Dong Won Kim

Bio: Dong Won Kim is an academic researcher from Chonbuk National University. The author has contributed to research in topics: Tardiness & Job scheduler. The author has an hindex of 2, co-authored 2 publications receiving 332 citations.

Unrelated parallel machine scheduling with setup times using simulated annealing

Abstract: This paper presents a scheduling problem for unrelated parallel machines with sequence-dependent setup times, using simulated annealing (SA). The problem accounts for allotting work parts of L jobs into M parallel unrelated machines, where a job refers to a lot composed of N items. Some jobs may have different items while every item within each job has an identical processing time with a common due date. Each machine has its own processing times according to the characteristics of the machine as well as job types. Setup times are machine independent but job sequence dependent. SA, a meta-heuristic, is employed in this study to determine a scheduling policy so as to minimize total tardiness. The suggested SA method utilizes six job or item rearranging techniques to generate neighborhood solutions. The experimental analysis shows that the proposed SA method significantly outperforms a neighborhood search method in terms of total tardiness.

Unrelated parallel machine scheduling with setup times and a total weighted tardiness objective

Abstract: This paper presents several search heuristics and their performance in batch scheduling of parallel, unrelated machines. Identical or similar jobs are typically processed in batches in order to decrease setup times and/or processing times. The problem accounts for allotting batched work parts into unrelated parallel machines, where each batch consists of a fixed number of jobs. Some batches may contain different jobs but all jobs within each batch should have an identical processing time and a common due date. Processing time of each job of a batch is determined according to the machine group as well as the batch group to which the job belongs. Major or minor setup times are required between two subsequent batches depending on batch sequence but are independent of machines. The objective of our study is to minimize the total weighted tardiness for the unrelated parallel machine scheduling. Four search heuristics are proposed to address the problem, namely (1) the earliest weighted due date, (2) the shortest weighted processing time, (3) the two-level batch scheduling heuristic, and (4) the simulated annealing method. These proposed local search heuristics are tested through computational experiments with data from dicing operations of a compound semiconductor manufacturing facility.

Scheduling and lot sizing with sequence-dependent setup: A literature review

Abstract: This paper reviews the literature related to the class of scheduling problems that involve sequence-dependent setup times (costs), an important consideration in many practical applications. It focuses on papers published within the last decade, addressing a variety of machine configurations including single machine, parallel machine, flow shop, and job shop systems and reviews the optimization and heuristic solution methods used for each category. Since lot sizing is so intimately related to scheduling, this paper reviews work that integrates these issues in relationship to each configuration. This paper provides a perspective of this line of research, gives conclusions, and discusses fertile research opportunities posed by this class of scheduling problems.

Heuristics for the Unrelated Parallel Machine Scheduling Problem with Setup Times

Abstract: The problem addressed in this paper is the non-preemptive unrelated parallel machine scheduling problem with the objective of minimizing the makespan. Machine-dependent and job sequence-dependent setup times are considered, all jobs are available at time zero, and all times are deterministic. This is a NP-hard problem and in this paper, optimal solutions are found for small problems only. For larger problems, a new meta-heuristic, Meta-RaPS, is introduced and its performance is evaluated by comparing its solutions to the solutions of an existing heuristic for the same problem. The results show that Meta-RaPS found all optimal solutions for the small problems and outperformed the solutions obtained by the existing heuristic for larger problems.

Optimization of production scheduling with time-dependent and machine-dependent electricity cost for industrial energy efficiency

Abstract: In many industrialized countries, manufacturing industries pay stratified electricity charges depending on the time of day (i.e., peak-load, mid-load, and off-peak-load). In contrast, the emerging smart grid concept may demand that industries pay real-time hourly electricity costs so as to use energy most efficiently. This paper deals with the production and energy efficiency of the unrelated parallel machine scheduling problem. This method allows the decision maker to seek a compromise solution using the weighted sum objective of production scheduling and electricity usage. Reliability models are used to consider the energy cost aspect of the problem. This paper aims to optimize the weighted sum of two criteria: the minimization of the makespan of production and the minimization of time-dependent electricity costs. We suggest a hybrid genetic algorithm with our blank job insertion algorithm and demonstrate its performance in simulation experiments.