The document introduces a Generalized Capital Investment Planning (GCIP) framework for modeling capital investment problems at different time scales (repair/correction, retrofit/commission, revamp/construction). It presents a small example modeled using the Industrial Modeling Framework (IMF), which uses a unit-operation-port-state superstructure and models capacity and capital as flows. The example is solved using MILP and the optimal solution expands capacity from 1.0 to 1.5 units at a cost of $0.75 and profit of $3.25 over three periods.

1. Generalized Capital Investment Planning w/ Sequence-Dependent Setups
Industrial Modeling Framework (GCIP-SDSU-IMF)
i n d u s t r IAL g o r i t h m s LLC (IAL)
www.industrialgorithms.com
June 2014
Introduction to “Generalized” Capital Investment Planning, UOPSS and QLQP
Presented in this short document is a description of what we call the “Generalized” Capital
Investment Planning (GCIP) problem where conventional capital investment planning (CIP), and
specifically for the “retrofit” problem, is discussed in Sahinidis and Grossmann (1989) and Liu
and Sahinidis (1996). CIP is the optimization problem where it is desired to expand the capacity
and/or extend the capability (conversion) of either the “expansion” of an existing unit or the
“installation” of a new unit (Jackson and Grossmann, 2002).
Figure 1 shows the three types of CIP problems as defined in Vazacopoulos et. al. (2014) and
Menezes (2014) with its capital cost and time scales.
Figure 1. Three Types of Capital Investment Planning Problems.
The shortest CIP problem is the “repair” problem which is typically referred to as Maintenance
Planning or Turnaround and Inspection (T&I) Planning and has a “correction” stage that is
placed in between the exiting unit before the correction and the improved unit after the
correction. The medium-term CIP problem is the “retrofit” problem and is often referred to as
Capacity Planning or Production Design Synthesis and has a “commission” stage that is placed
in between the existing unit before the commission and the expanded/extended unit after the
commission. The longest CIP problem is the “revamp” problem which is sometimes referred to
as Facilities Planning and Process Design Synthesis and has a “construction” stage that is
placed in between the existing unit before the construction and the expanded/extended unit
after the construction. If the unit does not previously exist then this is an installation versus an
expansion/extension. To our knowledge this is the first time a connection between the different

2. types of CIP problems has been made i.e., repair/correction (operational), retrofit/commission
(tactical/debottlenecking) and revamp/construction (strategic).
There are two other salient aspects of our general CIP formulation that sets our formulation
apart from all other formulations found in the literature. The first is the modeling of sequence-
dependent setups, switchovers or changeovers to manage the realistic situation that a
correction, commission or construction stage, activity or task must be planned or scheduled in
between the existing and expanded/extended units. This is handled using the appropriate
variables and constraints found in Kelly and Zyngier (2007) which albeit intended for discrete-
time scheduling problems with repetitive maintenance, can be easily applied here to CIP
problems which are also modeled in discrete-time. The second is the modeling of capacity (and
capability) and capital as “flows” or quantities. This is the notion that the correction, commission
and construction stages actually produce or create capacity and/or capability which can then be
used or consumed by the unit in subsequent time-periods but there is of course a charge for the
capacity/capability known as the capital cost expenditure. Net present value (NPV) adjustments
due to inflationary or deflationary trends over time can be applied to the cost of capital and is
simply reflected as a modification to the cost weights in the objective function.
For this discussion our focus is on the more widely studied retrofit problem where a small IMPL
flowsheet example is presented in Figure 2.
Figure 2. Small Generalized CIP Example Flowsheet with “Capacity” and “Capital” Flows.
The flowsheet is drawn using our unit-operation-port-state superstructure (UOPSS) described in
Kelly (2004), Kelly (2005) and Zyngier and Kelly (2012). The diamond shapes or objects are the
perimeter unit-operations where we consume material “A” and produce material “B” in the same
proportions. We have just one process unit with three operations of “Existing”, “Commission”
and “Expand” as shown by the square boxes with an “x” through it indicating it is a continuous-

3. process type. The first dotted line box highlights that only one unit-operation can be active or
setup at a time i.e., a unary resource or the unit commitment constraint. The small circles are
the in-ports (w/o “x”) and out-ports (w/ “x”) where these ports have the attributed lower and
upper yields available similar to the modeling of generalized network-flow problems i.e., Leontief
input/output models with intensities, bill-of-material or transfer coefficients.
The operations of Commission and Expand each have a special out-port and in-port labeled as
“cpt” which stands for the outflow and inflow of capacity respectively. There is a “Charge” unit-
operation which will only be setup if the Commission unit-operation is active and its purpose is
to convert the variable capacity to a variable and fixed capital cost using a simple linear
equation (see the “cpl” labeled ports):
capital cost = alpha * (capacity_new – capacity_old) + beta * setup
where the “alpha” coefficient or parameter is applied to the incremental or delta capacity change
and the “beta” is applied to the setup variable if the Charge unit-operation is on or open. The
out-port on the Charge unit-operation labeled “cpt” is the flow of capacity charged or dispatched
to the “Capacity” pool unit-operation (triangle shape). During the one time-period when the
Charge unit-operation is active, the flow of capacity to the Capacity pool must be enough
capacity to operate the Expand operation for as many time-periods left in the planning time-
horizon. For example, if we have a three (3) time-period future horizon and the Commission
operation starts in time-period one (1) then enough capacity must be fed or sent to the Capacity
pool unit-operation for time-periods two (2) and three (3).
The “cpt” in-port on the Expand operation will draw only up to the maximum allowable or upper
limit of the expanded capacity allowed from the Capacity pool and this will control the capacity
charge-size, throughput or flow through the unit-operation for the Expand operation. In order to
do this, the lower yield bound on the “cpt” in-port is configured as one (1.0) and the upper yield
bound as infinity (inf) or some large number. This will regulate the throughput of the Expand
unit-operation as follows:
flow,cpt >= 1.0 * flow,unitop
flow,cpt <= inf * flow,unitop
which simplifies to:
flow,unitop <= 1.0/1.0 * flow,cpt = flow,cpt
flow,unitop >= 1.0/inf * flow,cpt = 0.0
The complete configuration of this small generalized CIP problem can found in the IMPL file
GCIP-SDSU-IMF.IML found in Appendix A.
Industrial Modeling Framework (IMF), IMPL and SIIMPLE
To implement the mathematical formulation of this and other systems, IAL offers a unique
approach and is incorporated into our Industrial Modeling Programming Language we call IMPL.
IMPL has its own modeling language called IML (short for Industrial Modeling Language) which
is a flat or text-file interface as well as a set of API's which can be called from any computer
programming language such as C, C++, Fortran, C#, VBA, Java (SWIG), Python (CTYPES)
and/or Julia (CCALL) called IPL (short for Industrial Programming Language) to both build the
model and to view the solution. Models can be a mix of linear, mixed-integer and nonlinear

4. variables and constraints and are solved using a combination of LP, QP, MILP and NLP solvers
such as COINMP, GLPK, LPSOLVE, SCIP, CPLEX, GUROBI, LINDO, XPRESS, CONOPT,
IPOPT, KNITRO and WORHP as well as our own implementation of SLP called SLPQPE
(Successive Linear & Quadratic Programming Engine) which is a very competitive alternative to
the other nonlinear solvers and embeds all available LP and QP solvers.
In addition and specific to DRR problems, we also have a special solver called SECQPE
standing for Sequential Equality-Constrained QP Engine which computes the least-squares
solution and a post-solver called SORVE standing for Supplemental Observability, Redundancy
and Variability Estimator to estimate the usual DRR statistics. SECQPE also includes a
Levenberg-Marquardt regularization method for nonlinear data regression problems and can be
presolved using SLPQPE i.e., SLPQPE warm-starts SECQPE. SORVE is run after the
SECQPE solver and also computes the well-known "maximum-power" gross-error statistics
(measurement and nodal/constraint tests) to help locate outliers, defects and/or faults i.e., mal-
functions in the measurement system and mis-specifications in the logging system.
The underlying system architecture of IMPL is called SIIMPLE (we hope literally) which is short
for Server, Interfacer (IML), Interacter (IPL), Modeler, Presolver Libraries and Executable. The
Server, Presolver and Executable are primarily model or problem-independent whereas the
Interfacer, Interacter and Modeler are typically domain-specific i.e., model or problem-
dependent. Fortunately, for most industrial planning, scheduling, optimization, control and
monitoring problems found in the process industries, IMPL's standard Interfacer, Interacter and
Modeler are well-suited and comprehensive to model the most difficult of production and
process complexities allowing for the formulations of straightforward coefficient equations,
ubiquitous conservation laws, rigorous constitutive relations, empirical correlative expressions
and other necessary side constraints.
User, custom, adhoc or external constraints can be augmented or appended to IMPL when
necessary in several ways. For MILP or logistics problems we offer user-defined constraints
configurable from the IML file or the IPL code where the variables and constraints are
referenced using unit-operation-port-state names and the quantity-logic variable types. It is also
possible to import a foreign *.ILP file (row-based MPS file) which can be generated by any
algebraic modeling language or matrix generator. This file is read just prior to generating the
matrix and before exporting to the LP, QP or MILP solver. For NLP or quality problems we offer
user-defined formula configuration in the IML file and single-value and multi-value function
blocks writable in C, C++ or Fortran. The nonlinear formulas may include intrinsic functions
such as EXP, LN, LOG, SIN, COS, TAN, MIN, MAX, IF, NOT, EQ, NE, LE, LT, GE, GT and CIP,
LIP, SIP and KIP (constant, linear and monotonic spline interpolations) as well as user-written
extrinsic functions (XFCN). It is also possible to import another type of foreign file called the
*.INL file where both linear and nonlinear constraints can be added easily using new or existing
IMPL variables.
Industrial modeling frameworks or IMF's are intended to provide a jump-start to an industrial
project implementation i.e., a pre-project if you will, whereby pre-configured IML files and/or IPL
code are available specific to your problem at hand. The IML files and/or IPL code can be
easily enhanced, extended, customized, modified, etc. to meet the diverse needs of your project
and as it evolves over time and use. IMF's also provide graphical user interface prototypes for
drawing the flowsheet as in Figure 1 and typical Gantt charts and trend plots to view the solution
of quantity, logic and quality time-profiles. Current developments use Python 2.3 and 2.7
integrated with open-source Dia and Matplotlib modules respectively but other prototypes
embedded within Microsoft Excel/VBA for example can be created in a straightforward manner.

5. However, the primary purpose of the IMF's is to provide a timely, cost-effective, manageable
and maintainable deployment of IMPL to formulate and optimize complex industrial
manufacturing systems in either off-line or on-line environments. Using IMPL alone would be
somewhat similar (but not as bad) to learning the syntax and semantics of an AML as well as
having to code all of the necessary mathematical representations of the problem including the
details of digitizing your data into time-points and periods, demarcating past, present and future
time-horizons, defining sets, index-sets, compound-sets to traverse the network or topology,
calculating independent and dependent parameters to be used as coefficients and bounds and
finally creating all of the necessary variables and constraints to model the complex details of
logistics and quality industrial optimization problems. Instead, IMF's and IMPL provide, in our
opinion, a more elegant and structured approach to industrial modeling and solving so that you
can capture the benefits of advanced decision-making faster, better and cheaper.
“Generalized” Capital Investment Planning Synopsis
At this point we explore further the solution to the small retrofit generalized CIP problem fully
defined in Figure 2 and Appendix A. The future planning time-horizon is arbitrarily configured as
three (3) months and with one (1) month time-periods. The existing or old capacity of the
process is 1.0 quantity-units per month and the new capacity is 1.5. The capital cost is
computed with an alpha = 0.5 ($ per quantity-units) and a beta = 0.5 ($ per setup-units). The
cost for material A is $0.0 per quantity-unit and the price for B is $1.0 per quantity-unit and we
do not apply any NPV given the relatively short horizon.
The problem is solved using MILP with a provably optimal objective function of $3.25. If we
apply no expansion then the profit would be $3.0 since the existing capacity is 1.0 for three (3)
time-periods. Since the profit is $0.25 more than $3.0 then there has been an expansion where
the Commission operation is setup or started in time-period one (1). To perform an expansion
of 1.5 – 1.0 = 0.5 quantity-units then the capital cost required is 0.5 * 0.5 + 0.5 = $0.75. Given
the timing of the Commission stage, this implies that the Expand stage occurs in time-periods
two (2) and three (3) which is enforced by the sequence-dependent setup modeling i.e., after
the Commission stage only the Expand stage can be setup for the rest of the horizon. With an
expansion capital cost of $0.75 and a revenue for the sale of material B of 1.0 + 1.5 + 1.5 = $4.0
for the three (3) planning periods then this leaves a profit of $4.0 - $0.75 = $3.25 which is the
same value found by the MILP. The Gantt chart for this example is found in Figure 3.

6. Figure 3. Gantt Chart for Small Generalized CIP Example.
From the Gantt chart we can verify the timing for the Commission and Expand unit-operation on
the Process unit. The interesting detail is the Capacity pool holdup or inventory trend of
capacity which is shown as the grey line inside the black horizon bar. We can see a charge of
capacity in time-period one (1) and a continuous draw or dispatch out in time-periods two (2)
and three (3).
In summary, we have shown how to use IMPL, which is a “generalized network-flow using
setups (and properties)” approach to solving diverse decision-making and data-mining problems
found in the process industries, to model and solve a typical capital investment planning
problem. We have applied the concepts of “sequence-dependent setups” and the notion of
“capacity/capability and capital flows” to formulate the CIP problem in a unique and novel way
without having to explicitly code the sets, parameters, variables and constraints in an algebraic
modeling language such as AMPL, AIMMS, GAMS, MOSEL, OPL etc. Instead, IMPL allows the
modeler or user the ability to configure the problem using semantic variables such as flows,
yields and setups which is more intuitive and natural.
References
Sahinidis, N.V., Grossmann, I.E., Fornari, R.E., Chathrathi, M., "Optimization model for long
range planning in the chemical industry", Computers & Chemical Engineering, 13, 1049, (1989).
Liu, M-L., Sahinidis, N.V., “Long range planning in the process industries: a projection
approach”, Computers & Operations Research, 23, 237, (1996).
Jackson, R.J., Grossmann, I.E., “High-level optimization for the retrofit planning of process
networks”, Industrial & Engineering Chemistry Research, 41, 3762-3770, (2000).
Kelly, J.D., "Production modeling for multimodal operations", Chemical Engineering Progress,
February, 44, (2004).

7. Kelly, J.D., "The unit-operation-stock superstructure (UOSS) and the quantity-logic-quality
paradigm (QLQP) for production scheduling in the process industries", In: MISTA 2005
Conference Proceedings, 327, (2005).
Kelly, J.D., Zyngier, D., "An improved MILP modeling of sequence-dependent switchovers for
discrete-time scheduling problems", Industrial & Engineering Chemistry Research, 46, 4964,
(2007).
Zyngier, D., Kelly, J.D., "UOPSS: a new paradigm for modeling production planning and
scheduling systems", ESCAPE 22, June, (2012).
Vazacopoulos, A., Kelly, J.D., Menezes, B.C, Grossmann, I.E., “A general approach for
capital investment planning of oil-refineries using MILP and sequence-dependent
setups”, INFORMS Analytics & Operations Research, Boston, March, (2014).
Menezes, B.C., “Quantitative methods for strategic investment planning in the oil-
refining industry”, PhD Thesis, Federal University of Rio de Janeiro (UFRJ), August,
(2014).
Appendix A - GCIP-SDSU-IMF.IML File
i M P l (c)
Copyright and Property of i n d u s t r I A L g o r i t h m s LLC.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Calculation Data (Parameters)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sCalc,@sValue
START,-1.0
BEGIN,0.0
END,3.0
PERIOD,1.0
LARGE,10000.0
ALPHA,0.5
BETA,0.5
OLDCAPACITY,1.0
NEWCAPACITY,1.5
&sCalc,@sValue
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Chronological Data (Periods)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
@rPastTHD,@rFutureTHD,@rTPD
START,END,PERIOD
@rPastTHD,@rFutureTHD,@rTPD
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Construction Data (Pointers)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperation,@sType,@sSubtype,@sUse
A,,perimeter,,
B,,perimeter,,
Capacity,,pool,,
Capital,Cost,perimeter,,
Charge,,processc,,
Process,Commission,processc,,
Process,Existing,processc,,
Process,Expand,processc,,
&sUnit,&sOperation,@sType,@sSubtype,@sUse
&sUnit,&sOperation,&sPort,&sState,@sType,@sSubtype
A,,a,,out,
B,,b,,in,
Capacity,,i,,in,
Capacity,,o,,out,
Capital,Cost,cpl,,in,
Charge,,cpl,,out,

8. Charge,,cpt,,out,
Charge,,i,,in,
Process,Commission,a,,in,
Process,Commission,b,,out,
Process,Commission,cpt,,out,
Process,Existing,a,,in,
Process,Existing,b,,out,
Process,Expand,a,,in,
Process,Expand,b,,out,
Process,Expand,cpt,,in,
&sUnit,&sOperation,&sPort,&sState,@sType,@sSubtype
&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState
A,,a,,Process,Commission,a,
A,,a,,Process,Existing,a,
A,,a,,Process,Expand,a,
Capacity,,o,,Process,Expand,cpt,
Charge,,cpl,,Capital,Cost,cpl,
Charge,,cpt,,Capacity,,i,
Process,Commission,b,,B,,b,
Process,Commission,cpt,,Charge,,i,
Process,Existing,b,,B,,b,
Process,Expand,b,,B,,b,
&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Capacity Data (Prototypes)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperation,@rRate_Lower,@rRate_Upper
Process,Existing,0.0,OLDCAPACITY
Process,Commission,0.0,OLDCAPACITY
Process,Expand,0.0,LARGE
Charge,,0.0,NEWCAPACITY
&sUnit,&sOperation,@rRate_Lower,@rRate_Upper
&sUnit,&sOperation,@rHoldup_Lower,@rHoldup_Upper
Capacity,,0.0,NEWCAPACITY*END
&sUnit,&sOperation,@rHoldup_Lower,@rHoldup_Upper
&sUnit,&sOperation,&sPort,&sState,@rTotalRate_Lower,@rTotalRate_Upper
ALLINPORTS,0.0,LARGE
ALLOUTPORTS,0.0,LARGE
&sUnit,&sOperation,&sPort,&sState,@rTotalRate_Lower,@rTotalRate_Upper
&sUnit,&sOperation,&sPort,&sState,@rTeeRate_Lower,@rTeeRate_Upper
ALLINPORTS,0.0,LARGE
ALLOUTPORTS,0.0,LARGE
Process,Expand,cpt,,0.0,NEWCAPACITY
&sUnit,&sOperation,&sPort,&sState,@rTeeRate_Lower,@rTeeRate_Upper
&sUnit,&sOperation,&sPort,&sState,@rYield_Lower,@rYield_Upper,@rYield_Fixed
Process,Existing,a,,1.0,1.0,
Process,Existing,b,,1.0,1.0,
Process,Commission,a,,1.0,1.0,
Process,Commission,b,,1.0,1.0,
Process,Commission,cpt,,0.0,LARGE,
Process,Expand,a,,1.0,1.0,
Process,Expand,b,,1.0,1.0,
Charge,,i,,1.0,1.0
Charge,,cpl,,ALPHA,ALPHA,BETA-ALPHA*OLDCAPACITY
Charge,,cpt,,0.0,0.0
Process,Expand,cpt,,1.0,LARGE,
&sUnit,&sOperation,&sPort,&sState,@rYield_Lower,@rYield_Upper,@rYield_Fixed
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Constriction Data (Practices/Policies)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperation,@rUpTiming_Lower,@rUpTiming_Upper
Process,Commission,1.0,1.0
Process,Expand,END,
&sUnit,&sOperation,@rUpTiming_Lower,@rUpTiming_Upper
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Consolidation Data (Partitioning)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperation,&sOperationGroup
Process,Existing,ExistingGroup
Process,Expand,ExpandGroup
&sUnit,&sOperation,&sOperationGroup
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Compatibility Data (Phasing, Prohibiting, Purging, Postponing)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperationGroup,&sOperationGroup,@sOperation
Process,ExistingGroup,ExpandGroup,Commission

9. &sUnit,&sOperationGroup,&sOperationGroup,@sOperation
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Cost Data (Pricing)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperation,&sPort,&sState,@rFlowPro_Weight,@rFlowPer1_Weight,@rFlowPer2_Weight,@rFlowPen_Weight
A,,a,,0.0
B,,b,,1.0
Capital,Cost,cpl,,-1.0
&sUnit,&sOperation,&sPort,&sState,@rFlowPro_Weight,@rFlowPer1_Weight,@rFlowPer2_Weight,@rFlowPen_Weight
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Content Data (Past, Present Provisos)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperation,@rHoldup_Value,@rStart_Time
Capacity,,0.0,0.0
&sUnit,&sOperation,@rHoldup_Value,@rStart_Time
&sUnit,&sOperation,@rSetup_Value,@rStart_Time
Process,Existing,1,START
&sUnit,&sOperation,@rSetup_Value,@rStart_Time
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Command Data (Future Provisos)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperation,@rSetup_Lower,@rSetup_Upper,@rBegin_Time,@rEnd_Time
ALLPARTS,0,1,BEGIN,END
&sUnit,&sOperation,@rSetup_Lower,@rSetup_Upper,@rBegin_Time,@rEnd_Time
&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState,@rSetup_Lower,@rSetup_Upper,@rBegin_Time,@rEnd_Time
ALLPATHS,0,1,BEGIN,END
&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState,@rSetup_Lower,@rSetup_Upper,@rBegin_Time,@rEnd_Time
&sUnit,&sOperation,&sPort,&sState,@rYield_Lower,@rYield_Upper,@rYield_Target,@rBegin_Time,@rEnd_Time
Charge,,cpt,,1.0*(END-1.0),1.0*(END-1.0),,0.0,1.0
Charge,,cpt,,1.0*(END-2.0),1.0*(END-2.0),,1.0,2.0
Charge,,cpt,,1.0*(END-3.0),1.0*(END-3.0),,2.0,3.0
&sUnit,&sOperation,&sPort,&sState,@rYield_Lower,@rYield_Upper,@rYield_Target,@rBegin_Time,@rEnd_Time