Multiprocessor task scheduling in multistage hyrid flowshops: a genetic algorithm approach

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The objective is to minimize the make-span, that is, the completion time of all the tasks in the last stage. This problem is of practical interest in the textile and process industries. A genetic algorithm (GA) is developed to solve the problem. The GA is tested against a lower bound from the literature as well as against heuristic rules on a test bed comprising 400 problems with up to 100 jobs, 10 stages, and with up to five processors on each stage. For small problems, solutions found by the GA are compared to optimal solutions, which are obtained by total enumeration. For larger problems, optimum solutions are estimated by a statistical prediction technique. Computational results show that the GA is both effective and efficient for the current problem. Test problems are provided in a web site at www.benchmark.ibu.edu.tr/mpt-h; fsp

Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing systems. We propose a new approach based on limited discrepancy search to solve the problem. Our method is tested with reference to a proposed lower bound as well as the best-known solutions in literature. Computational results show that the developed approach is efficient in particular for large-size problems

Efficiency of the solution representations for the hybrid flow shop scheduling problem with makespan objective

In this paper we address the classical hybrid flow shop scheduling problem with makespan objective. As this problem is known to be NP-hard and a very common layout in real-life manufacturing scenarios, many studies have been proposed in the literature to solve it. These contributions use different solution representations of the feasible schedules, each one with its own advantages and disadvantages. Some of them do not guarantee that all feasible semiactive schedules are represented in the space of solutions –thus limiting in principle their effectiveness– but, on the other hand, these simpler solution representations possess clear advantages in terms of having consistent neighbourhoods with well-defined neighbourhood moves. Therefore, there is a trade-off between the solution space reduction and the ability to conduct an efficient search in this reduced solution space. This trade-off is determined by two aspects, i.e. the extent of the solution space reduction, and the quality of the schedules left aside by this solution space reduction. In this paper, we analyse the efficiency of the different solution representations employed in the literature for the problem. More specifically, we first establish the size of the space of semiactive schedules achieved by the different solution representations and, secondly, we address the issue of the quality of the schedules that can be achieved by these representations using the optimal solutions given by several MILP models and complete enumeration. The results obtained may contribute to design more efficient algorithms for the hybrid flow shop scheduling problem.Ministerio de Ciencia e Innovación DPI2016-80750-

A NeuroGenetic Approach for Multiprocessor Scheduling

This chapter presents a NeuroGenetic approach for solving a family of multiprocessor scheduling problems. We address primarily the Job-Shop scheduling problem, one of the hardest of the various scheduling problems. We propose a new approach, the NeuroGenetic approach, which is a hybrid metaheuristic that combines augmented-neural-networks (AugNN) and genetic algorithms-based search methods. The AugNN approach is a nondeterministic iterative local-search method which combines the benefits of a heuristic search and iterative neural-network search. Genetic algorithms based search is particularly good at global search. An interleaved approach between AugNN and GA combines the advantages of local search and global search, thus providing improved solutions compared to AugNN or GA search alone. We discuss the encoding and decoding schemes for switching between GA and AugNN approaches to allow interleaving. The purpose of this study is to empirically test the extent of improvement obtained by using the interleaved hybrid approach instead of applied using a single approach on the job-shop scheduling problem. We also describe the AugNN formulation and a Genetic Algorithm approach for the JobShop problem. We present the results of AugNN, GA and the NeuroGentic approach on some benchmark job-shop scheduling problems

New efficient constructive heuristics for the hybrid flowshop to minimise makespan: A computational evaluation of heuristics

This paper addresses the hybrid flow shop scheduling problem to minimise makespan, a well-known scheduling problem for which many constructive heuristics have been proposed in the literature. Nevertheless, the state of the art is not clear due to partial or non homogeneous comparisons. In this paper, we review these heuristics and perform a comprehensive computational evaluation to determine which are the most efficient ones. A total of 20 heuristics are implemented and compared in this study. In addition, we propose four new heuristics for the problem. Firstly, two memory-based constructive heuristics are proposed, where a sequence is constructed by inserting jobs one by one in a partial sequence. The most promising insertions tested are kept in a list. However, in contrast to the Tabu search, these insertions are repeated in future iterations instead of forbidding them. Secondly, we propose two constructive heuristics based on Johnson’s algorithm for the permutation flowshop scheduling problem. The computational results carried out on an extensive testbed show that the new proposals outperform the existing heuristics.Ministerio de Ciencia e Innovación DPI2016-80750-

ROBUST OPTIMIZATION OF STOCHASTIC HYBRID JOB-SHOP SCHEDULING WITH MULTIPROCESSOR TASK

Due to the large number of uncertainties in the production workshop, the actual performance of the scheduling scheme deviated significantly from the theoretical value. In order to enhance its anti-jamming capability, this paper developed the robust optimization of stochastic hybrid job-shop scheduling with multiprocessors tasks. Firstly, predictable uncertainties were abstracted into processing time variations and described by scenario analysis in the modeling process. Secondly, based on the analysis of the advantages and disadvantages of traditional robust optimization models, a new Expected Cmax and the Worst scenario Model (ECWM) was proposed. The model improved the single-index robust optimization model and avoided the disadvantage that the Max Regret Model is computationally intensive. Finally, the effectiveness of ECWM is verified by simulation experiments. The results show that the scheduling obtained by ECWM has good average performance and anti-risk ability, which indicates that the model achieves a good balance in scheduling performance enthusiasm and risk resistance