Side constraints in network models fall into several categories that have special structure. They are frequently used when the flow through an arc must be proportional to the flow through another arc. Such constraints are called proportionality constraints and are useful in models where production is subject to refining or modification into different materials. The amount of each output, or any waste, evaporation, or reduction can be specified as a proportion of input.

Typically the arcs near the supply nodes carry raw materials and the arcs near the demand nodes carry refined products. For example, in a model of the milling industry, the flow through some arcs may represent quantities of wheat. After the wheat is processed, the flow through other arcs might be flour. For others it might be bran. The side constraints model the relationship between the amount of flour or bran produced as a proportion of the amount of wheat milled. Some of the wheat can end up as neither flour, bran, nor any useful product, so this waste is drained away via arcs to a waste node.

Consider the network fragment in Figure 6.2. The arc Wheat_Mill conveys the wheat milled. The cost of flow on this arc is the milling cost. The capacity of this arc is the capacity of the mill. The lower flow bound on this arc is the minimum quantity that must be milled for the mill to operate economically. The constraints

Wheat_Mill Mill_Flour

Wheat_Mill Mill_Bran

force every unit of wheat that is milled to produce 0.3 units of flour and 0.2 units of bran. Note that it is not necessary to specify the constraint

Wheat_Mill Mill_Other

since flow conservation implies that any flow that does not traverse through Mill_Flour or Mill_Bran must be conveyed through Mill_Other. And, computationally, it is better if this constraint is not specified, since there is one less side constraint and fewer problems with numerical precision. Notice that the sum of the proportions must equal 1.0 exactly; otherwise, flow conservation is violated.

Blending or quality constraints can also influence the recipes or proportions of ingredients that are mixed. For example, different raw materials can have different properties. In an application of the oil industry, the amount of products that are obtained could be different for each type of crude oil. Furthermore, fuel might have a minimum octane requirement or limited sulphur or lead content, so that a blending of crudes is needed to produce the product.

The network fragment in Figure 6.3 shows an example of this.

The arcs MidEast_Port and USA_Port convey crude oil from the two sources. The arc Port_Refinery represents refining while the arcs Refinery_Gasoline and Refinery_Diesel carry the gas and diesel produced. The proportionality constraints

Port_Refinery Refinery_Gasoline

Port_Refinery Refinery_Diesel

capture the restrictions for producing gasoline and diesel from crude. Suppose that, if only crude from the Middle East is used, the resulting diesel would contain 5 units of sulphur per liter. If only crude from the USA is used, the resulting diesel would contain 4 units of sulphur per liter. Diesel can have at most 4.75 units of sulphur per liter. Some crude from the USA must be used if Middle East crude is used in order to meet the 4.75 sulphur per liter limit. The side constraint to model this requirement is

MidEast_Port USA_Port Port_Refinery

Since Port_Refinery MidEast_Port USA_Port, flow conservation allows this constraint to be simplified to

MidEast_Port USA_Port

If, for example, 120 units of crude from the Middle East is used, then at least 40 units of crude from the USA must be used. The preceding constraint is simplified because you assume that the sulphur concentration of diesel is proportional to the sulphur concentration of the crude mix. If this is not the case, the relation

Port_Refinery Refinery_Diesel

is used to obtain

MidEast_Port USA_Port Refinery_Diesel

which equals

MidEast_Port USA_Port Refinery_Diesel

An example similar to this Oil Industry problem is solved in the section Introductory Example.

Side constraints are also used in models in which there are capacities on transportation or some other shared resource, or there are limits on overall production or demand in multicommodity, multidivisional or multiperiod problems. Each commodity, division or period can have a separate network coupled to one main system by the side constraints. Side constraints are used to combine the outputs of subdivisions of a problem (either commodities, outputs in distinct time periods, or different process streams) to meet overall demands or to limit overall production or expenditures. This method is more desirable than doing separate local optimizations for individual commodity, process, or time networks and then trying to establish relationships between each when determining an overall policy if the global constraint is not satisfied. Of course, to make models more realistic, side constraints may be necessary in the local problems.

Figure 6.4 shows two network fragments. They represent identical production and distribution sites of two different commodities. Suffix com1 represents commodity 1 and suffix com2 represents commodity 2. The nodes Factorycom1 and Factorycom2 model the same factory, and nodes City1com1 and City1com2 model the same location, city 1. Similarly, City2com1 and City2com2 are the same location, city 2. Suppose that commodity 1 occupies 2 cubic meters, commodity 2 occupies 3 cubic meters, the truck dispatched to city 1 has a capacity of 200 cubic meters, and the truck dispatched to city 2 has a capacity of 250 cubic meters. How much of each commodity can be loaded onto each truck? The side constraints for this case are

Factorycom1_City1com1 Factorycom2_City1com2

Factorycom1_City2com1 Factorycom2_City2com2

In many cases, the flow through an arc might actually represent the flow or movement of a commodity from place to place or from time period to time period. However, sometimes an arc is included in the network as a method of capturing some aspect of the problem that you would not normally think of as part of a network model. For example, in a multiprocess, multiproduct model (Figure 6.5), there might be subnetworks for each process and each product. The subnetworks can be joined together by a set of arcs that have flows that represent the amount of product produced by process . To model an upper limit constraint on the total amount of product that can be produced, direct all arcs carrying product to a single node and from there through a single arc. The capacity of this arc is the upper limit of product production. It is preferable to model this structure in the network rather than to include it in the side constraints because the efficiency of the optimizer is affected less by a reasonable increase in the size of the network.

It is often a good strategy when starting a project to use a small network formulation and then use that model as a framework upon which to add detail. For example, in the multiprocess, multiproduct model, you might start with the network depicted in Figure 6.5. Then, for example, the process subnetwork can be enhanced to include the distribution of products. Other phases of the operation could be included by adding more subnetworks. Initially, these subnetworks can be single nodes, but in subsequent studies they can be expanded to include greater detail.

The NETFLOW procedure accepts the side constraints in the same dense and sparse formats that the LP procedure provides. Although PROC LP can solve network problems, the NETFLOW procedure generally solves network flow problems more efficiently than PROC LP.