience analysis for project scheduling with renewable resource constraint and uncertain activity durations. Journal of Industrial & Management Optimization, 2016, 12 (2) : 719-737. doi: 10.3934/jimo.2016.12.719 [5] Yunqiang Yin, T. C. E. Cheng, Jianyou Xu, Shuenn-Ren Cheng, Chin-Chia Wu. Single-machine scheduling with past-sequence-dependent delivery times and a linear deterioration. Journal of Industrial & Management Optimization, 2013, 9 (2) : 323-339. doi: 10.3934/jimo.2013.9.323 [6] Jiping Tao, Ronghuan Huang, Tundong Liu. A $2.28$-competitive algorithm for online scheduling on identical machines. Journal of Industrial & Management Optimization, 2015, 11 (1) : 185-198. doi: 10.3934/jimo.2015.11.185 [7] Xavier Gràcia, Xavier Rivas, Narciso Román-Roy. Constraint algorithm for singular field theories in the k-cosymplectic framework. Journal of Geometric Mechanics, 2020, 12 (1) : 1-23. doi: 10.3934/jgm.2020002 [8] Zheng Chang, Haoxun Chen, Farouk Yalaoui, Bo Dai. Adaptive large neighborhood search Algorithm for route planning of freight buses with pickup and delivery. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020045 [9] Didem Cinar, José António Oliveira, Y. Ilker Topcu, Panos M. Pardalos. A priority-based genetic algorithm for a flexible job shop scheduling problem. Journal of Industrial & Management Optimization, 2016, 12 (4) : 1391-1415. doi: 10.3934/jimo.2016.12.1391 [10] Jingwen Zhang, Wanjun Liu, Wanlin Liu. An efficient genetic algorithm for decentralized multi-project scheduling with resource transfers. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020140 [11] Guo Zhou, Yongquan Zhou, Ruxin Zhao. Hybrid social spider optimization algorithm with differential mutation operator for the job-shop scheduling problem. Journal of Industrial & Management Optimization, 2021, 17 (2) : 533-548. doi: 10.3934/jimo.2019122 [12] Jiping Tao, Zhijun Chao, Yugeng Xi. A semi-online algorithm and its competitive analysis for a single machine scheduling problem with bounded processing times. Journal of Industrial & Management Optimization, 2010, 6 (2) : 269-282. doi: 10.3934/jimo.2010.6.269 [13] Xuewen Huang, Xiaotong Zhang, Sardar M. N. Islam, Carlos A. Vega-Mejía. An enhanced Genetic Algorithm with an innovative encoding strategy for flexible job-shop scheduling with operation and processing flexibility. Journal of Industrial & Management Optimization, 2020, 16 (6) : 2943-2969. doi: 10.3934/jimo.2019088 [14] Ling Lin, Dong He, Zhiyi Tan. Bounds on delay start LPT algorithm for scheduling on two identical machines in the $l_p$ norm. Journal of Industrial & Management Optimization, 2008, 4 (4) : 817-826. doi: 10.3934/jimo.2008.4.817 [15] Y. K. Lin, C. S. Chong. A tabu search algorithm to minimize total weighted tardiness for the job shop scheduling problem. Journal of Industrial & Management Optimization, 2016, 12 (2) : 703-717. doi: 10.3934/jimo.2016.12.703 [16] Le Thi Hoai An, Tran Duc Quynh, Kondo Hloindo Adjallah. A difference of convex functions algorithm for optimal scheduling and real-time assignment of preventive maintenance jobs on parallel processors. Journal of Industrial & Management Optimization, 2014, 10 (1) : 243-258. doi: 10.3934/jimo.2014.10.243 [17] Behrad Erfani, Sadoullah Ebrahimnejad, Amirhossein Moosavi. An integrated dynamic facility layout and job shop scheduling problem: A hybrid NSGA-II and local search algorithm. Journal of Industrial & Management Optimization, 2020, 16 (4) : 1801-1834. doi: 10.3934/jimo.2019030 [18] Ran Ma, Jiping Tao. An improved 2.11-competitive algorithm for online scheduling on parallel machines to minimize total weighted completion time. Journal of Industrial & Management Optimization, 2018, 14 (2) : 497-510. doi: 10.3934/jimo.2017057 [19] Xiaoxiao Yuan, Jing Liu, Xingxing Hao. A moving block sequence-based evolutionary algorithm for resource investment project scheduling problems. Big Data & Information Analytics, 2017, 2 (1) : 39-58. doi: 10.3934/bdia.2017007 [20] Michel Lavrauw, Geertrui Van de Voorde. Locally repairable codes with high availability based on generalised quadrangles. Advances in Mathematics of Communications, 2020  doi: 10.3934/amc.2020099

2019 Impact Factor: 1.366