Effective selection and allocation of material handling equipment for stochastic production material demand problems using genetic algorithm

被引:16
|
作者
Poon, T. C. [1 ]
Choy, K. L. [1 ]
Cheng, C. K. [1 ]
Lao, S. I. [1 ]
Lam, N. Y. [1 ]
机构
[1] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Hong Kong, Peoples R China
关键词
RFID; GA; Stochastic production demand problem; FLEXIBLE MANUFACTURING SYSTEM; INVENTORY MODEL; WAREHOUSES; MANAGEMENT; OPERATIONS; FRAMEWORK; MACHINE;
D O I
10.1016/j.eswa.2011.04.033
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper addresses the stochastic production demand problem in a manufacturing company. The objective of this research is to minimize the waiting time of production workstations and reduce stochastic production material problems through coordinating pickup and delivery orders in a warehouse. RFID technology is adopted to visualize the actual status of operations in production and warehouse environments. A mathematical model is developed to address this problem and a meta-heuristic algorithm using genetic algorithm (GA) is also developed to improve performance. Computational experiments are undertaken to examine the performance of the algorithm when dealing with congestion in cases of heavy and normal demand for production material. The overall result shows that the algorithm efficiently minimizes the total makespan of the production shop floor. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:12497 / 12505
页数:9
相关论文
共 50 条
  • [21] Efficiency analysis of mechanical reducer equipment of material handling industry using Sunflower Optimization Algorithm and Material Generation
    Jena, Sourav
    Jeet, Siddharth
    Bagal, Dilip Kumar
    Baliarsingh, Asini Kumar
    Nayak, Dillip Ranjan
    Barua, Abhishek
    MATERIALS TODAY-PROCEEDINGS, 2022, 50 : 1113 - 1122
  • [22] Availability and Unloading Capacity Assessment of Multi-state Material Handling System, Operate in a Stochastic Environment and Material Handling Stochastic Demand
    Finish, Sagi
    Felshin, Marina
    Frenkel, Ilia
    Khvatskin, Lev
    2016 SECOND INTERNATIONAL SYMPOSIUM ON STOCHASTIC MODELS IN RELIABILITY ENGINEERING, LIFE SCIENCE AND OPERATIONS MANAGEMENT (SMRLO), 2016, : 357 - 364
  • [23] A novel hybrid clonal selection algorithm for the corridor allocation problem with irregular material handling positions
    Liu, Juniqi
    Zhang, Zeqiang
    Gong, Juhua
    Chen, Feng
    Yin, Tao
    Zhang, Yu
    COMPUTERS & INDUSTRIAL ENGINEERING, 2022, 168
  • [24] SELECTION OF THE MOST SUITABLE MATERIAL HANDLING SYSTEM IN PRODUCTION
    Berlec, T.
    Tansek, B.
    Kusar, J.
    INTERNATIONAL JOURNAL OF SIMULATION MODELLING, 2021, 20 (01) : 64 - 75
  • [25] ICMESE: Intelligent consultant system for material handling equipment selection and evaluation
    Park, YB
    JOURNAL OF MANUFACTURING SYSTEMS, 1996, 15 (05) : 325 - 333
  • [26] ADVISER - A COMPUTER-AIDED MATERIAL HANDLING EQUIPMENT SELECTION SYSTEM
    CHU, HK
    EGBELU, PJ
    WU, CT
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 1995, 33 (12) : 3311 - 3329
  • [27] An evaluation of input and output of expert systems for selection of material handling equipment
    Hassan, Mohsen M. D.
    JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT, 2014, 25 (07) : 1049 - 1067
  • [28] Evaluation and selection of material handling equipment in iron and steel industry using analytic hierarchy process
    Varun, Sajja
    Harshita, Raj
    Pramod, Sesha
    Nagaraju, Dega
    FRONTIERS IN AUTOMOBILE AND MECHANICAL ENGINEERING, 2017, 197
  • [29] Solving Material Handling Equipment Selection Problems in an Industry with the Help of Entropy Integrated COPRAS and ARAS MCDM techniques
    Shankha Shubhra Goswami
    Dhiren Kumar Behera
    Process Integration and Optimization for Sustainability, 2021, 5 : 947 - 973
  • [30] Solving Material Handling Equipment Selection Problems in an Industry with the Help of Entropy Integrated COPRAS and ARAS MCDM techniques
    Goswami, Shankha Shubhra
    Behera, Dhiren Kumar
    PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY, 2021, 5 (04) : 947 - 973