Goal seeking & optimization
|Transport routing optimization|
Warehouse & inventory operations
Goal seeking is the process of identifying those decision options which satisfy or approximate closely declared objectives. Usually these are decisions made to make the most effective use of available resources to achieve some objective. The most common goal seeking techniques are optimization procedures where the decision-maker wishes to achieve an operational objective associated with the lowest cost, the lightest weight, the most dense mass, highest profit, lowest energy consumption or other selection criteria.
Online decision analysis systems provide a convenient way to access powerful and cost-effective optimization solutions in a range of business operations including transport, warehouse and inventory management (logistics), agricultural production planning for food, fibre and bio-industrial feedstock production cycle optimization, minimum stock carrying under just-in-time (JIT) operations, identification of best combinations of use of manufacturing resources and the optimization of complex blending processes in such applications as animal rations, dietary standards in foods and flavour blending in such products as coffee.
Short term or medium term optimization conditions?
Many optimization procedures are applied to "short term" resource allocation problems. This means that the constraints facing the decision-maker, such as available equipment, trucks, storage capacity and other items cannot be altered in the short term. The objective is then to maximise the probability of achieving some objective, such as profit, subject to those known constraints.
It is, however, possible to make use of short term optimization information to guide medium term strategic investment. This is because the opportunity cost of removing constraints is apparent from the "short term return" to each constraint. This topic is described in more detail under Enterprise models & strategies.
Farm planning is an optimization process which assists farmers select the most profitable combination of crops according to expected market conditions, available farm equipment and labour force, land quality and cash available. One of the most common optimizations algorithms applied for this type of work is linear programming1 using the Simplex2 progam. Such farm planning information is detailed enough for producing good medium term strategic planning.
Agricultural research, development & dissemination
More advanced decision analysis can be applied to the analysis of environmental factors which, in combination with plant genotypes, determine agricultural productivity. This locational-state analysis relates bioclimate, soil conditions and plant genetics so as to structure a spatial analysis used to identify the optimial geographic locations for plant selection and performance trials and subsequent field demonstrations in areas with the highest probabilities of successful production of target crops such as grain, vegetable, fruit, fibre and feedstocks for bio-industries. For further information on this particular aspect see the reference to Locational-State Theory in the Leading Edge section.
Components & blending
A common application of optimization routines is to minimise the cost of products manufactured from many purchased components or ingredients. Thus agricultural commodities used to make up animal feeds have varying amounts of ingredients essential in the feed (such as proteins, vitamins and other components to ensure efficient animal production. Simplex procedures can be applied to spot commodity prices so as to purchase the commodities which, in combination, provide the lowest cost ration meeting minimum nutritional requirements. Similarly manufacturers who use standard components made by many different suppliers can shift the mix of sourcing according to the changes in demand for their product lines and according to component costs.
1 Linear programming was invented by Leonid Vitaliyevich Kantorovich in 1939; 2 The Simplex method of linear programming was developed by George Bernard Dantzig in 1953.
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