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What is Local Search Descent (LSD) Method

Chapter 21
Handbook of Research on Artificial Intelligence Techniques and Algorithms
A method to find a local minimum or maximum of a function. With a specified continuous function the gradient descent method is an effective method. In a discrete environment, like in combinatorial optimization, the idea to perturb a current solution (looking for neighboring solutions) and to look in an iterated way for improving solutions until a local minimum is found.
Published in Chapter:
Robust Vehicle Routing Solutions to Manage Time Windows in the Case of Uncertain Travel Times
Gerrit K. Janssens (Hasselt University, Belgium), Kusuma Soonpracha (Kasetsart University, Thailand), Tharinee Manisri (Sripatum University, Thailand), and Anan Mungwattana (Kasetsart University, Thailand)
Copyright: © 2015 |Pages: 24
DOI: 10.4018/978-1-4666-7258-1.ch021
Abstract
The vehicle routing problem requires mostly meta-heuristics to find approximate solutions. Congestion makes it hard for planners to find good routes because travel times are uncertain. The problem is handled by building scenarios with a range of possible travel times to represent the uncertainty. The main goal is to find a robust solution, which performs “well” in bad scenarios. The experiments show that robust results are obtained in a computationally reasonable time, which means that the concept can be used by practitioners. Most applications are commercial, but also social applications exist. An earthquake or a flood might lead to road disruptions. Higher traffic delays appear due to lower speed on flooded roads or due to time spent on finding alternative routes. Routing like evacuation of wounded or delivery of food is hardly studied. The work is inspired by the 2011 flooding in Thailand.
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