2019
DOI: 10.1016/j.future.2019.05.066
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Stackelberg model based game theory approach for assortment and selling price planning for small scale online retailers

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Cited by 18 publications
(7 citation statements)
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“…(1) cold chain transport and path optimization of fresh produce [9,10], with research methods being mainly algorithmic [11,12]. (2) Contractual coordination of fresh produce supply chains [13,14], with game theory as the main method [15,16]. (3) Fresh produce supply chain process optimization [17,18], the main method is empirical analysis [19,20].…”
Section: Fresh Products Supply Chain Coordinationmentioning
confidence: 99%
“…(1) cold chain transport and path optimization of fresh produce [9,10], with research methods being mainly algorithmic [11,12]. (2) Contractual coordination of fresh produce supply chains [13,14], with game theory as the main method [15,16]. (3) Fresh produce supply chain process optimization [17,18], the main method is empirical analysis [19,20].…”
Section: Fresh Products Supply Chain Coordinationmentioning
confidence: 99%
“…The maxi-min mathematical model of game theory was used for selecting the appropriate marketing strategy with respect to rival's strategies for two brands of beverage manufacturer (Baradaran, 2019). A game-theoretical model with two strategies is developed to support the small-scale e-tailer in assortment planning (AP) decision-making (Saberi et al, 2019). The first strategy offers the product with supreme quality by procuring it from the main powerful supplier as a non-cooperative Stackelberg supply chain model in which the supplier plays a leader and the e-tailor is a follower; the second strategy offers the product from a less popular brand as an assortment planning problem model while considering utility degradation of providing alternative brand to the customers.…”
Section: Game Theory and Marketingmentioning
confidence: 99%
“…Step 5: Equations (13) and (14) are used to update the velocities and positions of followers particles, respectively; compare the fitness functions of the particles; and retain the population best position in the memory library.…”
Section: Implementation Steps Of the Ipso Algorithmmentioning
confidence: 99%
“…Step 17: From the memory library, we choose particles to replace particles with worse fitness among the population, and the immune system generates a new generation of particles p 1 . Then, the algorithm continues to update the particle velocities and positions by using Equations (13) and (14).…”
Section: Implementation Steps Of the Ipso Algorithmmentioning
confidence: 99%
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