Enhancing the Credibility of Agent-Based Model for the Study of Workers' Group Behavior by Comparing Simulation Data with Survey Data

Seat and aisle interferences are assumed to be linked with a prolonged boarding time along with several other aspects related to airplane boarding such as: luggage handling, luggage distribution inside the cabin, number of passengers, passengers’ physique characteristics, group behavior, seat selection, aircraft occupancy, aircraft design, etc. Based on these assumptions, a series of proposed boarding methods, underlying their efficiency starting from the absence or limited presence of these types of interferences, are proposed. The present paper aims to analyze whether the different types of seat or aisle interferences do matter for the overall boarding time by considering 24 boarding methods proposed in the literature. A series of specific elements related to interferences, such as: the average waiting time, the average number of interferences based on their types, and the average number of interference-affected passengers, have been considered. Also, the presence of multiple interferences in different parts of the aircraft has been analyzed in order to offer a complete picture of the considered situation. An agent-based model in NetLogo 6.0.4, fed with values form field trials within the literature is created and used for simulations, which enables the agents to act like real passengers involved in an airplane boarding process.

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Agent-based modeling and simulation of the decision behaviors of e-retailers

Industrial Management & Data Systems ◽

10.1108/imds-07-2017-0321 ◽

2018 ◽

Vol 118 (5) ◽

pp. 1094-1113 ◽

Cited By ~ 2

Author(s):

Guoyin Jiang ◽

Shan Liu ◽

Wenping Liu ◽

Yan Xu

Keyword(s):

Simulation Model ◽

Agent Based Modeling ◽

Price Adjustment ◽

Agent Based Model ◽

Online Markets ◽

Simulation Data ◽

Content Type ◽

Agent Based ◽

Integrated Simulation ◽

Decision Behaviors

Purpose Social media facilitates consumer exchanges on product opinions and provides comprehensive knowledge of online products. The interaction between consumers and e-retailers evolves into a collective set of dynamics within a complex system. Agent-based modeling is well suited to stimulate such complex systems. The purpose of this paper is to integrate agent-based model and technique for order performance by similarity to ideal solution (TOPSIS) to simulate decision behaviors of e-retailers in competitive online markets. Design/methodology/approach An agent-based network model using the TOPSIS driven by actual price data is developed. The authors ran an experimental model to simulate interactions between online consumers and e-retailers and to record simulation data. A nonparametric test is used to conduct data analysis and evaluate the sensibility of parameters. Findings Simulation results showed that different profits could be obtained for various brands under different social network structures. E-retailers could achieve more profits through cross-selling than single-selling; however, the highest profits can be achieved when some adopt cross-selling, whereas others use single-selling. From a game perspective, the equilibrium for price-adjustment frequency can be determined from the simulation data. Thus, price adjustment differences significantly affect e-retailer profit. Originality/value This study provides new insights into the evolutionary dynamics of online markets. This work also indicates how to build an integrated simulation model with an agent-based model and TOPSIS and how to use an integrated simulation model and interpret its results.

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