In our recent research, we showed results of the comparative study on effects of using several kinds of scheduling evaluation criteria as the fitness function of a genetic algorithm for job-shop scheduling. From these results, we obtained that the idle time criterion sometimes can provide a good makespan-minimizing schedule for a job-shop scheduling problem. In this paper, according to the abov...

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...

Flexible Job Shop Problem (FJSP) is an extension of classical Job Shop Problem (JSP). The FJSP extends the routing flexibility of the JSP, i.e assigning machine to an operation. Thus it makes it more difficult than the JSP. In this study, Cooperative Coevolutionary Genetic Algorithm (CCGA) is presented to solve the FJSP. Makespan (time needed to complete all jobs) is used as the performance eva...

Job shop scheduling problems occurs in automobile industry, aerospace industry, printing industry, and chemical industry. Job shop scheduling problems are real world and complex. The present work assesses the effect of change in setup times levels on make span, maximum flow time and maximum tardiness measures. The model of the job shop is developed in PROMODEL simulation software. Three levels ...

In this paper we study specially structured two stage flow shop scheduling problem with jobs in a string of disjoint job blocks having sequence independent setup times separated from processing times each associated with their respective probabilities including job weightage. In flow shop scheduling optimization of elapsed time may not always result in optimization of utilization time. Here, th...

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...

Manufacturing scheduling is an important but difficult task. In order to effectively solve such combinatorial optimization problems, this paper presents a novel Lagrangian relaxation neural network (LRNN) for separable optimization problems by combining recurrent neural network optimization ideas with Lagrangian relaxation (LR) for constraint handling. The convergence of the network is proved, ...

We consider in this paper a scheduling problem given by n tasks of same processing time d, an out-tree, communication delays all equal to c d and an integer t. The problem is to nd the minimum volume of a feasible schedule with makespan t. Studying the dominance properties of such schedules, we prove that this problem is polynomial using a dynamic programming algorithm.

This paper describes a genetic algorithm approach to the dynamic job shop scheduling problem with jobs arriving continually. Both deterministic and stochastic models of the dynamic problem were investigated. The objective functions examined were weighted flow time, maximum tardiness, weighted tardiness, weighted lateness, weighted number of tardy jobs, and weighted earliness plus weighted tardi...