Minimizing maximum tardiness for unrelated parallel machines
TL;DR: In this article, the scheduling of n jobs, all requiring a single stage of processing, on m unrelated parallel machines is considered, and the scheduling objective is to minimize the maximum tradiness.
About: This article is published in International Journal of Production Economics.The article was published on 1994-03-01. It has received 35 citations till now. The article focuses on the topics: Fair-share scheduling & Flow shop scheduling.
Citations
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14 Oct 1996TL;DR: In this article, the authors proposed several heuristics to schedule independent jobs on machines with different processing speeds in order to minimize two due-date-related criteria: total lateness and total tardiness.
Abstract: In semiconductor manufacturing, the rapid development of manufacturing equipment allows product processing to be achieved by machines of different generations whose processing speed may be different. In this study, we propose several heuristics to schedule independent jobs on machines with different processing speeds. The scope of this research is limited to a static and deterministic problem. The scheduling objective is to minimize two due-date-related criteria: total lateness and total tardiness, respectively.
1 citations
01 Jan 2012
TL;DR: A hybrid algorithm that combines differential evolution with particle swarm optimization, namely HDEPSO, is proposed in order to solve the unrelated parallel machine scheduling problem with sequence-dependant setup times for minimizing total tardiness and workload imbalance.
Abstract: This paper addresses the unrelated parallel machine scheduling problem with sequence-dependant setup times for minimizing total tardiness and workload imbalance. The mixed integer linear programming is proposed to model the studied problem. This problem is shown to be NP-hard in the strong sense. Thus, we propose a hybrid algorithm that combines differential evolution with particle swarm optimization, namely HDEPSO, in order to solve the given problem. Our objective is to achieve faster convergence rate and obtain better pareto optimal solutions. In order to demonstrate the efficiency and reliability of the proposed algorithm, a number of test problems are solved. The HDEPSO results are compared with two well-known multi- objective genetic algorithms in the literature, i.e. NSGA-II and SPEA-II based on some comparison metrics. Computational experiments indicate the superiority of the HDEPSO compared to these two genetic algorithms.
1 citations
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25 Oct 2010TL;DR: A hybrid method based on simulated annealing and genetic algorithm is proposed to solve the problem of scheduling of n jobs on non-identical parallel machines and the results were compared with those obtained by genetic algorithm.
Abstract: The problem of scheduling of n jobs on m non-identical parallel machines is considered. All jobs can be processed on all machines and the processing time and cost of each job depend on the machine on which the job is performed. Jobs cannot be split or divided and all jobs are available at time zero. The goal is to minimize cost which is composed of two parts: earliness-tardiness cost and production cost. The problem is formulated as a MILP model. A hybrid method based on simulated annealing and genetic algorithm is proposed to solve this problem. After parameter tuning of the algorithm, the proposed algorithm was tested on different combinations of jobs and machines and the results were compared with those obtained by genetic algorithm.
1 citations
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01 Dec 2007TL;DR: In this research heuristics based on guided search, record-to-record travel, and tabu lists from the tabu search (TS) are presented to minimize the maximum completion time (i.e., makespan or Cmax) and maximum tardiness, respectively, to promote schedule performance.
Abstract: This research deals with the problem of scheduling N jobs on M unrelated parallel machines. Each job has a due date and requires a single operation. A setup that includes detaching one die and attaching another from the appropriate die type is incurred if the type of the job scheduled is different from the last job on that machine. Due to the mechanical structure of machines and the fitness of dies to each, the processing time for a job depends on the machine on which the job is processed, and some jobs are restricted to be processed on certain machines. Furthermore, the required detaching and attaching times depend on both the die type and the machine. This type of problems may be encountered, for example, in plastics forming industry where unrelated parallel machines are used to process different components and setups for auxiliary equipment (e.g., dies) are necessary. This type of scheduling problems is also frequently encountered in injection molding departments where many different parallel machines are also used to produce different components and for which setups are required for attaching or detaching molds. In general, the dies (or molds) are quite expensive (tens of thousands dollars each) and thus the number of each type of dies available is limited. Therefore, dies should be considered as secondary resources, the fact of which distinguishes this research from many past studies in unrelated parallel-machine scheduling in which secondary resources are not restricted. This type of problems is NP-hard (So, 1990). When dealing with a large instance encountered in industry, in the worst case, it may not be able to obtain an optimal solution in a reasonable time. In this research heuristics based on guided search, record-to-record travel, and tabu lists from the tabu search (TS) are presented to minimize the maximum completion time (i.e., makespan or Cmax) and maximum tardiness (i.e., Tmax), respectively, to promote schedule performance. Computational characteristics of the proposed heuristics are evaluated through extensive experiments. The rest of this research is organized in six sections. Previously related studies on parallel machine scheduling are reviewed in Section 2. The record-to-record travel and tabu lists are briefly described in Section 3. The proposed heuristic to minimize makespan and the computational results are reported in Section 4. The proposed heuristic to minimize maximum tardiness and the computational results are reported in Section 5. Conclusions and suggestions for future research are discussed in Section 6. O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m
Cites methods from "Minimizing maximum tardiness for un..."
...Suresh & Chaudhuri (1994) presented a GAP-EDD algorithm to minimize maximum tardiness....
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References
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01 Jan 1974TL;DR: In this article, the authors present an introduction to Sequencing and Scheduling in the context of the Operational Research Society (ORS) and the International Journal of Distributed Sensor Networks (ILS).
Abstract: (1977). Introduction to Sequencing and Scheduling. Journal of the Operational Research Society: Vol. 28, No. 2, pp. 352-353.
2,640 citations
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TL;DR: Reading theory of scheduling as one of the reading material to finish quickly to increase the knowledge and happiness in your lonely time.
Abstract: Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading theory of scheduling as one of the reading material to finish quickly.
2,356 citations
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TL;DR: The problem of this paper is that of scheduling several one-stage tasks on several processors, which are capable of handling the tasks with varying degrees of efficiency, to minimize the total loss, which is a sum of losses associated with the individual tasks.
Abstract: The problem of this paper is that of scheduling several one-stage tasks on several processors, which are capable of handling the tasks with varying degrees of efficiency, to minimize the total loss, which is a sum of losses associated with the individual tasks.
Each task has a deadline; the individual loss associated with it is a function of amount of time between the deadline and the time of completion if the former precedes the latter, and zero otherwise.
843 citations
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TL;DR: A critical review of a particular segment of scheduling research in which the due to date assignment decision is of primary interest is presented, observing that while the static single- machine problem with constant or common due dates has been well researched, very little or no work has been done on the dynamic multi-machine problem with sophisticated due date assignment methods.
498 citations
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TL;DR: In this article, the problem of scheduling semiconductor burn-in operations is modeled as batch processing machines, where the processing time of a batch is equal to the largest processing time among all jobs in the batch.
Abstract: In this paper, we study the problem of scheduling semiconductor burn-in operations, where burn-in ovens are modeled as batch processing machines. A batch processing machine is one that can process up to B jobs simultaneously. The processing time of a batch is equal to the largest processing time among all jobs in the batch. We present efficient dynamic programming-based algorithms for minimizing a number of different performance measures on a single batch processing machine. We also present heuristics for a number of problems concerning parallel identical batch processing machines and we provide worst case error bounds.
433 citations