PPT-ANALYSIS OF A LOCAL SEARCH HEURISTIC FOR FACILITY LOCATION

Madhukar R Korupolu C Greg Plaxton Rajmohan Rajaraman Proceedings of the ninth annual ACMSIAM Symposium on Discrete Algorithms SODA 1998 LOCAL SEARCH TECHNIQUE. Chapter 5, Part A. Overview. Facility Planning. Long-Range Capacity Planning. Facility Location. Wrap-Up: What World-Class Companies Do. Facility Planning. HOW MUCH long range capacity is needed. WHEN additional capacity is needed. Heuristic - a “rule of thumb” used to help guide search. often, something learned experientially and recalled when needed. Heuristic Function - function applied to a state in a search space to indicate a likelihood of success if that state is selected. Feedback: Tutorial 1. Describing a state.. Entire state space vs. incremental development.. Elimination of children.. Closed and the solution path.. Generation of children – effects on search.. Heuristic Search. Idea: give the algorithm “hints” about the desirability of different states . Use an . evaluation function. . to rank nodes and select the most promising one for expansion. Greedy best-first search. Initialize. . the . frontier . using the . starting state. While the frontier is not empty. Choose a frontier node to expand according to . search strategy . and take it off the frontier. If the node contains the . Initialize. . the . frontier . using the . starting state. While the frontier is not empty. Choose a frontier node to expand according to . search strategy . and take it off the frontier. If the node contains the . Heuristic - a “rule of thumb” used to help guide search. often, something learned experientially and recalled when needed. Heuristic Function - function applied to a state in a search space to indicate a likelihood of success if that state is selected. John W. Chinneck, M. . Shafique. Systems and Computer Engineering. Carleton University, Ottawa, Canada. Introduction. Goal: . Find a . good quality. integer-feasible MINLP solution . quickly. .. Trade off accuracy for speed. . the . frontier . using the . starting state. While the frontier is not empty. Choose a frontier node to expand according to . search strategy . and take it off the frontier. If the node contains the . . the . frontier . using the . starting state. While the frontier is not empty. Choose a frontier node to expand according to . search strategy . and take it off the frontier. If the node contains the . Winter 2018. Introduction to Artificial Intelligence. Prof. Richard Lathrop. Reading: R&N 3.5-3.7. Outline. Review limitations of uninformed search methods . Informed (or heuristic) search. Problem-specific heuristics to improve efficiency . Continued. Before we continue. Breadth-First. Depth-First. Uniform Cost. Iterative-Deepening. Before we continue. Breadth-First. S,A,B,D,C,G. Depth-First. S,A,C,D,B,G. Uniform Cost. S,A,B,D,C,G. Iterative-Deepening. Continued. Before We Start. HW1 extended to Monday. Submit online (now working) and bring paper print out. Questions?. Competency Demo next Wednesday. Study Guide Posted. We will have some discussion time on Monday. often, something learned experientially and recalled when needed. Heuristic Function - function applied to a state in a search space to indicate a likelihood of success if that state is selected. heuristic search methods are known as “weak methods” because of their generality and because they do not apply a great deal of knowledge .