A composite algorithm for multiprocessor scheduling

Abstract

A composite algorithm is developed for the classical problem of scheduling independent jobs on identical parallel machines with the objective of minimizing the makespan. The algorithm at first obtains a family of initial partial solutions and combines these partial solutions until a feasible solution is generated. Then local search procedures are used for improving the solution. The effectiveness of this approach is evaluated through extensive computational comparisons with recent improvement heuristics for different classes of benchmark instances.

A characterization of optimal multiprocessor schedules and new dominance rules

“…Kashan and Karimi (2009) presented a discrete particle swarm optimization algorithm and a hybrid version, that makes use of an efficient local search algorithm to further improve on the makespan. Paletta and Vocaturo (2011) developed a composite heuristic. In the construction phase, families of partial solutions are combined until a feasible solution is generated.…”

Minimizing Makespan on Identical Parallel Machines

“…Improvement heuristics have been proposed in Finn & Horowitz, 1979;Langston, 1982;Fatemi Ghomi & Jolai Ghazvini, 1998. In addition, metaheuristic procedures have been developed in Hübscher & Glover, 1994;Thesen, 1998;Dell'Amico et al, 2008;Paletta & Vocaturo, 2011. Many polynomial time constructive approaches, having a known worst-case performance ratio, have been developed to solve P||C max (for an overview, see Cheng & Sin, 1990;Lawler, Lenstra, Rinnooy Kan, & Shmoys, 1993;Hoogeveen, Lenstra, & Van de Velde, 1997;Chen, Potts, & Woeginger, 1998;Mokotoff, 2001). The most important constructive approaches to solve P||C max are:…”