An approximation scheme for minimizing the makespan of the parallel identical multi-stage flow-shops

被引:7
|
作者
Tong, Weitian [1 ,2 ]
Miyano, Eiji [3 ]
Goebel, Randy [2 ]
Lin, Guohui [2 ]
机构
[1] Georgia Southern Univ, Dept Comp, Statesboro, GA 30460 USA
[2] Univ Alberta, Dept Comp Sci, Edmonton, AB T6G 2E8, Canada
[3] Kyushu Inst Technol, Dept Syst Design & Informat, Iizuka, Fukuoka 8208502, Japan
基金
加拿大自然科学与工程研究理事会;
关键词
Multiprocessor scheduling; Flow-shop scheduling; Makespan; Linear program; Polynomial-time approximation scheme; SCHEDULING PROBLEM; 2-STAGE FLOWSHOP; HYBRID FLOWSHOP; ALGORITHMS; MACHINES; STAGE; HEURISTICS; COMPLEXITY;
D O I
10.1016/j.tcs.2017.09.018
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In the parallel k-stage flow-shops problem, we are given m identical k-stage flow-shops and a set of jobs. Each job can be processed by any one of the flow-shops but switching between flow-shops is not allowed. The objective is to minimize the makespan, which is the finishing time of the last job. This problem generalizes the classical parallel identical machine scheduling (where k = 1) and the classical flow-shop scheduling (where m = 1) problems, and thus it is NP-hard. We present a polynomial-time approximation scheme (PTAS) for the problem, when m and k are fixed constants. The key technique is to partition the jobs into big jobs and small jobs, enumerate over all feasible schedules for the big jobs, and handle the small jobs by solving a linear program and employing a "sliding" method. Such a technique has been used in the design of PTAS for several flow-shop scheduling variants. Our main contributions are the non-trivial application of this technique and a valid answer to the open question in the literature. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:24 / 31
页数:8
相关论文
共 50 条
  • [1] A PTAS for the Multiple Parallel Identical Multi-stage Flow-Shops to Minimize the Makespan
    Tong, Weitian
    Miyano, Eiji
    Goebel, Randy
    Lin, Guohui
    Frontiers in Algorithmics, FAW 2016, 2016, 9711 : 227 - 237
  • [2] A polynomial-time approximation scheme for an arbitrary number of parallel identical multi-stage flow-shops
    Gong, Mingyang
    Lin, Guohui
    Miyano, Eiji
    Su, Bing
    Tong, Weitian
    ANNALS OF OPERATIONS RESEARCH, 2024, 335 (1) : 185 - 204
  • [3] A polynomial-time approximation scheme for an arbitrary number of parallel identical multi-stage flow-shops
    Mingyang Gong
    Guohui Lin
    Eiji Miyano
    Bing Su
    Weitian Tong
    Annals of Operations Research, 2024, 335 : 185 - 204
  • [4] A polynomial-time approximation scheme for an arbitrary number of parallel two-stage flow-shops
    Dong, Jianming
    Jin, Ruyan
    Luo, Taibo
    Tong, Weitian
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2020, 281 (01) : 16 - 24
  • [5] Minimizing makespan in reentrant flow-shops using hybrid tabu search
    Chen, Jen-Shiang
    Pan, Jason Chao-Hsien
    Wu, Chien-Kuang
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2007, 34 (3-4): : 353 - 361
  • [6] Minimizing makespan in reentrant flow-shops using hybrid tabu search
    Jen-Shiang Chen
    Jason Chao-Hsien Pan
    Chien-Kuang Wu
    The International Journal of Advanced Manufacturing Technology, 2007, 34 : 353 - 361
  • [7] Minimizing makespan and flowtime in a parallel multi-stage cellular manufacturing company
    Saracoglu, Ilkay
    Suer, Gursel A.
    Gannon, Patrick
    ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2021, 72
  • [8] A fully polynomial time approximation scheme for scheduling on parallel identical two-stage openshops
    Dong, Jianming
    Jin, Ruyan
    Hu, Jueliang
    Lin, Guohui
    JOURNAL OF COMBINATORIAL OPTIMIZATION, 2019, 37 (02) : 668 - 684
  • [9] Randomized approximation schemes for minimizing the weighted makespan on identical parallel machines
    Sun, Ruiqing
    JOURNAL OF COMBINATORIAL OPTIMIZATION, 2024, 47 (03)
  • [10] Minimizing the makespan on two identical parallel machines with mold constraints
    Chung, Tsuiping
    Gupta, Jatinder N. D.
    Zhao, Haidan
    Werner, Frank
    COMPUTERS & OPERATIONS RESEARCH, 2019, 105 : 141 - 155