A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in chang...

  In this paper, we consider a flow shop scheduling problem with bypass consideration for minimizing the sum of earliness and tardiness costs. We propose a new mathematical modeling to formulate this problem. There are several constraints which are involved in our modeling such as the due date of jobs, the job ready times, the earliness and the tardiness cost of jobs, and so on. We apply adapte...

The various industries such as automobile industry, aerospace industry, printing industry, and chemical industry operate on job shop system. Job shop scheduling problems are complex and real world. The present work assesses the effect of change in shop utilizations levels on total setups and mean setup time measures. The simulation model of the job shop is developed for investigation purpose. T...

This thesis deals mainly with makespan minimization in Flow-Shop and hybrid Flow-Shop scheduling problems where mixed blocking constraints are considered. In Flow-Shop scheduling problem, a set of N jobs must be executed on a set of M machines. All jobs require the same operation order that must be executed according to the same manufacturing process. Each machine can only execute one job at an...

Many problems in industry are a form of open-shop scheduling problem (OSSP). We describe a hybrid approach to this problem which combines a Genetic Algorithm (GA) with simple heuristic schedule building rules. Excellent performance is found on some benchmark OSS problems, including improvements on previous best-known results. We describe how our approach can be simply amended to deal with the m...

Practical Constraint Satisfaction Problems (CSPs) such as design of integrated circuits or scheduling generally entail large search spaces with hundreds or even thousands of variables, each with hundreds or thousands of possible values. Often, only a very tiny fraction of all these possible assignments participates in a satisfactory solution. This article discusses techniques that aim at reduci...

The problem of finding good solutions to scheduling problems is very important to real manufacturing systems, since the production rate and production costs are very dependent on the schedules used for controlling the work in the system. Most research in scheduling focuses on optimisation of static problems, where all problem data are known before scheduling starts. However many real world opti...

The indeterministic multi-layer job-shop scheduling problem, which is the extension of the traditional job-shop scheduling, is introduced in this paper. The framework and some key issues of the problem are discussed. A multi-agent reinforcement learning approach, named memory-evolution-based MAS reinforcement learning algorithm, is breifly introduced too. Experiment results show that our approa...

Job shop scheduling problem is one of the most important problems in the combinatorial optimization problems and it is applied to various fields about engineering. Many works have been reported for this problem using Genetic Algorithm (GA). The GA is one of the most powerful optimization methods based on the mechanics of natural evolution. In this paper, we propose a new algorithm for the job s...

The assumption of classical shop scheduling problems that each job visits each machine only once (Baker, 1974) is often violated in practice. A new type of manufacturing shop, the reentrant shop has recently attracted attention. The basic characteristic of a re-entrant shop is that a job visits certain machines more than once. For example, in semiconductor manufacturing, consequently, each wafe...