Planning and scheduling are as decision making processes which they have important roles in production systems and industries. According that, job shop scheduling is one of NPhard problems to solve multi-objective decision making approaches. So, the problem is known as uncertain with many variables in optimal solution view. Finding optimal solutions are essential task in scheduling of jobs betw...

In 1980 Miyazaki considered the weight of a job in which weight shows the relative priority over some other job in a sequencing of different jobs. After that some researchers such as Ghanshiam Das, Singh and Singhal [1981], Panwalker [1991] Sotskov, Allahverdi and Lai [2000], Chandramouli (2005) and Belwal, Mittal (2008) had considered the idea of N-job 2 machine flow shop scheduling problem wi...

Genetic algorithms are a very popular heuristic which have been successfully applied to many optimization problems within the last 30 years. In this chapter, we give a survey on some genetic algorithms for shop scheduling problems. In a shop scheduling problem, a set of jobs has to be processed on a set of machines such that a specific optimization criterion is satisfied. According to the restr...

The Job Shop Scheduling Problem is a strongly NP-hard problem of combinatorial optimisation and one of the best-known machine scheduling problem. Taboo Search is an effective local search algorithm for the job shop scheduling problem, but the quality of the best solution found depends on the initial solution. To overcome this problem we present a new approach that uses a population of Taboo Sea...

We study a generalized job-shop problem called the body shop scheduling problem (BSSP). This problem arises from the industrial application of welding in a car body production line, where possible collisions between industrial robots have to be taken into account. BSSP corresponds to a job-shop problem where the operations of a job have to follow alternating routes on the machines, certain oper...

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...

The scheduling of manufacturing processes aims to find sequences of jobs on given machines optimal by a selected criterion, e.g. minimal completion time of all operations. With respect to NP-hardness of these problems and the necessity to solve them by heuristic methods, the problem representation and the effectiveness of their procedures is substantial for computations to be completed in a rea...

The job-shop scheduling is concerned with arranging processes and resources. Proper schedules are very important for the manufacturers, but can cause serious problems because of the enormous solution space. Pressure from the competitive enterprises is the main reason why time is becoming one of the most important success factors. Scheduling tools allow production to run efficiently. The goal in...

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...

Constraint-based scheduling (CBS) is a highly efficient and widely applied method in solving resourceconstrained project scheduling problems – including job-shop scheduling problems. However, many recent studies suggest that practical problems highly differ from benchmark instances used in theoretical scheduling research, generated intentionally to challenge solvers. Although industrial problem...