• Journal of the Korea Safety Management & Science
• /
• v.6 no.2
• /
• pp.141-154
• /
• 2004

The objective of this paper is to develop the efficient heuristic method for solving the minimum makespan problem of the job shop scheduling. The proposed heuristic method is based on a constraint satisfaction problem technique and a improved randomizing search algorithm. In this paper, ILOG programming libraries are used to embody the job shop model, and a constraint satisfaction problem technique is developed for this model to generate the initial solution. Then, a improved randomizing search algorithm is employed to overcome the increased search time of constrained satisfaction problem technique on the increased problem size and to find a improved solution. Computational experiments on well known MT and LA problem instances show that this approach yields better results than the other procedures.

• Industrial Engineering and Management Systems
• /
• v.6 no.2
• /
• pp.119-124
• /
• 2007

The most difficult and time-intensive issue in the successful implementation of genetic algorithms is to find good parameter setting, one of the most popular subjects of current research in genetic algorithms. In this study, we present a new efficient experimental design method for parameter optimization in a genetic algorithm for general job shop scheduling problem using the Taguchi method. Four genetic parameters including the population size, the crossover rate, the mutation rate, and the stopping condition are treated as design factors. For the performance characteristic, makespan is adopted. The number of jobs, the number of operations required to be processed in each job, and the number of machines are considered as noise factors in generating various job shop environments. A robust design experiment with inner and outer orthogonal arrays is conducted by computer simulation, and the optimal parameter setting is presented which consists of a combination of the level of each design factor. The validity of the optimal parameter setting is investigated by comparing its SN ratios with those obtained by an experiment with full factorial designs.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.31 no.3
• /
• pp.17-23
• /
• 2008

In this research article, scheduling a casting sequence in a job-shop type foundry involving a variety of casts made of an identical alloy but with different shapes and II weights, has been investigated. The objective is to produce the assigned mixed orders satisfying due dates and obtaining the highest ingot efficiency simultaneously. Implementing simple integer programming instead of complicated genetic algorithms accompanying rigorous calculations proves that it can provide a feasible solution with a high accuracy for a complex, multi-variable and multi-constraint optimization problem. Enhancing the ingot efficiency under the constraint of discrete ingot sizes is accomplished by using a simple and intelligible algorithm in a standard integer programming. Employing this simple methodology, a job-shop type foundry is able to maximize the furnace utilization and minimize ingot waste.

• Journal of the Korea Safety Management & Science
• /
• v.5 no.1
• /
• pp.83-92
• /
• 2003

Scheduling plays an important role in shop floor planning. A scheduling shows the planned time when processing of a specific job will start on each machine that the job requires. It also indicates when the job will be completed on every process. Thus, it is a timetable for both jobs and machines. There is only one server available and arriving work require services from this server. Job are processed by the machine one at a time. The most common objective is to sequence jobs on the severs so as to minimize the penalty for being late, commonly called tardiness penalty. Based on other objectives, many criteria may serve as s basis for developing job schedules. The process also comprises all strategic planning, capital investments, management decisions, and tasks necessary to create a new product. manufacturing processes must be created so that the product can be produced in the product facility. Purchasing new equipment and training workers may be required if new technology is to be used. Tools, fixtures, and the sequence of steps in the manufacturing processes must all be developed to allow rapid, high-quality, cost effective production. Also, it may be needed to be rearrange the production facility to adapt to the new manufacturing processes. Therefore, this study tries to proposed that Scheduling by customer needs group for minimizing the problem and reducing inventory, product development time, cycle time, and order lead time.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2002.05a
• /
• pp.429-434
• /
• 2002

For companies assembling end products from sub assemblies or components, MRP (Material Requirement Planning) logic is frequently used to synchronize and pace the production activities for the required parts. However, in MRP, the planning of operational-level activities is left to short term scheduling. So, we need a good scheduling algorithm to generate feasible schedules taking into account shop floor characteristics and multi-level job structures used in MRP. In this paper, we present a GA (Genetic Algorithm) solution for this complex scheduling problem based on a new gene to reflect the machine assignment, operation sequences and the levels of the operations relative to final operation. The relative operation level is the control parameter that paces the completion timing of the components belonging to the same branch in the multi-level job hierarchy. In order to revise the fixed relative level which solutions are confined to, we apply large step transition in the first step and GA in the second step. We compare the genetic algorithm and 2-phase optimization with several dispatching rules in terms of tardiness for about forty modified standard job-shop problem instances.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.9
• /
• pp.2213-2221
• /
• 2015

The ondemand governor used in android-based smartphone platforms is a CPU frequency scaling technique. The ondemand governor sets the CPU operating frequency depending on the CPU utilization rate. Job scheduling affects the CPU utilization rate. The power consumption is proportional to the value of operating frequency. Consequently, CPU frequency scaling and CPU utilization rate have an effect on power consumption in a smartphone. In this paper, we evaluated the performance of job scheduling techniques incorporating the ondemand governor in terms of CPU utilization, power consumption, and job deadline miss ratio.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.14 no.24
• /
• pp.111-121
• /
• 1991

This paper considers a non preemtive single processor scheduling problem in which each set have the two common due dates. The objective of the problem is to minimize the weighted mean absolute deviation of job completion times about such two common due dates under the assumption that each job has a different weight. Such a job sequence is an W-shaped sequence. We propose three heuristic solution methods based on several dominance conditions. Numerical examples are presented. The performance comparison is made among three heuristic scheduling procedures.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.23 no.59
• /
• pp.11-20
• /
• 2000

The Job Shop Scheduling Problem(JSSP) is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on single genetic algorithm(SGA) and parallel genetic algorithm (PGA) to address JSSP. In this scheduling method, new genetic operator, generating method of initial population are developed and island model PGA are proposed. The scheduling method based on PGA are tested on standard benchmark JSSP. The results were compared with SGA and another GA-based scheduling method. The PGA search the better solution or improves average of solution in benchmark JSSP. Compared to traditional GA, the proposed approach yields significant improvement at a solution.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.05a
• /
• pp.615-619
• /
• 2004

This paper considers a variation of customer order scheduling problems. The variation is the case where machine-job assignment is fixed, and the objective is to minimize the sum of the completion times of the batches. In customer order scheduling problems, jobs are dispatched in batches. While a machine can process only one job at a time, multiple machines can simultaneously process jobs in a batch. We first establish a couple of lower bounds. Then, we develop a dynamic programming (DP) algorithm that runs in exponential time on the number of batches when there exist two machines. For the same problem with arbitrary number of machines, we present two simple heuristics, which use simple scheduling rules such as shortest batch first and shortest makespan batch first rules. Finally, we empirically evaluate the heuristics.

• IE interfaces
• /
• v.12 no.4
• /
• pp.617-624
• /
• 1999

In order to maximize an availability of machine and utilization of space, the parallel machines scheduling problem with space limit is frequently discussed in the industrial field. In this paper, we consider a scheduling problem for assembly machine in ship engine assembly shop. This paper considers the parallel machine scheduling problem in which n jobs having different release times, due dates and space limits are to be scheduled on m parallel machines. The objective function is to minimize the sum of earliness and tardiness. To solve this problem, a heuristic is developed. The proposed heuristic is divided into three modules hierarchically: job selection, machine selection and job sequencing, solution improvement. To illustrate its effectiveness, a proposed heuristic is evaluated with a large number of randomly generated test problems based on the field situation. Through the computational experiment, we determine the job selection rule that is suitable to the problem situation considered in this paper and show the effectiveness of our heuristic.