Optimization decision variables

The optimum operation of a multiproduct column is one that maximizes an overall objective function, such as the annual profit. The optimization decision variables are the duration and reflux ratio policy of each product. 

In achieving optimization, decision variables that are within the control of the planner, such as when to manufacture an order or when and how much of a raw material needs to be ordered, must be balanced with the inherent constraints or limitations that are placed upon supply. 

Its objective is to select tiie best possible decision for a given set of circumstances without having to enumerate all of the possibilities and involves maximization or minimization as desired. In optimization decision variables are variables in the model which you have control over. Objective function is a fimction (mathematical model) that quantifies the quality of a solution in an optimization problem. Constraints must be considered, conditions that a solution to an optimization problem must satisfy and restrict decision variables are determined by defining relationships among them. It must be found the values of die decision variables that maximize (minimize) the objective function value, while staying widiin the constraints. The objective function and all constraints are linear functions (no squared terms, trigonometric functions, ratios of variables) of the deeision variables [59, 60]. 

Recognizing that, due to unavoidable variability in the decision variables, one has to operate within a zone of the decision space, and not at a single point, we might still believe that finding the optimal pointwise solution, X, as usual, would be enough. The assumption behind such a... 

Another perspective for production simulation is automatic capacity utilization optimization of multi-product systems. As discussed, this task may be very difficult because of the many different variables and boundary conditions. In an environment integrating optimization and simulation, the optimizer systematically varies the important decision variables in an external loop while the simulation model carries out production planning with the specified variables in the internal loop (see Gunther and Yang [3]). The target function, for example total costs or lead times, can be selected as required. The result of optimization is a detailed proposal for the sequence of the placed orders. 

In Section 1.5 we briefly discussed the relationships of equality and inequality constraints in the context of independent and dependent variables. Normally in design and control calculations, it is important to eliminate redundant information and equations before any calculations are performed. Modem multivariable optimization software, however, does not require that the user clearly identify independent, dependent, or superfluous variables, or active or redundant constraints. If the number of independent equations is larger than the number of decision variables, the software informs you that no solution exists because the problem is overspecified. Current codes have incorporated diagnostic tools that permit the user to include all possible variables and constraints in the original problem formulation so that you do not necessarily have to eliminate constraints and variables prior to using the software. Keep in mind, however, that the smaller the dimensionality of the problem introduced into the software, the less time it takes to solve the problem. 

As mentioned earlier, nonlinear objective functions are sometimes nonsmooth due to the presence of functions like abs, min, max, or if-then-else statements, which can cause derivatives, or the function itself, to be discontinuous at some points. Unconstrained optimization methods that do not use derivatives are often able to solve nonsmooth NLP problems, whereas methods that use derivatives can fail. Methods employing derivatives can get stuck at a point of discontinuity, but the function-value-only methods are less affected. For smooth functions, however, methods that use derivatives are both more accurate and faster, and their advantage grows as the number of decision variables increases. Hence, we now turn our attention to unconstrained optimization methods that use only first partial derivatives of the objective function. 

Another type of widely used modeling system is the spreadsheet solver. Microsoft Excel contains a module called the Excel Solver, which allows the user to enter the decision variables, constraints, and objective of an optimization problem into the cells of a spreadsheet and then invoke an LP, MILP, or NLP solver. Other spreadsheets contain similar solvers. For examples using the Excel Solver, see Section 7.8, and Chapters 8 and 9. 

At each iteration, NLP algorithms form new estimates not only of the decision variables x but also of the Lagrange multipliers A and u. If, at these estimates, all constraints are satisfied and the KTC are satisfied to within specified tolerances, the algorithm stops. At a local optimum, the optimal multiplier values provide useful sensitivity information. In the NLP (8.25)-(8.26), let V (b, c) be the optimal value of the objective/at a local minimum, viewed as a function of the right-hand sides of the constraints b and c. Then, under additional conditions (see Luenberger, 1984, Chapter 10)... 

The Excel Solver. Microsoft Excel, beginning with version 3.0 in 1991, incorporates an NLP solver that operates on the values and formulas of a spreadsheet model. Versions 4.0 and later include an LP solver and mixed-integer programming (MIP) capability for both linear and nonlinear problems. The user specifies a set of cell addresses to be independently adjusted (the decision variables), a set of formula cells whose values are to be constrained (the constraints), and a formula cell designated as the optimization objective. The solver uses the spreadsheet interpreter to evaluate the constraint and objective functions, and approximates derivatives, using finite differences. The NLP solution engine for the Excel Solver is GRG2 (see Section 8.7). 

To apply these ideas to a general optimization problem, let the system state vector q correspond to the objects to be optimized (job sequences, vehicle routes, or vectors of decision variables), denoted by x. The system energy level corresponds to the objective function fix). As in Section 10.5.1, let N(x) denote a neighborhood of x. The following procedure (Floquet et al., 1994) specifies a basic SA algorithm ... 

This problem is very small, however, with only two decision variables. As the number of decision variables increases, the number of iterations required by evolutionary solvers to achieve high accuracy increases rapidly. To illustrate this, consider the linear project selection problem shown in Table 10.9. The optimal solution is also shown there, found by the LP solver. This problem involves determining the optimal level of investment for each of eight projects, labeled A through H, for which fractional levels are allowed. Each project has an associated net present value (NPV) of its projected net profits over the next 5 years and a different cost in each of the 5 years, both of which scale proportionately to the fractional level of investment. Total costs in each year are limited by forecasted budgets (funds available in... 

Table 10.10 shows the performance of the evolutionary solver on this problem in eight runs, starting from an initial point of zero. The first seven runs used the iteration limits shown, but the eighth stopped when the default time limit of 100 seconds was reached. For the same number of iterations, different final objective function values are obtained in each run because of the random mechanisms used in the mutation and crossover operations and the randomly chosen initial population. The best value of 811.21 is not obtained in the run that uses the most iterations or computing time, but in the run that was stopped after 10,000 iterations. This final value differs from the true optimal value of 839.11 by 3.32%, a significant difference, and the final values of the decision variables are quite different from the optimal values shown in Table 10.9. 

B, and E are also shown. Although B is reasonably near its optimal value, A and E are far from theirs. This performance is comparable to that of the evolutionary algorithm in the Extended Excel Solver, shown in Table 10.10. If the decision variables in this problem must be binary, however, then OPTQUEST finds the optimal solution, whose objective value is 767, in only 116 iterations. The evolutionary algorithm found this same optimal solution in one of two runs using 1000 iterations. 

Although, as explained in Chapter 9, many optimization problems can be naturally formulated as mixed-integer programming problems, in this chapter we will consider only steady-state nonlinear programming problems in which the variables are continuous. In some cases it may be feasible to use binary variables (on-off) to include or exclude specific stream flows, alternative flowsheet topography, or different parameters. In the economic evaluation of processes, in design, or in control, usually only a few (5-50) variables are decision, or independent, variables amid a multitude of dependent variables (hundreds or thousands). The number of dependent variables in principle (but not necessarily in practice) is equivalent to the number of independent equality constraints plus the active inequality constraints in a process. The number of independent (decision) variables comprises the remaining set of variables whose values are unknown. Introduction into the model of a specification of the value of a variable, such as T = 400°C, is equivalent to the solution of an independent equation and reduces the total number of variables whose values are unknown by one. 

In optimization using a process simulator to represent the model of the process, the degrees of freedom are the number of decision variables (independent variables) whose values are to be determined by the optimization, hence the results of an optimization yield a fully determined set of variables, both independent and dependent. Chapter 2 discussed the concept of the degrees of freedom. Example 15.1 demonstrates the identification of the degrees of freedom in a small process. 

In optimization using a modular process simulator, certain restrictions apply on the choice of decision variables. For example, if the location of column feeds, draws, and heat exchangers are selected as decision variables, the rate or heat duty cannot also be selected. For an isothermal flash both the temperatures and pressure may be optimized, but for an adiabatic flash, on the other hand, the temperature is calculated in a module and only the pressure can be optimized. You also have to take care that the decision (optimization) variables in one unit are not varied by another unit. In some instances, you can make alternative specifications of the decision variables that result in the same optimal solution, but require substantially different computation time. For example, the simplest specification for a splitter would be a molar rate or ratio. A specification of the weight rate of a component in an exit flow stream from the splitter increases the computation time but yields the same solution. 

Optimization methods calculate one best future state as optimal result. Mathematical algorithms e.g. SIMPLEX or Branch Bound are used to solve optimization problems. Optimization problems have a basic structure with an objective function H(X) to be maximized or minimized varying the decision variable vector X with X subject to a set of defined constraints 0 leading to max(min)//(X),Xe 0 (Tekin/Sabuncuoglu 2004, p. 1067). Optimization can be classified by a set of characteristics ... 

Decision variables decision variables representing the planning decisions of the planner and being decided in the optimization model e.g. production volumes... 

Relaxation of hard constraints is critical for optimization-based planning models used in industry practice with more than even 100,000 constraints and specifically for hard integer programming problems (Fisher 2004). Hard constraints set hard minimum and maximum boundaries for decision variables that have to be fulfilled. It may occur that no solution exists fitting all constraints at the same time. Planners have difficulties to identify manually constraints leading to infeasibility. Value chain planning model infeasibility is mainly caused by volume-related constraints of material flows e.g. by bounding sales quantities, inventories, transportation quantities, production and procurement quantities. Examples in literature for relaxation methods to e.g. transportation problems is presented by Klose/Lidke (2005)... 

Optimization model decision variables, objective functions and constraints are defined in the optimization model. 

The model areas have limited complexity considering single indices such as products or resources. Complexity increases combining basis indices like transportation lanes and products. A planning horizon of 12 periods will lead later to optimization models with more than 10,000 decision variables already for this limited scope. 

Finally, finite elements are added as decision variables in (27) not just to ensure accurate approximation (of the state and control profiles), but also to provide optimal points of discontinuity for the control profile. This dual purpose led Cuthrell and Biegler (1987) to distinguish some elements as finite-and super-elements. These roles can be combined, however, if one considers the NLP formulation of the optimal control problem given below ... 

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