MAD Dispersion Measure Makes Extremal Queue Analysis Simple

A notorious problem in queueing theory is to compute the worst possible performance of the GI/G/1 queue under mean-dispersion constraints for the interarrival- and service-time distributions. We address this extremal queue problem by measuring dispersion in terms of mean absolute deviation (MAD) instead of the more conventional variance, making available methods for distribution-free analysis. Combined with random walk theory, we obtain explicit expressions for the extremal interarrival- and service-time distributions and, hence, the best possible upper bounds for all moments of the waiting time. We also obtain tight lower bounds that, together with the upper bounds, provide robust performance intervals. We show that all bounds are computationally tractable and remain sharp also when the mean and MAD are not known precisely but are estimated based on available data instead. Summary of Contribution: Queueing theory is a classic OR topic with a central role for the GI/G/1 queue. Although this queueing system is conceptually simple, it is notoriously hard to determine the worst-case expected waiting time when only knowing the first two moments of the interarrival- and service-time distributions. In this setting, the exact form of the extremal distribution can only be determined numerically as the solution to a nonconvex nonlinear optimization problem. Our paper demonstrates that using mean absolute deviation (MAD) instead of variance alleviates the computational intractability of the extremal GI/G/1 queue problem, enabling us to state the worst-case distributions explicitly.

Deep Reinforcement Learning-Based Workload Scheduling for Edge Computing

Edge computing is a new paradigm for providing cloud computing capacities at the edge of network near mobile users. It offers an effective solution to help mobile devices with computation-intensive and delay-sensitive tasks. However, the edge of network presents a dynamic environment with large number of devices, high mobility of users, heterogeneous applications and intermittent traffic. In such environment, edge computing often suffers from unbalance resource allocation, which leads to task failure and affects system performance. To tackle this problem, we proposed a deep reinforcement learning(DRL)-based workload scheduling approach with the goal of balancing the workload, reducing the service time and the failed task rate. Meanwhile, We adopt Deep-Q-Network(DQN) algorithms to solve the complexity and high dimension of workload scheduling problem. Simulation results show that our proposed approach achieves the best performance in aspects of service time, virtual machine(VM) utilization, and failed tasks rate compared with other approaches. Our DRL-based approach can provide an efficient solution to the workload scheduling problem in edge computing.

Design and Performance Estimation of Mixed-Robotic Fulfillment System

E-commerce retailers face the challenge to assemble a large number of time-critical picking orders. Common parts-to-picker autonomous intelligent warehouses such as automated vehicle storage and retrieval system and robotic mobile fulfillment system are often a little ill-suited for these prerequisites. A mixed-robotic fulfillment system is a hybrid robot picking system based on multi-device collaboration. It is a fusion innovation of traditional automated vehicle storage and retrieval system and robotic mobile fulfillment system. This paper comprehensively considers the characteristics of the system and customer demand, through the construction of a queuing network model to evaluate the performance of the system. A series of problems such as order service time, throughput capacity, and vehicle quantity configuration are analyzed experimentally. The validity of the model is verified by a simulation model.

Continuous Fixed-Bed Column Studies of Textile Effluent Treatment using Multi-Walled Carbon Nanotubes Originated from Rosmarinus officinalis Oil

A continuous adsorption study in a fixed-bed column was carried out using Multi-walled Carbon Nanotubes derived from Rosmarinus officinalis oil as an adsorbent for removing the textile dye Acid blue 40 from an aqueous solution. The adsorbent, MWNTs were prepared from Rosmarinus officinalis oil as a precursor to Fe/Mo catalyst supported on silica at 650 ºC under N2 atmosphere by spray pyrolysis process characterized by scanning electron microscopy, Transmission Electron microscopy, and Raman spectroscopy. The effects of adsorbent bed height (2–6 cm), initial ion concentration (20– 60 mg/L), and flow rate (10–30 mL/min) on the column performance were analyzed. The breakthrough curve was analyzed using the mathematical models of Thomas, Yoon-Nelson, and bed depth service time. The Thomas model at different conditions defined the behaviors of the breakthrough curves. The bed depth service time model showed good agreement with the experimental data. The high values of correlation coefficients (R2 0.9875) obtained indicate the validity of the bed depth service time model for the present column system.

Social Queues (Cues): Impact of Others’ Waiting in Line on One’s Service Time

The traditional queueing literature assumes that service time is largely independent of social influences. However, queues are social systems; and social considerations are therefore likely to impact customers’ service time decision to the extent they have control. Through a series of experiments, we show that when others are waiting in line, customers tend to accelerate their own service time, and in doing so, sacrifice their own consumption utility. This behavior is driven by concern for others. Notably, the effect is diminished when they themselves have waited, as it is perceived as fair to let others wait if one also had to wait. We further show that obscuring the visibility between customers in service and those waiting in line diminishes the negative effect of others queueing on one’s own service time. This paper was accepted by Jay Swaminathan, operations management.

Discrete Time Multi-servers Loss Systems and Stochastic Approximation with Delayed Groups of Customers

The stochastic approximation procedure with delayed groups of delayed customers is investigated. The Robbins-Monro stochastic approximation procedure is adjusted to be usable in the presence of delayed groups of delayed customers. Two loss systems are introduced to get an accurate description of the proposed procedure. Each customer comes after fixed time-intervals with the stage of the following customer is accurate according to the outcome of the preceding one, where the serving time of a customer is assumed to be discrete random variable. Some applications of the procedure are given where the analysis of their results is obtained. The analysis shows that efficiencies of the procedure can be increased by minimizing the number of customers of a group irrespective of their service times that may take maximum values. Efficiencies depend on the maximum service time of the customer and on the number of customers of the group. The most important result is that efficiencies of the procedure are increased by increasing the service time distributions as well as service times of customers .This new situation can be applied to increase the number of served customers where the number of served groups will also be increased. The results obtained seem to be acceptable. In general, our proposal can be utilized to other stochastic approximation procedures to increase the production in many fields such as medicine, computer sciences, industry, and applied sciences.

A Packet Delay Assessment Model in the Data Link Layer of the LTE

The issues of modeling and evaluating the characteristics of the LTE data link layer functioning are considered. Transmitting packets in the data link layer are represented by a probabilistic-temporal graph consisting of two subgraphs. The first subgraph describes the operation of the HARQ protocol, and the second subgraph describes the operation of the ARQ protocol. The first subgraph is nested within the second subgraph. The probabilities of correct reception, non-error detection, and retransmission of packets in the MAC and RLC layers and generating functions of the packet service time based on the HARQ and ARQ protocols are determined. With the help of generating functions, the average value, variance, and coefficient of variation of the packet service time are determined. To calculate the average packet delay time in the LTE data link layer, the type of queuing system is selected, taking into account the coefficient of variation of the packet service time. The analysis of packets' delay time in the network's data link layer is carried out for different values of the intensity of packet arrival and the probabilities of a bit error in the physical layer of the network. For the sustainable functioning of the data link layer of the network, the limit values of the intensity of the arrival of packets are determined for a given probability of a bit error in the physical layer of the network.

Underwater Inspection Using ROV

ADNOC Drilling operates a growing fleet of 22 jack up units. These units require various inspections and tests to ensure that their integrity is maintained while conducting the drilling operations. One of these inspections is the underwater inspection which is required to be carried out twice every 5 years. Traditionally, this inspection is carried out by divers at the shipyard where it is safe for divers to carry out cleaning, visual inspections and NDT of structural welds. Moving the rig to a drydock or a shipyard is a costly and involves a lot of activities related to safety in addition to the out of service time. Loss of revenue is experienced while the rig is out of service, as well as costs associated to the survey, shipyard costs, vessel costs etc. all combining to create an expensive inspection process. ADNOC Drilling Marine and Group Technology adapted a new method for performing the full scope of the underwater inspection offshore using small remotely operated vehicles (ROV), most of the scope is carried out while the rig remains in full operation (while drilling).

Automated System for Restaurant Services

The study proposes a smart restaurant system and analyses its benefits to be able to determine system potential advantages in restaurants. Service time is one of the main criteria that can be improved to enhance the speed of the customer service as well as to increase the number of restaurant visitors. To develop the system, solutions found in scientific literature, software and their different architectures are analysed. It has been found out that it is possible to decrease the average restaurant service load time by 52.76 %. Two hypotheses have been proposed for further research in order to determine how a smart restaurant service system can increase chef’s efficiency and how the use of different algorithms can decrease chef’s workload during peak hours.