In this paper we present an overview of the research devoted to these problems with emphasis on the case of the optimal makespan on identical parallel machines Scheduling problems with more than one machine involve both resource allocation and sequencing rather than simply sequencing
WhatsApp— The first algorithm we propose is a list scheduling algorithm analogical to the LPT rule longest processing time first for P ∥ C max sorts jobs in non increasing order of sums of means and variances μ j σ j 2 with ties broken by larger μ each step a job is taken and assigned to the machine i ∈ {1 ⋯ m} with the currently largest probability
WhatsApp— This work addresses a new bi objective parallel machine selection and job scheduling problem with release dates and resource consumption It consists in optimally selecting subcontractors machines from a set of geographically dispersed locations and scheduling the orders jobs to the selected subcontractors for processing while meeting
WhatsApp— Moreover the authors suggest that studying the lot sizing problem on parallel machines and more general models are interesting subjects for future works Among the methods proposed to solve the ILSSP on parallel machines Kang et al 1999 Meyr 2002 and James and Almada Lobo 2011 deserve to be highlighted
WhatsAppPinedo demonstrated that the least flexible job LFJ rule was optimal for minimizing makespan in a parallel machine environment with equal processing times when there are machine eligibility restrictions the machine eligibility sets are nested and no release time constraint exists The results presented in this paper demonstrate that for
WhatsApp— This paper considers jointly scheduling the production and resource constrained maintenance activities in a manufacturing setting with unrelated parallel machines In particular a single maintenance activity needs to be scheduled on each machine in one of its available time windows and the maintenance activities require a
WhatsApp— We examine a parallel machine scheduling problem with a job splitting property sequence dependent setup times and limited setup operators for minimizing makespan Jobs are split into arbitrary job sections that can be processed on different machines simultaneously When a job starts to be processed on a machine a setup that
WhatsApp— As far as we know there is no study investigating unrelated parallel machine scheduling while considering simultaneously the total weighted completion time and the machine usage costs comprising both variable and fixed costs although some branch and price algorithms are available after the work of Chen and Powell 1999 that is the first to
— 1 Introduction Parallel batch machines are encountered in many industries such as semiconductor industry [1] [2] aircraft industry [3] [4] steel working industry [5] [6] and glass manufacturing industry [7] [8] A batch machine can process multiple jobs simultaneously as a batch which updates the conventional single item machine on which
WhatsApp— Four scheduling sub problems are involved in the electrode foil production process AGV selection machine selection distribution sequence of the AGVs and processing sequence of the machines Zou et al proposed two heuristic algorithms and a simulated annealing acceptance criterion and solved a multi bin automated guided
WhatsApp— In such situations the delay in processing a job may result in an increased effort to accomplish the job More details on single machine parallel machine and dedicated machine scheduling problems with time dependent precessing times are provided in the book published by Gawiejnowicz [2] Job s deterioration was introduced by Gupta
WhatsApp— Integrated production planning and preventive maintenance scheduling for synchronized parallel machines Author links open overlay panel Yu Liu a b Qin Zhang a Zhiyuan Ouyang a Hong Zhong Huang a b Show more Add to Mendeley In the CCGA the optimization problem is decomposed into a set of low dimensional sub problems and
WhatsApp— 5 Uniformly Fine Grained Data Parallel Computing for Machine Learning Algorithms 89 Meichun Hsu Ren Wu and Bin Zhang Overview of a GP GPU 91 Uniformly Fine Grained Data Parallel Computing on a GPU 93 The k Means Clustering Algorithm 97 The k Means Regression Clustering Algorithm 99 Implementations
WhatsApp— The structure of the parallel machine scheduling problem is as follows A manufacturing order contains a total of n jobs J = {1 2 n} each job can be assigned on any identical parallel machine M = {1 2 m} and the processing time of the job p j is the same on every identical parallel machine In actual production the value of n
WhatsApp— Since the end of the twentieth century serial parallel machine tools have drawn considerable interest because these machines offer the advantages of both serial and parallel mechanisms As a result 2 and 3 DOF parallel mechanisms have been increasingly proposed and studied Some have been used in industry and the exploration of such
WhatsApp— We consider the unrelated parallel machine scheduling problem with sequence and machine dependent setup times and due date constraints There are N jobs each having a due date and requiring a single operation on one of the M machines A setup is required if there is a switch from processing one type of job to another Due to the
WhatsApp— This paper considers jointly scheduling the production and resource constrained maintenance activities in a manufacturing setting with unrelated parallel machines In particular a single maintenance activity needs to be scheduled on each machine in one of its available time windows and the maintenance activities require a
— 1 Introduction Parallel batch machines are encountered in many industries such as semiconductor industry [1] [2] aircraft industry [3] [4] steel working industry [5] [6] and glass manufacturing industry [7] [8] A batch machine can process multiple jobs simultaneously as a batch which updates the conventional single item machine on which
WhatsAppThis work considers the unrelated parallel machines scheduling problem with family setups and resource constraints In this problem jobs are grouped into families and setup times are required between jobs belonging to different families The processing of a job requires a certain amount of resource from a machine which is supplied by upstream
WhatsApp— Scheduled Model Parallel Machine Learning Jin Kyu Kim1 Qirong Ho2 Seunghak Lee1 Xun Zheng1 Wei Dai1 Garth A Gibson1 Eric P Xing1 1School of Computer Science As a result all machines sub updates can be easily aggregated This convenient property does not ap ply to model parallel algorithms which introduce new sub
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