### 3.3 Abstracting the Structure of the Problem

 Concepts We shall now consider the structure of our simple oil blending model. What are the classes of object with which we are dealing and whose behaviour is essentially similar? They are the: components: LGO, HGO, WAXD, ARES and VRES qualities: SG, VBI, Sulphur We might also consider that there was a class of objects: products: HFO because a similar matrix would exist for other products. We shall disregard this for now. We shall use individual lower case letters as subscripts to denote these classes of object. To be specific we shall use: r for components; q for qualities Next let us consider the data items which we have used in our matrix. These are: the cost of component r, COSTr the value of quality q of component r, QUALqr the maximum value of quality q in the blend, QMAXq the demand for product, DEMAND the availability of component r, AVAILr What are our decision variables? These are: the quantity of component r to use in the blend, xr. To complete our armoury of notation we shall use the symbols Σj to mean 'sum over j' and є to mean 'is an element of'. Writing the Structure Symbolically Then we can write our problem informally as minimize Σr Cost of r * Quantity of r used subject to: 1. the quality constraints for each q Σr Quality q of r * Quantity of r used ≤ Max quality * Total quantity made 2. the demand constraint Σr Quantity of r used = Demand 3. the availability constraints for each r Quantity of r used ≤ Availability of r We can write this more formally as: minimize Σr COSTr xr subject to 1. the quality constraints `Σr QUALqr xr ≤ QMAXq Σr xr for all q` 2. the demand constraint Σr xr = DEMAND 3. the availability constraints `xr ≤ AVAILr for all r` If we are being formal we should also constrain each xr variable to be non-negative: `xr ≥ 0 for all r` although in practice this is assumed by convention. Improving the Quality Constraints This mathematical formulation may appear more verbose than our simple matrix. Indeed, it occupies much more space when presented with the data as an input file for an algebraic modelling language such as XPRESS. But this is an unfair comparison. Our mathematical formulation remains the same whether we are working with 5 or 50 components and 3 or 30 qualities. It also holds true for blends of any product and for making batches of the product today, tomorrow and at any time. More important than this, it provides a much better way of thinking about our problem. We can manipulate the mathematical formulation and consider how to improve it. If we wish to add minimum quality constraints all that we need do is to add the data items: the minimum value of quality q in the blend, QMINq and the minimum quality constraints: `Σr QUALqr xr ≥ QMINq Σr xr for all q` It is now a trivial piece of manipulation to write the quality constraints as: `Σr (QUALqr - QMAXq ) xr ≤ 0 for all q Σr (QUALqr - QMINq ) xr ≥ 0 for all q` Now that we are being precise we should record the units in which we measure the data items, decision variables and constraints. Let us say that we are working in tonnes, i.e. AVAILr , DEMAND and xr are all measured in tonnes. We shall use \$ as our currency so COSTr is measured in \$/tonne. The objective is thus measured in \$ while the availability and demand constraints are measured in tonnes. We need to check that for each row (i.e. constraint or the objective), each term (i.e. coefficient multiplied by decision variable) has the same units and that these correspond to the units we have declared for the row. For these rows they do. What should we do about qualities? What are the units of quality q? Specific gravity is the dimensionless ratio of the density of the material divided by the density of water. VBI is a numerical index which is derived by taking the logarithm of the logarithm of a number measured in centiStokes, which has dimensions Mass Length-1 Time-1. Sulphur is usually measured as a % by weight, i.e. is dimensionless but calculated as tonnes of Sulphur / 100 tonnes of material. In order to avoid getting bogged down in technical details, when dealing with qualities we shall use the term "units of quality q" as the equivalent of \$ for costs. Then the units of QUALqr , QMAXq , and QMINq are units of quality q. But what are the units of the quality constraints? More seriously, are the quality constraints correct? In Blending by Weight and by Volume we saw that some qualities blended by weight while others blended by volume. VBI and Sulphur content are examples of qualities which blend by weight, so the quality constraints we have defined are correct for those qualities. But specific gravity blends by volume, so the constraints which we require are `Σr (QUALqr - QMAXq ) vr ≤ 0 for all q Σr (QUALqr - QMINq ) vr ≥ 0 for all q` where vr is the volume of component r in the blend. Now vr = xr / (ρWATER * SGr ) so we have `Σr { (QUALqr - QMAXq ) / (ρWATER * SGr ) } xr ≤ 0 for all q Σr { (QUALqr - QMINq ) / (ρWATER * SGr ) } xr ≥ 0 for all q` or, cancelling out the ρWATER `Σr { (QUALqr - QMAXq ) / SGr } xr ≤ 0 for all q Σr { (QUALqr - QMINq) / SGr } xr ≥ 0 for all q` These are the form of quality constraint which we require for qualities which blend by volume. The fact that we happened to be considering specific gravity is irrelevant to the appearance of SGr as the divisor of the term Σr (QUALqr - QMAXq ) and Σr (QUALqr - QMINq ): SGr divides these terms in the quality constraints for all qualities which blend by volume. The advantages of an algebraic notation should now have become more clear. It would have been far harder to have developed these constraints correctly if we had been working with specific instances of the constraints rather than algebra. The notation is a prop to our thoughts. `previous contents next`