Exact Lexicographic Scheduling and Approximate Rescheduling

TL;DR

This paper introduces a two-stage robust scheduling method combining exact lexicographic scheduling for initial plans with approximate rescheduling for recovery, improving efficiency under uncertainty in industrial resource allocation.

Contribution

It presents a novel two-stage approach using exact lexicographic scheduling and approximate rescheduling, with analytical and computational validation for handling uncertainty.

Findings

Lexicographic optimization enhances rescheduling efficiency.

The proposed method provides a quantifiable robustness trade-off.

A new lexicographic branch-and-bound algorithm improves computational performance.

Abstract

In industrial resource allocation problems, an initial planning stage may solve a nominal problem instance and a subsequent recovery stage may intervene to repair inefficiencies and infeasibilities due to uncertainty, e.g.\ machine failures and job processing time variations. In this context, we investigate the minimum makespan scheduling problem, a.k.a.\ $P||C_{\max}$, under uncertainty. We propose a two-stage robust scheduling approach where first-stage decisions are computed with exact lexicographic scheduling and second-stage decisions are derived using approximate rescheduling. We explore recovery strategies accounting for planning decisions and constrained by limited permitted deviations from the original schedule. Our approach is substantiated analytically, with a price of robustness characterization parameterized by the degree of uncertainty, and numerically. This analysis is based on optimal substructure imposed by lexicographic optimality. Thus, lexicographic optimization enables more efficient rescheduling. Further, we revisit state-of-the-art exact lexicographic optimization methods and propose a lexicographic branch-and-bound algorithm whose performance is validated computationally.