By reducing traffic congestion in large cities and providing a safe place to leave vehicles while their owners are doing their activities, parking facilities play an important role in society by ...reducing congestion and the negative externalities (e.g. CO2 generation, noise, congestion) caused by vehicular traffic, being this one detrimental to society. Additionally, parking lots must establish strategies that allow them to be profitable and remain competitive. We determined the rate of a parking lot with a 20% margin, considering the rate of actual arrivals and the lost arrivals rate when the parking lot is full and the time a vehicle spends in the parking lot. In the results we found the queuing theory is applied to a private parking lot, in addition to establishing an optimal pricing policy, operating policies are established that allow it to generate additional revenue.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We show how certain active transport processes in living cells can be modeled in terms of a directed search process with stochastic resetting and delays. Two particular examples are the motor-driven ...intracellular transport of vesicles to synaptic targets in the axons and dendrites of neurons, and the cytoneme-based transport of morphogen to target cells during embryonic development. In both cases, the restart of the search process following reset has a finite duration with two components: a finite return time and a refractory period. We use a probabilistic renewal method to explicitly calculate the splitting probabilities and conditional mean first passage times (MFPTs) for capture by a finite array of contiguous targets. We consider two different search scenarios: bounded search on the interval 0, L, where L is the length of the array, with a refractory boundary at x = 0 and a reflecting boundary at x = L (model A), and partially bounded search on the half-line (model B). In the latter case there is a non-zero probability of failure to find a target in the absence of resetting. We show that both models have the same splitting probabilities, and that increasing the resetting rate r increases (reduces) the splitting probability for proximal (distal) targets. On the other hand the MFPTs for model A are monotonically increasing functions of r, whereas the MFPTs of model B are non-monotonic with a minimum at an optimal resetting rate. We also formulate multiple rounds of search-and-capture events as a G/M/∞ queue and use this to calculate the steady-state accumulation of resources in the targets.
On-Demand Service Platforms Taylor, Terry A
Manufacturing & service operations management,
09/2018, Volume:
20, Issue:
4
Journal Article
Peer reviewed
An on-demand service platform connects waiting-time-sensitive customers with independent service providers (agents). This paper examines how two defining features of an on-demand service ...platform—delay sensitivity and agent independence—impact the platform’s optimal per-service price and wage. Delay sensitivity reduces expected utility for customers and agents, which suggests that the platform should respond by decreasing the price (to encourage participation of customers) and increasing the wage (to encourage participation of agents). These intuitive price and wage prescriptions are valid in a benchmark setting without uncertainty in the customers’ valuation or the agents’ opportunity costs. However, uncertainty in either dimension can reverse the prescriptions: Delay sensitivity increases the optimal price when customer valuation uncertainty is moderate. Delay sensitivity
decreases
the optimal wage when agent opportunity cost uncertainty is high and expected opportunity cost is moderate. Under agent opportunity cost uncertainty, agent independence
decreases
the price. Under customer valuation uncertainty, agent independence
increases
the price if and only if valuation uncertainty is sufficiently high.
The online appendix is available at
https://doi.org/10.1287/msom.2017.0678
.
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The Internet of Things (IoT) is large scale by nature, which is manifested by the massive number of connected devices as well as their vast spatial existence. Cellular networks, which provide ...ubiquitous, reliable, and efficient wireless access, will play fundamental rule in delivering the first-mile access for the data tsunami to be generated by the IoT. However, cellular networks may have scalability problems to provide uplink connectivity to massive numbers of connected things. To characterize the scalability of cellular uplink in the context of IoT networks, this paper develops a traffic-aware spatiotemporal mathematical model for IoT devices supported by cellular uplink connectivity. The developed model is based on stochastic geometry and queueing theory to account for the traffic requirement per IoT device, the different transmission strategies, and the mutual interference between the IoT devices. To this end, the developed model is utilized to characterize the extent to which cellular networks can accommodate IoT traffic as well as to assess and compare three different transmission strategies that incorporate a combination of transmission persistency, backoff, and power-ramping. The analysis and the results clearly illustrate the scalability problem imposed by IoT on cellular network and offer insights into effective scenarios for each transmission strategy.
We study how coding in distributed storage reduces expected download time, in addition to providing reliability against disk failures. The expected download time is reduced because when a content ...file is encoded with redundancy and distributed across multiple disks, reading only a subset of the disks is sufficient for content reconstruction. For the same total storage used, coding exploits the diversity in storage better than simple replication, and hence gives faster download. We use a novel fork-join queueing framework to model multiple users requesting the content simultaneously, and derive bounds on the expected download time. Our system model and results are a novel generalization of the fork-join system that is studied in queueing theory literature. Our results demonstrate the fundamental trade-off between the expected download time and the amount of storage space. This trade-off can be used for design of the amount of redundancy required to meet the delay constraints on content delivery.
•Developed an integrated analytical model for overlapping operations at a container terminal.•Developed a novel iterative algorithm for solving the network.•Validated the model at heavy and non-heavy ...traffic conditions.•Developed insights for efficient stack layouts.
With the growing worldwide trade, container terminals have grown in number and size. To increase operational efficiency, many new terminals are now automated. The key focus is on improving seaside processes, where a distinction can be made between single quay crane operations (all quay cranes are either loading or unloading containers) and overlapping quay crane operations (some quay cranes are loading while others are unloading containers). Using a network of open and semi-open queues, we develop a new integrated stochastic model for analyzing the performance of overlapping loading and unloading operations that capture the complex stochastic interactions among quayside, vehicle, and stackside processes. The analytical model is solved using an iterative algorithm based on the parametric decomposition approximation approach. The system performance is tested at varying container traffic levels. We find that the percent absolute errors in throughput times compared to simulation are less than 10% for all cases. Using these integrated models, we are able to generate design insights and also rapidly analyze what-if scenarios. For example, we show that the best yard layout configurations for single (either loading or unloading) operations and the best for overlapping (both loading and unloading) operations largely overlap. The best configurations have relatively few stack blocks and many rows per block. The model is generic and amenable to obtain other design and operational performance insights.
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On-demand matching between waiting passengers and idle drivers is one of the most important components in a ride-sourcing system. A variety of matching mechanisms have been developed to meet ...different needs of ride-sourcing platforms, e.g. mitigating supply–demand imbalance, maximizing platform revenue. In this paper, we focus on a block matching system, a special type of matching mechanism, where the region of interest is partitioned into blocks, and on-demand matching is separately and simultaneously conducted in each block. Block matching can bring many benefits, such as limiting order assignment with long pick-up distance, simplifying the process of deployment, etc. However, it still remains a challenging yet interesting issue to determine the block size for the matching system, which is a key decision variable governing passengers’ waiting time. To solve the problem, we model the ride-sourcing system with block matching via a M/M/c queue, in which the service rate is endogenous and partially determined by passengers’ average pick-up time. Based on the model, we find that the average queueing time of passengers decreases with block size increasing, while the average pick-up time may increase instead. In addition, the average total waiting time (sum of average queueing and pick-up time) become nearly invariant to the change of block size when the block size is large, which we call plateau phenomenon. In the plateau, ride-sourcing platforms can choose the block size based on other standards while the average total waiting time is always maintained at the nearly lowest value. The findings are verified via an agent-based simulation study, demonstrating that the proposed model can be an effective tool to approximate block matching system.
•Model the ride-sourcing system with block matching via a M/M/c queue.•Spell out endogenous relationship between service rate and average pick-up time.•Study the impacts of block size on key market measurements.•Total waiting time becomes nearly invariant to block size at large block size.•The theoretic results are validated by an agent-based simulator.
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Unmanned aerial vehicles (UAVs) can be conveniently deployed for environmental monitoring, firefighting, disaster rescue, and so on. However, the utmost challenge is how to transfer the important and ...urgent information to the control center as quick as possible in face of communication and computation constraints. As one of the promising technologies, mobile edge computing (MEC) technology can be deployed on UAVs to support computation-intensive and latency-critical applications. Therefore, a joint communication and computation optimization model is established for a MEC enabled UAV network, which includes a centralized MEC enabled top-UAV and a swarm of distributed bottom-UAVs. Using stochastic geometry, the successful transmission probability results for a single link and a group of links are derived based on the three-dimensional distribution of UAV swarm. Moreover, the optimal response delay is theoretically achieved with the closed-form solutions by using stochastic geometry and queueing theory. In contrast to the conventional UAVs without MEC capabilities, the optimal response delay is achieved by using our proposed joint communication and computation optimization algorithm in the MEC enabled UAV swarm scenario. The performances of the proposed algorithm are evaluated based on the results from the simulation system and the hardware testbed.
In the context of fog computing, we consider a simple case where data centers are installed at the edge of the network and assume that if a request arrives at an overloaded data center, then it is ...forwarded to a neighboring data center with some probability. Data centers are assumed to have a large number of servers, and traffic at some of them is assumed to cause saturation. In this case, the other data centers may help to cope with this saturation regime by accepting some of the rejected requests. Our aim is to qualitatively estimate the gain achieved via cooperation between neighboring data centers. After proving some convergence results related to the scaling limits of loss systems for the process describing the number of free servers at both data centers, we show that the performance of the system can be expressed in terms of the invariant distribution of a random walk in the quarter plane. By using and developing existing results in the technical literature, explicit formulas for the blocking rates of such a system are derived.
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