reduction of manufacturing time of wagon-tippler tanmoy das 08im6020 industrial engineering and...

Reduction of Manufacturing Time of Wagon-Tippler

Tanmoy Das08IM6020

Industrial Engineering And Management

Company Profile

• TRF Limited, a TATA Enterprise, originally TATA-ROBINS-FRASER Limited was promoted in 1962.

• TRF, is a pioneer in India to provide solutions in bulk material handling/processing systems and equipments and Port and

Yard equipments. • It provides engineering solutions in the processing plants for

core industries like power, mines, ports, steel, cement and

fertilizers, dam construction, chemicals/ processes etc.

PRODUCT RANGE OF TRF

• Vibrating Equipment -Mechanical• Vibrating Equipment-Electromagnetic• Crushers• Underground Mining Vehicles• Wagon Tipplers• Apron Feeders• Plough Feeders• Rotary Table Feeders etc.

Current Production Life cycleOrder from the customer

Issue of SODS (Sales Order Data-Sheet)

Drawing of the product

Instruction – Sheet to shop-floor

Purchase Requisition and Purchase order

Manufacturing

Dispatch

Problem statement

Reduction of high manufacturing time for wagon-tippler.

Problem description:Main time consuming areas are-

• Design of equipments• Production planning• Procurement of items from outside• Manufacturing (non-productive time)• Assembly

Literature Survey

• Sarin and Lefoka (1993) worked on job shop scheduling problem to develop a heuristic approach for developing the priority sequence of jobs with a view of minimizing make span.

• Rajendran (1996) developed efficient and new dispatching rules to minimize mean flow time, maximum flow time, variance of flow time and proportion of tardy jobs.

Literature Survey cont..

• Kumar and Srinivasan (1995) worked on this type of problem and used GA (Generic Algorithm ) to solve the problem.

• Naderi and Jolai (2008) developed a Variable Neighborhood Search (VNS) considering the setup times to minimize the make span.

Objective

• To reduce manufacturing make span for different equipments.

• To reduce high non-productive time.• Identify and reduce the unnecessary work

elements• Identify and standardize the items with high

production volume and manufacture in a lot to reduce setup time.

Main Parts of Wagon Tippler

WAGON

TOP CLAMP

END RING

CRADLEMOTOR

SIDE BEAM

WAGON TIPPLER

WAGON

TOP CLAMP

END RING

CRADLE

MOTOR

SIDE BEAM

PARTS OF WAGON TIPPLER

Major Time Components(in minutes)

Cradle assembly

79082

Side beam assembly

51930

Top clamp assembly

14254

Pivot assembly

4858

Cradle seating beam

3348

Main Four Workstation of Manufacturing

PreparationPreparation

Bending, Drilling,Boring

Bending, Drilling,Boring

WeldingWelding

InspectionInspection

A typical work station

Input objects

Output objects

ParametersNo. of m/csNo of WorkersTotal available time in a shift

Node

Transformation

Decision Agent

NodeNode NodeNode

Decision Agent

Decision Agent

Global Decision Agent

Problem Formulation

• Let us consider– There are ‘n’ workstation.– Each part will pass through all the ‘n’

workstations.

Decision will be taken in two levels,Global decision agent will try to minimize the make span.Local decision agent will try to assign parts in m/c depending upon m/c and worker availability.

For the Local decision agent,

• Assign the jobs to machines with min setup times S1,S2,S3… Sm

• S1<=S2<=S3…..<=Sm

• Considering »

» .

,1

*m

i jj

M dj Mi for all i

1

m

j

dj Ni for all i

Notations

• S1,S2,..,Sm are the setup time of part j in workstation i

• Mi = available worker in workstation j• Mi,j = worker required for part j in w/s i• Ni = available machines in w/s i• dj = 1 if part j is loaded in w/s i

=0 if part j is not loaded in w/s i

For the Global decision agent,

• Objective is to minimize total make span

• Subjected to Fi-1 ≤ Fi – ti

• Si is the setup time of part j in w/s i• Fi = finish time of part j in w/s i• di = processing time of part j in w/s i

1 1

maxn n

i ii i

C S t

Reference

• SARIN, S. and LEFOKA, M. (1993), “Scheduling heuristic for the n –Job m-Machine flow shop”, International Journal of Management Science, Vol. 21, No. 2, pp. 229-234.

• Kumar, N.S.H. and Srinivasan, G. (1996), “A genetic algorithm for job shop scheduling - A case study”, Computers in Industry, Vol. 31, pp. 155-160.

• Holthaus, O. and Rajendran, C. (1997),“Efficient dispatching rules for scheduling in a job shop”, International Journal of Production Economics , Vol. 48, pp. 87-105.

Thank You