Game theory
Elham Sadat Mousavi; Ashkan Hafezalkotob; Ahmad Makui; Mohammad Kazem Sayadi
Abstract
This paper presents an optimization model for hotel pricing in the competitive environment following the Covid-19 epidemic, in which the government intervenes by offering appropriate tariffs and hotels use incentive policies such as discounts to attract customers. we consider the government as the leader ...
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This paper presents an optimization model for hotel pricing in the competitive environment following the Covid-19 epidemic, in which the government intervenes by offering appropriate tariffs and hotels use incentive policies such as discounts to attract customers. we consider the government as the leader and the hotels as the followers of the Stalkberg model, then apply the Nash equilibrium to determine the optimal price and demand of hotels in competitive conditions, taking into account the discount. By considering a government utility function, the optimal level of government tariffs is determined. The results indicate that government intervention in the tourism industry includes measures that benefit tourism. Because the government can increase the hotel revenue and expand tourism in favor of hoteliers by reducing its profits. Extensive analysis has been performed on five-star, four-star, and three-star hotels in a tourist area in Iran, and some of the most important managerial insights have been explained.
Engineering Modeling
Salman Abbasi Siar; Mohammad Ali Keramati; Mohammad Reza Motadel
Abstract
Because of the dissemination of Impulse Buying (IB) behavior in consumers its academic studies have increased over the last decade. Because in large stores, sales have to be increased, the behavior of consumers in IB to be taken into account by the researchers and managers of the stores. The purpose ...
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Because of the dissemination of Impulse Buying (IB) behavior in consumers its academic studies have increased over the last decade. Because in large stores, sales have to be increased, the behavior of consumers in IB to be taken into account by the researchers and managers of the stores. The purpose of this paper is to model agent-based the IB behavior of consumers (customers), with regards to the factors of discount and swarm in the purchase. In terms of executive purpose and with Agent-Based Modeling (ABM) approach, the present paper examines the existing reality of consumer IB behavior. This paper develops consumption models, examines and analyzes Consumer Behavior (CB) under the NetLogo software environment. In comparing the optimal points of discounts and sales volume in both discount and swarm-discount functions that lead the stores to maximize profits and sales volume simultaneously, it can be debated that with running this model (swarm-discount) stores would be gaining more sales by less discounts. Results could describe customer behavior by implementing discount and swarm factors. Understanding the customer behavior prepared the comparing possibility of customer behavior in store in each introduced mathematical model. The contributions could be considered in two points of view. On the applicable view, this research can provide the managers and decision makers with significant information, includes possibility of forecasting sales volume and incomes of any policies in stores, so the comparing of policies and strategies analysis would be possible. This method is rather less expensive, because of virtual environment nature. Users of this model can study other sections by changing the research assumptions.
Supply chain management
Javid Ghahremani-Nahr; Hamed Nozari; Seyyed Esmaeil Najafi
Abstract
The mathematical model of a multi-product multi-period multi-echelon closed-loop supply chain network design under uncertainty is designed in this paper. The designed network consists of raw material suppliers, plants, warehouses, distribution centers, and customer zones in forward chain and collection ...
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The mathematical model of a multi-product multi-period multi-echelon closed-loop supply chain network design under uncertainty is designed in this paper. The designed network consists of raw material suppliers, plants, warehouses, distribution centers, and customer zones in forward chain and collection centers, repair centers, recovery/decomposition center, and disposal center in the reverse chain. The goal of the model is to determine the quantities of products and raw material transported between the supply chain entities in each period by considering different transportation mode, the number and locations of the potential facilities, the shortage of products in each period, and the inventory of products in warehouses and plants with considering discount and uncertainty parameters. The robust possibilistic optimization approach was used to control the uncertainty parameter. At the end to solve the proposed model, five meta-heuristic algorithms include genetic algorithm, bee colony algorithm, simulated annealing, imperial competitive algorithm, and particle swarm optimization are utilized. Finally, some numerical illustrations are provided to compare the proposed algorithms. The results show the genetic algorithm is an efficient algorithm for solving the designed model in this paper.
