Volume 34, Issue 4 (IJIEPR 2023)                   IJIEPR 2023, 34(4): 1-25 | Back to browse issues page

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Aghazadeh S M, Farvaresh H. Efficient resource management and pricing in a two-echelon supply chain with cooperative advertising: A bi-level programming approach. IJIEPR 2023; 34 (4) :1-25
URL: http://ijiepr.iust.ac.ir/article-1-1879-en.html
1- PhD student of Industrial Engineering, Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
2- Associate Professor, Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran , farvaresh@uok.ac.ir
Abstract:   (607 Views)
The growing online marketplace has opened a plethora of opportunities for businesses across various industries. Manufacturers, seeking to bypass intermediaries and directly reach end-users, have been increasingly adopting online sales channels in addition to their traditional retail sales. A key challenge, however, lies in determining optimal pricing strategies and advertising investments for both manufacturers and retailers while considering various constraints. This study contemplates a two-echelon supply chain model involving one manufacturer and two retailers. The manufacturer sells its product both through retailers (offline channel) and directly to consumers via an online channel. The model features both global and local advertising. The influence of global advertising is realized through distinct advertising channels, each with a unique impact on demand. To further motivate retailers, the manufacturer contributes to the cost of local advertising. In response to these challenges, this research formulates a bi-level model and employs the concept of Variational Inequalities to solve it. The model also contends with production capacity and budget constraints, leading to a Generalized Nash-Stackelberg game. The validity of the model and the efficacy of the solution method are assessed through numerical experiments performed. Finally, a set of valuable managerial insights are provided.
Full-Text [PDF 837 kb]   (317 Downloads)    
Type of Study: Research | Subject: Operations Research
Received: 2023/08/30 | Accepted: 2023/11/22 | Published: 2023/12/9

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