This research introduces three heuristic algorithms for solving job-shop scheduling problems, job-shop scheduling problems with multi-purpose machines, and open-shop scheduling problems. All these algorithms are based on the particle swarm optimization algorithm, and generate solutions that belong to the class of parameterized active schedules via their specific decoding procedures. Comparison ...

Flexible job shop schedule is very important in both fields of combinatorial optimization and production management. In this paper, a simulationmodel is presented to solve themulti-objective flexible job shop scheduling problem. The proposed model has been coded by Matlab which is a special mathematical computation language. After modeling the pending problem, the model is validated by five rep...

The Flexible Job Shop problem is a generalization of the classical job shop scheduling problem in which for every operation there is a group of machines that can process it. The problem is to assign operations to machines and to order the operations on the machines so that the operations can be processed in the smallest amount of time. This models a wide variety of problems encountered in real ...

We describe a formal framework for modeling and prototyping complex systems. Our framework consists of a meta-model for discrete event systems, a language based upon this meta-model and a software environment for editing and validating system descriptions. The possibilities for using our framework for decision support systems are indicated and illustrated by a job shop planning example.

The job-shop scheduling problem is one of the most difficult problems in scheduling. This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint sa...

In this paper the job shop scheduling problem is considered. Since this well known problem is NP hard nding an optimal solution for a given instance is often very diicult. Although newest hardware solutions are used it is nearly impossible to solve even instances of moderate size (like 15 jobs processed on 15 machines) in a reasonable time. Due to this fact several heuristic algorithms have bee...

This work studies the performance of two Mixed Integer Linear Programming (MILP) models to solve scheduling problems in a flexible job shop environment with recirculation and assembly using a due-date-based objective function. The models convey different approaches both in the modelling of time (discrete and continuous approaches) as well as in the assignment of jobs to machines. The comparison...

This paper describes a GA for job shop scheduling problems. Using the Giffler and Thompson algorithm, we created two new operators, THX crossover and mutation, which better transmit temporal relationships in the schedule. The approach produced excellent results on standard benchmark job shop scheduling problems. We further tested many models and scales of parallel GAs in the context of job shop...

In this survey we review methods to analyze open queueing network models for discrete manufacturing systems. We focus on design and planning models for job shops. The survey is divided in two parts: in the first we review exact and approximate decomposition methods for performance evaluation models for single and multiple product class networks. The second part reviews optimization models of th...

The recently emerged Fourth Industrial Revolution (Industry 4.0) is characterised by the introduction of the new Cyber-Physical System (CPS) concepts and the Internet of Things (IoT) paradigm. These new collaborating computational entities offer a broad range of opportunity to consider with a different perspective. One of the perennial problems of the manufacturing operation is the scheduling p...