Energy efficiency, robustness, and makespan optimality in job-shop scheduling problems

被引:13
|
作者
Salido, Miguel A. [1 ]
Escamilla, Joan [1 ]
Barber, Federico [1 ]
Giret, Adriana [1 ]
Tang, Dunbing [2 ]
Dai, Min [2 ]
机构
[1] Univ Politecn Valencia, Inst Automat & Informat Ind, Camino Vera S-N, Valencia 46071, Spain
[2] Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing, Jiangsu, Peoples R China
基金
美国国家科学基金会;
关键词
Energy Efficiency; Job-Shop Scheduling; Parameter Relationship; Robustness; CONSUMPTION; FRAMEWORK; SEARCH;
D O I
10.1017/S0890060415000335
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Many real-world problems are known as planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. The traditional scheduling models consider performance indicators such as processing time, cost, and quality as optimization objectives. However, most of them do not take into account energy consumption and robustness. We focus our attention in a job-shop scheduling problem where machines can work at different speeds. It represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The main goal of the paper is focused on the analysis of three important objectives (energy efficiency, robustness, and makespan) and the relationship among them. We present some analytical formulas to estimate the ratio/relationship between these parameters. It can be observed that there exists a clear relationship between robustness and energy efficiency and a clear trade-off between robustness/energy efficiency and makespan. It represents an advance in the state of the art of production scheduling, so obtaining energy-efficient solutions also supposes obtaining robust solutions, and vice versa.
引用
收藏
页码:300 / 312
页数:13
相关论文
共 50 条
  • [41] Solving Open Job-Shop Scheduling Problems by SAT Encoding
    Koshimura, Miyuki
    Nabeshima, Hidetomo
    Fujita, Hiroshi
    Hasegawa, Ryuzo
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2010, E93D (08) : 2316 - 2318
  • [42] A multi-objective PSO for job-shop scheduling problems
    Sha, D. Y.
    Lin, Hsing-Hung
    EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (02) : 1065 - 1070
  • [43] A heuristic combination method for solving job-shop scheduling problems
    Hart, E
    Ross, P
    PARALLEL PROBLEM SOLVING FROM NATURE - PPSN V, 1998, 1498 : 845 - 854
  • [44] Preemptive job-shop scheduling problems with a fixed number of jobs
    Peter Brucker
    Svetlana A. Kravchenko
    Yuri N. Sotskov
    Mathematical Methods of Operations Research, 1999, 49 (1) : 41 - 76
  • [45] A Noncompact Formulation for Job-Shop Scheduling Problems in Traffic Management
    Lamorgese, Leonardo
    Mannino, Carlo
    OPERATIONS RESEARCH, 2019, 67 (06) : 1586 - 1609
  • [46] Parallel bat algorithm for optimizing makespan in job shop scheduling problems
    Thi-Kien Dao
    Pan, Tien-Szu
    Trong-The Nguyen
    Pan, Jeng-Shyang
    JOURNAL OF INTELLIGENT MANUFACTURING, 2018, 29 (02) : 451 - 462
  • [47] Solving Fuzzy Job-Shop Scheduling Problems with a Multiobjective Optimizer
    Thanh-Do Tran
    Varela, Ramiro
    Gonzalez-Rodriguez, Ines
    Talbi, El-Ghazali
    KNOWLEDGE AND SYSTEMS ENGINEERING (KSE 2013), VOL 2, 2014, 245 : 197 - 209
  • [48] The Implementation and Improvements of Genetic Algorithm for Job-Shop Scheduling Problems
    Parinov, Oleg
    GECCO-2010 COMPANION PUBLICATION: PROCEEDINGS OF THE 12TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2010, : 2055 - 2057
  • [49] Parallel bat algorithm for optimizing makespan in job shop scheduling problems
    Thi-Kien Dao
    Tien-Szu Pan
    Trong-The Nguyen
    Jeng-Shyang Pan
    Journal of Intelligent Manufacturing, 2018, 29 : 451 - 462
  • [50] Parallel Reactive Tabu Search for Job-Shop Scheduling Problems Considering Energy Management
    Kawaguchi, Shuhei
    Kokubo, Tatsuya
    Fukuyama, Yoshikazu
    2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2017,