midterm 1 specific practice Flashcards
Name the five elements of a descision model
decision variables, parameters, constraints, outputs, objective functions
Describe decision variables
represents quantities that can control or change(things the solver finds and fills in yellow cells)
Describe parameters
inputs that influence decision making but cannot control (blue colored cells, values given to us)
Describe outputs
results that model predicts based on decision variables and parameters( i think all outputs the solver fills in)
Describe objective function
outputs that represent measures of performance (the green revenue cell)
Describe contraints
interaction of inputs that limit feasibility of decisions (the blue cells that have conditional operators with other blue cells typically)
Optimization: describe optimization given a set of parameters
finding the best feasible combination of values for the decision variables
Describe a feasible combination of values for the decision variables
one that meets all the constraints of the model
The objective function determines what is best in terms of either ____ or ___ it’s value
maximizing or minimizing
Not all mathematical models are optimization models, true or false?
true, some just descrbie logical relationship between inputs and outputs
list the color of cells for Parameter, Decision Variable, Objective function (output), Calculation
blue, yellow, green, white
What are the three key questions to ask first?
What are the ___ to be made?
What are the ___ on these descisions?
What is the overall _____ for these decisions?
decisions, constraints, measure of performance?
Example Maximization and Feasibility: Regarding the diagram of feasibility range, what points would be the optimal solutions?
the corner points
Regarding graph and Feasibility: What does it mean to have redundant constriants?
a feasibility line or constraint that is outside feasibility already, but could become relevant later
Name the four Special conditions in LP Models
multiple optimal solutions, redundant constraints, unboundedness, infeasibility
Describe multiple optimal solutions
graph, when there is a line segment same as objective function instead of point so it has multiple optimal solutions
Describe redundant constraints
a feasibility line that doesnt effect current feasibility
Describe unboundedness
no limits, goes to infinite
Describe infeasibility
constraints to not define any feasible region, check sign of operation
List the types of models: Allocation Model, Covering, Blending, Transportation and assignment, transshipment, multiperiod and inventory models, netwrok models with yield, cash flow problem,
Allocation Model, Covering, Blending, Transportation and assignment, transshipment, multiperiod and inventory models, netwrok models with yield, cash flow problem,
List the types of models
Allocation Model, Covering, Blending, Transportation and assignment, transshipment, multiperiod and inventory models, netwrok models with yield, cash flow problem,
Describe Allocation Model
Maximizing and objective, with less than constraints on capacity
Describe Covering problems
minimizing, subject to greater than or equal constrains on coverage
Describe Blending problems
nonlinear, convert to linear constraints
Transportation and assignment models: Describe network models
linear programs with special structure, describes configurations of flow in a connected system (nodes and arcs)
Describe transshipment model
Multiple stages of flow (multiple nodes) instead of just one
Describe multiperiod and inventory models
flow and network but with multiple periods, with conservation of flow
