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**Dynamic Programming In this handout A shortest path example**

Deterministic Dynamic Programming Inventory example Resource allocation example

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Dynamic Programming Dynamic programming is a widely-used mathematical technique for solving problems that can be divided into stages and where decisions are required in each stage. The goal of dynamic programming is to find a combination of decisions that optimizes a certain amount associated with a system.

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**A typical example: Shortest Path**

Ben plans to drive from NY to LA Has friends in several cities After 1 day’s driving can reach Columbus, Nashville, or Louisville After 2 days of driving can reach Kansas City, Omaha, or Dallas After 3 days of driving can reach Denver or San Antonio After 4 days of driving can reach Los Angeles The actual mileages between cities are given in the figure (next slide) Where should Ben spend each night of the trip to minimize the number of miles traveled?

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**Shortest Path: network figure**

680 Columbus 2 Kansas City 5 610 790 790 1050 550 Denver 8 1030 580 540 New York 1 900 Nashville 3 760 Omaha 6 Los Angeles 10 660 940 Stage 1 1390 Stage 5 San Antonio 9 770 790 510 700 Stage 4 270 Louisville 4 Dallas 7 830 Stage 2 Stage 3

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**Shortest Path problem: Solution**

The problem is solved recursively by working backward in the network Let cij be the mileage between cities i and j Let ft(i) be the length of the shortest path from city i to LA (city i is in stage t) Stage 4 computations are obvious: f4(8) = 1030 f4(9) = 1390

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Stage 3 computations Work backward one stage (to stage 3 cities) and find the shortest path to LA from each stage 3 city. To determine f3(5), note that the shortest path from city 5 to LA must be one of the following: Path 1: Go from city 5 to city 8 and then take the shortest path from city 8 to city 10. Path 2: Go from city 5 to city 9 and then take the shortest path from city 9 to city 10. Similarly,

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Stage 2 computations Work backward one stage (to stage 2 cities) and find the shortest path to LA from each stage 2 city.

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Stage 1 computations Now we can find f1(1), and the shortest path from NY to LA. Checking back our calculations, the shortest path is 1 – 2 – 5 – 8 – 10 that is, NY – Columbus – Kansas City – Denver – LA with total mileage 2870.

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**General characteristics of Dynamic Programming**

The problem structure is divided into stages Each stage has a number of states associated with it Making decisions at one stage transforms one state of the current stage into a state in the next stage. Given the current state, the optimal decision for each of the remaining states does not depend on the previous states or decisions. This is known as the principle of optimality for dynamic programming. The principle of optimality allows to solve the problem stage by stage recursively.

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Division into stages The problem is divided into smaller subproblems each of them represented by a stage. The stages are defined in many different ways depending on the context of the problem. If the problem is about long-time development of a system then the stages naturally correspond to time periods. If the goal of the problem is to move some objects from one location to another on a map then partitioning the map into several geographical regions might be the natural division into stages. Generally, if an accomplishment of a certain task can be considered as a multi-step process then each stage can be defined as a step in the process.

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States Each stage has a number of states associated with it. Depending what decisions are made in one stage, the system might end up in different states in the next stage. If a geographical region corresponds to a stage then the states associated with it could be some particular locations (cities, warehouses, etc.) in that region. In other situations a state might correspond to amounts of certain resources which are essential for optimizing the system.

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Decisions Making decisions at one stage transforms one state of the current stage into a state in the next stage. In a geographical example, it could be a decision to go from one city to another. In resource allocation problems, it might be a decision to create or spend a certain amount of a resource. For example, in the shortest path problem three different decisions are possible to make at the state corresponding to Columbus; these decisions correspond to the three arrows going from Columbus to the three states (cities) of the next stage: Kansas City, Omaha, and Dallas.

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**Optimal Policy and Principle of Optimality**

The goal of the solution procedure is to find an optimal policy for the overall problem, i.e., an optimal policy decision at each stage for each of the possible states. Given the current state, the optimal decision for each of the remaining states does not depend on the previous states or decisions. This is known as the principle of optimality for dynamic programming. For example, in the geographical setting the principle works as follows: the optimal route from a current city to the final destination does not depend on the way we got to the city. A system can be formulated as a dynamic programming problem only if the principle of optimality holds for it.

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**Recursive solution to the problem**

The principle of optimality allows to solve the problem stage by stage recursively. The solution procedure first finds the optimal policy for the last stage. The solution for the last stage is normally trivial. Then a recursive relationship is established which identifies the optimal policy for stage t, given that stage t+1 has already been solved. When the recursive relationship is used, the solution procedure starts at the end and moves backward stage by stage until it finds the optimal policy starting at the initial stage.

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**Solving Inventory Problems by DP**

Main characteristics: Time is broken up into periods. The demands for all periods are known in advance. At the beginning of each period, the firm must determine how many units should be produced. Production and storage capacities are limited. Each period’s demand must be met on time from inventory or current production. During any period in which production takes place, a fixed cost of production as well as a variable per-unit cost is incurred. The firm’s goal is to minimize the total cost of meeting on time the demands.

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**Inventory Problems: Example**

Producing airplanes 3 production periods No inventory at the beginning Can produce at most 3 airplanes in each period Can keep at most 2 airplanes in inventory Set-up cost for each period is 10 Determine a production schedule to minimize the total cost (the DP solution on the board). Period 1 2 3 Demand Unit cost 5 4

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**Resource Allocation Problems**

Limited resources must be allocated to different activities Each activity has a benefit value which is variable and depends on the amount of the resource assigned to the activity The goal is to determine how to allocate the resources to the activities such that the total benefit is maximized

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**Resource Allocation Problems: Example**

A college student has 6 days remaining before final exams begin in his 4 courses He wants allocate the study time as effectively as possible Needs at least 1 day for each course and wants to concentrate on just one course each day. So 1, 2, or 3 days should be allocated to each course He estimates that the alternative allocations for each course would yield the number of grade points shown in the following table: How many days should be allocated to each course? (The DP solution on the board). Courses Days 1 2 3 4 5 6 7 9

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