OPTEX MATHEMATICAL MODELING AND MANAGEMENT SYSTEM

MATHEMATICAL MODELING
MANAGEMENT SYSTEM

OUR MISSION:
BRING THE BENEFITS OF OPTIMIZATION TECHNOLOGY
TO SOCIETY:
 ENABLING PEOPLE TO APPLY OPTIMIZATION TECHNOLOGY
SUCCESSFULLY INTO THEIR ORGANIZATIONS
 BEING ENTREPRENEURS OF NEW COMPANIES
THAT BRING THE BENEFITS OF OPTIMIZATION TECHNOLOGY TO SOCIETY

Why do you choose to
programming in any
specific optimization
technology when you can
programming in all tools
at the same time with
only one effort ?FICO™
XPRESS-MOSEL
CPLEX-OPL-ODM
IMPRESS

Why do you choose to
programming in any
specific optimization
technology when you can
programming in all tools
at the same time with
only one effort ?
The best way is to have
the mathematical models
in a meta-platform and in
a second phase go to any
specific commercial
platform.

As a part of its process of
technological innovation,
DW has developed an
optimization technology
called
OPTEX
Mathematical Modeling
Management System
which is oriented to
designing, implementing
and setting up large scale
optimization models for
the real word .

OPTEX IS A META-FRAMEWORK
ORIENTED TOWARDS THE DESIGN, IMPLEMENTATION AND SETUP OF DECISION
SUPPORT SYSTEMS BASED IN MATHEMATICAL PROGRAMMING WITH SPECIAL
EMPHASIS IN THE DEVELOPMENT OF FINAL USER APPS:
 ALGEBRAIC FORMULATION IS INDEPENDENT FROM ANY PROGRAMMING
LANGUAGE
 CAN BE CONNECTED WITH ANY DATA SERVER
THEREBY GENERATING APPS USING MULTIPLE COMMERCIAL OR NONCOMMERCIAL
TECH ACCORDING TO CLIENTS’ NEEDS

OPTEX Mathematical Modeling System,
was developed to support
DecisionWare’s mathematical modeling
projects since 1991.

SUPPORTS ALL STAGES OF THE
MATHEMATICAL MODELING PROCESS

MATHEMATICAL
MODELING PROCESS
REAL
WORLD

DSS
DATABASE
OPTMER(P): =
Maximizar RMin
sujeto a:
RMin t= 1,Tb= 1,NB{c= 1,NC((DEMcdbt-DNS+
cdbt)Pcbt)
+ (VBOLSA+
hdbt-VBOLSA-
hdbt)CMhdbt
- ECPhdbt PPO + VBOLSA+
hdbt CPOhbt} - VENFt PFIt } h,d
VBOLSA+
hdbt-VBOLSA-
hdbt = Hhdbt + Thdbt - c= 1,NC(DEMdbt-DNS+
dbt)
h,d,b,t
DNS+
dbt - DNS-
dbt = DEMcdbt - EVcbt c,d,b,t
ECPhdbt  c= 1,NCEVcbt - DHIhdbt - CTbt h,b
VENFt b= 1,NB {c= 1,NCEVcbt - CTbt} - EFt t
VENFt  0 t
EVcbt  0 c,b,t
ECPhdbt  0 h,d,b,t
DNS+
cdbt , DNS-
cdbt  0 c,d,b,t
VBOLSA+
hdbt , VBOLSA-
hdbt  0 h,d,b,t
MATHEMATICAL
MODELING PROCESS
ALGEBRAIC MODEL DATA MODEL
MODELERS
REAL
WORLD

G.R.G.
0-1
BALAS-BENDERS
LAGRAGIAN
RELAXATION
BENDERS THEORY
BRANCH &
BOUND
P.L.
FLUJO EN
REDES
G.R.G.
/PC
G.R.G.
/PL
D.F.P.
x, p
OPTIMIZATION SOLVER
DSS
DATABASE
OPTMER(P): =
Maximizar RMin
sujeto a:
cdbt)Pcbt)
+ (VBOLSA+
hdbt-VBOLSA-
hdbt)CMhdbt
VBOLSA+
hdbt-VBOLSA-
dbt)
h,d,b,t
DNS+
dbt - DNS-
VENFt  0 t
DNS+
cdbt , DNS-
VBOLSA+
hdbt , VBOLSA-
NUMERICAL MODEL
MATHEMATICAL
MODELING PROCESS
MATRIX
GENERATION
MODELERS
REAL
WORLD

G.R.G.
0-1
BALAS-BENDERS
LAGRAGIAN
RELAXATION
BENDERS THEORY
BRANCH &
BOUND
P.L.
FLUJO EN
REDES
G.R.G.
/PC
G.R.G.
/PL
D.F.P.
x, p
OPTIMIZATION SOLVER
DSS
DATABASE
DSS
DATABASE
OPTMER(P): =
Maximizar RMin
sujeto a:
cdbt)Pcbt)
+ (VBOLSA+
hdbt-VBOLSA-
hdbt)CMhdbt
VBOLSA+
hdbt-VBOLSA-
dbt)
h,d,b,t
DNS+
dbt - DNS-
VENFt  0 t
DNS+
cdbt , DNS-
VBOLSA+
hdbt , VBOLSA-
NUMERICAL MODEL
MATHEMATICAL
MODELING PROCESS
MATRIX
GENERATION
DECISION MAKERS
MODELERS
REAL
WORLD

G.R.G.
0-1
BALAS-BENDERS
LAGRAGIAN
RELAXATION
BENDERS THEORY
BRANCH &
BOUND
P.L.
FLUJO EN
REDES
G.R.G.
/PC
G.R.G.
/PL
D.F.P.
x, p
OPTIMIZATION SOLVER
DSS
DATABASE
DSS
DATABASE
OPTMER(P): =
Maximizar RMin
sujeto a:
cdbt)Pcbt)
+ (VBOLSA+
hdbt-VBOLSA-
hdbt)CMhdbt
VBOLSA+
hdbt-VBOLSA-
dbt)
h,d,b,t
DNS+
dbt - DNS-
VENFt  0 t
DNS+
cdbt , DNS-
VBOLSA+
hdbt , VBOLSA-
NUMERICAL MODEL
MATHEMATICAL
MODELING PROCESS
MATRIX
GENERATION
DECISION MAKERS
MODELERS
REAL
WORLD
THIRD PART PROVIDER

G.R.G.
0-1
BALAS-BENDERS
LAGRAGIAN
RELAXATION
BENDERS THEORY
BRANCH &
BOUND
P.L.
FLUJO EN
REDES
G.R.G.
/PC
G.R.G.
/PL
D.F.P.
x, p
OPTIMIZATION SOLVER
DSS
DATABASE
DSS
DATABASE
OPTMER(P): =
Maximizar RMin
sujeto a:
cdbt)Pcbt)
+ (VBOLSA+
hdbt-VBOLSA-
hdbt)CMhdbt
VBOLSA+
hdbt-VBOLSA-
dbt)
h,d,b,t
DNS+
dbt - DNS-
VENFt  0 t
DNS+
cdbt , DNS-
VBOLSA+
hdbt , VBOLSA-
NUMERICAL MODEL
MATHEMATICAL
MODELING PROCESS
MATRIX
GENERATION
DECISION MAKERS
MODELERS
REAL
WORLD
MAY BE
THIRD PART PROVIDER

A DECISION SUPPORT SYSTEM
IS AS A DECISION MAKING CHAIN
INTEGRATED BY A COLLECTION
OF MODELS AND DATA FLOW

PTA
Industrial Operations
Tactical Planning
DEM
Long/Medium/Short
Demand Planning
INV
Inventory
Policy
Medium / Short Term
Demand Projections
Inventory
Policy
Production
Goals
POD
Production
Schedule
DIS
Distribution
Schedule
Distribution
Goals
PCO
Sourcing
Sourcing
Goals
Production
Orders
Distribution
Orders
Sourcing
Orders
PES
Supply Chain Design
Short / Medium Term
Market Scenarios
Expansion
Plans
DSS
Short / Medium Term
Market Scenarios

PTA
Industrial Operations
Tactical Planning
DEM
Long/Medium/Short
Demand Planning
INV
Inventory
Policy
POD
Production
Schedule
DIS
Distribution
Schedule
PCO
Sourcing
PES
Supply Chain Design
DSS
COMMON
DATA MODEL
INFORMATION
SYSTEM

SUPPORTS DESIGN, IMPLEMENTATION,
START UP AND MAINTENANCE OF COMPLEX
DECISION SUPPORT SYSTEMS,
USING AN UNIFIED DEVELOPMENT ENVIRONMENT

ALGEBRAIC LANGUAGES
• Algebraic Programming Language
• Database Algebraic Language
USER INTERFACE
• Based in database tables
• Operates in LANs and WANs (“Cloud Computing”)
• Visual Interface (MS-Windows)
• Filling the blanks parameterization
SERVICES
• Data-Model Generator
• Final User Interface Generator
• General Language Model Generator (C, Java …), includes Matrix Generator
• Algebraic Language Model Generator (GAMS, IBM ILOG OPL, MOSEL , AIMMS … )
PROBLEM SOLUTION
• Basic problems: LP, MIP, QP, MIQP, NLP
• Large Scale Theory: Benders Partition, Lagrangean Relaxation, Disjunctive Programming, …
• Links to multiple optimization libraries (GUROBI, IBM CPLEX, XPREXX, COIN-MP, … )
• Automatic Generation of Non-anticipative Multistage Stochastic Programming (MSP)
• Parallel solution in computers grids
CONNECTIVITY
• ERP/WMS/TMS/AMS: Enterprise Information Systems
• GIS: Geographic Information Systems
• ASP: Applications Service Provider (MS-Project, Google MAPS, …)
ELEMENTS

ALGEBRAIC LANGUAGEs:
• Programming Language
• Database Language

ALGEBRAIC LANGUAGES OBJECTS
MATHEMATICAL DEFINITIONS
• Index, Sets, Parameters, Variables, Equations,
Objective Functions, Planning Horizons, Decision
Trees
DECISION SUPPORT SYSTEMS
• Problems = (Equations, Variables, Objective
Functions)
• Model = (Problems, Data Flows)
• DSS = (Models, Data Flows)
DATA MODEL
• DSN, Data Tables, Fields, Shell Windows, Data
Windows, Menus

OPTEX DATABASE
ALGEBRAIC LANGUAGE

OPTEX- DATABASE ALGEBRAIC LANGUAGE
SQL
Server
Internet - Intranet
0
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1 s t Q t r 2 n d Q t r
0
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0
2 0
4 0
6 0
8 0
0
2 0
4 0
6 0
8 0
MM
Server
MATHEMATICAL
MODEL
SERVER
INFORMATION
SYSTEM
0
2 0
4 0
6 0
8 0
0
2 0
4 0
6 0
8 0
EASY DEVELOPMENT MATHEMATICAL MODELS
IN A LAN-WAN ENVIRONMENT USING THE POWER
OF THE DATABASE SERVERS

SQL
Server
Internet - Intranet
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0
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4 0
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0
2 0
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2 0
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8 0
MM
Server
MATHEMATICAL
MODEL
SERVER
INFORMATION
SYSTEM
0
2 0
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6 0
8 0
0
2 0
4 0
6 0
8 0
THE IMPLEMENTATION OF A
DECISION SUPPORT SYSTEMS IS BASED IN
A FILLING THE BLANKS PROCESS

MATHEMATICAL MODELS BASIC ELEMENTS
ARE STORED IN A DATA BASE

JVB-08/94OPTEX
Min t j h CTt(GTjth)
sujeto a:
GDzth = uTN(z) LDuzth
GDzth + GHAzth + DEFzth = DEMzth
ENuth - jL1(u) GTEjuth
- vL2(u) LLvuth = 0
. . . . .
z  NOD
t = 1,T
h = 1,NH
z  NOD
t = 1,T
h = 1,NH
u  LIN
t = 1,T
h = 1,NH
INDEXESINDEXES

JVB-08/94OPTEX
sujeto a:
. . . .
z  NOD
t = 1,T
h = 1,NH
z  NOD
t = 1,T
h = 1,NH
u  LIN
t = 1,T
h = 1,NH
SETSSETS

DATABASE
CONNECTIVITY

AUTOMATIC GENERATION OF
MATHEMATICAL MODEL- DATA MODEL
SQL CONNECTIVITY

JVB-08/94OPTEX
sujeto a:
. . . .
z  NOD
t = 1,T
h = 1,NH
z  NOD
t = 1,T
h = 1,NH
u  LIN
t = 1,T
h = 1,NH
PARAMETERSPARAMETERS

TIPO DE SERIE INTERPRETACIÓN
E
ESCALÓN
()
I
IMPULSO
(PULSE)
P
POLI LÍNEA
(POLY LINE)
MULTIPLES FORMS OF
DATA INTERPRETATION

JVB-08/94OPTEX
sujeto a:
. . . .
z  NOD
t = 1,T
h = 1,NH
z  NOD
t = 1,T
h = 1,NH
u  LIN
t = 1,T
h = 1,NH
VARIABLES

JVB-08/94OPTEX
sujeto a:
. . . .
z  NOD
t = 1,T
h = 1,NH
z  NOD
t = 1,T
h = 1,NH
u  LIN
t = 1,T
h = 1,NH
CONSTRAINTS

MODELSPROBLEMS DSSs
COORDINATION DECISIONS
OVER SPACE AND TIME

MO
IL
MO
WO MO
Tiempo
FOR DISCRETE TIME MOODELS, THE
PLANNING HORIZON MAY BE IN YEARS,
MONTHS, DAY, HOURS, MINUTES, …

PROBLEMS
MODELS
OPTEX – DECISION SUPPORT SYSTEM ELEMENTS
A PROBLEM IS A COLLECTION OF CONSTRAINTS
A MODEL IS A COLLECTION OF PROBLEMS
CONNECTED BY A DATA FLOW AND A MODEL CONTROL

A DECISION SUPPORT SYSTEM IS A COLLECTION OF
MODELS AND DATA FLOW ALL USING THE SAME DATA MODEL
AND THE SAME FRAMEWORK
PTA
Aggregated Industrial
Operations
Tactical Plannings
DEM
Demand
Long/Medium/Short
Term
INV
Inventory
Policies
Demand Forecasting
Medium/Short Term
Demand Stages
Medium/Short Term
Inventory
Policies
Production
Goals
POD
Production
Scheduling
DIS
Distribution
scheduling
Distribution
Goals
PCO
Sourcing
Scheduling
Consumption
Goals
Production
Orders
Distriution
Orders
Purchase
Orders
PES
Supply Chain Design
Marjet Stages
Long/Medium Term
Expansion
Plans
DSS
DSS
MODELS
OPTEX – DECISION SUPPORT SYSTEMS ELEMENTS

ADVANCED OPTIMIZATION
INVESTMENTS COORDINATOR
INTERZONE
COORDINATOR
SECTOR 1
STOCHASTIC 1
INTERZONE
COORDINATOR
SECTOR 1
STOCHASTIC 1
INTERSECTOR OPERATIONS
COORDINATOR
STOCHASTIC CONDITION 1
DYNAMIC
COORD.
ZONA S.1
DYNAMIC
COORD.
ZONA S.ZS
DYNAMIC
COORD.
ZONE 1.1
DYNAMIC
COORD.
ZONA 1.Z1
1 T2 T-1 1 T2 T-1 1 T2 T-1 1 T2 T-1
TIME
PARTITION
INVESTMENTS
SECTOR
ZONE
DECOMPOSITION
MULTILEVEL
SYSTEM
INTERZONE
COORDINATOR
SECTOR 1
STOCHASTIC H
INTERZONE
COORDINATOR
SECTOR 1
STOCHASTIC H
COORDINATOR
STOCHASTIC CONDITION H
DYNAMIC
COORD.
ZONA S.1
DYNAMIC
COORD.
ZONA S.ZS
DYNAMIC
COORD.
ZONE 1.1
DYNAMIC
COORD.
ZONA 1.Z1
1 T2 T-1 1 T2 T-1 1 T2 T-1 1 T2 T-1
RANDOM
OPERATIONS

LARGE SCALE OPTIMIZATION
AND
DECISION SUPPORT SYSTEM ELEMENTS
MODELSPROBLEMS DSSs

NON-ANTICIPATIVE STOCHASTIC OPTIMIZATION
PROBABILISTICS CONSTRAINTS
BENDERS PARTITIONING THEORY
LAGRANGIAN RELAXATION
PARAMETRIC PROGRAMMING
DISJUNCTIVE PROGRAMMING
AUTOMATIC LINEARIZATION
….

MODELSPROBLEMS DSSs
Problem =  (Equations, Variables, Objective Functions)
Model =  (Problems, Data Flows)
DSS =  (Models, Data Flows)

INTEGRATED
MODEL
INVESTMENTS
-
OPERATIONS
OPTEX- LARGE SCALE METHODOLOGIES

INVESTMENTS
COORDINATOR
INTERZONE
COORDINATOR
SECTOR 1
STOCHASTIC 1
INTERZONE
COORDINATOR
SECTOR 1
STOCHASTIC 1
COORDINATOR
STOCHASTIC CONDITION 1
DYNAMIC
COORD.
ZONA S.1
DYNAMIC
COORD.
ZONA S.ZS
DYNAMIC
COORD.
ZONE 1.1
DYNAMIC
COORD.
ZONA 1.Z1
1 T2 T-1 1 T2 T-1 1 T2 T-1 1 T2 T-1TIME
PARTITION
INVESTMENTS
SECTOR
ZONE
DECOMPOSITION
MULTILEVEL
SYSTEM
INTERZONE
COORDINATOR
SECTOR 1
STOCHASTIC H
INTERZONE
COORDINATOR
SECTOR 1
STOCHASTIC H
COORDINATOR
STOCHASTIC CONDITION H
DYNAMIC
COORD.
ZONA S.1
DYNAMIC
COORD.
ZONA S.ZS
DYNAMIC
COORD.
ZONE 1.1
DYNAMIC
COORD.
ZONA 1.Z1
1 T2 T-1 1 T2 T-1 1 T2 T-1 1 T2 T-1
RANDOM
OPERATIONS
PROBLEMS <-> MODELS

HYDRAULIC SYSTEM
PROBLEM: MODBENCO
CCP, CGH, CGS, COE, CSP, EQE, SQE
yk
ELECTRIC SYSTEM
PROBLEM: MODBENUNNU
DUN, NUN
pk
vk
OPTEX- BENDERS IMPLEMENTATION

HYDRAULIC SYSTEM
PROBLEM: MODBENCO
CCP, CGH, CGS, COE, CSP, EQE, SQE
yk
ELECTRIC SYSTEM
PROBLEM: MODBENNU
DUN, NUN
pk
vk
MODEL: MODBENNU
OPTEX- BENDERS IMPLEMENTATION

SUt(xt-1,xt): =
{Wt(xt-1,xt) =
Min dt
Tut |
Btut = bt - Et-1xt-1 - Atxt
Gtut = gt
utR+
}
SGt(xj
t-1):=
{aT(xj
t-1) Min =
ct
Txt + Wt(xt-1,xt) + at+1(xt)
|
Wt(xt-1,xt) + (pt
k)TAtxt 
qT(pt
k,dt
k) - (pt
k)TEt-1xj
t-1 kIU(t,j)
xtR+
at+1(xt) + kI1(t+1,j) yk,j
t+1 (pt+1
k)Tetxt
t
j jIJ(t) }
pt
k
SUt(xt-1,xt): =
{Wt(xt-1,xt) =
Min dt
Tut |
Gtut = gt
utR+
}
SUt(xt-1,xt): =
{Wt(xt-1,xt) =
Min dt
Tut |
Gtut = gt
utR+
}
p1
k
pT
k
t=1 t=T
x1 x1 xT
x1
xT-1
yk,j
t+1
yk,j
t+1
SGt(xj
t-1):=
{aT(xj
t-1) Min =
ct
|
Wt(xt-1,xt) + (pt
qT(pt
k,dt
k) - (pt
k)TEt-1xj
t-1 kIU(t,j)
xtR+
at+1(xt) + kI1(t+1,j) yk,j
t+1 (pt+1
k)Tetxt
t
j jIJ(t) }
SGt(xj
t-1):=
{aT(xj
t-1) Min =
ct
|
Wt(xt-1,xt) + (pt
qT(pt
k,dt
k) - (pt
k)TEt-1xj
t-1 kIU(t,j)
xtR+
at+1(xt) + kI1(t+1,j) yk,j
t+1 (pt+1
k)Tetxt
t
j jIJ(t) }
PROBLEM-MODEL CONCEPTUALIZATION IS ORIENTED TO ALLOW THE
MODELERS TO IMPLEMENT LARGE SCALE METHODOLOGIES LIKE
BENDERS THEORY AND LAGRANGEAN RELAXATION
USING A GENERAL PARTITION-DECOMPOSITION FRAMEWORK

PLANNING
HORIZONS
MULTI-STAGE
STOCHASTIC
DECISIONS
TREE
MATHEMATICAL MODELS
ADVANCED ELEMENTS

Scenario H
Scenario 1
Scenario 2
ARBOL DE DECISIONES DE
MULTIPLES ETAPAS
t = 1 t = 2 t = 3 t = 4
OPTEX- MULTISTAGE STOCHASTIC OPTIMIZATION
OPTEX HAS TOOLS ORIENTED TO DEVELOP
MULTISTAGE STOCHASTIC OPTIMIZATION MODELS
AUTOMATIC CONVERSION OF A
DETERMINISTIC MODEL INTO STOCHASTIC
MULTI-STAGE
DECISION TREE

MULTI-STAGE
DECISION TREE
N1
e = 1 e = 2 e = 3
t
1 13 25 36
N21
N22
N21
N22
N21
N22
N21
N22
Hidrology 1988
Hidrology 1992
Hidrology 1985
Hidrology 1990
High Demand High Price
Hidrology 1988 Low Demand Low Price
Low Demand Low PriceHidrology 1990
Low Demand Low PriceHidrology 1992
High Demand Low Price
High PriceLow Demand
Hidrology 1988
Hidrology 1988
0.125
0.0625
UNCERTAINTY DIMENSIONS
• Demand
• Fuel Prices
• Water Inflows
• Others

OPTEX HAS TOOLS ORIENTED TO DEVELOP
MULTISTAGE STOCHASTIC OPTIMIZATION
INCLUDING MULTIPLES TYPES OF RISK CONSTRAINTS
Conditional Value-at-Risk (CVaR)
Cost Probability Function
Desvío
Estándar
(s)
VaR
b=0.05
1.645 s
Cost - f(x|w)a(b)
f ( f(x|w) )
jb( f(x|w) )
OPTEX- MULTISTAGE STOCHASTIC OPTIMIZATION

AUTOMATIC CONVERSION
OF A DETERMINISTIC MODEL
INTO A STOCHASTIC MODEL

DETERMINISTIC CASE
t = 1 t = 2
Mean
Demand
Deterministics
Investment
Decisions
Deterministics
Future Operations
Decisions

TWO-STAGE DECISION TREE FOR
DEMAND: UNCERTAINTY DIMENSION
t = 1 t = 2
Scenario
Demand 10
Scenario
Demand 1
Scenario
Demand 2
Deterministics
Investment
Decisions
0.10
0.10
Uncertainty
Future Operations
Decisions

Demand 10
Demand 1
Demand 2
0.10
0.10
Demand 10
Demand 1
Demand 2
0.10
0.10
WITHOUT Extrem Event
0.90
0.10
t = 1 t = 2
Deterministics
Investment
Decisions Uncertainty
Future Operations
Decisions
TWO-STAGE DECISION TREE FOR
DEMAND: UNCERTAINTY DIMENSION 1
EXTREME EVENT: UNCERTAINTY DIMENSION 2
WITH Extrem Event

THE AUTOMATIC CONVERSION IMPLIES:
1. TO INCLUDE THE INDEXES RELATED WITH THE

2. TO DEFINE A DECISION TREE
3. TO SPECIFY THE NON ANTICIPATIVE VARIABLES
4. TO SPECIFY THE PARAMETERS WITH THE
2.
3.
4.

5. TO LINK THE MODEL WITH THE DECISION TREE

6. TO INCLUDE IN THE TABLES THE FIELDS
ASSOCIATED TO THE UNCERTAINTY DIMENSIONS

OPTEX PROGRAMMING
ALGEBRAIC LANGUAGE
(like GAMS, AMPL, LPL, …)

OPTEX PROGRAMMING ALGEBRAIC LANGUAGE

Internet-Intranet
0
2 0
4 0
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SERVIDOR
MODELOS
MATEMÁTICOS
OPTEX
WIDE AREA NETWORK
DOCUMENTATION
 OPTEX generates automatically the following documentation:
 Algebraic Formulation
 Information system data model
 Connectivity with other data models
Remote Access Server
Connectivity

RTF DOCUMENT
GENERATED BY OPTEX

OPTEX – MATHEMATICAL PROBLEM FORMATS
OPTEX-MMS incorporates optimization methodologies depending on
the optimization library that is being used.
• LINEAR PROGRAMMING (LP)
• MIXED INTEGER PROGRAMMING (MIP).
• MIXED BINARY PROGRAMMING (BP)
• QUADRATIC PROGRAMMING (QP)
• QUADRATIC MIXED PROGRAMMING (QMP)
• QUADRATIC PROGRAMMING (QP-QR)
• INTEGER QUADRATIC PROGRAMMING (QMP-QR)
• NON-LINEAR PROGRAMMING (NLP)
• MIXED COMPLEMENTARITY PROGRAMMING (MCP)

MODELS
GAMS – MPS
IBM ILOG OPL
MOSEL – AIMMS - …
MODEL RESULTS
(PRIMAL – DUAL)
PROGRAMMING
ALGEBRAIC LANGUAGE
DATABASE
ALGEBRAIC LANGUAGE
OPTEX
PROCESSOR
MODELS
C PROGRAMS
LIB or DDL LIBRARY

OPTEX
WIDE AREA NETWORK
Internet-Intranet
0
2 0
4 0
6 0
8 0
SERVIDOR
MODELOS
MATEMÁTICOS
Remote access
server connectivity
CLOUD SERVER
ALGEBRAIC
LANGUAGE
SOLVER
C ANSI
SOLVER
CLOUD LINK

Internet
0
2 0
4 0
6 0
8 0
MATHEMATICAL
MODEL’S
ERVER
OPTEX
ERP
DATABASE
Connectivity
OPTEX
Graphic User Interface
OPTEX
Processor
ODBC
USUARIOS
ILIMITADOS
OPTIMIZATION LIBRARY
CPLEX
FICO™
XPRESS

OPTEX – C DSS PROGRAM STRUCTURE
I/O
Routines
MODELs
Routines
Main
OPTEX-COINLP
LINK
Routine
COINLP
Routines
CPLEX
Routines
CONSTRAINTs
Routines
OPTEX-CPLEX
LINK
Routine
OPTEX-xxxxx
LINK
Routine
XXXXX
Routines
PROBLEMs
Routines
Routines
DSS.LIB or DSS.DLL
DSS
DATABASE

OPTEX – C DSS PROGRAM STRUCTURE
MODELs
Routines
OPTEX-COINLP
LINK
Routine
COINLP
Routines
CPLEX
Routines
CONSTRAINTs
Routines
OPTEX-CPLEX
LINK
Routine
OPTEX-xxxxx
LINK
Routine
XXXXX
Routines
PROBLEMs
Routines
Routines
DSS.LIB or DSS.DLL
DSS
DATABASE
USER
Routines
OPTEX-USER
LINK
Routine
Customized Visual User Interface
USER
ERP

USER
TRANSACTIONAL
DATABASE
.TXT
VV_rrr.OPT
RR_rrr.OPT
XXX.EXE
OPTEX: PRODUCTION PHASE

USER
TRANSACTIONAL
DATABASE
XXX.EXE
OPTEX: PRODUCTION PHASE

Customized Web Visual User Interface

Internet
0
2 0
4 0
6 0
8 0
OPTEX
ERP
DATABASE
Connectivity
OPTEX
ODBC
OPTEX
Processor
CPLEX
FICO™
Xpress
MATHEMATICAL
MODEL’S
ERVER

Internet
0
2 0
4 0
6 0
8 0
OPTEX
ERP
DATABASE
OPTEX
ODBC
OPTEX
Processor
CPLEX
MATHEMATICAL
MODEL’S
ERVER
Connectivity
FICO™
Xpress

MATHEMATICAL MODEL
GAMS ALGERAIC LANGUAGE

Internet
OPTEX
ERP
DATABASE
OPTEX
OPTEX
Processor
ODBC
CPLEX
OPL
0
2 0
4 0
6 0
8 0
Connectivity
MATHEMATICAL
MODEL’S
ERVER

MATHEMATICAL MODEL
IBM-OPL LANGUAGE

Internet
OPTEX
ERP
DATABASE
OPTEX
OPTEX
Processor
ODBC
0
2 0
4 0
6 0
8 0
Connectivity
MATHEMATICAL
MODEL’S
ERVER
UNDER DEVELOPMENT

MATHEMATICAL MODEL
MOSEL ALGERAIC LANGUAGE

Internet
OPTEX
ERP
DATABASE
OPTEX
OPTEX
Processor
ODBC
0
2 0
4 0
6 0
8 0
Connectivity
MATHEMATICAL
MODEL’S
ERVER
UNDER DEVELOPMENT
CPLEX
FICO™
Xpress

MATHEMATICAL MODEL
AIMMS ALGERAIC LANGUAGE

SOLVING
AMPL MODELS
(UNDER DEVELOPMENT)

MATHEMATICAL MODEL
AIMMS ALGERAIC LANGUAGEMATHEMATICAL MODEL
AMPL ALGERAIC LANGUAGE

SOLVING
iAL IMPRESS MODELS
(UNDER DEVELOPMENT)

SUPER STRUCTURE
&sUnit,&sOperation,&sPort,&sState

IMPRESS
SIMM:
MATHEMATICAL
MODEL
INFORMATION
SYSTEM
SIDI:
INDUSTRIAL
DATA
INFORMATION
SYSTEM
(UOPSS) IMPRESS Files

INFORMATION SYSTEMS CONNECTIVITY

INDUSTRIAL DATA
INFORMATION SYSTEM

INFORMATION
SYSTEM
sujeto a:
GDzth - uTN(z) LDuzth = 0
Sistema Descripción
Capacidad
Térmica (MW)
EEB.
ISA.
EPM
COR
Energía Eléctrica de Bogotá
Interconexión Eléctrica S.A.
Empresas Públicas de Medellín
CORELCA
45
67
0
78
MATHEMATICAL MODEL
INFORMATION SYSTEM
INDUSTRIAL DATA
INFORMATION SYSTEM

IMPLEMENTATION OF THE
INDUSTRIAL DATA
INFORMATION SYSTEM

RELACIÓN SIMM - SIDI
INDEX
Parameter
Restricción
IndexesVariable
Indexes
Indexes
ENTITY
ENTITIES
RELATIONS
SIMM:
MATHEMATICAL
MODEL
INFORMATION
SYSTEM
SIDI:
INDUSTRIAL
DATA
INFORMATION
SYSTEM
IndexesSets

IN OPTEX THE IMPLEMENTATION OF THE
INDUSTRIAL DATA INFORMATION SYSTEM IS
BASED IN A FILLING THE BLANKS GUIDED
PROCESS, SIMILAR TO THE PROCESS TO
IMPLEMENTATION OF THE MATHEMATICAL
MODELS.
THE MODELER DOESN’T NEED TO BE AN SPECIALIST
IN DATABASES LANGUAGES AND INFORMATION
SYSTEMS
IMPLEMENTATION INDUSTRIAL DATA INFORMATION SYSTEM

TABLES DEFINITION
FIELDS DEFINITION
INDEX TABLES DEFINITION RELATIONAL FIELDS DEFINITION

MENU DEFINITION

INDUSTRIAL DATA
INFORMATION SYSTEM
IS A COLLECTION OF:
DATA TABLES, SHELL WINDOWS, DATA
WINDOWS AND MENUS ORIENTED TO THE
FINAL USER
INDUSTRIAL DATA INFORMATION SYSTEM

THE DATABASE OF THE INFORMATION SYSTEM
IS A COLLECTION OF RELATIONAL DATA TABLES
ORIENTED TO MANAGE LARGE AMOUNT OF DATA, LIKE IN
THE REAL WORLD MODELS.

OPTEX GENERATES, ON-LINE, DATA
WINDOWS WITH A COLLECTION OF
WINDOWS-TOOLS THAT HELP THE USER IN
THE LABOR OF DATA CAPTURE.
THE DATA WINDOWS ARE JOINT IN A SHELL
WINDOWS IN A RELATIONAL APPROACH.

HIERARCHIC INFORMATION SYSTEM FOR MODELS RESULTS
SCENARIO FAMILY
ROOT DIRECTORY
Family
No. 1
Directory
Family
No. E
Directory
Family
No. n
Directory
Scenario
No. E-X
Directory
Scenario
No. E-X
Directory
Tables
Parameters
Tables
Resulting
Parameters
Tables
Variable
Results
Tables
Parameters
Results
Tables
Variable
Results
Scenario
No. E-X
Directory
Tables
Parameter
Results
Tables
Variable
Results
AUTOMATICALLY, OPTEX GENERATES A HIERARCHIC INFORMATION
SYSTEM TO STORE THE RESULTS OF THE MODELS USING THE
CONCEPTS OF SCENARIOS AND FAMILY OF SCENARIOS.

OPTEX STORES IN TABLES
THE MATRIX AND THE
VECTORS RESULT OF THE
MATRIX GENERATION.
THIS ALLOWS THE
DEVELOPER TO VISUALIZE
AND CHECK THE VALIDITY
OF HIS MODELING

OPTEX STORES
THE RESULTS
IN DATA
TABLES
AND/OR IN
TEXT FILES
AND/OR IN
EXCEL FILES

OPTEX PROVIDES TOOLS
FOR VISUALIZATION OF
LITTLE MODELS.
FOR LARGE SCALE MODELS
THE STRUCTURE OF
RESULTS TABLES ARE
ORIENTED TO USE IN
MULTIDIMENSIONAL
ANALYSIS DATA TOOLS

RELATIONAL INFORMATION SYSTEM
OPTEX
INFORMATION
SYSTEM

TABLES DEFINITION
FIELDS DEFINITION
INDEX TABLES DEFINITION RELATIONAL FIELDS DEFINITION

BREWING PLANTS BREWING PLANT
PRODUCT
BREWING PLANT
HOURS
BREWING PLANT
RESOERCE
PRODUCT
BREWING PLANT
INITIAL CONDITIONS
BREWING PLANT
RESOURCE
BREWING PLANT
FACTORY

PACKING PLANTS
PACKING PLANT
DISTRIBUTION CENTER
PACKING PLANT
RESOURCE
PACKING PLANT
FACTORY

OPTEX FORM WINDOW TO CAPTURE/MODIFY DATA
INCLUDING HELP TOOLS

OPTEX
PROCESSOR
ARCHIVOS
CONECTIVIDAD
SOFTWARE TERCEROS
IBM JVIEW,- MS PROJECT
XML – OLAP SERVER
EXCEL
OUTPUT DATA
EXCEL
INPUT DATA
0
2 0
4 0
6 0
8 0

Customized EXCEL Visual User Interface

ERP – TMS
AMS - WMS
DECISION
SUPPORT
INFORMATION
SYSTEM
COMPANY
ERP
INFORMATION
SYSTEM
MAPING
CONNECTIVITY

DECISION
SUPPORT
INFORMATION
SYSTEM ERP
TMS
WMS
INFORMATION
SYSTEM
XML
MAPING
ODBCs
Web Services
DECISIONMAKERS
OR SCIENTISTS
VISUALIZATION
TOOLS
CONNECTIVITY

XML CODE
MDX SERVER
MONDRIAN
INTERFACE
JRUBICK(PC) – OPENI(WEB)

CLIENT – SERVER ARCHITECTURE

OPTEX
SQL DATABASE
OPTEX - SERVER
MATHEMATICAL
MODELING PROCESSOR
OPTEX CLIENT

OPTEX
SQL DATABASE
ERP/TMS/WMS
DATABASE
OPTEX
OLAP DATABASE
OPTEX - SERVER
MATHEMATICAL
MODELING PROCESSOR
OPTEX CLIENT
VISUALIZATION
SERVER

CLOUD LINK
EXCEL
PROGRAMS IN DIFERENT
LANGUAGES
C – GAMS – IBM OPL –
MOSEL – AIMMS - AMPL
MPS
MODEL

OPTEX
CLOUD SERVER
EXCEL
OUTPUT DATA
EXCEL
INPUT DATA

OPCHAIN
OPTIMIZING THE VALUE CHAIN

To capitalize its expertise in mathematical optimization projects,
DW created OPCHAIN, a brand through which we have grouped
the solutions developed by DW, in different areas of application
using mathematical programming methodologies and technologies.
In 2012, OPCHAIN has accumulated the experience of more than
thirty-five (35) years in engineering problem solving and business
analytics using mathematical programming models. OPCHAIN
models are fully programmable, easy to customize for each client,
and are easily integrated with other IT solutions in organizations.
OPCHAIN
OPTIMIZING THE VALUE CHAIN

OPCHAIN-SCO
SUPPLY CHAIN OPTIMIZATION
OPCHAIN-TSO
TRANSPORT SYSTEMS OPTIMIZATION
OPCHAIN-RSO
RETAIL CHAIN OPTIMIZATION
OPCHAIN-RPO
REGIONAL PLANING OPTIMIZATION
OPCHAIN-ESO
ENERGY SYSTEMS OPTIMIZATION
OPCHAIN-BANK
BANK SYSTEMS OPTIMIZATION
OPCHAIN-EDO
EDUCATIONAL SYSTEMS OPTIMIZATION
OPCHAIN-MINES
MINES SYSTEMS OPTIMIZATION

OPTEX Mathematical Modeling System,
was developed to support
DecisionWare’s mathematical modeling
projects since 1991.
OPTEX supports the development of all
multi-model OPCHAIN-DSS developed
by

SERVICES
 TO SELL OPTEX MATHEMATICAL MODELING MANAGEMENT SYSTEM
 TO SELL OPCHAIN-MODELS IN ANY PLATFORM
 (INCLUDING SOURCE CODE)
 TO CONVERT MODELS FROM ANY PLATFORM TO ANY PLATFORM
 TO DEVELOPMENT ON DEMAND MODELS IN ANY PLATFORM
 ON DEMAND OPTIMIZATION IN THE CLOUD

OPTEX MATHEMATICAL MODELING AND MANAGEMENT SYSTEM

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