Robustness and stability measures for scheduling policies in a single machine environment

Abstract

Scheduling is a decision making process that concerns allocation of limited resources (machines, material handling equipment, operators, tools, fixtures, etc.) to competing tasks (operations of jobs) over time with the goal of optimizing one or more objectives. The output of this decision process is time/machine/operation assignments. In classical scheduling theory, the objective is generally maximizing some measure of system performance. In addition to classical performance measures two new criteria are used in modern scheduling literature: "robustness" and "stability". In this thesis, we propose several robustness and stability measures and policies. Two new surrogate measures are also developed since the exact measures are difficult to calculate. These surrogate measures are embedded in a tabu search algorithm to generate robust and stable schedules for a single machine subject to random machine breakdowns. We show that our surrogate measures are better than well-known and commonly used average slack method.