Strategizing optimum location

Problem definition: Strategizing optimum location for Manufacturing 2) Service Problem context and background: To better illustrate the case, we’ll consider a typical food processing industry, e.g. an abattoir that supplies packaged meat directly to Retail stores. The method used in IP is called "Hub location", solved using hub-and-spoke networks, which minimize transport cost across the network. Arriving at a precise location requires a combined methodology of "simplistic" transportation techniques (e.g. MODI) to derive least "time" in movement of goods, linear programming network "flow" designs which provide for optimum allocation of resources, and break-even analysis to determine the rate at which disposal services have to be "regulated". The first two will strategize manufacturing concerns of the abattoir, whereas the last one is a service roadmap.
Analysis and theoretical briefing: The standard design of an abattoir goes somewhat like this: a 60 m2 by 30 m2 rectangular Plan consisting mainly of 1)Production module (Pi): P1 slaughter floor, P2 lairage (store area for animals prior to slaughter), P3 chiller, P4 tripe room, P5 meat cutting and processing. 2)Service module (Si): S1 water supply, S2 effluent disposal, S3 solid waste and blood disposal, S4 hide and skin processing, S5 electric light and power. For our case study, we restrict our discussions on a simplistic level only.
Let us examine how standard Operation procedures apply in this case. Using linear programming, we need to mainly determine the quantity and variety (e.g. cattle, pigs, sheep and goat, etc.) of slaughter needed/day based on market demand. in this example the "objective function" is market revenue (Ri) out of beef (x1) and pork (x2), and the constraints are space requirements, and market "demand". There may also be some extraneous constraints that we may ignore. In notational analysis, we have R1x1 + R2x2 to be maximized, where R1 and R2 denote revenues. We have to optimize bearing the some constraints: e.g. x1 + x2 &lt. A which is 1800 m2 in our case. Once all constraints have been identified, we can have strategically optimized resource allocation, which will ensure better network flow in the longer run.
Using MODI method, we can minimize transport cost across both Production and Service outlets. The design possibility based on variables talked earlier, can be shown as:

Since our objective is to minimize "time" factor in transport, we have to design our abattoir facilities in such a manner that it streamlines flow of goods (animals), manpower (butchers), effluents (blood, serum etc.). Before proceeding toward MODI method, we must factor in a streamlined layout encompassing the principle of optimized operations. The areas as shown for various outlets above have been approximated from considering the "weighted" importance of each location, vis–vis all others. Precise calculations can be arrived at by calculating the weighted "center of gravity" of the entire layout, from which other locations can be pin-pointed.
Using MODI method, we can determine optimum location of each transit point (Pi, Si) by repeatedly finding unused routes with the largest negative improvement index each time. Once the largest index is identified, we need trace only one closed path to achieve optimum symmetrical location.
We will now further apply our skills to service area using "break even analysis". The overall objective of the abattoir is to maintain "hygiene" and "quick movement". which it does by defining issues properly, e.g. achieving a certain level of cleanliness always to be maintained. Once that is laid out, we can extend our domain to incorporate discussions on optimum areas for various service outlets, and appropriateness of Si location vis–vis other Si. It is an easy numerical calculation, and does not require advanced Math. We will, thus have information on a large no. of areas, e.g. "optimum" water supply rate (discharge in m3/ sec), purchase of equipment like incinerators and other disposal units based on "optimum" effluents production, manpower needs, etc.
Areas (Y-axis)
S5 S1 S2 S4 S3 Preferred Hygiene level B.E. point Disposal rate of effluents (X-axis)
Conclusion: Thus, we have seen that a good combined Operations technique can solve the problem in relation to strategically best locations for any industry.