Hybrid tabu search and a truncated branch-and-bound for the unrelated parallel machine scheduling problem

Abstract

We consider the problem of scheduling a number of jobs on a number of unrelated parallel machines in order to minimize the makespan. We develop three heuristic approaches, i.e., a genetic algorithm, a tabu search algorithm and a hybridization of these heuristics with a truncated branch-and-bound procedure. This hybridization is made in order to accelerate the search process to near-optimal solutions. The branchand-bound procedure will check whether the solutions obtained by the meta-heuristics can be scheduled within a tight upper bound. We compare the performances of these heuristics on a standard data set available in the literature. Moreover, the influence of the different heuristic parameters is examined as well. The computational experiments reveal that the hybrid heuristics are able to compete with the best known results from the literature.

A Monte Carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system

“…Neamatian Monemi et al (2015) considered a special parallel machine scheduling problem founded in maintenance planning of heterogeneous wells and also in the context of workover rig scheduling and solved it by a learning mechanism guided hyper-heuristic algorithm. Sels et al (2015) designed a genetic algorithm, a tabu search algorithm and a hybrid branch-andbound procedure to minimize makespan on unrelated parallel machines. Hsieh et al (2015) proposed controllable processing times for such problem where processing time of a job can be controlled by the allocation of extra resources.…”

Parallel machine scheduling with dynamic resource allocation via a master–slave genetic algorithm

“…Constraint (9) ensures that jobs are properly linked in machine, i.e., if a given job h is processed on a given machine j , a predecessor i must exist on the same machine. Constraint (10) indicates that each machine can process only one job at any time. Constraint (11) gives the range of variables.…”

“…Each job has three processing modes, and the amount of resources in all the three processing modes is set to 1, 2, 3, respectively. The processing time of all jobs in the first mode p i 1 is generated from the uniform distribution U [10,50], and the processing time in the second mode and third mode is calculated as in (15), where α is a coefficient that is set to 0.6. Based on this method, 28 test instances are generated in total.…”

“…In recent years, the studies on parallel machine scheduling have received much interest [9][10][11][12]. In real-world manufacturing or service systems, a job may need different kinds of resources to be processed, e.g., human, water, and electricity.…”

Job scheduling and resource allocation in parallel‐machine system via a hybrid nested partition method

“…operation room scheduling in hospital [5], gate assignment scheduling in airports [6], and process scheduling in a computer central processing unit [7]. Recently, many studies on PMS have been done [8][9][10][11].…”