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Chapter 3. Introduction to Linear Programming. Introduction. Linear programming Programming means planning Model contains linear mathematical functions An application of linear programming Allocating limited resources among competing activities in the best possible way
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Chapter 3 Introduction to Linear Programming
Introduction • Linear programming • Programming means planning • Model contains linear mathematical functions • An application of linear programming • Allocating limited resources among competing activities in the best possible way • Applies to wide variety of situations
3.1 Prototype Example • Wyndor Glass Co. • Produces windows and glass doors • Plant 1 makes aluminum frames and hardware • Plant 2 makes wood frames • Plant 3 produces glass and assembles products
Prototype Example • Company introducing two new products • Product 1: 8 ft. glass door with aluminum frame • Product 2: 4 x 6 ft. double-hung, wood-framed window • Define the problem: What mix of products would be most profitable (determine the production rate of each product to maximize the profit)? • Assuming company could sell as much of either product as could be produced
Prototype Example • Products produced in batches of 20 • Data needed (data gathering process) • Number of hours of production time available per week in each plant for new products • Production time used in each plant for each batch of each new product • Profit per batch of each new product
Prototype Example • Formulating the model (decision variables, objective function and constraints) x1 = number of batches of product 1 produced per week x2 = number of batches of product 2 produced per week Z = total profit per week (thousands of dollars) from producing these two products • From bottom row of Table 3.1
Prototype Example • Constraints (see Table 3.1) • Classic example of resource-allocation problem • Most common type of linear programming problem
Prototype Example • Problem can be solved graphically • Two dimensional graph with x1 and x2 as the axes • First step: sketch the feasible region (values of x1 and x2in this region satisfy the constraint restrictions) • See Figures 3.1 and Figure 3.2 • Next step: find outa point in the feasible region that maximizes value of Z = 3x1 + 5x2 • See Figure 3.3
Prototype Example Rewrite z = 3x1 + 5x2 as x2 = -3x1/5 + z/5, the graph of this is a line with slope -3/5 and y-interception z/5. To maximize z is equivalent to maximizing the y-intercept z/5 (among all parallel line segments in the feasible region having slope -3/5). Figure 3.3 illustrates three parallel lines of different y-intercepts. Note that since |-3/5| < |-3/2| (-3/2 is the slope of the third constraint boundary), the lines of the objective function is less steep. The objective function line on the feasible region that maximizes the y-intercept is the line having the corner point (2, 6) on the line (the only point from the feasible region is on the line).
3.2 The Linear Programming Model • General problem terminology and examples • Resources: money, particular types of machines, vehicles, or personnel • Activities: investing in particular projects, advertising in particular media, or shipping from a particular source • Problem involves choosing levels of activities to maximize overall measure of performance
The Linear Programming Model • Standard form The first m constraints are called the functional constraints. The last n constraints are nonnegativity constraints.
The Linear Programming Model • Other legitimate forms • Minimizing (rather than maximizing) the objective function • Functional constraints with greater-than-or-equal-to inequality • Some functional constraints in equation form • Some decision variables may be negative
The Linear Programming Model • Feasible solution • Solution for which all constraints are satisfied • Might not exist for a given problem • Infeasible solution • Solution for which at least one constraint is violated • Optimal solution • Has most favorable value of objective function (the largest value if the objective function is to be maximized, or the smallest value if the objective function is to be minimized) • Might not exist for a given problem (no feasible solutions or unbounded Z) • Might have multiple optimal solutions
The Linear Programming Model • Corner-point feasible (CPF) solution • Solution that lies at the corner of the feasible region (note that there are corner point solutions that do not lie in the feasible region, so they are corner-point infeasible solutions).
The Linear Programming Model • Linear programming problem with feasible solution and bounded feasible region • Must have CPF solutions and optimal solution(s) • Best CPF solution must be an optimal solution
The Linear Programming Model Example: a problem having no feasible solutions
The Linear Programming Model Example: a problem having multiple optimal solutions
The Linear Programming Model Example: a problem having feasible solutions but no optimal solution (unbounded Z)
The Linear Programming Model More examples (sketch the constraint boundaries and see): • No feasible solutions: x1 ≤ 1, x2 ≤ 2, x1 + x2 ≥ 5, x1 ≥ 0, x2 ≥ 0. 2. No optimal solutions (unbounded Z): maximize Z = 3x1 + 5x2 s.t. x1 + x2 ≥ 4, x1 ≥ 0, x2 ≥ 0 3. Multiple optimal solutions: maximize Z = 2x1 + 2x2 s.t. x1 + x2 ≤ 2, x1 ≥ 0, x2 ≥ 0 All points on the line segment between (2,0) and (0, 2) on the feasible region are optimal solutions and Z = 4.
3.3 Assumptions of Linear Programming • A Linear Programming model must simultaneously satisfy all four assumptions below 1. Proportionality assumption • The contribution of each activity to the value of the objective function (or left-hand side of a functional constraint) is proportional to the level of the activity (cjxj terms or aijxj terms) • If assumption does not hold, one must use nonlinear programming (Chapter 13)
Assumptions of Linear Programming 2. Additivity • Every function in a linear programming model is the sum of the individual contributions of the activities (∑cjxj or ∑aijxj) 3. Divisibility • Decision variables in a linear programming model may have any real number values • Including noninteger values • Assumes activities can be run at fractional values
Assumptions of Linear Programming 4. Certainty • Value assigned to each parameter (cj and aij and bi) of a linear programming model is assumed to be a known constant • Seldom satisfied precisely in real applications • Sensitivity analysis used
3.4 Additional Examples • The optimal solutions of the examples in this section can be obtained by using Excel Solver (introduced in Section 3.5) and are available on the course website. • Example 1: Design of radiation therapy for Mary’s cancer treatment • Goal: select best combination of beams and their intensities to generate best possible dose distribution • Dose is measured in kilorads This is an example of a cost-benefit-trade-off problem (it seeks the best trade-off between some cost and benefits).
Example 1: Radiation Therapy Design • Linear programming model • Using data from Table 3.7
Example 1: Radiation Therapy Design • A type of cost-benefit tradeoff problem
Example 2: Regional Planning It’slike the prototype example, it’s another resource-allocation problem).
Example 3: Personnel Scheduling It’s a cost-benefit-trade-off problem.
Example 3: Personnel Scheduling Without the integer constraints, due to the special structure of the model, the optimal solution turns out to have integer values anyway.
Example 4: Distributing Goods through a Distribution Network It’s a minimum cost flow problem.
Example 4: Distributing Goods through a Distribution Network
Example 4: Distributing Goods through a Distribution Network
Example 5: Reclaiming Solid Wastes • SAVE-IT company collects and treats four types of solid waste materials • Materials amalgamated into salable products • Three different grades of product possible • Fixed treatment cost covered by grants • Objective: maximize the net weekly profit • Determine amount of each product grade • Determine mix of materials to be used for each grade
Example 5: Reclaiming Solid Wastes It’s an example of a blending problem (find the best blend of ingredients into final products to meet certain specifications).
Example 5: Reclaiming Solid Wastes • Decision variables (for i = A, B, C; j = 1,2,3,4) = number of pounds of material j allocated to product grade i per week
3.5 Formulating and Solving Linear Programming Models on a Spreadsheet • Excel and its Solver add-in • Popular tools for solving small linear programming problems