Time In Queue Research Articles

RANCANG BANGUN PURWARUPA SISTEM REGISTRASI MASUK PINTU TOLL UNTUK KENDARAAN MENGGUNAKAN LONG RANGE RADIO (LORA)

The inefficiency of toll gate systems is one of the main causes of traffic congestion on toll roads. This condition requires an innovative solution to detect and process vehicle information before the vehicle reaches the toll gate. This study aims to develop a prototype that uses ESP32, LoRa, GPS, OLED, and Internet of Things (IoT) technology to provide a solution to this problem. The developed prototype uses ESP32 as the main microcontroller, LoRa for long-distance communication, GPS to track the vehicle's location, and OLED as the information display. This system is designed for vehicles to perform real-time registration. The test results show that this prototype can function well in registering vehicle data that will enter the toll gate remotely. With the implementation of this technology, it is expected to reduce the waiting time for toll road users caused by the long queue times of vehicles at conventional toll gate registration systems.

(Invited) Particles Removal on Challenging Surfaces

In semiconductor manufacturing, particle removal is critical for high yield. This study investigates cleaning efficiency on challenging surfaces like deep trenches, wafer bevel, and bonded dies, using various methods. Intentional contamination was applied using silica, silicon nitride, and ceria particles. Cleaning methods tested include high-velocity biphasic sprays, cryogenic processes, and polymer peel techniques. Results show that while polymer peeling offers excellent particle removal efficiency (PRE) on blanket surfaces and wafer bevels, it struggles with high aspect ratio features due to polymer film dewetting. The study provides a new methodology for generating cleaning tool fingerprints and highlights the importance of key contamination parameters, such as particle nature, media characteristics, and queue time. The findings underscore the need for evolving cleaning processes to address the complexities of modern semiconductor surfaces.

A Noise Validation for Quantum Circuit Scheduling Through a Service-Oriented Architecture

Progress in the realm of quantum technologies is paving the way for a multitude of potential applications across different sectors. However, the reduced number of available quantum computers, their technical limitations, and the high demand for their use are posing some problems for developers and researchers. Mainly, users trying to execute quantum circuits on these devices are usually facing long waiting times in the task queues. In this context, this work proposes a Service-Oriented architecture to reduce waiting times and optimize quantum computer usage by scheduling the circuits from different users into combined circuits that are executed at the same time. To validate this proposal, different widely known quantum algorithms have been selected and executed in combined circuits. The obtained results are then compared with the results of executing the same algorithms in an isolated way. This allowed us to measure the impact of the use of the scheduler against quantum noise. Among the obtained results, it has been possible to verify that the noise suffered by executing a combination of circuits through the proposed scheduler does not critically affect the outcomes.

Pickup and delivery problem with electric vehicles and time windows considering queues

The electric vehicle, as a green and sustainable technology, has gained tremendous development and application recently in the logistics distribution system. However, the increasing workload and limited infrastructure capacity pose challenges for electric vehicles in the pickup and delivery operating system, including task allocation, electric vehicle routing, and queue scheduling. To address these issues, this paper introduces a pickup and delivery problem with electric vehicles and time windows considering queues, which considers queue scheduling for multiple electric vehicles when operating at the same site. A novel mixed integer linear programming model is proposed to minimize the cost of travel distance and queue time. An adaptive hybrid neighborhood search algorithm is developed to solve the moderately large-scale problem. Experimental results demonstrate the effectiveness of the model and adaptive hybrid neighborhood search algorithm. The competitive performance of the developed algorithm is further confirmed by finding 9 new best solutions for the pickup and delivery problem with electric vehicles and time windows benchmark instances. Moreover, the results and sensitivity analysis of objective weight costs highlight the impact and importance of considering queues in the studied problem and obtain some management insights.

A DQN based approach for large-scale EVs charging scheduling

This paper addresses the challenge of large-scale electric vehicle (EV) charging scheduling during peak demand periods, such as holidays or rush hours. The growing EV industry has highlighted the shortcomings of current scheduling plans, which struggle to manage surge large-scale charging demands effectively, thus posing challenges to the EV charging management system. Deep reinforcement learning, known for its effectiveness in solving complex decision-making problems, holds promise for addressing this issue. To this end, we formulate the problem as a Markov decision process (MDP). We propose a deep Q-learning (DQN) based algorithm to improve EV charging service quality as well as minimizing average queueing times for EVs and average idling times for charging devices (CDs). In our proposed methodology, we design two types of states to encompass global scheduling information, and two types of rewards to reflect scheduling performance. Based on this designing, we developed three modules: a fine-grained feature extraction module for effectively extracting state features, an improved noise-based exploration module for thorough exploration of the solution space, and a dueling block for enhancing Q value evaluation. To assess the effectiveness of our proposal, we conduct three case studies within a complex urban scenario featuring 34 charging stations and 899 scheduled EVs. The results of these experiments demonstrate the advantages of our proposal, showcasing its superiority in effectively locating superior solutions compared to current methods in the literature, as well as its efficiency in generating feasible charging scheduling plans for large-scale EVs. The code and data are available by accessing the hyperlink: https://github.com/paperscodeyouneed/A-Noisy-Dueling-Architecture-for-Large-Scale-EV-ChargingScheduling/tree/main/EV%20Charging%20Scheduling.

Analisis Sistem Antrean untuk Mengoptimalkan Pelayanan pada Coffeeshop Teman Sejalan .Co di Karanganyar

Teman Sejalan.co is a Coffeeshop located in Karanganyar and was established in December 2022. Operations in the Teman Sejalan.co Coffeeshop business certainly include from the initial order to pickup. This is related to the queuing system which must improve its service so that customers do not leave if there is a queue. This research uses the Single Channel Multiphase method which is in accordance with the current conditions of Teman Sejalan.co's servers and services. This data was obtained by going directly to the research object during the "Makgedebrews" event by calculating the customer queue time with the number of customers at that time being 103 customers. The results of the research regarding the server line service queue system with H1 and H2 were not proven to be true because the number of customers who were enthusiastic about the event held by Teman Sejalan.co. The use of 1 server lane is not optimal at events that are quite popular with customers which causes queues to pile up, so it is better to use 2 lanes at future events so that it is effective when it is busy.

En-route recharging scheduling strategy for electric trucks serving inter-terminal container transportation

ABSTRACT Container ports are increasingly deploying electric trucks (ETs) for inter-terminal transportation (ITT). However, en-route recharging activities, which reduce ITT efficiency, are necessary for ETs because of limited battery capacity. This study proposes a real-time strategy to schedule the en-route recharging activities for ETs. The strategy dynamically monitors the ITT system, and simultaneously designates charging piles for ETs and determines their positions in the service sequences. An ET may be designated to a remote but shorter-queued pile. The total queue time of the ETs served by a designated pile can be reduced by adjusting the service sequence. The effectiveness is demonstrated in comparison with an alternative strategy in a real-world case. The results indicate that the proposed strategy effectively reduces container throughput time by 21.8% and ET energy consumption by 12.3%. Sensitivity analyses suggest that the strategy is applicable for ports with multiple charging stations and moderate ET fleet size.

Study of an Impedance Function for Mixed Traffic Flows Considering the Travel Time–Cost Characteristics of Long-Distance Electric Vehicle Trips

To quantify the travel time and cost characteristics of mixed traffic involving electric vehicles (EVs) and fuel-powered vehicles on roads, in this paper, we comprehensively consider three factors affecting road impedance: queue length, waiting time, and service rate. Initially, a time characteristic function and a cost characteristic function for mixed traffic impedance are constructed. From the perspective of travel time, we consider the impact of EV penetration on the actual road capacity and introduce a capacity coefficient to modify the BPR (Bureau of Public Roads) road impedance function. Given that different types of vehicles might need to wait at charging stations, we employ queuing theory to calculate the queuing time at these stations and construct an impedance model that considers travel time. From the perspective of travel costs, we account for the energy consumption costs and road usage fees for different types of vehicles. The energy consumption cost for travel mileage is obtained by multiplying the unit mileage energy consumption cost of mixed traffic by the travel mileage. For road usage fees, we adopt the conventional method of multiplying the per-kilometer rate for each vehicle type by the travel mileage, thus constructing an impedance model that incorporates travel costs. Finally, in the numerical analysis section, based on the vehicle travel mileage, we categorize travel into short-, medium-, and long-distance trips for analysis. With the constructed mixed traffic impedance model, we conduct a detailed analysis of the travel time and cost characteristics of mixed traffic over different travel distances. We explore the specific impacts of the electric vehicle penetration rate, traffic flow volume, and travel mileage on road impedance. The results indicate that as the penetration rate of electric vehicles increases, the total energy consumption of the transportation system significantly decreases. Moreover, at high electric vehicle penetration rates, although an increase in traffic flow leads to higher traffic impedance and longer travel times, the overall travel costs are reduced. This demonstrates that increasing the penetration rate of electric vehicles positively contributes to reducing the energy consumption and costs of transportation systems.

Investigation of Cu Corrosion Defects Induced by Humidity Imbalance in the Cu Damascene Process of Semiconductors

As integrated circuit design rules shrink in semiconductor, Copper (Cu) has been employed as the back end of line (BEOL) interconnect metal to enhance device performance through reduced resistivity and resistive-capacitive delay.[1] However, Cu metal lines are prone to degradation caused by Cu oxidation-induced defects, including extrusions and voids, owing to their inherent properties.[2] Consequently, it becomes essential to regulate the oxidation rate during the chip fabrication process, particularly when the Cu metal line is exposed during the Damascene process. This control is crucial for ensuring long-term reliability and achieving a higher yield.[3]From the learning of earlier technological nodes, we are aware to and already precisely controlling both process queue time and wafer surface humidity. We must exercise control over the queue time, addressing both inter-process intervals and in-process queuing. [4] This underscores the importance of managing queue time and maintaining low environmental humidity to mitigate wafer surface defects when the FOUP (Front Opening Unified Pod) is opened at the EFEM (Equipment Front End Module).[5] Those humidity and queue times related Cu corrosion defects have been mitigated by N2 purge in FOUP and reducing the wafer quantity. However, dividing the lot, typically consisting of 25 wafers, by half or less to reduce queue times leads to an imbalance in low humidity due to N2 purging. This occurs when the FOUP is opened in EFEM.This paper examines Cu corrosion under low humidity conditions in a FOUP with a small quantity of wafers, specifically examining the influence of queue time during N2 purging in the EFEM. Through simulation of the interaction between the inlet air entering the EFEM and the purged N2 gas, a vulnerable area is identified where copper oxidation takes place. In addition, we propose an arrangement to mitigate high humidity, supported by experiments conducted under real equipment conditions.Fig. 1 shows Cu corrosion defects after ILD etching at around 20% RH in the FOUP. This defect is only observed in the lower slot of the FOUP on the N2 purged load port. Figure 2 illustrates a chart depicting the humidity inside the FOUP under various conditions. The humid region within the FOUP when opened on the N2 purged load port without wafers. (Fig.2a) While the extent of the humid area is contingent upon the flow rate of the N2 purge and the intake of air through the Fan Filter Unit (FFU), the vortex of the inlet air from the cleanroom (<42% RH) is notably observed at the bottom and front side of the FOUP. Therefore, wafers stored in the lower slot of the FOUP face an increased risk of Cu corrosions when subjected to elevated humidity levels during processes like ILD etching and subsequent cleaning. Specifically, an extended queue time amplifies the probability of Cu defects occurring in this high-humidity slot compared to slots with lower humidity. We observed an unintended increase to 28.2% RH in specific configurations when reducing the quantity of wafers to minimize queue time. (Fig. 2b)Hence, we propose an arrangement for wafers that maintains low humidity with small quantity. When placing 12 wafers in the FOUP, an arrangement with equal spacing from the first slot to the 25th slot ensures uniform N2 flow, thereby reducing the highest humidity from 28.2% RH to 14.0% RH by half. (Fig.3a) Alternatively, positioning all wafers on the upper section is another method. (Fig.3b) Therefore, by halving the quantity of wafers and reducing the process wait time by half, and applying the optimal wafer arrangement that maintains low humidity balance even with a small number of wafers, we confirmed the absence of Cu corrosion.

Scheduling in a Simple Assembly Robotics Cell to Minimize Earliness and Tardiness

Background: Robotics Assembly Cells (RAC) have been designed to meet the flexibility requirements demanded by today's globalized market. The objective is to manufacture a vast variety of products at a low cost, which requires equipment with a high level of flexibility, such as robots. The need to schedule a great variety of jobs in an RAC is a very relevant issue, as efficiency and productivity depend on the sequence in which jobs are scheduled. Studies around this matter have developed models with analytical and heuristic approaches, as well as simulation methods and genetic algorithms, seeking to improve performance measures based mainly on time, utilization, and costs. Method: The purpose of this article is to formulate an exact mathematical model using mixed-integer linear programming (MILP) to optimize small scheduling problems. The objective is to minimize the measure of performance related to the tardiness and earliness of jobs. This optimization aims to mitigate the effects of delays in product deliveries, queue times, and work-in-process inventory in subsequent processes. Doing so facilitates adherence to agreed-upon delivery deadlines and prevents bottlenecks in the assembly cell. Results: The proposed mathematical model generates optimal solutions to the job scheduling problem in the assembly cell, which serves as a case study. This addresses the need to minimize tardiness to meet delivery deadlines or minimize earliness while avoiding an increase in work-in-process inventories. The model ensures that optimal scheduling decisions are made to optimize both delivery performance and inventory levels. Conclusions: Due to the NP-hard complexity of the scheduling problem under study, the proposed mathematical model demonstrates computational efficiency in solving scheduling problems with fewer than 20 jobs. The model is designed to handle such smaller-scale problems within a reasonable computational time frame, considering the inherent complexity of the scheduling problem.

Research on the capacity of charging stations based on queuing theory and energy storage scheduling optimization sharing strategy

The rapid development of electric vehicles (EVs) has led to the continuous expansion of charging infrastructure, but it has also resulted in the low utilization of urban charging stations. Numerous cities have made the decision to open to the public electric bus (EB) charging stations for EVs in an effort to address the issue of charging difficulty for EVs in urban areas and increase the use of charging stations. However, when EB charging stations are open to the public, they inevitably create a load on the power grid during peak hours, and also increase the queuing time for EV users. To address these issues, this paper proposes an operational model where EVs can use the EB charging station from 6:00 AM to 8:00 PM daily, while EBs can charge at other times. In order to reduce grid load during periods of peak electricity demand and lower electricity costs, the model makes use of energy storage facilities to charge during off-peak hours and discharge during peak hours. Queue times are also decreased by optimizing the number of chargers using the M/M/s/K queuing model. The research results indicate that during peak hours at the charging station, the probability of electricity consumption exceeding the storage battery's capacity is only 3.562 %. After five years of operation, the charging station has saved 5.6610 % on electricity costs. Taking the K1 bus route in Jinan, Shandong Province as a case study, it was found that the optimal configuration involves 22 chargers. This operational model and energy storage strategy provide a feasible solution for EB charging stations, contributing positively to the sustainable operation of charging stations.

Admission Control in Multi-server Systems Under Binary Reward Structure

We study a multi-server queueing system where a customer is satisfied (and generates a unit revenue) if their queueing time is at most a given constant. If the queueing time of the admitted customer exceeds this constant, the customer gets served, but is unsatisfied and generates no revenue. Such queueing systems arise in the context of modeling service systems where excessive delays are of concern. A key challenge is how to design an admission control policy to maximize the number of satisfied customers per unit time in the long run, assuming that we can observe the number of customers in the system at any time. We call this the binary reward structure system and show that a threshold-type admission policy is optimal. The optimal threshold policy has to be computed numerically. Hence we propose a square-root admission policy to approximate the optimal admission control policy, and compare the performance of these two policies. We derive an analytical upper bound on the performance of optimal admission control policy by deriving an optimal admission policy assuming we have full information over the queueing time of the admitted customers. This is equivalent to a queueing system where customers abandon the queue (i.e., leave without service) if their queueing time exceeds the given constant. We demonstrate that the optimal policy that includes customer abandonment, or alternatively, the optimal policy under full information, the optimal threshold policy, and the square-root admission policy, all exhibit identical performance in the asymptotic regions of the parameter space. Our numerical results indicate that the worst optimality gap of the square-root admission policy is within 3.9% of the optimal revenue, and implementing the square-root admission policy in the observable queueing system leads to a revenue loss that is at most 5.6% of the maximum possible revenue rate in the full information system. We also compare the binary reward structure with the more common linear reward structure where the system incurs holding cost per unit queueing time per customer. In addition, we also show that the analysis based on queueing time is applicable to the system time as well.

Joint optimization of metro travel reservation and compartment capacity allocation for an overcrowded metro line

ABSTRACT In some mega cities, metro lines often experience severe congestion during peak hours, posing serious operational risks. To alleviate passenger delay and address overcrowded conditions, this study proposes a novel model for metro travel reservation and carriage reserved capacity. The model comprises three components: off-station queuing restrictions, dynamic loading constraints, and carriage reserved capacity constraints. A particle swarm optimization algorithm is applied to allocate reservation quota by considering time-variant and location-dependent passenger demand and train supply. The paper applies the model to Nanjing Metro Line 3, using historical data to estimate passenger demand and compartment capacity, and evaluates its performance in four scenarios. The results demonstrate proposed approach efficiently obtains high-quality joint reservation plans, resulting in a notable reduction of approximately 20% in platform delay time and 50% in off-platform queuing time in suitable scenarios. The paper also discusses the implementation challenges and limitations, and provides recommendations for effective application.

Adaptive federated reinforcement learning for critical realtime communications in UAV assisted vehicular networks

This paper sheds the light on road active safety measurements implemented in unmanned aerial vehicles assisted vehicular networks. Despite the great potential of deploying high computing drones, the drone battery life is the major concern, on one hand. On the other hand, road active safety is a critical real-time process that should be tackled in a tight time window in vehicular networks. To meet the mentioned concerns, we adopt federated machine learning on the local vehicles, sending local updates to drone servers. Moreover, a dynamic frequency adaptation framework is proposed to achieve the optimal trade-off between the road active safety performance and drone’s energy consumption. The thresholds for the local update frequency are calibrated according to road safety measurements (i.e., collision rate, risky and impolite driving time on the road) and drone energy consumption. Additionally, an accurate mathematical modeling based on M/G/1 multi-class was conducted in order to access the queuing time at the drone.

The computational and energy cost of simulation and storage for climate science: lessons from CMIP6

Abstract. The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature.

An intelligent partial charging navigation strategy for electric vehicles

ABSTRACT The rise of electric vehicles (EVs) has highlighted the issue of insufficient charging infrastructure, a major barrier to their widespread adoption. This problem is due to the limited capacity of existing charging networks and the performance constraints of EV batteries. Intelligent route planning, which optimizes travel time and energy use, could be a solution. Traditional studies on electric vehicle route planning oversimplify the charging process, assuming a linear relationship between charging level and time, neglecting its actual nonlinear nature. Additionally, these studies underestimate the impact of dynamically changing traffic conditions and charging station queue times on travel time. Integrating these factors into route planning can significantly reduce energy consumption and travel time. Our work proposes an intelligent partial charging strategy, relaxing full recharging restrictions, and fully considering the nonlinear battery characteristics, dynamic traffic conditions, and charging station queue times. Employing an improved genetic algorithm for route planning, our method demonstrates a dual advantage – reducing computational complexity and saving an average of 15% in travel time. Experimental results over a 400 km2 traffic road network validate the efficiency of our approach, outperforming existing methods facing similar challenges.

Chaotic Harris Hawks Optimization Algorithm for Electric Vehicles Charge Scheduling

Electric Vehicle (EV) technology and migration are hindered by battery sizing, short driving ranges, and optimal operations. This article focuses on developing a strategy for scheduling EV charging in a specific region, addressing waiting time, charging time, and uneven scheduling due to unevenly distributed charging stations (CS). The proposed approach optimizes CS using separate queues for different levels, reducing waiting time and costs during peak hours. Which considers trade-offs between time-aware fairness and overall waiting time, and factors like reachability, battery state of charge, depth of discharge limits, and charging rate constraints. A bi-objective formulation and online scheduling algorithm based on dynamic schedulable time, energy demand fluctuation and user’s prioritization are proposed. The aim is to allocate a charging station to each EV by considering travel needs and battery specifics, with the objective of minimizing travel time, queue time, recharging time, and energy costs. To achieve this, the scheduling system utilizes the Chaotic Harris Hawks Optimization (CHHO), an enhanced iteration of the previously discussed metaheuristic, the Harris Hawk Optimization. Validation of the system is conducted through Vehicular Ad-hoc Network (VANET) simulation and comparison with alternative algorithms Exponential Harris Hawk Optimization, Grey Wolf Optimizer and Random allocation. The outcomes demonstrate noteworthy decreases in travel time, queue time, recharging time, and energy costs, all while adhering to set constraints.

Dynamic departure pushback control at airports: Part A—Linear penalty‐based algorithms and policies

AbstractAirport surface congestion can lead to significantly long taxi‐out times, thus resulting in increased fuel‐burn costs as well as excessive emissions of greenhouse gases. To curtail this undesirable syndrome, in this article, we propose a new penalty‐based dynamic departure pushback control (PDPC) strategy, which employs a linear penalty function dependent not only on the taxiway queue limit but also on the current queue length to ration the pushback frequency at airports, and trades taxiway queueing times with gate‐hold delays to minimize the total operational cost (fuel‐burn and gate‐hold costs). Using data from Beijing Capital International (PEK) airport, four different departure pushback control policies, namely: (i) no‐control (baseline case); (ii) traditional ‐control; (iii) PDPC with a constant taxiway limit; and (iv) PDPC with varying taxiway limits; are compared. Detailed Monte Carlo simulations, which showcase the sensitivity of the total cost function to various problem parameters are presented, and our results indicate that deploying the PDPC policy results in a 42% reduction in total operational costs and a 68% reduction in fuel‐burn (kg) as compared to the baseline case. To analytically reinforce these simulation results, an iterative Markov chain‐based optimization algorithm is also developed to estimate the optimal values of the pushback rate and taxiway queue limit that minimize the total cost function. Such an analytical framework is very useful in the absence of reliable airport data as it only requires estimates of the historical pushback request rates and service times at the taxiway, while yet retaining the capability to closely mirror the simulation results. Our Monte Carlo simulations as well as the Markov chain optimization model validate the strength and impact of the proposed PDPC policy, and demonstrate its practical efficacy in reducing airport surface congestion when applied using data from PEK airport.

Peningkatan Produksi dan Daya Saing Kopi di Desa Sumanding Kecamatan Kembang Kabupaten Jepara

One of the coffee farmers and processors in the Kembang Jepara district, Mr. Eko Siswanto, made a business called Vinzoei with coffee products branded The Boyo (Mitra), which has problems in the field of low production capacity. This is because partners process coffee using third-party services, which causes long queue times and high production costs. The product packaging design is still very traditional and unattractive. To overcome these problems, the methods used include outreach, mentoring, monitoring, and evaluation. The results of the increase in benefits from this regional partnership program are an increase in production capacity by 25%, an increase in coffee quality control knowledge by 100%, an increase in assets in the form of a grinder tool by 100%, an increase in product packaging design by 100%, an increase in knowledge about good business management by 100%.

WiSeCart: Sensor-based Smart-Cart with Self-Payment Mode to Improve Shopping Experience and Inventory Management

Superstores are often rippled with people from a variety of the pyramid structure from lower, middle and higher-levels of the pyramid. These malls often transform onto a busy-bee hub with people wishing to explore the merits of discounted product prices and special offers. This, however causes a surge in traffic coupled with the enticing promotions that then lead to endless queues at the various cash-sales point and check-out counters. This case often poses inherent challenges both for business owners and customers due to time constraint, product substitution and quantity tracking in such malls. The study proposes WiSeCart – a wireless sensor-based shopping cart with near feature compatibility feat. Result shows that the integration of a self-payment system via the NFC tech provisions the system with: (a) reduced queue time so that customers can finish their shopping quicker with fast-paced checkout process, (b) yields increase inventory management efficiency with reduced errors from customer data entry using NFC communication stickers, and (c) improved data management capability that allow for further study of customer behaviour for improved customer experience and service delivery, which will in turn equip business owner with better managerial decision support. Keywords: wireless sensor-networks, smart-carts, shopping trolley, customer experience, superstores Journal Reference Format: Brizimor, S.E., Okpor, M.D., Yoro, R.E., Emordi, F.U., Ifioko, A.M., Odiakaose, C.C., Ojugo, A.A., Ejeh, P.O., Abere, R.A., Ako, R.E., Geteloma, V.O., (2024): WiSeCart: Sensor-based Smart-Cart with Self-Payment Mode to Improve Shopping Experience and Inventory Management. Social Informatics, Business, Politics, Law, Environmental Sciences &amp; Technology Journal. Vol. 10, No. 1. Pp 53-74. www.isteams/socialinformaticsjournal. dx.doi.org/10.22624/AIMS/SIJ/V10N1P7