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Enhancing a simulation-based Allocator Software with machine learning and genetic algorithms for improving the gate assignment problem

Assigning gates to flights considering physical, operational, and temporal constraints is known as the Gate Assignment Problem. This article proposes the novelty of coupling a commercial stand and gate allocation software with an off-the-grid optimization algorithm. The software provides the assignment costs, verifies constraints and restrictions of an airport, and provides an initial allocation solution. The gate assignment problem was solved using a genetic algorithm. To improve the robustness of the allocation results, delays and early arrivals are predicted using a random forest regressor, a machine learning technique and in turn they are considered by the optimization algorithm. Weather data and schedules were obtained from Zurich International Airport. Results showed that the combination of the techniques result in more efficient and robust solutions with higher degree of applicability than the one possible with the sole use of them independently.

Check-in allocation improvements through the use of a simulation-optimization approach

The aeronautical industry is still under expansion in spite of the problems it is facing due to the increase in oil prices, limited capacity, and novel regulations. The expansion trends translate into problems at different locations within an airport system and are more evident when the resources to cope with the demand are limited or are reaching to theirs limits. In the check-in areas they are appreciated as excessive waiting times which in turn are appreciated by the customers as bad service levels. The article presents a novel methodology that combines an evolutionary algorithm and simulation in order to give the best results taking into account not only the mandatory hard and soft rules determined by the internal policies of an airport terminal but also the quality indicators which are very difficult to include using an abstract representation. The evolutionary algorithm is developed to satisfy the different mandatory restrictions for the allocation problem such as minimum and maximum number of check-in desks per flight, load balance in the check-in islands, opening times of check-in desks and other restrictions imposed by the level of service agreement. Once the solutions are obtained, a second evaluation is performed using a simulation model of the terminal that takes into account the stochastic aspects of the problem such as arriving profiles of the passengers, opening times physical configurations of the facility among other with the objective to determine which allocation is the most efficient in real situations in order to maintain the quality indicators at the desired level.

Digital marketing activities by Dutch place management partnerships: A resource-based view

In most shopping areas, there are place management partnerships (PMPs) that aim to increase the competitiveness of the area. Collective digital marketing activities, such as the adoption and update of collective websites and social media pages, provide opportunities in this regard. Currently, the extent to which digital marketing activities are being employed varies widely among PMPs. However, studies investigating the factors that influence the uptake of digital marketing activities are lacking. This study applies a resource-based view to fill this gap, using data from an online survey about collective digital marketing activities among 164 official representatives of PMPs in urban shopping areas in the Netherlands. Regression analyses were employed to examine the extent to which the resources of PMPs influence the adoption and update frequency of the two most often used digital marketing channels: websites and social media pages. The results revealed that while the adoption of collective digital marketing channels is strongly influenced by the physical resources that characterize the shopping area itself, the update frequency of these channels is influenced more by the organizational resources of PMPs. In addition, the strategic choice of PMPs to deploy human and financial resources for the benefit of collective digital marketing activities leads to increased use of these activities. This effect is reinforced by the fact that digital marketing skills gained through experience contribute to a higher update frequency of the adopted channels. As such, this study provides empirical evidence on the influence of PMPs shared resources upon their digital marketing activities.

Resource-Efficient Image Buffer Architecture for Neighborhood Processors

Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx BlockRAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution (1080 × 1920) @103 fcps.

Opt-Sim approach for the gate allocation problem in covid-19 times

This study tackles the gate allocation problem (GAP) at the airport terminal, considering the current covid-19 pandemic restrictions. The GAP has been extensively studied by the research community in the last decades, as it represents a critical factor that determines an airport's capacity. Currently, the airport passenger terminal operations have been redesigned to be aligned and respect the covid-19 regulation worldwide. This provides operators with new challenges on how to handle the passengers inside the terminal. The purpose of this study is to come up with an efficient gate allocator that considers potential issues derived by the current pandemic, i.e., avoid overcrowded areas. A sim-opt approach has been developed where an evolutionary algorithm (EA) is used in combination with a dynamic passenger flow simulation model to find a feasible solution. The EA aims to find a (sub)optimal solution for the GAP, while the simulation model evaluates its efficiency and feasibility in a real-life scenario. To evaluate the potential of the Opt-Sim approach, it has been applied to a real airport case study.

Planning and resource allocation of a hybrid IoT network using artificial intelligence