Modern Methods For Manufacturing Planning And Scheduling

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MODERN METHODS
mentioned in the literature as a Multi-Mode Resource-Constrained
Multi-Project Scheduling Problem (MMRCMPSP) with activity splitting
FOR MANUFACTURING
[Buddhakulsomsiri 2006]. The scheduling of this type of assembly
production lines is a very complex task. It is defined as a large
PLANNING
combinatorial order problem.
Unfortunately, many manufacturers have ineffective production
AND SCHEDULING
scheduling systems. They produce goods and ship them to their customers,
but they use a broken collection of independent plans that are frequently
ignored, periodic meetings where unreliable information is shared,
expediters who run from one crisis to another, and ad-hoc decisions
IVANA SIMEONOVOVA
, ALEKSANDAR GYUROV
,
1
2
made by persons [McKay 2004].
SIMEON SIMEONOV1
In this publication is presented the implementation of various
Brno University of Technology, Faculty of Mechanical Engineering
1
optimization methods for planning and management of manufacturing
Technical University of Varna, Faculty of Mechanical Engineering
2
company.
DOI: 10.17973/MMSJ.2015_10_201519
2. MANUFACTURING MODEL CONFIGURATION
Scheduling is an important tool for manufacturing and engineering,
e-mail: y78756@stud.fme.vutbr.cz
where it can have a major impact on the productivity of a process. In
The paper deals with modern methods which can be used for
manufacturing, the purpose of scheduling is to minimize the production
planning and scheduling of manufacturing systems. Creating
time and costs, by telling a production facility when to make, with which
of production plans, using different software products is
staff, and on which equipment. Production scheduling aims to maximize
discussed. Advanced planning and scheduling methods are
the efficiency of the operation and reduce costs [Magalhaes 2003].
used for planning and scheduling. The outputs as Gantt charts
Manufacturing model configurations (models) are presented using
are presented. Different optimization methods are applied for
different software products. Models include all processes from receiving
schedule optimization. Production plan, for every machine is
of raw materials, manufacturing to purchasing. The purchases raw
created, after applying the optimization. Scheduling by using
materials are ‘Steel-M1’ and ‘Gear-X’ and the produces products are
Lekin, ‘Spreadsheets’ and Asprova is described and compared.
AX100 AX200 BX100 BX200 CX100 and CX200. They are produced
through cutting, additional processing, assembly, and in the end they
KEYWORDS
are packed and sold.
APS Systems, modern methods, production planning,
The flow of processes for producing each product is shown below:
manufacturing processes, Asprova, Gantt chart
• Steel-M1 – Cutting – Processing – Inspection1 – Shaft-A;
• Steel-M1 – Cutting – Processing – Inspection1 – Shaft-B;
• Steel-M1 – Cutting – Processing – Inspection1 – Shaft-C;
1. INTRODUCTION
• A, Gear-X – Assembly – Inspection3 – AX;
Manufacturing facilities are complex, dynamic and stochastic systems. From
• B, Gear-X – Assembly – Inspection3 – BX;
the beginning of organized manufacturing, workers, supervisors, engineers,
• C, Gear-X – Assembly – Inspection3 – CX;
and managers have developed many clever and practical methods
• AX – Packing – AX100;
for controlling production activities. Many manufacturing organizations
• AX – Packing – AX200;
generate and update production schedules, which are plans that state
• BX – Packing – BX100;
when certain controllable activities (e.g., processing of jobs by resources)
• BX – Packing – BX200;
should take place. Production schedules coordinate activities to increase
• CX – Packing – CX100;
productivity and minimize operating costs. A production schedule can
• CX – Packing – CX200;
identify resource conflicts, control the release of jobs to the shop, ensure
2.1 Production scheduling using ‘Lekin’
that required raw materials are ordered in time, determine whether delivery
promises can be met, and identify time periods available for preventive
LEKIN
[Pinedo 2012] is a scheduling system developed at the Stern
®
maintenance [Herrmann 2007].
School of Business, NYU. LEKIN
was designed as an educational
®
Given the limitations of applying information technology or analyzing
tool with the main purpose of introducing the students to scheduling
problems mathematically, let to the conclusion that a comprehensive
theory and its applications. Besides that, the system’s extensibility allows
approach is needed to improve production scheduling. As modern
(and encourages) to use it in algorithm development. The project has
manufacturing moves towards manufacture-to-order and virtual environments
been directed by Professor Michael L. Pinedo and Associate Professor
that both increase the complexity of the operation and demand an
Xiuli Chao.
increasing rapid response time, it becomes more important than ever
The framework defines the type of scheduling problem that is to be
that manufacturing enterprises have ‘Advanced Planning and Scheduling’
entered. For scheduling the simulation model of the order is picked
(APS) systems that can reliably schedule complicated operations quickly.
a “Flexible Job Shop” environment. Once the framework has been
At the same time, scheduling plays an increasing role in many service
selected, the number of the machines and jobs is to be specified. In
industries, such as the transportation, computer, and communication
the current project 6 workcenters are needed. Different jobs are added
industries [Pinedo 2012].
to the workcenters and their machines. After adding the jobs other
The iterative use of simulation and scheduling is presented as a powerful
settings like processing time, release date, repeatability of the work
technique for making all-round productivity improvement recommendations.
and the job route, in the machine environment, are also specified.
Using a Scheduling tool also allows the plant to use both proactive and
Full list of every jobs and the total amount of time needed for
reactive methods to respond to sequence and priority changes while
manufacturing is shown in the Fig.1. The total production time is
maintaining productivity levels. The schedule so generated is then used
131 hours.
as an input into the simulation [Vasudevan 2008].
Once the relevant data of the problem has been entered, the scheduling
[Angelidis 2013] presents a specific custom-built simulator designed
process is started. The ‘Early Due Date(EDD)’ dispatching rule is set in
to support solution approaches for scheduling problems in complex
order to generate a schedule. The generated gantt chart of the jobs is
assembly lines found in industrial environments, which is often
show in Fig.2.
2015 | OCTOBER |
|
SCIENCE JOURNAL
635

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