DECISION WITH ARTIFICIAL NEURAL NETWORKS IN DISCRETE EVENT SIMULATION MODELS ON A TRAFFIC SYSTEM

Silva, Marília Gonçalves Dutra da; Rangel, João José de Assis; Silva, David Vasconcelos Corrêa da; Peixoto, Túlio Almeida; Matias, Ítalo de Oliveira

doi:10.1590/0101-7438.2016.036.01.0133

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Articles • Pesqui. Oper. 36 (1) • Jan-Apr 2016 • https://doi.org/10.1590/0101-7438.2016.036.01.0133 copy

DECISION WITH ARTIFICIAL NEURAL NETWORKS IN DISCRETE EVENT SIMULATION MODELS ON A TRAFFIC SYSTEM

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

This work aims to demonstrate the use of a mechanism to be applied in the development of the discrete-event simulation models that perform decision operations through the implementation of an artificial neural network. Actions that involve complex operations performed by a human agent in a process, for example, are often modeled in simplified form with the usual mechanisms of simulation software. Therefore, it was chosen a traffic system controlled by a traffic officer with a flow of vehicles and pedestrians to demonstrate the proposed solution. From a module built in simulation software itself, it was possible to connect the algorithm for intelligent decision to the simulation model. The results showed that the model elaborated responded as expected when it was submitted to actions, which required different decisions to maintain the operation of the system with changes in the flow of people and vehicles.

Keywords:
simulation; computational intelligence; artificial neural networks

Module	Name	Description	Configuration Data
Create	E1	Responsible for the creation of entities	T. Arrival:
			LOGN (2.78, 3.84) seconds
			T. First Arrival: 2 seconds
			Max. Arrival: Infinite
Hold	L1	Holds the entities in a queue	Expression:
		until the condition is satisfied	VariableSemaforo = 1
Assign	C1	Adds a variable to the model	Type: Variable
			Name: VariableCarStreet 1
			Value: VariableCarStreet 1 + 1
Write	W1	Writes values of a variable on file	Name of the variable:
			VariableCarStreet 1
			File: exit.txt
End	-	Ends the list of commands	-

Module	Name	Description	Configuration Data
Create	E2	Responsible for the creation of entities	T. Arrival:
			LOGN (3.5, 2.5) seconds
			T. First Arrival: 2 seconds
			Max. Arrival: Infinite
Hold	L2	Holds the entities in a queue	Expression:
		until the condition is satisfied	VariableSemaforo = 0
Assign	C2	Adds a variable to the model	Type: Variable
			Name: VariableCarStreet 2
			Value: VariableCarStreet 2 + 1
Write	W2	Writes values of a variable on file	Name of the variable:
			VariableCarStreet 2
			File: exit2.txt
End	-	Ends the list of commands	-

Parameter	Real		Simulation A		Simulation B
	Average	CI (95%)	Average	CI (95%)	Average	CI (95%)
A.T.O.M.T (s)	55.33	[47.29; 63.37]	49.11	[46.88; 51.33]	52.33	[50.06; 54.60]
A.T.O.S.T. (s)	40.12	[34.16; 46.08]	40.98	[31.57; 50.38]	40.93	[39.71; 42.16]
A.Q.P.C.C. (un)	28.77	[18.47; 39.07]	28.81	[22.10; 35.50]	30.57	[28.18; 32.95]
A.Q.V.P.M.C. (un)	21.57	[13.85; 29.28]	18.65	[16.64; 20.66]	20.73	[19.91; 21.55]
Error (%)			2.0558		1.2998

Parameter	Real		Scenario 1		Scenario 2
	Average	CI (95%)	Average	CI (95%)	Average	CI (95%)
A.T.O.M.T (s)	55,33	[47,29; 63,37]	63,15	[57,64; 68,66]	45,40	[43,59; 47,21]
A.T.O.S.T (s)	40,12	[34,16; 46,08]	39,72	[38,65; 40,49]	43,96	[37,26; 50,66]
A.Q.P.C.C (un)	28,77	[18,47; 39,07]	28,58	[27,29; 29,87]	62,48	[52,79; 72,16]
A.Q.V.P.M.C (un)	21,57	[16,42; 34,72]	43,27	[38,90; 47,65]	16,65	[15,52; 17,79]
Error (%)			1.5810		1.6282

Module	Name	Description	Configuration Data
Create	E4	Responsible for the creation of entities	T. Arrival: Const. (1s)
			T. First. Arrival: 1 second
			Max. Arrival: Infinite
Assign	C4	Adds a variable to the model	Type: Variable
			Name: time
			Value: time + 1
X (Decisor)	X1	Deviates the execution of a function	Type: ANN with 2 ways
			Training file: C:\DataTreiningANN5Neurons_
			Modified.CSV
			Algorithm: ResilientPropagation
			Exp. Neuron 1: L1.Segurar1.queue
			Exp. Neuron 2: L2. Segurar2.queue
			Exp. Neuron 3: L3. Segurar3.queue
			Exp. Neuron 4: time
			Exp. Neuron 5: variablesemaforo
			Number Neurons Input Layer: 5
			Number Neurons Hidden Layer: 4
			Number Neurons Output Layer: 1
X (Decisor)	X2	Deviates the execution of a function	Type: 2 ways by condition
			Condition: variablesemaforo = 0
X (Decisor)	X3	Deviates the execution of a function	Type: 2 ways by condition
			Condition: variablesemaforo = 1
Write	W4	Writes values of a variable on file	Variable name: time
			File: semaforo1.txt
Write	W5	Writes values of a variable on file	Variable name: time
			File:semaforo1.txt
Assign	C5	Adds a variable to the model	Type: Variable
			Name: variablecarstreet1
			Value: 0
Assign	C6	Adds a variable to the model	Type: Variable1
			Name: variablecarstreet2
			Value: 0
Assign	C7	Adds a variable to the model	Type: Variable
			Name: variableperson
			Value: 0
Assign	C8	Adds a variable to the model	Type: Variable
			Name: time
			Value: 0
Assign	C9	Adds a variable to the model	Type: Variable
			Name: variablesemaforo
			Value: 0

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