This paper presents three techniques for using an iterated local search algorithm to improve the performance of a state-of-the-art branch and bound algorithm for job shop scheduli...
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
Searches that include both feasible and infeasible solutions have proved to be efficient algorithms for solving some scheduling problems. Researchers conjecture that these algorit...
Recently the problem of automatic composition of workflows has been receiving increasing interest. Initial investigation has shown that designing a practical and scalable composit...
Researchers often express probabilistic planning problems as Markov decision process models and then maximize the expected total reward. However, it is often rational to maximize ...
In this paper, we present a motion planning framework for a fully deployed autonomous unmanned aerial vehicle which integrates two sample-based motion planning techniques, Probabi...
We present a new algorithm for conformant probabilistic planning, which for a given horizon produces a plan that maximizes the probability of success under quantified uncertainty ...
Policy Reuse is a method to improve reinforcement learning with the ability to solve multiple tasks by building upon past problem solving experience, as accumulated in a Policy Li...
Supply chains are ubiquitous in the manufacturing of many complex products. Traditionally, supply chains have been created through the intricate interactions of human representati...