In this centennial year of Erlang's 1917 paper that solved the M/M/m queue, we derive the residence time without resorting to the (by now) conventional machinery of probability theory and Markov chains. Addendum added June 27, 2018.
10:30-12:00 - 2104B Simulation, stochastic programming and modeling Stream: Simulation, stochastic programming and model- ing (contributed) Contributed session Chair: Benjamin Legros Chair: Dinesh Sharma 1 - Mathematical analysis of machine repair problems with common cause failure, hot spares and multiple repair- men Dinesh Sharma We study the machine repairable system comprising M operating ma- chines, H spares and more than one repairman where "the partial server vacation" is applied on some of the repairmen. In this system, the first repairman never takes vacation and always available for servicing of failed machines while other repairmen goes to random length vacation whenever the number of failed machines are less than N, N +1 respec- tively. Machines may breakdown individually or due to common cause according to Poisson process. Vacation time and service time of repair- men follows the exponential distribution. Recursive approach is used to obtain the steady state probabilities. A cost model is developed to determine the optimum value of failed machine maintaining the sys- tem availability and other performance measures. Sensitivity analysis is investigated for optimal conditions and also analyzes the reliability characteristics of the system. 2 - Unintended consequences of optimizing a queue disci- pline for a service level defined by a percentile of the waiting time Benjamin Legros 4 - Single-period newsvendor problem under random end- of-season demand Subrata Mitra Newsvendor problems, which have attracted the attention of re- searchers since 1950’s, have wide applications in various indus- tries. There have been many extensions to the standard single-period newsvendor problem. In this paper, we consider the single-period, single-item and single-stage newsvendor problem under random end- of-season demand, and develop a model to determine the optimal order quantity and expected profit. We prove that the optimal order quantity and expected profit thus obtained are lower than their respective values obtained from the standard newsvendor formulation. We also provide numerical examples and perform sensitivity analyses to compute the extent of deviations of the ’true’ optimal solutions from the newsven- dor solutions. We observe that the deviations are most sensitive to the ratio of the means of the demand distributions. The deviations are also found sensitive to the contribution margin, salvage price, coe cients of variation of the demand distributions and correlation between sea- sonal and end-of-season demands. We provide broad guidelines for managers as to when the model developed in this paper should be used and when the standard newsvendor formulation would su ce to deter- mine the order quantity. Finally, we present the concluding remarks and directions for future research. ⌅ TB-23 Tuesday, 10:30-12:00 - 2105 MADM principles 2 Stream: Multiple criteria decision analysis Invited session Chair: Jung-Ho Lu 1 - A hybrid multiple attributes decision-making model for of the waiting time. This may create an incentive for managers to mod- ify the traditional first-come-first-served discipline of service. For this purpose, we consider the analysis of the M/M/s queue under the queue- ing discipline which minimizes a given percentile of the waiting time. We prove that a strict non-preemptive priority should be given to the oldest customer who has waited less than the acceptable waiting time. We derive closed-form expressions of the performance measures un- der this discipline, and evaluate the unintended consequences that this discipline may have on service levels and on sta ng decisions. In par- ticular, we show that although this discipline may reduce sta ng costs, it leads to excessive wait for non-prioritized customers. 3 - Morphing M/M/m: A new view of an old queue Neil Gunther 2017 is the centenary of A.K. Erlang’s paper on waiting times in an M/D/m queue. M/M/m queues are used to model call centers, multi- cores & the Internet. Unfortunately, those who should be using M/M/m models often don’t know applied probability theory. Our remedy de- fines a morphing approximation to M/M/m that’s accurate within 10% for typical applications+. The morphing residence-time formula is both simpler and more intuitive than the exact solution involving the Erlang-C function. We have also developed an animation of this mor- phing process. An outstanding challenge, however, has been to eluci- date the nature of the corrections that transform the approximate mor- phing solution to the exact Erlang solution. In this presentation, we show: 1) the morphing solutions correspond to the m-roots of unity in the complex z-plane; 2) the exact solutions can be expressed as a rational function with poles; 3) these poles lie inside the unit disk and converge around the Szego curve with increasing m-servers; 4) the correction factor for the morphing model is defined by the deflated polynomial; 5) the pattern of poles in the z-plane provides a conve- nient visualization of how the morphing solutions di↵er from the exact solutions. 2 88 c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 2 / 24
use queueing theory (OR) Many don’t have a background in applied probability theory (OR) Modeling often considered “unrealistic” by mgmt (OR) A significant issue for OR in general Simplest M/M/1 queue can be derived algebraically So can closed M/M/1/N/N (from Little’s law) Avoids probabilities, BD dynamics, Markov chains, etc. IT people like that How to derive M/M/m algebraically ? cf. Erlang’s approach of 1917 c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 4 / 24
time: R = S + W = S + QS = S + (λR)S = S + R(λS) = S + R ρ R = S 1 − ρ (1) No probability theory used Mean waiting time W = ρS 1 − ρ cf. A.K. Erlang 1909 W = ρS 2(1 − ρ) for “simpler” M/D/1 queue (Not 1st textbook example) c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 5 / 24
result ! The Erlang solution is R2 = S 1 − ρ2 (2) Since ρ < 1, ρ2 << 1, so (1 − ρ2) > (1 − ρ) Hence, R2 < R1 How to derive this algebraically? Think: ρ2 represents m = 2 server “dependency” of some kind c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 6 / 24
! Guess m = 3 mean residence time is: R3 = S 1 − ρ3 Wrong! Correct expression is R3 = S + S 3(1 − ρ) 9ρ3 2 + ρ(4 + 3ρ) (3) which is inscrutable and does not simplify c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 7 / 24
because of a coincidence Exact expression for R2 — corresponding to (3) for R3 — is: R2 = S + S 2(1 − ρ) 2ρ2 1 + ρ (4) which just happens to simplify to (2) R2 = S 1 − ρ2 Last factor in (4) comes from the Erlang C function which is completely unintuitive and gets more complicated as m gets bigger. c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 8 / 24
= S + S m(1 − ρ) C(m, a) (5) where C(m, a) was published 1 by Agner Erlang exactly 100 years ago C(m, a) = am m!(1 − ρ) m−1 k=0 ak k! + am m!(1 − ρ) (6) a = mρ is the (telephonic) traffic intensity measured in Erlangs Arnold Allen: “Solving (6) is an unnatural act” Can code an algorithm but all intuition is lost 1Tore O. Engset published later c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 9 / 24
and animation S/m ! ? ! m S A miracle happens Morphing queues As λ increases, M/M/m appears to “morph” from m parallel M/M/1 queues into a single M/M/1 queue that is m-times faster Already know correct R1 for M/M/1 from eqn. (1) Just need to formalize the “small miracle” in the middle c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 10 / 24
involves a truncated geometric series φm(ρ) = 1 1 + ρ + ρ2 + . . . + ρm−1 φm(ρ) produces the approximate mean residence time for M/M/m Rφ m S 1 − ρm (7) Intuitive derivation without probability theory Eqn. (7) is not identical to the Erlang solution Morphing Rφ m underestimates exact RE m Close enough for Guerrilla style operational estimates † See Analyzing Computer System Performance with Perl::PDQ, Springer (2004) c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 12 / 24
for m = 1, 2 Approximate for m ≥ 3 Error generally < 10% 2 Can also develop by playing The Multiserver Numbers Game (2011) Erlang Morphing m = {1, 2, 4, 8, 16, 128} 0.0 0.2 0.4 0.6 0.8 1.0 ρ 1 2 3 4 R/S 0 0.05 0.10 0.15 Can we find analytic corrections? Rφ m → RE m 2 TB-22 audience: Is there a bound on the morphing error? (See Addendum) c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 13 / 24
! a = !S C(m, ρ) = B(m, ρ) 1 − [1 − B(m, ρ)] ρ m S S/m ! (1"#)! m S Many diagrams and simulations later it became clear that a visual/intuitive approach to finding morphing model corrections was doomed: C(m, ρ) affects Rm through waiting times Wm only C(m, ρ) arises from the Poisson dsn not a geometric series Correction terms in ρ are as complicated as C(m, ρ) itself! As ρ → 0, M/M/m behaves like an infinite server, not parallel queues Erlang’s 1917 paper contains no intuitive insights c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 14 / 24
z = ρx + iρy Rm (z) poles determined by location of Pm (z) zeros Morphing approximation (m = 8) 1 all zeros lie on unit disk 2 symmetric roots of unity Erlang solution (m = 8) 1 7 zeros inside unit disk 2 asymmetric left & right half-planes c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 17 / 24
1 Re() - Im() Szego curve Figure 1: RE m zeros lie inside the unit disk but outside the Szeg˝ o bound c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 18 / 24
→ x + iy ≡ z, morphing model poles in the normalized residence time Rm = Rm /S are determined by a simple polynomial of degree m Rφ m (z) = 1 1 − zm (8) viz., the roots of unity zm = 1. Derivation requires a geometric series 1 + z + z2 + · · · + zm truncated at m servers (Theorem 1). 2 From Theorem 2, complex Erlang residence time can be written as RE m (z) = Pm−1(z) Pm (z) (9) Eq. (9) is a rational function associated with the Poisson distribution whose (real) CDF is F(k, m) = e−a m k=0 ak k! with a = mρ. The summation is a truncated exponential series. c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 19 / 24
= 1, 2, . . . , can be regarded as polynomials of degree n However, the zeros of those successive polynomials move outside the unit disk |z| ≤ 1 in Figure 1 as n → ∞ Szeg˝ o (1926) tamed this behavior by scaling the roots: zn → ζn = zn /n He proved ζn converge, as n → ∞, to a complex function: |ζn e1−ζn | = 1 (10) Eq. (10) is the Szeg˝ o bound: teardrop shaped (red) curve in Fig. 1 Solve (10) numerically using the Lambert W function Remarkably, the zeros of (9) are self-scaling and therefore also converge on the Szeg˝ o curve (no explicit rescaling required) c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 20 / 24
thy required) But RE m is inscrutible to derive in the same way Derived the morphing approximation Rφ m (Theorem 1) Rφ m can now be corrected to produce RE m (Theorem 2) 3 Missing terms arise from a complicated polynomial Pm (ρ) Not what I had in mind when I started (analytically or visually) Complex analysis is not familiar to most IT people either But it’s correct and reveals how Rφ m is related algebraically to RE m Convenient visualization by extending ρ from R → C A new perspective for future work? Fairly certain Erlang didn’t look at M/M/m this way 3 TB-22 audience suggests publication in top-tier journal (along with proofs) c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 21 / 24
S 1 − ρm (11) to the exact Erlang solution (5) Rexact = S + C(m, ρ) S m(1 − ρ) (12) has a relative error defined by ∆Rm,ρ = Rexact − Rmorph Rexact (13) A member of the IFORS audience asked if there was an upper bound on ∆R We can now answer in the affirmative c 2018 Performance Dynamics Morphing M/M/m July 3, 2018 22 / 24
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