Integrated strategic and tactical supply chain planning with price-sensitive demands

被引:23
作者
Fattahi, M. [1 ]
Mahootchi, M. [1 ]
Husseini, S. M. Moattar [1 ]
机构
[1] Amirkabir Univ Technol, Dept Ind Engn & Management Syst, Tehran, Iran
关键词
Supply chain network design; Strategic and tactical decisions; Capacity expansion; Mixed-integer linear programming; Dynamic pricing approach; NETWORK DESIGN PROBLEM; FACILITY LOCATION; PLANT LOCATION; WAREHOUSE LOCATION; MULTIPERIOD; DECISIONS; MODELS;
D O I
10.1007/s10479-015-1924-3
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
This paper deals with the supply chain network design and planning for a multi-commodity and multi-layer network over a planning horizon with multiple periods in which demands of customer zones are considered to be price dependent. These prices determine the demands using plausible price-demand relationships of customer zones. The net income of the supply chain is maximized, while satisfying budget constraints for investment in network design. In addition, a new approach is considered for capacity planning to make the problem more realistic. In this regard, when production plants are opened and expanded, capacity options are taken into account for manufacturing operations. Furthermore, several aspects relevant to real world applications are captured in the problem. Different interconnected time periods in the planning horizon are considered for strategic and tactical decisions in the problem and then, a mixed-integer linear programming (MILP) model is developed. The performance and applications of the model are investigated by several test problems with reasonable sizes. The numerical results illustrate that obtained solutions after solving the MILP model by using CPLEX solver are acceptable. Moreover, using an alternative pricing approach, a tight upper bound is provided for the problem. In addition, a deep sensitivity analysis is conducted to show the validity and performance of the proposed model.
引用
收藏
页码:423 / 456
页数:34
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