Transcript Facilities design - Georgia Institute of Technology

Facilities design
Main Topics
• Discrete vs. Continuous Flow and Repetitive Manufacturing
• Process vs. Product-focused designs and the other currently
used variations
• Designing Layouts
– Systematic Layout Planning (SLP) for Process-focused layouts
– Flow Patterns for Product-focused layouts
• (Assembly) Line Balancing
• Cell Formation
• (Warehousing and its design issues – in a sequel presentation)
Operation Process Chart Example
for discrete part manufacturing
(borrowed from Francis et. al.)
Discrete vs. Continuous Flow and
Repetitive Manufacturing Systems
(Figures borrowed from Heizer and Render)
A typical (logical) Organization of the
Production Activity in
Repetitive Manufacturing
Assembly Line 1: Product Family 1
S1,1
Raw
Material
& Comp.
Inventory
S1,i
S1,2
S1,n
Fabrication (or Backend Operations)
Dept. 1
S2,1
S2,2
Dept. 2
Dept. j
S2,i
Assembly Line 2: Product Family 2
Finished
Item
Inventory
Dept. k
S2,m
Synchronous Transfer Lines: Examples
(Pictures borrowed from Heragu)
Major Layout Types
(borrowed from Francis et. al.)
Advantages and Limitations of the various
layout types (borrowed from Francis et. al.)
Advantages and Limitations of the various layout
types (cont. - borrowed from Francis et. al.)
Selecting an appropriate layout
(borrowed from Francis et. al.)
The product-process matrix
Production
volume
& mix
Process
type
Jumbled
flow (job
Shop)
Low volume,
low standardization
Commercial
printer
Disconnected
line flow
(batch)
High volume, high
standardization,
commodities
Void
Heavy
Equipment
Connected
line flow
(assembly
Line)
Continuous
flow
(chemical
plants)
Multiple products, Few major products,
low volume
high volume
Auto
assembly
Void
Sugar
refinery
Cell formation in group technology:
A clustering problem
Partition the entire set of parts to be produced on the plant-floor into
a set of part families, with parts in each family characterized by
similar processing requirements, and therefore, supported by the
same cell.
Part-Machine Indicator Matrix
P1
P2
P3
P4
P5
P6
P1
P3
P2
P4
P5
P6
M1
1
M2
M3
1
1
M4
1
M5
M4
1
1
1
1
1
1
M1
1
1
1
1
M6
1
1
M7
1
1
1
M6
1
M2
M3
M5
1
1
1
1
1
1
1
1
M7
1
1
Clustering Algorithms for Cellular Manufacturing
Row & Column Masking
P1
P2
P3
P4
P5
P6
P1
P3
P2
P4
P5
P6
M1
1
M2
M3
1
1
M4
1
M5
M4
1
1
1
1
1
1
M1
1
1
1
1
M6
1
1
M7
1
1
1
M6
1
M2
M3
M5
1
1
1
1
1
1
1
1
M7
1
1
Clustering Algorithms for Cellular Manufacturing:
Similarity Coefficients - Motivation
P1
P2
P3
P4
P5
P6
P1
P3
P2
P4
P5
P6
M1
1
1
M2
M3
1
1
M4
1
M5
1
M4
1
1
1
1
1
1
M1
1
1
1
1
M6
1
1
M7
1
1
1
M6
1
M2
M3
M5
1
1
1
1
1
1
1
1
M7
1
1
Clustering Algorithms for Cellular Manufacturing:
Similarity Coefficients - Definitions
• P(Mi) = set of parts supported by machine Mi
• |P(Mi)| = cardinality of P(Mi), i.e., the number of elements
of this set
• SC(Mi,Mj) = |P(Mi)P(Mj)| / |P(Mi)P(Mj)| =
|P(Mi)P(Mj)| / (|P(Mi)|+|P(Mj)|-|P(Mi)P(Mj)|)
• Notice that: 0  SC(Mi,Mj)  1.0, and the closer this value is
to 1.0 the greater the similarity among the part sets supported
by machines Mi and Mj.
• By picking a desired threshold, one can cluster together all
machines that have a similarity coefficient greater than or
equal to this threshold.
Flow Patterns for Product-focused Layouts
(borrowed from Francis et. al.)
Design of Process-based layouts
Arrange spatially the facility departments in a way that
• facilitates the flow of parts through the facility by minimizing
the material handling / traveling effort;
• observes additional practical constraints arising from, e.g.,
• processing/operational requirements
• safety/health considerations
• aesthetics
• building features
• etc.
Prevailing Methodology:
Systematic Layout Planning (SLP)
1. Material
Flows
2. Activity
Relationships
3. REL
Chart
4. REL
Diagram
5. Space
Requirements
6. Space REL
Diagram
7. Space
Availability
8. Layout
Alternatives
Departments  Activities
Example on SLP
Developed in class – c.f. your class notes!
Synchronous Transfer Lines: Examples
(Pictures borrowed from Heragu)
Balancing Synchronous Transfer Lines
• Given:
– a set of m tasks, each requiring a certain (nominal) processing
time t_i, and
– a set of precedence constraints regarding the execution of these
m tasks,
• assign these tasks to a sequence of k workstations, in a way that
– the total amount of work assigned to each workstation does not
exceed a pre-defined cycle time c, (constraint I)
– the precedence constraints are observed, (constraint II)
– while the number of the employed workstations k is
minimized. (objective)
• Remark: The problem is hard to solve optimally, and
quite often it is addressed through heuristics.
Heuristics for Assembly Line Balancing
Developed in class – c.f. your class notes!
Asynchronous Production Lines
• Each part moves to the next station upon finishing
processing at its current station, provided that there is
available buffering capacity at the next station, without
coordinating its movement with other parts in the
system.
• Some reasons for adopting an asynchronous
operational mode:
– Lack / High cost of synchronizing material handling
equipment
– (Highly) variable processing times at or among the different
stations
– Frequent equipment failures
Buffers, WIP and Congestion
Typical quantities of interest:
• Times spent at different part of the system (“cycle” times)
• Material accumulated at different parts of the system (WIP)
Estimates for these quantities can be obtained either through
• Queueing theory (G/G/1 models), or
• Simulation
The G/G/1 model
TH
TH
Station Parameters: (m: number of machines)
• Production rate / Throughput: TH
• Mean effective processing time: te
• St. deviation of effective processing time: e
• Coefficient of variation (CV) of effective processing time: ce = e / te
• Machine utilization u = TH * te (TH*te / m)
• Coefficient of variation of inter-arrival times: ca
• Coefficient of variation of inter-departure times: cd
Evaluating the key performance measures:
• CTq = [(ca2 + ce2) / 2]*[u / (1-u)] * te [(ca2 + ce2) / 2]*[u(2(m+1))-1 /(m (1-u))] * te
• CT = CTq + te
• WIPq = TH * CTq
• WIP = TH * CT = WIPq + u WIPq + m*u
• cd2 = u2 * ce2 + (1-u2) * ca2
1+(1-u2)(ca2-1)+u2(ce2-1)/m
Evaluating an entire Production Line
TH
Key observations:
• For a stable system, the average production rate of every station
will be equal to TH.
• For every pair of stations, the inter-departure times of the first
constitute the inter-arrival times of the second.
• Then, the entire line can be evaluated on a station by station basis,
working from the first station to the last, and using the equations for
the basic G/G/1 model.
Taking into consideration machine failures
Definitions:
• Base machine processing time: t0
• Coefficient of variation for base processing time: c0 = 0 / t0
• Mean time to failure: mf
• Mean time to repair: mr
• Coefficient of variation of repair times: cr = r / mr
• Machine Availability A = mf / (mf + mr)
Then,
• te = t0 / A (or equivalently 1/te = A * (1/t0) )
• e2 = (0/A)2 + (mr2+ r2)(1-A)(t0/A)
• ce2 = e2 / te2 = c02 + (1+cr2)A(1-A)mr/t0
Example on the Analysis and Design of
Asynchronous Production Lines through
the presented G/G/m model
Developed in class – c.f. your class notes!
Reading Assignment
• From your textbook:
–
–
–
–
Chapter 1: Section 1.9
Chapter 10: Sections 10.1-10.3, 10.6
Chapter 8: Section 8.10
(for those of you interested to see something more, read also Sections
10.4, 10.5 and 10.7)