A demand-based relocation of warehouses and green routing

被引:5
|
作者
Balaji, K. S. [1 ]
Ramasubramanian, B. [1 ]
Vinay, M. Sai Satya [1 ]
Reddy, D. Tejesh [1 ]
Dheeraj, Ch [1 ]
Subash, K. Teja [1 ]
Anbuudayasankar, S. P. [1 ]
机构
[1] Amrita Vishwa Vidyapeetham, Dept Mech Engn, Amrita Sch Engn, Coimbatore 641112, Tamil Nadu, India
关键词
Warehouse; MILP; Dynamic programming; Green routing; THERMAL UNIT COMMITMENT; PROGRAMMING APPROACH; LOCATION; SELECTION; FACILITY; HYBRID;
D O I
10.1016/j.matpr.2021.03.476
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The circumstantial surge in demand for products, despite meticulous demand forecasting, calls for robust supply chain enhancement. One of the ways to optimize it without altering other facets of the supply chain is warehouse relocation. In this study, the data obtained from an enterprise is fed to the Mixed Integer Linear Programming (MILP) algorithm to determine the case for relocation of warehouses. The objective of this paper is to find the appropriate location of the warehouse and determine the economical route using dynamic programming. The choice of economical routing would help in the conservation of fuel and emission of greenhouse gases as green routing is applied. This work would help the supply chain managers to optimize their logistics design, minimize operating costs and attain sustainability in their supply chain. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rd International Conference on Materials, Manufacturing and Modelling.
引用
收藏
页码:8438 / 8443
页数:6
相关论文
共 50 条
  • [41] Examining Consumer Perceptions of Demand-Based Ticket Pricing in Sport
    Shapiro, Stephen L.
    Drayer, Joris
    Dwyer, Brendan
    SPORT MARKETING QUARTERLY, 2016, 25 (01): : 34 - 46
  • [42] Demand-based charging strategy for wireless rechargeable sensor networks
    Dong, Ying
    Wang, Yuhou
    Li, Shiyuan
    Cui, Mengyao
    Wu, Hao
    ETRI JOURNAL, 2019, 41 (03) : 326 - 336
  • [43] Demand-Based Optimal Design of Storage Tank with Inerter System
    Zhang, Shiming
    Zhang, Ruifu
    Zhao, Zhipeng
    SHOCK AND VIBRATION, 2017, 2017
  • [44] Demand-based web surveillance of sexually transmitted infections in Russia
    Domnich, Alexander
    Arbuzova, Eva K.
    Signori, Alessio
    Amicizia, Daniela
    Panatto, Donatella
    Gasparini, Roberto
    INTERNATIONAL JOURNAL OF PUBLIC HEALTH, 2014, 59 (05) : 841 - 849
  • [45] Model-based automation system for demand-based heating and ventilation
    Spasokukotskiy, K
    Horn, M
    Tränkler, H
    TECHNISCHES MESSEN, 2003, 70 (04): : 206 - 213
  • [46] Repetition and Reconfiguration: Demand-Based Confabulation in Initial Eyewitness Memory
    Sharps, Matthew J.
    Herrera, Megan
    Dunn, Laurel
    Alcala, Emanuel
    JOURNAL OF INVESTIGATIVE PSYCHOLOGY AND OFFENDER PROFILING, 2012, 9 (02) : 149 - 160
  • [47] Traffic Demand-based Cooperation Strategy in Cognitive Radio Networks
    Dai, Zhiyu
    Wong, Vincent W. S.
    2014 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), 2014, : 1832 - 1837
  • [48] Are only demand-based policy incentives enough to deploy electromobility?
    Nanaki, Evanthia A.
    Kiartzis, Spyros
    Xydis, George A.
    POLICY STUDIES, 2022, 43 (02) : 370 - 386
  • [49] DDM: A Demand-based Dynamic Mitigation for SMT Transient Channels
    Zhang, Yue
    Zhu, Ziyuan
    Meng, Dan
    2019 IEEE INTL CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, BIG DATA & CLOUD COMPUTING, SUSTAINABLE COMPUTING & COMMUNICATIONS, SOCIAL COMPUTING & NETWORKING (ISPA/BDCLOUD/SOCIALCOM/SUSTAINCOM 2019), 2019, : 614 - 621
  • [50] Demand-based planning of rural water systems in developing countries
    Hopkins, OS
    Lauria, DT
    Kolb, A
    JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE, 2004, 130 (01): : 44 - 52