Design of a Resource Scheduling Tool for Production Project Management

2012 ◽  
Vol 220-223 ◽  
pp. 165-168
Author(s):  
Zeng Bin ◽  
Jing Zhang
Keyword(s):  
Project Management ◽  
Production Planning ◽  
Manufacturing Systems ◽  
Resource Scheduling ◽  
Minimal Amount ◽  
Job Shops ◽  
Front End ◽  
Production Processes ◽  
Repetitive Nature ◽  
Scheduling Tool

Production planning is clearly needed in all manufacturing systems, and that is also the case for job shops. When dealing with a small number of products or jobs repetitive scheduling becomes a major issue. In this situation, the same tasks are performed sequentially and repeated from job to job. To help with this problem, a resource scheduling tool is developed that would work alongside Microsoft Project. This tool should act as a front end to Microsoft Project and allow for the creation of a schedule with a minimal amount of work for the scheduling manager. Due to the repetitive nature of production processes, the tool should be able to eliminate most of the manual scheduling currently done exclusively in MS Project.

scholarly journals Queue Length Forecasting in Complex Manufacturing Job Shops

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 322-338
Author(s):  
Marvin Carl May ◽  
Alexander Albers ◽  
Marc David Fischer ◽  
Florian Mayerhofer ◽  
Louis Schäfer ◽  
...  
Keyword(s):  
Operations Management ◽  
Queue Length ◽  
Manufacturing Systems ◽  
Production Control ◽  
Data Sets ◽  
Job Shops ◽  
Static Data ◽  
Queue Lengths ◽  
Operational Excellence

Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, such as queuing times, queue length, and production speed. However, accurate predictions of queue lengths have long been overlooked as a means to better understanding manufacturing systems. In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based queue forecasting models. Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. The main findings concluded that accurate queue length prediction, even with minimal available data, is feasible by applying a variety of techniques, which can enable further research and predictions.

Simulationsgestützte Produktionsplanung flexibler Fertigungssysteme*/Simulation-based production planning of flexible manufacturing systems

2019 ◽  
Vol 109 (04) ◽  
pp. 242-249
Author(s):  
A. Selmaier ◽  
T. Donhauser ◽  
T. Lechler ◽  
J. Zeitler ◽  
J. Franke
Keyword(s):  
Production Planning ◽  
Flexible Manufacturing ◽  
Material Flow ◽  
Flexible Manufacturing Systems ◽  
Manufacturing Systems ◽  
Flow Simulation ◽  
Limited Extent ◽  
Time Data ◽  
High Data ◽  
Simulation Based

Während sich das Verhalten starr verketteter Systeme relativ einfach mittels Materialflusssimulationen modellieren lässt, sind herkömmliche Simulationsansätze für flexible Fertigungssysteme aufgrund des hohen Datenerhebungs- sowie Parametrisieraufwands nur bedingt geeignet. Jedoch kann durch das automatische Übertragen von Echtzeitdaten in das Simulationsmodell der aktuelle Zustand solcher Systeme deutlich verbessert abgebildet werden. Der Beitrag stellt ein Konzept für die simulationsgestützte Produktionsplanung schnellveränderlicher Systeme vor.   While the behaviour of rigidly linked systems is relatively easy to model by means of material flow simulation, traditional simulation approaches are only suitable to a limited extent for flexible manufacturing systems due to the high data collection and parameterization effort. However, the use of real-time data can significantly improve the simulation of such systems. This paper presents an approach for simulation-based production planning of rapidly changing systems.

MINIMUM AVERAGE COST PRODUCTION PLANNING IN STOCHASTIC MANUFACTURING SYSTEMS

1998 ◽  
Vol 08 (07) ◽  
pp. 1251-1276 ◽  
Author(s):  
SURESH P. SETHI ◽  
HANQIN ZHANG ◽  
QING ZHANG
Keyword(s):  
Control Problem ◽  
Production Planning ◽  
Average Cost ◽  
Production Cost ◽  
Manufacturing Systems ◽  
Production Control ◽  
Production Capacity ◽  
Production Rates ◽  
Planning Problems

Recently, the production control problem in stochastic manufacturing systems has generated a great deal of interest. The goal is to obtain production rates to minimize total expected surplus and production cost. This paper reviews the research devoted to minimum average cost production planning problems in stochastic manufacturing systems. Manufacturing systems involve a single or parallel failure-prone machines producing a number of different products, random production capacity, and constant demands.

scholarly journals Effect of work-force availability on manufacturing systems operations of job shops

Procedia CIRP ◽  
2021 ◽  
Vol 103 ◽  
pp. 152-157
Author(s):  
Maria Chiara Magnanini ◽  
Marcello Colledani ◽  
Oleksandr Melnychuk ◽  
Davide Caputo
Keyword(s):  
Manufacturing Systems ◽  
Work Force ◽  
Job Shops

Shaping Mega-Science Projects and Practical Steps for Success

2018 ◽  
pp. 5690-5704
Author(s):  
Phil Crosby
Keyword(s):  
Project Management ◽  
Large Scale ◽  
High Technology ◽  
Engineering Science ◽  
Front End ◽  
Science Projects ◽  
Start Up ◽  
Successful Design

Too many large engineering/science projects fail in terms of budget overruns, schedule slippage, or under-performance, and this has profound implications not only for the construction and commissioning organisations, but also for the funders (public or private), and the clients or users. Successful design and delivery is therefore not only a commercial necessity but also a societal imperative. Success in complex mega-projects is not easily achieved and is interpreted differently by various stakeholders, moreover there is growing recognition of the importance of front-end shaping. In this chapter, the author addresses the inception, planning and feasibility phases of complex mega-projects in some depth, based on extant and updated research of large scale high-technology science projects. Five key success drivers are explained, and when addressed together, are shown to be especially potent. This chapter draws out subtle aspects of mega-project management shown to be crucial at the preliminary, or start-up, phase.

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