Single-machine scheduling with deteriorating jobs under a series-parallel graph constraint

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Pengaruh Learning EffectBergantung-Waktu dan Deteriorasi terhadap Penjadwalan Mesin Tunggal

Learning effect and deterioration do not fragmentary happen all the time. If both of them simultaneously found, the processing time of the job will increase yet decrease from the plan at once. The actual processing time of jobs are defined by function of their starting times and positions in the sequence. The effect of learning and deterioration on single machine scheduling at this paper is applied at a paper-mill. Learning effect as a result of regular performance-evaluation at this paper-mill reduce the effect of deterioration up to 206,5509 hours. Routing jobs by Earlier Due Date (EDD) rule construct the optimal result under maximum lateness case in this paper than either Most Urgent Job (MUJ) or Shortest Processing Time (SPT) do. The maximum lateness under EDD rule is 13,6% less than sequence that is recently used in that paper-mill

Scheduling Jobs with Linear Model of Simultaneous Ageing and Learning Effects

In the paper, we introduce some new scheduling model in which learning and aging effects are both considered simultaneously. In this model the actual processing time of the jobs depends only on its position in a schedule and can be described by the piecewise linear function. For single-processor problem with introduced model, we show that the problem of minimizing the makespan criterion for independent jobs with release dates is strongly NPhard, but some special cases of this problem are polynomially solvable. Based on those special cases, we propose 4 heuristic algorithms and we experimentally examine their usefulness for solving the general problem

Stochastic single machine scheduling problem as a multi-stage dynamic random decision process

In this work, we study a stochastic single machine scheduling problem in which the features of learning effect on processing times, sequence-dependent setup times, and machine configuration selection are considered simultaneously. More precisely, the machine works under a set of configurations and requires stochastic sequence-dependent setup times to switch from one configuration to another. Also, the stochastic processing time of a job is a function of its position and the machine configuration. The objective is to find the sequence of jobs and choose a configuration to process each job to minimize the makespan. We first show that the proposed problem can be formulated through two-stage and multi-stage Stochastic Programming models, which are challenging from the computational point of view. Then, by looking at the problem as a multi-stage dynamic random decision process, a new deterministic approximation-based formulation is developed. The method first derives a mixed-integer non-linear model based on the concept of accessibility to all possible and available alternatives at each stage of the decision-making process. Then, to efficiently solve the problem, a new accessibility measure is defined to convert the model into the search of a shortest path throughout the stages. Extensive computational experiments are carried out on various sets of instances. We discuss and compare the results found by the resolution of plain stochastic models with those obtained by the deterministic approximation approach. Our approximation shows excellent performances both in terms of solution accuracy and computational time

Scheduling Problems with Learning and Ageing Effects: A Survey

In recent years, many papers concerning scheduling problems with simultaneous learning and ageing effects were published. In this paper, the state of the art of research concerning these problems is presented. In order to facilitate understanding this subject, the scheduling problems where these effects occur separately, are firstly explained. Then, the papers devoted to scheduling problems combining the effects of learning and ageing are discussed. Particular attention was paid on practical applications of the considered scheduling problems. After thorough analysis it turned out that both scheduling problems with learning effect, and with ageing effect, as well as, in particular, the problems with models merging learning and ageing effects do not have any reasonable real-life applications. This is because the learning and ageing effects are in general long time horizon phenomena observed in repetitive systems and scheduling theory concerns either with repetitive short-horizon planning problems or single long-horizon projects. Therefore, there is no sense to continue research considering these scheduling problems from practical (computer engineering, automatic control, technical and economical) point of view, unless such reasonable real-life example appears

Common due date early

Ankara : The Department of Industrial Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.Thesis (Master's) -- Bilkent University, 2013.Includes bibliographical references leaves 91-96.This study considers a scheduling problem with position-dependent deteriorating jobs and a maintenance activity in a single machine. Even in the absence of maintenance activity and deterioration problem is NP-hard. A solution comprises the following: (i) positions of jobs, (ii) the position of the maintenance activity, (iii) starting time of the first job in the schedule. After the maintenance activity, machine will revert to its initial condition and deterioration will start anew. The objective is to minimize the total weighted earliness and tardiness costs. Jobs scheduled before (after) the due-date are penalized according to their earliness (tardiness) value. Polynomial (O(n log n)) time solutions are provided for some special cases. No polynomial solution exists for instances with tight due-dates. We propose a mixed integer programming model and efficient algorithms for the cases where mathematical formulation is not efficient in terms of computational time requirements. Computational results show that the proposed algorithms perform well in terms of both solution quality and computation time.Şirvan, FatmaM.S

A Decomposition-Based Two-Stage Optimization Algorithm for Single Machine Scheduling Problems with Deteriorating Jobs

10.1155/2015/340769Mathematical Problems in Engineering201534076