13.3.17 Combined Methods
Real systems may involve more than one of the models discussed above. Thus, a
production control problem usually comprises a combination of inventory, allocation and queuing models.
The normal method of resolving such combined models includes ‘solving’ them
(a) one at a time in (b) logical sequence. However, OR does, sometimes, combine
these models to elaborate some type of master model, which also considers the
interaction between individual models.
OR techniques are ‘application specific’. Maximum benefit can be derived by
selecting the most appropriate technique for each specific area or problem. The
appropriate selection of OR is an equally important task. Each technique has its own
advantages and limitations. In certain cases, the computation is easy. Some are
amenable to computer applications. In all such cases, the ability of the manager is
tested in an appropriate selection of OR technique. Some examples are listed below
in Table 13.3. This is, by no means, an exhaustive list.
13.4 Operations Research in Infrastructure
13.4.1 Suitability to Infrastructure
Operations research is suitable for solving problems in infrastructure because of the
different functions that it can handle, namely:
(1) it uses scientific methods to work towards a decision, by a scientific analysis
and data interpretation, thus improving the objectivity of the results obtained.
(2) it decomposes complex problems into simple ones, thus providing a clearer
view of problems and solutions which enable a better insight in the overall
solution.
(3) it can handle trade-offs between conflicting objectives, for example pumping
water from underground costs energy, while distributing water from a surface
reservoir will save the energy, but deplete the reservoir. But not pumping the
underground water at the right time, also means foregoing it (see also Chap. 11
on resilience).
(4) It can promote integration of different subsystems. It has been seen earlier that
infrastructure consists of a network of sub-systems. These sub-systems can be
studied in order to optimise the same, single goal of the infrastructure network,
with sub-optimisation of some sub-systems – trade-off oblige, if necessary.
(5) it enables resource optimisation, by analysing material characteristics and value
engineering in the calculations.
(6) it attempts cost minimisation, through linear programming, CPM in project
management, EOQ in inventory purchases, assignment algorithm in
transportation, etc.
(7) it tries to minimise time required through the transportation algorithm, queuing
theory, PERT in project management, etc.
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13 Modelling in Infrastructure Planning
Real systems may involve more than one of the models discussed above. Thus, a
production control problem usually comprises a combination of inventory, allocation and queuing models.
The normal method of resolving such combined models includes ‘solving’ them
(a) one at a time in (b) logical sequence. However, OR does, sometimes, combine
these models to elaborate some type of master model, which also considers the
interaction between individual models.
OR techniques are ‘application specific’. Maximum benefit can be derived by
selecting the most appropriate technique for each specific area or problem. The
appropriate selection of OR is an equally important task. Each technique has its own
advantages and limitations. In certain cases, the computation is easy. Some are
amenable to computer applications. In all such cases, the ability of the manager is
tested in an appropriate selection of OR technique. Some examples are listed below
in Table 13.3. This is, by no means, an exhaustive list.
13.4 Operations Research in Infrastructure
13.4.1 Suitability to Infrastructure
Operations research is suitable for solving problems in infrastructure because of the
different functions that it can handle, namely:
(1) it uses scientific methods to work towards a decision, by a scientific analysis
and data interpretation, thus improving the objectivity of the results obtained.
(2) it decomposes complex problems into simple ones, thus providing a clearer
view of problems and solutions which enable a better insight in the overall
solution.
(3) it can handle trade-offs between conflicting objectives, for example pumping
water from underground costs energy, while distributing water from a surface
reservoir will save the energy, but deplete the reservoir. But not pumping the
underground water at the right time, also means foregoing it (see also Chap. 11
on resilience).
(4) It can promote integration of different subsystems. It has been seen earlier that
infrastructure consists of a network of sub-systems. These sub-systems can be
studied in order to optimise the same, single goal of the infrastructure network,
with sub-optimisation of some sub-systems – trade-off oblige, if necessary.
(5) it enables resource optimisation, by analysing material characteristics and value
engineering in the calculations.
(6) it attempts cost minimisation, through linear programming, CPM in project
management, EOQ in inventory purchases, assignment algorithm in
transportation, etc.
(7) it tries to minimise time required through the transportation algorithm, queuing
theory, PERT in project management, etc.
368
13 Modelling in Infrastructure Planning