Abstract
Although simulation is typically considered as relevant and highly applicable, the use of simulation is limited in reality. Many organizations have tried to use simulation to analyze their business processes at some stage. However, few are using simulation in a structured and effective manner. This may be caused by a lack of training and limitations of existing tools, but in this chapter, we argue that there are also several additional and more fundamental problems. First of all, the focus is mainly on design while managers would also like to use simulation for operational decision making (solving the concrete problem at hand rather than some abstract future problem). Second, there is limited support for using existing artifacts such as historical data and workflow schemas. Third, the behavior of resources is modeled in a rather naive manner. This chapter focuses on the last problem. It proposes a new way of characterizing resource availability. The ideas are described and analyzed using CPN Tools. Experiments show that it is indeed possible to capture human behavior in business processes in a much better way. By incorporating better resource characterizations in contemporary tools, business process simulation can finally deliver on its outstanding promise.
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Notes
- 1.
The interested reader can look up the declarations that would initialize this model with \( \lambda = {1 \mathord{\left/{\vphantom {1 {100}}} \right.} {100}} \), \( \mu = {1 \mathord{\left/{\vphantom {1 {15}}} \right.} {15}} \), and one resource “r1” characterized by h = 1,000, a = 0.2, and c = 200 in Appendix 6.
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Appendices
Appendix 1: Declarations for CPN Model in Sect. 3.2
The colset, variable, and function declarations of the CPN model have been listed in the ML language.
Colset Declarations
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colset CID = int timed;
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colset Tm = int;
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colset Work= int;
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colset Case = product CID * Tm * Work timed;
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colset Queue = list Case;
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colset Res= string timed;
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colset Hor = int;
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colset Av = int with 1..100;
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colset Chunk = int;
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colset Info = product Hor * Av * Chunk;
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colset RWC = product Res * Work * Chunk timed;
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colset RT = product Res * Tm timed;
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colset RI = product Res * Info timed;
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colset CR = product Case * RT timed;
Variable Declarations
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var i: CID;
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var t, t1, t2, done: Tm;
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var w, w1, w2: Work;
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var r: Res;
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var h: Hor;
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var a: Av;
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var c, c1: Chunk;
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var q: Queue;
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var hac : Info;
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val Rinit = [("r1", (1000, 20, 200))];
Function Declarations
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fun x1([]) = [] | x1((x, (h, a, c))::r) = (x, 0, c)::x1(r);
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fun x2([]) = [] | x2((x, y):: r) = x :: x2(r);
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fun Mtime() = IntInf.toInt(time()):int;
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fun Dur() = floor(exponential(1.0/15.0));
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fun IAT() = floor(exponential(1.0/100.0));
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fun min(x, y) = if x < y then x else y;
Appendix 2: Task Parameters for Base Scenario Described in Sect. 3.4
Task | Parameters | |
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(a) | Register | Resources r a = 1 Arrival rate \( {\lambda_a} = {1 \mathord{\left/{\vphantom {1 {50}}} \right.} {50}} \) Service rate \( {\mu_a} = {1 \mathord{\left/{\vphantom {1 {18}}} \right.} {18}} \) Utilization ρ a = 0.36 |
(b) | Classify | Resources r b = 2 Arrival rate \( {\lambda_b} = {1 \mathord{\left/{\vphantom {1 {50}}} \right.} {50}} \) Service rate \( {\mu_b} = {1 \mathord{\left/{\vphantom {1 {36}}} \right.} {36}} \) Utilization ρ b = 0.36 |
(c) | Phone garage | Resources r c = 3 Arrival rate \( {\lambda_c} = {1 \mathord{\left/{\vphantom {1 {100}}} \right.} {100}} \) Service rate \( {\mu_c} = {1 \mathord{\left/{\vphantom {1 {100}}} \right.} {100}} \) Utilization ρ c = 0.33 |
(d) | Check insurance | Resources r d = 2 Arrival rate \( {\lambda_d} = {1 \mathord{\left/{\vphantom {1 {100}}} \right.} {100}} \) Service rate \( {\mu_d} = {1 \mathord{\left/{\vphantom {1 {70}}} \right.} {70}} \) Utilization ρ d = 0.35 |
(e) | Decide | Resources r e = 2 Arrival rate \( {\lambda_e} = {1 \mathord{\left/{\vphantom {1 {100}}} \right.} {100}} \) Service rate \( {\mu_e} = {1 \mathord{\left/{\vphantom {1 {70}}} \right.} {70}} \) Utilization ρ e = 0.35 |
(f) | Pay | Resources r f = 1 Arrival rate \( {\lambda_f} = {1 \mathord{\left/{\vphantom {1 {200}}} \right.} {200}} \) Service rate \( {\mu_f} = {1 \mathord{\left/{\vphantom {1 {70}}} \right.} {70}} \) Utilization ρ f = 0.35 |
(g) | Send Letter | Resources r g = 2 Arrival rate \( {\lambda_g} = {1 \mathord{\left/{\vphantom {1 {50}}} \right.} {50}} \) Service rate \( {\mu_g} = {1 \mathord{\left/{\vphantom {1 {36}}} \right.} {36}} \) Utilization ρ g = 0.36 |
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van der Aalst, W.M.P., Nakatumba, J., Rozinat, A., Russell, N. (2010). Business Process Simulation. In: Brocke, J.v., Rosemann, M. (eds) Handbook on Business Process Management 1. International Handbooks on Information Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00416-2_15
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