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Www.izmirekonomi.edu.tr Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, 2007 1 ISE 102 Introduction to Linear Programming (LP)

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Presentation on theme: "Www.izmirekonomi.edu.tr Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, 2007 1 ISE 102 Introduction to Linear Programming (LP)"— Presentation transcript:

1 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, ISE 102 Introduction to Linear Programming (LP) Asst. Prof. Dr. Mahmut Ali GÖKÇE Industrial Systems Engineering Dept. İzmir University of Economics

2 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Introduction to Linear Programming Many managerial decisions involve trying to make the most effective use of an organization’s resources. Resources typically include:  Machinery/equipment  Labor  Money  Time  Energy  Raw materials These resources may be used to produce  Products (machines, furniture, food, or clothing)  Services (airline schedules, advertising policies, or investment decisions)

3 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 What is Linear Programming? Linear Programming is a mathematical technique designed to help managers plan and make necessary decisions to allocate resources Linear Programming (LP) is one the most widely used decision tools of Operations Research & Management Science (ORMS) In a survey of Fortune 500 corporations, 85 % of those responding said that they had used LP

4 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Brief History of LP LP was developed to solve military logistics problems during World War II In 1947, George Dantzig developed a solution procedure for solving linear programming problems (Simplex Method) This method turned out to be so efficient for solving large problems quickly

5 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 The simultaneous development of the computer technology established LP as an important tool in various fields Simplex Method is still the most important solution method for LP problems In recent years, a more efficient method for extremely large problems has been developed (Karmarkar’s Algorithm) History of LP (contd)

6 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 LP Problems A large number of real problems can be formulated and solved using LP. A partial list includes:  Scheduling of personnel  Production planning and inventory control  Assignment problems  Several varieties of blending problems including ice cream, steel making, crude oil processing  Distribution and logistics problems

7 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Aggregate Planning Develop a production schedule which  satisfies specified sales demands in future periods  satisfies limitations on production capacity  minimizes total production/inventory costs Scheduling Problem Produce a workforce schedule which  satisfies minimum staffing requirements  utilizes reasonable shifts for the workers  is least costly Typical Applications of LP

8 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Product Mix (“Blending”) Problem Develop the product mix which  satisfies restrictions/requirements for customers  does not exceed capacity and resource constraints  results in highest profit Logistics Determine a distribution system which  meets customer demand  minimizes transportation costs Typical Applications of LP (contd)

9 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Marketing Determine the media mix which  meets a fixed budget  maximizes advertising effectiveness Financial Planning Establish an investment portfolio which  meets the total investment amount  meets any maximum/minimum restrictions of investing in the available alternatives  maximizes ROI Typical Applications of LP (contd)

10 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 What do these applications have in common?  All are concerned with maximizing or minimizing some quantity, called the objective of the problem  All have constraints which limit the degree to which the objective function can be pursued Typical Applications of LP (contd)

11 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Fleet Assignment at Delta Air Lines Delta Air Lines flies over 2500 domestic flight legs every day, using about 450 aircrafts from 10 different fleets that vary by speed, capacity, amount of noise generated, etc. The fleet assignment problem is to match aircrafts (e.g. Boeing 747, 757, DC-10, or MD80) to flight legs so that seats are filled with paying passengers Delta is one the first airlines to solve to completion this fleet assignment problem, one of the largest and most difficult problems in airline industry Typical Applications of LP (contd)

12 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 An airline seat is the most perishable commodity in the world Each time an aircraft takes off with an empty seat, a revenue opportunity is lost forever The flight schedule must be designed to capture as much business as possible, maximizing revenues with as little direct operating cost as possible Fleet Assignment at Delta (contd)

13 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 The airline industry combines  the capital-intensive quality of the manufacturing sector  low profit margin quality of the retail sector Airlines are capital, fuel, and labor intensive Survival and success depend on the ability to operate flights along the schedule as efficiently as possible Fleet Assignment at Delta (contd)

14 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Both the size of the fleet and the number of different types of aircrafts have significant impact on schedule planning If the airline assigns too small a plane to a particular market: it will lose potential passengers If it assigns too large a plane: it will suffer the expense of the larger plane transporting empty seats Fleet Assignment at Delta (contd)

15 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Stating the LP Model Delta implemented a large scale linear program to assign fleet types to flight legs so as to minimize a combination of operating and passenger “spill” costs, subject to a variety of operation constraints

16 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 What are the constraints? Some of the complicating factors include:  number of aircrafts available in each fleet  planning for scheduled maintenance (which city is the best to do the maintenance?)  matching which crews have the skills to fly which aircrafts  providing sufficient opportunity for crew rest time  range and speed capability of the aircraft  airport restrictions (noise levels!)

17 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 The result?! The typical size of the LP model that Delta has to optimize daily is:  40,000 constraints  60,000 decision variables The use of the LP model was expected to save Delta $300 million over the 3 years (1997)

18 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Formulating LP Models An LP model is a model that seeks to maximize or minimize a linear objective function subject to a set of constraints An LP model consists of three parts:  a well-defined set of decision variables  an overall objective to be maximized or minimized  a set of constraints

19 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 PetCare Problem PetCare specializes in high quality care for large dogs. Part of this care includes the assurance that each dog receives a minimum recommended amount of protein and fat on a daily basis. Two different ingredients, Mix 1 and Mix 2, are combined to create the proper diet for a dog. Each kg of Mix 1 provides 300 gr of protein, 200 gr of fat, and costs $.80, while each kg of Mix 2 provides 200 gr of protein, 400 gr of fat, and costs $.60. If PetCare has a dog that requires at least 1100 gr of protein and 1000 gr of fat, how many kgs of each mix should be combined to meet the nutritional requirements at a minimum cost?

20 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 STEP 1: Understand the Problem STEP 2: Identify the decision variables STEP 3: State the objective function STEP 4: State the constraints LP Formulation Steps

21 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 PetCare Problem PetCare specializes in high quality care for large dogs. Part of this care includes the assurance that each dog receives a minimum recommended amount of protein and fat on a daily basis. Two different ingredients, Mix 1 and Mix 2, are combined to create the proper diet for a dog. Each kg of Mix 1 provides 300 gr of protein, 200 gr of fat, and costs $.80, while each kg of Mix 2 provides 200 gr of protein, 400 gr of fat, and costs $.60. If PetCare has a dog that requires at least 1100 gr of protein and 1000 gr of fat, how many kgs of each mix should be combined to meet the nutritional requirements at a minimum cost?

22 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 STEP 1: Understand the Problem STEP 2: Identify the decision variables x 1 : kgs of mix 1 to be used to feed the dog x 2 : kgs of mix 2 to be used to feed the dog STEP 3: State the objective function minimize0.8 x x 2 (total cost) STEP 4: State the constraints subject to 300 x x 2  1100 (protein constraint) 200 x x 2  1000(fat constraint) x 1  0(sign restriction) x 2  0(sign restriction) PetCare: LP Formulation

23 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Furnco manufactures desks and chairs. Each desk uses 4 units of wood, and each chair uses 3 units of wood. A desk contributes $40 to profit, and a chair contributes $25. Marketing restrictions require that the number of chairs produced must be at least twice the number of desks produced. There are 20 units of wood available. Formulate the Linear Programming model to maximize Furnco’s profit. Furnco Company Problem

24 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 x 1 : number of desks produced x 2 : number of chairs produced maximize40 x x 2 (objective function) subject to4 x x 2  20 (wood constraint) 2 x 1 - x 2  0(marketing constraint) x 1, x 2  0(sign restrictions) Furnco Company (contd)

25 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Farmer Jane owns 45 acres of land. She is going to plant each acre with wheat or corn. Each acre planted with wheat yields $200 profit; each with corn yields $300 profit. The labor and fertilizer used for each acre are as follows: WheatCorn Labor3 workers2 workers Fertilizer2 tons4 tons 100 workers and 120 tons of fertilizer are available. Formulate the Linear Programming model to maximize the farmer’s profit. Farmer Jane Problem

26 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 x 1 : acres of land planted with wheat x 2 : acres of land planted with corn maximize200 x x 2 (objective function) subject to x 1 + x 2  45 (land constraint) 3 x x 2  100(labor constraint) 2 x x 2  120(fertilizer constraint ) x 1, x 2  0(sign restrictions) Farmer Jane (contd)

27 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Truck-co manufactures two types of trucks: 1 and 2. Each truck must go through the painting shop and the assembly shop. If the painting shop were completely devoted to painting type 1 trucks, 800 per day could be painted, whereas if it were completely devoted to painting type 2 trucks, 700 per day could be painted. Is the assembly shop were completely devoted to assembling truck 1 engines, 1500 per day could be assembled, and if it were completely devoted to assembling truck 2 engines, 1200 per day could be assembled. Each type 1 truck contributes $300 to profit; each type 2 truck contributes $500. Formulate the LP problem to maximize Truckco’s profit. Truck-Co Company Problem

28 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 x 1 : number of type 1 trucks manufactured x 2 : number of type 2 trucks manufactured maximize300 x x 2 (objective function) subject to7 x x 2  5600(painting constraint) 12 x x 2  18000( assembly constraint ) x 1, x 2  0(sign restrictions) Truckco Company (contd)

29 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 McDamat's fast food restaurant requires different number of full time employees on different days of the week. The table below shows the minimum requirements per day of a typical week: Day of weekEmpl Reqd Day of week Empl Reqd Monday 7 Friday4 Tuesday 3 Saturday6 Wednesday 5 Sunday4 Thursday 9 Union rules state that each full-time employee must work 5 consecutive days and then receive 2 days off. The restaurant wants to meet its daily requirements using only full time personnel. Formulate the LP model to minimize the number of full time employees required. McDamat’s Fast Food Restaurant

30 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 McDamat’s Fast Food Restaurant (contd) Defining Decision Variables x i : number of employees beginning work on day i where i = Monday, …., Sunday Defining the Objective Function min Z = x mon + x tue + x wed + x thu + x fri + x sat + x sun

31 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Defining the Constraint Set x mon + x thu + x fri + x sat + x sun  7(Mon Reqts) x mon + x tue + x fri + x sat + x sun  3(Tue Reqts) x mon + x tue + x wed + x sat + x sun  5(Wed Reqts) x mon + x tue + x wed + x thu + x sun  9(Thu Reqts) x mon + x tue + x wed + x thu + x fri  4(Fri Reqts) x tue + x wed + x thu + x fri + x sat  6(Sat Reqts) x wed + x thu + x fri + x sat + x sun  4(Sun Reqts) Non-Negativity Condition (Sign Restriction) x mon, …., x sun  0 McDamat’s Fast Food Restaurant (contd)

32 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 A Multi-Period Production Planning Pr. Sailco Corporation must determine how many sailboats to produce during each of the next four quarters. The demand during each of the next four quarters is as follows: Quarters Demand At the beginning of the first quarter Sailco has an inventory of 10 sailboats. At the beginning of each quarter Sailco must decide how many sailboats to produce that quarter. Sailboats produced during a quarter can be used to meet demand for that quarter. Capacity Cost. Regular Time40 (sailboats)$400/sailboat Overtime$450/sailboat Inventory Holding Cost: $20/sailboat Determine a production schedule to minimize the sum of production and inventory holding costs during the next four quarters.

33 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 A Multiperiod PP Problem (contd) Defining Decision Variables R 1 : regular time production at quarter 1 R 2 : regular time production at quarter 2 … R t : regular time production at quarter t O t : overtime production at quarter t I t : inventory at the end of quarter t Defining the Objective Function min400 R R R R O O O O I I I I 4

34 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 A Multiperiod PP Problem (contd) Defining the Constraint Set 10 + R 1 + O 1 - I 1 = 40 I 1 + R 2 + O 2 - I 2 = 60 I 2 + R 3 + O 3 - I 3 = 75 I 3 + R 4 + O 4 - I 4 = 25 R 1  40 R 2  40 R 3  40 R 4  40 Non-Negativity Condition (Sign Restriction) R 1, R 2, R 3, R 4, O 1, O 2, O 3, O 4, I 1, I 2, I 3, I 4  0

35 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 An LP problem is an optimization problem for which we do the following:  We attempt to maximize (or minimize) a linear function of the decision variables. The function that is to be maximized (or minimized) is called the objective function  The values of the decision variables must satisfy a set of constraints. Each constraint must be a linear equation or linear inequality  A sign restriction is associated with each variable LP Summary

36 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Furnco Company Max40 x x 2 s.t.4 x x 2  20 2 x 1 - x 2  0 x 1, x 2  0 Graphical Solution Method X 2 Chairs X 1 Desks Z= Z=150 0 (2) (1) 6.67 (2,4) [180] [166.75]

37 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Farner Jane (modified) max200 x x 2 s.t x 1 + x 2  45 3 x x 2  x x 2  120 x 1 ≥ 10 x 1, x 2  0 Graphical Solution Method (contd) X 1 Wheat X 2 Corn (4) [2000] Z= (2) [6667] 45 (1) (3) (30,15) (20,20) (10,25) Z=7080

38 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Special Cases of the Feasible Region InfeasibleRedundant Constraint

39 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 More Special Cases of the Feasible Region Unbounded Feasible Region Unbounded Solution Unbounded Feasible Region Bounded Optimal Solution

40 Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics Spring, of 52 Special Cases of the Optimal Solution Multiple OptimaUnbounded Solution


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