An NP-hard problem like Flexible Job Shop Scheduling (FJSP) tends to be more complex and requires computational effort optimize the objectives with contradictory measures. This paper aims address FJSP combined objectives, minimization of make-span, maximum workload, total workload. proposes ‘Hybrid Adaptive Firefly Algorithm’ (HAdFA), a new enhanced version classic Algorithm (FA) embedded adapt...

Ideally, the solution to job-shop scheduling problem (JSP) should effectively reduce cost of manpower and materials, thereby enhancing core competitiveness manufacturer. Deep learning (DL) neural networks have certain advantages in handling complex dynamic JSPs with a massive amount historical data. Therefore, this paper proposes model based on DL. Firstly, data prediction was established for s...

The paper considers the dynamic job shop scheduling problem (DJSSP) with job release dates which arises widely in practical production systems. The principle characteristic of DJSSP considered in the paper is that the jobs arrive continuously in time and the attributes of the jobs, such as the release dates, routings and processing times are not known in advance, whereas in the classical job sh...

Job-shop scheduling problems constitute a big challenge in nowadays industrial manufacturing environments. Because of the size of realistic problem instances, applied methods can only afford low computational costs. Furthermore, because of highly dynamic production regimes, adaptability is an absolute must. In state-of-the-art production factories the large-scale problem instances are split int...

Two scheduling methods based on Extreme Value Theory (SEVAT) and Genetic Algorithms (GA) are developed. The SEVAT approach is a schedule building approach that creates a statistical profile of schedules through random sampling and predicts the potential' of a schedule alternative. The GA approach, on the other hand, is a schedule permutation approach, in which a population of schedules are init...

In this paper, we analyze the characteristics of the job shop scheduling problem. A new genetic algorithm for solving the agile job shop scheduling is presented to solve the job shop scheduling problem. Initial population is generated randomly. Two-row chromosome structure is adopted based on working procedure and machine distribution. The relevant crossover and mutation operation is also desig...

We introduce the multiple capacitated job shop scheduling problem as a generalization of the job shop scheduling problem. In this problem machines may process several operations simultaneously. We present an algorithm based on constraint satisfaction techniques to handle the problem e ectively. The most important novel feature of our algorithm is the consistency checking. An empirical performan...