مقاله انگلیسی رایگان در مورد استراتژی بار برای غربالگری چمدان در فرودگاه

Baggage handling system (BHS) is a type of logistic system installed at airports, transporting passenger bags automatically from sources to destinations (e.g., from check-in counters to departure areas). The major functionalities of a BHS are baggage check-in, transportation, screening, tracking, sortation, earlystorage, etc., and are implemented by various logistical equipment (Yu and Xu, 2010). The carriers to transfer baggage can be conveyors, trays, carts, or their combinations within the system. Recent technological advancement in the BHS is destination-coded vehicle (DCV) where the speed of every bag can be controlled individually (Mao et al., 2015). A BHS is made up by a number of subsystems on the basis of baggage processing flow (van de Laar, 2009). A subsystem is normally comprised of machines that handle the baggage such as screening machines, loading robots, etc., and a number of buffers (e.g., queue conveyors) which are necessary during peak period. A BHS consists of a number of cascaded queueing systems which can potentially cause a bottlenecking problem within the system (de Neufville, 1994; de Neufville and Odoni, 2013).

Therefore, it can be advantageous if practitioners are able to verify the performance of subsystems via simulative analysis ahead of actual system deployment. Solution providers are usually requested to assess their design via simulation and present the performance results to the airport management when tendering a new BHS project. Hafizogullari et al. highlighted the importance of proactive system-level simulation in (Hafizogullari et al., 2003) with an example of Lambert St. Louis International Airport. A number of simulative studies are available in different aspects of the BHS, such as check-in area (Le et al., 2007), merge area (Johnstone et al., 2015), and especially screening area (Sterchi and Schwaninger, 2015; Dorton and Liu, 2016; Leone and Liu, 2005). Sterchi et al. investigated the effects of screening time and false alarm rate on the baggage throughput of airport security screening checkpoint in (Sterchi and Schwaninger, 2015). Reference (Dorton and Liu, 2016) verified the impacts of baggage volume and false alarm rate on the performance (waiting times and number of queues) by modelling the system using queueing theory. Leone et al. suggested a way to estimate the total number of explosives detection system (EDS) machines required and evaluated also waiting times and queue lengths using discrete-event simulation (Leone and Liu, 2005).

This paper concentrates on a different topic which is the routing strategy in airport baggage screening. Currently, according to the industry standard from Transportation Security Administration of the United State (Transportation Security Administration, 2011), baggage screening subsystems implement the round-robin (RR) and first-available (FA) policies for bag assignment. It is a very simple algorithm which distributes bags in a circular way without knowing anything about the system states. It can be easily implemented by software on a programmable logic controller (PLC) (Haneyah et al., 2013). The RR is the most common practice in the industry. It is the only load balancing policy for screening subsystems as stated in the official bidding document of CharlotteDouglas International Airport (Charlotte Douglas International Airport, 2014). In the design document (San Francisco International Airport, 2011; hereafter SFO, 2011) of San Francisco International Airport, the RR can be complemented with the FA. Under the FA scheme, bags are dispatched to the first screening line where the queue conveyors are not full. This strategy requires information from screening lines, and is only used when the queue utilization is extremely high. Both the RR and the FA fall into the category of static routing. The main disadvantage for static routing is the inability to handle variability. At the presence of unbalanced queue lengths (due to variability), neither RR nor FA can react quickly to improve the situation, because of the lack of feedback mechanism. This paper introduces a dynamic policy, namely joinshortest-queue (JSQ), to the airport screening process. It is not a new policy as it has been used in job scheduling in computing, communication theory, and so on (Mukhopadhyay and Mazumdar, 2016; Jiang et al., 2012; Iyengar et al., 2015). This approach relies on the real-time state feedback from queues and servers. A variety of literature demonstrated the optimality of the JSQ policy when the servers follow the first-come-first-serve (FCFS) principle (Winston, 1977; Nelson and Philips, 1993; Akgun et al., 2011).