The introduction of NVidia’s powerful Tesla GPU hardware and Compute Unified Device Architecture (CUDA) platform enable many-core parallel programming. As a result, existing algorithms implemented on a GPU can run many times faster than on modern CPUs. Relatively little research has been done so far on GPU implementations of discrete optimisation algorithms. In this paper, two approaches to par...

This paper investigates an approach to the permutation flowshop scheduling problem based on Tabu Search with an additional memory structure called a ‘pruning pattern list’. The pruning pattern list approach allows a better use of the critical block information. A solution of the flowshop scheduling problem is represented by a permutation of job numbers. A pruning pattern is generated from a sol...

This research deals with a flexible flowshop scheduling problem with arrival and delivery of jobs in groups and processing them individually. Due to the special characteristics of each job, only a subset of machines in each stage is eligible to process that job. The objective function deals with minimization of sum of the completion time of groups on one hand and minimization of sum of the diff...

This paper focuses on a two-machine re-entrant flowshop scheduling problem with the objective of minimizing makespan. In the re-entrant flowshop considered here, all jobs must be processed twice on each machine, that is, each job should be processed on machine 1, machine 2 and then machine 1 and machine 2. We develop dominance properties, lower bounds and heuristic algorithms for the problem, a...

There are numerous instances of flowshop in the production process of process industry. When such characteristics as continuous production resulted from high-temperature environment or deteriorate intermediate products are took into consideration, it should be ensured that the waiting time of any job between two consecutive machines is not greater than a given value, which results in the flowsh...

A hybrid Taguchi-based genetic algorithm (HTGA) is developed for solving multi-objective flowshop scheduling problems (FSPs). Search performance is improved by using Taguchi-based crossover to avoid scheduling conflicts, and dynamic weights are randomly selected by a fuzzy inference system. The conventional approach to selecting dynamic weights randomly ignores small value for the objective whe...

One of the most important assumptions in production scheduling is that the machines are permanently available without any breakdown. In the real world of scheduling, machines can be made unavailable due to various reasons such as preventive maintenance and unpredicted breakdown. In this paper, we explore flowshop configuration under the assumption of condition-based maintenance to minimize expe...

Scheduling problems involving both earliness and tardiness costs have received significant attention in recent years. This type of problem became important with the advent of the justin-time (JIT) concept, where early or tardy deliveries are highly discouraged. In this work we examine the flowshop scheduling problem with no storage constraints and with blocking inprocess. In this latter environ...

Studies on scheduling with learning considerations have recently become important. Most studies focus on single-machine settings. However, numerous complex industrial problems can be modeled as flowshop scheduling problems. This paper thus focuses on minimizing the makespan in an m-machine permutation flowshop with learning considerations. This paper proposes a dominance theorem and a lower bou...

In this study, we proposed a discrete teaching-learning-based optimisation (DTLBO) for solving the flowshop rescheduling problem. Five types of disruption events, namely machine breakdown, new job arrival, cancellation of jobs, job processing variation and job release variation, are considered simultaneously. The proposed algorithm aims to minimise two objectives, i.e., the maximal completion t...