• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.7
• /
• pp.1702-1710
• /
• 2009

In this paper, we propose a problem space based search(PSBS) algorithm to solve parallel machine scheduling problem considering rework probabilities. For each pair of a machine and a job type, rework probability of each job on a machine can be known through historical data acquisition. Neighborhoods are generated by perturbing four problem data vectors (processing times, due dates, setup times, and rework probabilities) and evaluated through the efficient dispatching heuristic (EDDR). The proposed algorithm is measured by maximum lateness and the number of reworked jobs. We show that the PSBS algorithm is considerably improved from the result obtained by EDDR.

• Journal of the Society of Naval Architects of Korea
• /
• v.60 no.3
• /
• pp.202-211
• /
• 2023

The profile shops in shipyards produce section steels required for block production of ships. Due to the limitations of shipyard's production capacity, a considerable amount of work is already outsourced. In addition, the need to improve the productivity of the profile shops is growing because the production volume is expected to increase due to the recent boom in the shipbuilding industry. In this study, a scheduling optimization was conducted for a parallel welding line of the profile process, with the aim of minimizing tardiness and the number of set-up changes as objective functions to achieve productivity improvements. In particular, this study applied a dynamic scheduling method to determine the job sequence considering variability of processing time. A Markov decision process model was proposed for the job sequence problem, considering the trade-off relationship between two objective functions. Deep reinforcement learning was also used to learn the optimal scheduling policy. The developed algorithm was evaluated by comparing its performance with priority rules (SSPT, ATCS, MDD, COVERT rule) in test scenarios constructed by the sampling data. As a result, the proposed scheduling algorithms outperformed than the priority rules in terms of set-up ratio, tardiness, and makespan.

• Management Science and Financial Engineering
• /
• v.19 no.1
• /
• pp.29-36
• /
• 2013

This paper considers common due-date assignment and scheduling on parallel machines. The main decisions are: (a) deter-mining the common due-date; (b) allocating jobs to machines; and (c) sequencing the jobs assigned to each machine. The objective is to minimize the sum of the penalties associated with common due-date assignment, earliness and tardiness. As an extension of the existing studies on the problem, we consider sequence-dependent setup times that depend on the type of job just completed and on the job to be processed. The sequence-dependent setups, commonly found in various manufacturing systems, make the problem much more complicated. To represent the problem more clearly, a mixed integer programming model is suggested, and due to the complexity of the problem, two heuristics, one with individual sequence-dependent setup times and the other with aggregated sequence-dependent setup times, are suggested after analyzing the characteristics of the problem. Computational experiments were done on a number of test instances and the results are reported.

• Journal of Korean Institute of Industrial Engineers
• /
• v.15 no.2
• /
• pp.11-22
• /
• 1989

The problem of scheduling n-jobs on m-uniform parallel machines is considered, in which each job has a release time, a deadline, and a processing requirement. The job processing requirements are allocated to the machines so that the maximum of the load differences between time periods is minimized. Based on Federgruen's maximum flow network model to find a feasible schedule, a polynomially bounded algorithm is developed. An example to show the effectiveness of our algorithm is presented.

• Journal of Korean Institute of Industrial Engineers
• /
• v.24 no.3
• /
• pp.431-446
• /
• 1998

In this paper, we consider the problem of scheduling n jobs with sequence-dependent processing times on a set of parallel-identical machines. The processing time of each job consists of a pure processing time and a sequence-dependent setup time. The objective is to maximize the total remaining machine available time which can be used for other tasks. For the problem, we first propose a dynamic programming(DP) algorithm for sequencing jobs processed on a single machine. The algorithm is then extended to handle jobs on parallel-identical machines. Finally, we developed an improved version of the algorithm which generates optimal solutions using much smaller amount of memory space and computing time. Computational results are provided to illustrate the performance of the DP algorithms.

• Korean Management Science Review
• /
• v.17 no.1
• /
• pp.41-54
• /
• 2000

in the real world situations that some jobs need be processed only on certain limited machines frequently occur due to the capacity restrictions of machines such as tools fixtures or material handling equipment. In this paper we consider n-job non-preemptive and m parallel machines scheduling problem having two machines group. The objective function is to minimize the sum of earliness and tardiness with different release times and due dates. The problem is formulated as a mixed integer programming problem. The problem is proved to be Np-complete. Thus a heuristic is developed to solve this problem. To illustrate its suitability and efficiency a proposed heuristic is compared with a genetic algorithm and tabu search for a large number of randomly generated test problems in ship engine assembly shop. Through the experimental results it is showed that the proposed algorithm yields good solutions efficiently.

• IE interfaces
• /
• v.11 no.3
• /
• pp.65-75
• /
• 1998

This research considers the problem of scheduling jobs on parallel machines with non-common due dates and additional resource constraints. The objective is to minimize the total absolute deviation of job completion times about the due dates. Job processing times are assumed to be the same. This problem is motivated by restrictions that occur in the handling and processing of jobs in certain phases of semiconductor manufacturing and other production systems. We examine two problems. For the first of these, the number of different types of additional: resources and resource requirements per job are arbitrary. The problem is formulated as a zero-one integer linear programming and the Lagrangian relaxation approach is used. For the second case, there exists one single type of additional resource and the resource requirements per job are zero or one. We show how to formulate the problem as an assignment problem.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.13 no.21
• /
• pp.143-151
• /
• 1990

This paper studies a single machine scheduling problem in which all jobs have the common due date and penalties are assessed for jobs at different rates. The scheduling objective is to minimize the weighted mean absolute deviations(WMAD). This problem may provide greater flexibility in achieving scheduling objectives than the mean absolute deviation (MAD) problem. We propose three heuristic solution methods based on several dominance conditions. Numerical examples are presented. This article extends the results to the problem to the problem of scheduling n-jobs on m-parallel identical processors in order to minimize the weighted mean absolute deviation.

• Journal of Korean Institute of Industrial Engineers
• /
• v.25 no.3
• /
• pp.360-368
• /
• 1999

We consider the problem of scheduling n jobs with sequence-dependent processing times on a set of parallel-identical machines. The processing time of each job consists of a pure processing time and a sequence-dependent setup time. The objective is to maximize the total remaining machine available time which can be used for other tasks. For the problem, a hybrid genetic algorithm is proposed. The algorithm combines a genetic algorithm for global search and a heuristic for local optimization to improve the speed of evolution convergence. The genetic operators are developed such that parallel machines can be handled in an efficient and effective way. For local optimization, the adjacent pairwise interchange method is used. The proposed hybrid genetic algorithm is compared with two heuristics, the nearest setup time method and the maximum penalty method. Computational results for a series of randomly generated problems demonstrate that the proposed algorithm outperforms the two heuristics.

• Journal of Korean Institute of Industrial Engineers
• /
• v.24 no.1
• /
• pp.37-50
• /
• 1998

This paper considers the nonidentical parallel machine scheduling problem in which n jobs having different due dates are to be scheduled on m nonidentical parallel machines. For the make-to-order manufacturing environment, the objective is to minimize the number of tardy jobs. A 0-1 nonlinear programming model is formulated and a heuristic algorithm that allocates and sequences jobs to machines is developed. The proposed algorithm makes use of the concept of assignment problem based on the suitability measure as the cost coefficient. Computational experiments show that the proposed algorithm is superior to the existing one in some performance measures such as number of tardy jobs. In addition, this algorithm is appropriate for solving real industrial problems efficiently.