A job-shop scheduling problem is one of the classic scheduling problems considered to be NP-hard. In this paper, we presenta modified adaptiveevolutionary algorithm (EA) that uses speculative mutations, variable fitness functions and a pseudo-random number generator for solving job-shop scheduling problems. The algorithm was tested on well-known benchmark datainstances, such as Ft10, La01, Swv0...

Job shop scheduling(JSS) is a hard problem with most of the research focused on scenarios with the assumption that the shop parameters such as processing times, due dates are constant. But in the real world uncertainty in such parameters is a major issue. In this work, we investigate a genetic programming based hyper-heuristic approach to evolving dispatching rules suitable for dynamic job shop...

The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective approach of combining bacterial foraging strategy with particle swarm optimization for solving the minimum makespan problem of job shop scheduling is proposed. In the artificial bacterial foraging system, a novel chemotactic model is designe...

The Flexible Job Shop Scheduling Problem (FJSP) is one of the most general and difficult of all traditional scheduling problems. The Flexible Job Shop Problem (FJSP) is an extension of the classical job shop scheduling problem which allows an operation to be processed by any machine from a given set. The problem is to assign each operation to a machine and to order the operations on the machine...

Since their introduction, local search algorithms – and in particular tabu search algorithms – have consistently represented the state-of-the-art in solution techniques for the classical job-shop scheduling problem. This is despite the availability of powerful search and inference techniques for scheduling problems developed by the constraint programming community. In this paper, we introduce a...

In the last decade, various approximation approaches, such as dispatching rules, shifting bottleneck heuristic and local search methods, are proposed for the job shop scheduling problem. As one of the local search methods, taboo search provides a promising alternative for the job shop scheduling problem; however, it has to be tailored each time with respect to parameters for every instance in o...

The Flexible Job Shop scheduling Problem (FJSP) is a generalization of the classical Job Shop Problem in which each operation must be processed on a given machine chosen among a finite subset of candidate machines. The aim is to find an allocation for each operation and to define the sequence of operations on each machine so that the resulting schedule has a minimal completion time. We propose ...

The job shop scheduling problem is one of the most arduous combinatorial optimization problems. Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. This paper proposed a new effective approach based on the hybridization of the particle swarm optimization (PSO) an...

Scheduling is a combinatorial problem with important impact on both industry and commerce. If it is performed well it yields time and efficiency benefits and hence reduces costs. Genetic Algorithms have been applied to solve several types of scheduling problems; Flow Shop, Resource, Staff and Line Balancing have all been tackled. However Jobshop Scheduling is the most common problem of interest...

This paper introduces a modified shifting bottleneck approach to solve train scheduling and rescheduling problems. The problem is formulated as a job shop scheduling model and a mixed integer linear programming model is also presented. The shifting bottleneck procedure is a wellestablished heuristic method for obtaining solutions to the job shop and other machine scheduling problems. We modify ...