Scheduling on identical machines with conflicts to minimize the mean flow time

TL;DR

This paper studies scheduling jobs with conflict constraints on identical machines to minimize mean flow time, revealing NP-hardness results, identifying solvable cases, and proposing models and algorithms with computational evaluation.

Contribution

It provides new complexity results, polynomial-time solvable cases, and a genetic algorithm for the conflict-aware scheduling problem.

Findings

NP-hardness on two machines with conflicts

Polynomial-time solvable cases for specific conflict graphs

Genetic algorithm performs well on benchmark instances

Abstract

This paper addresses the problem of scheduling jobs on identical machines with conflict constraints, where certain jobs cannot be scheduled simultaneously on different machines. We focus on the case where conflicts can be represented by a simple undirected graph, and the objective is to minimize the mean flow time. We show that the problem is NP-hard even on two machines and two distinct processing times. For unit-time jobs, the problem becomes NP-hard when the number of machines increases to three. We also identify polynomial-time solvable cases for specific classes of conflict graphs. For the general problem, we propose mathematical models, lower bounds, and a genetic algorithm. We evaluate their performance through computational experiments on a wide range of instances derived from well-known benchmark instances in the literature.