Transshipment model considering environmental cost using mixed integer linear programming: Beef distribution problem

被引:0
作者
Januardi, Januardi [1 ]
Ramdhani, Zakia Puspa [1 ]
Harnaningrum, Rizky Novera [1 ]
机构
[1] Inst Teknol Sepuluh Nopember, Dept Ind Engn, Surabaya, Indonesia
关键词
branch and bound; environmental cost; green transshipment; mixed integer linear programming;
D O I
10.17535/crorr.2020.0013
中图分类号
F [经济];
学科分类号
02 ;
摘要
The operational research paper in the transportation model nowadays is heading to the environmental issue. One of the famous operational research models is transshipment. Transshipment is an expanded model of transportation, in each distribution center between the start to the destination point. In this research, the transshipment model is integrated into an environmental function. The challenge is to find the right shipment of each route from the start, distribution, and destination point considering the transportation cost and carbon emission. This research proposed a transshipment model by minimizing transportation and carbon emission cost using mixed{integer linear programming for model formulation. The solution searching used branch and bound method. This research analyzed the environmental objective function and constrain effect in the transshipment model. The model was tested in a beef distribution case study in Bogor, Indonesia that has eight source points, three distribution centers, and six destination points. The model was experimented using carbon emission limitation scenarios. The optimum result in source allocation, distribution and destination were different between the two scenarios. The carbon emission limitation affected carbon emission production and total cost.
引用
收藏
页码:155 / 164
页数:10
相关论文
共 50 条
[31]   Rolling Stock Scheduling by Mixed-Integer Linear Programming Considering Splitting and Combining [J].
Kato S. ;
Morito S. ;
Fukumura N. .
IEEJ Transactions on Industry Applications, 2022, 142 (04) :280-289
[32]   Dynamic Fleet Configuration Model for Optimizing Earthmoving Operations Using Mixed Integer Linear Programming [J].
Khallaf, Zaid ;
Alshibani, Adel ;
Alsawafy, Omar ;
Mohammed, Awsan ;
Bubshait, Abdulaziz .
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 2024, 150 (11)
[33]   Stochastic Optimization Model for Energy Management of a Hybrid Microgrid using Mixed Integer Linear Programming [J].
Franke, G. ;
Schneider, M. ;
Weitzel, T. ;
Rinderknecht, S. .
IFAC PAPERSONLINE, 2020, 53 (02) :12948-12955
[34]   A new mixed integer linear programming model for product development using quality function deployment [J].
Delice, Elif Kilic ;
Gungor, Zuelal .
COMPUTERS & INDUSTRIAL ENGINEERING, 2009, 57 (03) :906-912
[35]   A Satisficing Bi-Directional Model Transformation Engine using Mixed Integer Linear Programming [J].
Callow, Glenn ;
Kalawsky, Roy .
JOURNAL OF OBJECT TECHNOLOGY, 2013, 12 (01)
[36]   Distribution Network Restoration Using Mixed Integer Linear Programming Approach Based on Node State Variable [J].
Wang, Jiawei ;
Dong, Jinxi ;
Ren, Xiusheng ;
Wang, Zheng ;
Guo, Jing ;
Xu, Yahui ;
Liu, Wenxia .
2017 IEEE CONFERENCE ON ENERGY INTERNET AND ENERGY SYSTEM INTEGRATION (EI2), 2017, :310-315
[37]   Mixed-integer linear programming and constraint programming formulations for solving resource availability cost problems [J].
Kreter, Stefan ;
Schutt, Andreas ;
Stuckey, Peter J. ;
Zimmermann, Juergen .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2018, 266 (02) :472-486
[38]   Minimum Spillage from Reservoirs using Mixed Integer Linear Programming [J].
Almohseen, Kamel A. .
JOURNAL OF ENGINEERING RESEARCH, 2021, 9
[39]   An Algorithm for Hardware/Software Partitioning Using Mixed Integer Linear Programming [J].
Ralf Niemann ;
Peter Marwedel .
Design Automation for Embedded Systems, 1997, 2 :165-193
[40]   Inference of chemical reaction networks using mixed integer linear programming [J].
Willis, Mark J. ;
von Stosch, Moritz .
COMPUTERS & CHEMICAL ENGINEERING, 2016, 90 :31-43