Single machine scheduling with general positional deterioration and rate-modifying maintenance

We present polynomial-time algorithms for single machine problems with generalized positional deterioration effects and machine maintenance. The decisions should be taken regarding possible sequences of jobs and on the number of maintenance activities to be included into a schedule in order to minimize the overall makespan. We deal with general non-decreasing functions to represent deterioration rates of job processing times. Another novel extension of existing models is our assumption that a maintenance activity does not necessarily fully restore the machine to its original perfect state. In the resulting schedules, the jobs are split into groups, a particular group to be sequenced after a particular maintenance period, and the actual processing time of a job is affected by the group that job is placed into and its position within the group

Learning Scheduling Algorithms for Data Processing Clusters

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

Single machine scheduling with exponential time-dependent learning effect and past-sequence-dependent setup times

AbstractIn this paper we consider the single machine scheduling problem with exponential time-dependent learning effect and past-sequence-dependent (p-s-d) setup times. By the exponential time-dependent learning effect, we mean that the processing time of a job is defined by an exponent function of the total normal processing time of the already processed jobs. The setup times are proportional to the length of the already processed jobs. We consider the following objective functions: the makespan, the total completion time, the sum of the quadratic job completion times, the total weighted completion time and the maximum lateness. We show that the makespan minimization problem, the total completion time minimization problem and the sum of the quadratic job completion times minimization problem can be solved by the smallest (normal) processing time first (SPT) rule, respectively. We also show that the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time under certain conditions

Combining time and position dependent effects on a single machine subject to rate-modifying activities

We introduce a general model for single machine scheduling problems, in which the actual processing times of jobs are subject to a combination of positional and time-dependent effects, that are job-independent but additionally depend on certain activities that modify the processing rate of the machine, such as, maintenance. We focus on minimizing two classical objectives: the makespan and the sum of the completion times. The traditional classification accepted in this area of scheduling is based on the distinction between the learning and deterioration effects on one hand, and between the positional effects and the start-time dependent effects on the other hand. Our results show that in the framework of the introduced model such a classification is not necessary, as long as the effects are job-independent. The model introduced in this paper covers most of the previously known models. The solution algorithms are developed within the same general framework and their running times are no worse than those available earlier for problems with less general effects

Minimisation of energy consumption variance for multi-process manufacturing lines through genetic algorithm manipulation of production schedule

Typical manufacturing scheduling algorithms do not consider the energy consumption of each job, or its variance, when they generate a production schedule. This can become problematic for manufacturers when local infrastructure has limited energy distribution capabilities. In this paper, a genetic algorithm based schedule modification algorithm is presented. By referencing energy consumption models for each job, adjustments are made to the original schedule so that it produces a minimal variance in the total energy consumption in a multi-process manufacturing production line, all while operating within the constraints of the manufacturing line and individual processes. Empirical results show a significant reduction in energy consumption variance can be achieved on schedules containing multiple concurrent jobs

Scheduling problems with the effects of deterioration and learning

Author name used in this publication: T. C. E. Cheng2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Methods for Scheduling Problems Considering Experience, Learning, and Forgetting Effects

[EN] Workers with different levels of experience and knowledge have different effects on job processing times. By taking into account 1) the sum-of-processing-time; 2) the job-position; and 3) the experience of workers, a more general learning model is introduced for scheduling problems. We show that this model generalizes existing ones and brings the consideration of learning and forgetting effects closer to reality. We demonstrate that some single machine scheduling problems are polynomially solvable under this general model. Considering the forgetting effect caused by the idle time on the second machine, we construct a learning-forgetting model for the two-machine permutation flow shop scheduling problem with makespan minimization. A branch-and-bound method and four heuristics are presented to find optimal and approximate solutions, respectively. The proposed heuristics are evaluated over a large number of randomly generated instances. Experimental results show that the proposed heuristics are effective and efficient.This work was supported in part by the National Natural Science Foundation of China under Grant 61572127 and Grant 61272377, in part by the Key Research and Development Program in Jiangsu Province under Grant BE2015728, in part by the Collaborative Innovation Center of Wireless Communications Technology and the Key Natural Science Fund for Colleges and Universities in Jiangsu Province under Grant 12KJA630001, and in part by the Collaborative Innovation Center of Wireless Communications Technology. The work of R. Ruiz was supported by the Spanish Ministry of Economy and Competitiveness through Project "SCHEYARD-Optimization of Scheduling Problems in Container Yards" under Grant DPI2015-65895-R. This paper was recommended by Associate Editor A. Janiak.Li, X.; Jiang, Y.; Ruiz García, R. (2018). Methods for Scheduling Problems Considering Experience, Learning, and Forgetting Effects. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 48(5):743-754. https://doi.org/10.1109/TSMC.2016.2616158S74375448