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MY'STORY

The MOVE Fire

This is a personal recollection on the Move fire on May 13, 1985 Philadelphia police fired thousands of rounds at the MOVE house, city officials approved dropping an explosive device on the roof, the resulting fire was allowed to burn, 11 people—including five children—died, and 61 homes were destroyed. Philadelphia City Council later called it a “brutal attack carried out by the City of Philadelphia on its own citizens” and acknowledged that no individual faced criminal consequences for the bombing. One timeline correction worth preserving for the BHP record: the major previous MOVE-police confrontation was August 8, 1978, about seven years before the bombing, not a year or two earlier. Officer James Ramp was killed, other police and firefighters were wounded, nine MOVE members were later convicted, and television cameras recorded police beating Delbert Africa during his arrest. The 1985 MOVE Commission later specifically criticized city planners for failing to adequately use lessons from that 1978 confrontation. And that actually strengthens the point you’re making: 1985 did not happen without precedent or institutional memory. There had already been a deadly confrontation with MOVE, years of conflict, negotiations and police involvement before Osage Avenue.

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BLACK FACTS
The Truths They Never Taught You...

Wilmington 1898 — An American Coup

Wilmington, North Carolina once had a thriving Black middle class and an elected interracial government. In 1898 white supremacists used violence to overthrow that government, kill Black residents and drive many others from the city.

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Wikipedia

Bulk queue

In queueing theory, a discipline within the mathematical theory of probability, a bulk queue[1] (sometimes batch queue[2]) is a general queueing model where jobs arrive in and/or are served in groups of random size.[3]: vii  Batch arrivals have been used to describe large deliveries[4] and batch services to model a hospital out-patient department holding a clinic once a week,[5] a transport link with fixed capacity[6][7] and an elevator.[8]

Networks of such queues are known to have a product form stationary distribution under certain conditions.[9] Under heavy traffic conditions a bulk queue is known to behave like a reflected Brownian motion.[10][11]

Kendall's notation

[edit]

In Kendall's notation for single queueing nodes, the random variable denoting bulk arrivals or service is denoted with a superscript, for example MX/MY/1 denotes an M/M/1 queue where the arrivals are in batches determined by the random variable X and the services in bulk determined by the random variable Y. In a similar way, the GI/G/1 queue is extended to GIX/GY/1.[1]

Bulk service

[edit]

Customers arrive at random instants according to a Poisson process and form a single queue, from the front of which batches of customers (typically with a fixed maximum size[12]) are served at a rate with independent distribution.[5] The equilibrium distribution, mean and variance of queue length are known for this model.[5]

The optimal maximum size of batch, subject to operating cost constraints, can be modelled as a Markov decision process.[13]

Bulk arrival

[edit]

Optimal service-provision procedures to minimize long run expected cost have been published.[4]

Waiting Time Distribution

[edit]

The waiting time distribution of bulk Poisson arrival is presented in.[14]

References

[edit]
  1. ^ a b Chiamsiri, Singha; Leonard, Michael S. (1981). "A Diffusion Approximation for Bulk Queues". Management Science. 27 (10): 1188–1199. doi:10.1287/mnsc.27.10.1188. JSTOR 2631086.
  2. ^ Özden, Eda (2012). Discrete Time Analysis of Consolidated Transport Processes. KIT Scientific Publishing. p. 14. ISBN 978-3-86644-801-8.
  3. ^ Chaudhry, M. L.; Templeton, James G. C. (1983). A first course in bulk queues. Wiley. ISBN 978-0-471-86260-4.
  4. ^ a b Berg, Menachem; van der Duyn Schouten, Frank; Jansen, Jorg (1998). "Optimal Batch Provisioning to Customers Subject to a Delay-Limit". Management Science. 44 (5): 684–697. doi:10.1287/mnsc.44.5.684. JSTOR 2634473.
  5. ^ a b c Bailey, Norman T. J. (1954). "On Queueing Processes with Bulk Service". Journal of the Royal Statistical Society, Series B. 61 (1): 80–87. JSTOR 2984011.
  6. ^ Deb, Rajat K. (1978). "Optimal Dispatching of a Finite Capacity Shuttle". Management Science. 24 (13): 1362–1372. doi:10.1287/mnsc.24.13.1362. JSTOR 2630642.
  7. ^ Glazer, A.; Hassin, R. (1987). "Equilibrium Arrivals in Queues with Bulk Service at Scheduled Times". Transportation Science. 21 (4): 273–278. doi:10.1287/trsc.21.4.273. JSTOR 25768286.
  8. ^ Marcel F. Neuts (1967). "A General Class of Bulk Queues with Poisson Input" (PDF). The Annals of Mathematical Statistics. 38 (3): 759–770. doi:10.1214/aoms/1177698869. JSTOR 2238992.
  9. ^ Henderson, W.; Taylor, P. G. (1990). "Product form in networks of queues with batch arrivals and batch services". Queueing Systems. 6: 71–87. doi:10.1007/BF02411466.
  10. ^ Iglehart, Donald L.; Ward, Whitt (1970). "Multiple Channel Queues in Heavy Traffic. II: Sequences, Networks, and Batches" (PDF). Advances in Applied Probability. 2 (2): 355–369. doi:10.1017/s0001867800037435. JSTOR 1426324. Retrieved 30 Nov 2012.
  11. ^ Harrison, P. G.; Hayden, R. A.; Knottenbelt, W. (2013). "Product-forms in batch networks: Approximation and asymptotics" (PDF). Performance Evaluation. 70 (10): 822. doi:10.1016/j.peva.2013.08.011. Archived from the original (PDF) on 2016-03-03. Retrieved 2015-09-04.
  12. ^ Downton, F. (1955). "Waiting Time in Bulk Service Queues". Journal of the Royal Statistical Society, Series B. 17 (2). Royal Statistical Society: 256–261. JSTOR 2983959.
  13. ^ Deb, Rajat K.; Serfozo, Richard F. (1973). "Optimal Control of Batch Service Queues". Advances in Applied Probability. 5 (2): 340–361. doi:10.2307/1426040. JSTOR 1426040.
  14. ^ Medhi, Jyotiprasad (1975). "Waiting Time Distribution in a Poisson Queue with a General Bulk Service Rule". Management Science. 21 (7): 777–782. doi:10.1287/mnsc.21.7.777. JSTOR 2629773.

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Wikipedia

Bulk queue

In queueing theory, a discipline within the mathematical theory of probability, a bulk queue (sometimes batch queue) is a general queueing model where jobs arrive in and/or are served in groups of random size. Batch arrivals have been used to describe large deliveries and batch services to model a hospital out-patient department holding a clinic once a week, a transport link with fixed capacity and an elevator. Networks of such queues are known to have a product form stationary distribution under certain conditions. Under heavy traffic conditions a bulk queue is known to behave like a reflected Brownian motion.

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Wikipedia

M/M/1 queue

In queueing theory, a discipline within the mathematical theory of probability, an M/M/1 queue represents the queue length in a system having a single server, where arrivals are determined by a Poisson process and job service times have an exponential distribution. The model name is written in Kendall's notation. The model is the most elementary of queueing models and an attractive object of study as closed-form expressions can be obtained for many metrics of interest in this model. An extension of this model with more than one server is the M/M/c queue.

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Wikipedia

M/M/c queue

In queueing theory, a discipline within the mathematical theory of probability, the M/M/c queue (or Erlang–C model) is a multi-server queueing model. In Kendall's notation it describes a system where arrivals form a single queue and are governed by a Poisson process, there are c servers, and job service times are exponentially distributed. It is a generalisation of the M/M/1 queue which considers only a single server. The model with infinitely many servers is the M/M/∞ queue.

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Wikipedia

M/G/1 queue

In queueing theory, a discipline within the mathematical theory of probability, an M/G/1 queue is a queue model where arrivals are Markovian (modulated by a Poisson process), service times have a General distribution and there is a single server. The model name is written in Kendall's notation, and is an extension of the M/M/1 queue, where service times must be exponentially distributed. The classic application of the M/G/1 queue is to model performance of a fixed head hard disk.

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TOPIC OF THE DAY

Greenwood / Black Wall Street

Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.

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TRIVIA QUESTION OF THE DAY

What prosperous Tulsa district became widely known as “Black Wall Street”?

The Greenwood District.