A key issue in location and layout design has become to exploit the flexibility offered by new technologies and means of operations. Processing flexibility allows processors to be allocated a variety of products to be treated, each with a given performance rating. Spatial flexibility occurs when management accepts multi-ple centers or processors, either identical or having intersecting capabilities, to be distributed through the facility. The combination of processing and spatial flexibility has the potential to improve significantly the design performance. Simple examples can be seen in everyday life. Switching from a single centralized toilet area or break area in a facility to multiple smaller areas spread through the facility has significant impact on people movement. A chain adding another convenience store in a city both helps it reach new customers through better convenience and reshuffles its clientele among the new and existing stores.
Exploiting flexibility makes the design process more difficult as it involves treating the flows as vari-ables, rather than mere inputs, and dealing with capacity. The flows indeed become dependent on the rela-tive locations and performance of entities. Thus, to evaluate a design, one has to estimate how in future operations the flows will be assigned given the design and the operating policies. Given the estimated flow, one can then apply the travel and traffic scoring methods shown in Section 5.5.
For illustrative purposes, assume that in the case used in the previous sections, each center is devoted to a single process and is composed of a specific number of identical processors, as shown in processor layout 1 in Figure 5.9. For simplicity purposes, assume also that the processing times for each process are product independent as stated in Table 5.9.
E
D B
F
J
I A
C MP
PF H
G
FIGuRE 5�8 Alternative layout 1 with superimposed qualitative relationships.
TABLE 5�6Flow Estimation for Layout Alternative From/ToMPABCDEFGHIJPFLoadedEmptyTotal From MP150 50 300 500 0 500 A 70 2512525 + 15 90 175 175 350 B 65 15 10 651005 130 130 260 C 5010540 + 60 175 215 215 430 D 25 10 40 25 4025 + 35 100 100 200 E260 45215300 15430 790 4751265 F150150300 300 300 600 G 350 350 0 350 H 40150 15110 300 300 315 615 I 300190 180500 500 6701170 J 65 35 25 6020 45 125 125 250 PF 490170 320 0 980 980 Loaded0175130215100 4753000315 6701259803485 Empty500175130215100 790300350300 50012503485 Total to500350260430200126560035061511702509806970 Entries: Trips/period. Shaded and italics: Empty. Bold: High relative value. Squared: Both loaded and empty travel. Loaded trip entries: From the output station of source center to the input station of destination center. Empty trip entries: From the input station of source center to the output station of destination center.
TABLE 5�7Travel Evaluation for Layout Alternative From/ToMPABCDEFGHIJPFLoadedEmpty TotalAverage MP36002000120068000680014 A56015001250320225028003080588017 B91063044039080011019801300328013 C100014702320105019303910584014 D5504408004501840254024404180662033 E10405402150660025534401083531901402511 F30001200210021004200630011 G56005600560016 H76010501210360036003020662011 I18001520144050005000476097608 J58594572512001020202547151785650026 PF3920170019200754075408 Loaded041504480362520952250660002355102201450105754780014 Empty5800203014952150171057202250513027303360414003696511 Total580061805975577542557970885051305085135805590105758476512 Average1218231321615158122211141112 Total flow*1000700520860400253012007001230234050019606970 Total travel*1260012060925511615108752199515150107301170523340120901811584765 Average travel*131718142791315101024912 Entries: Trips/period, * meters/period. Shaded and italics: Empty. Bold: High relative value. Underline: Low value given high flow.
TABLE 5�8Evaluation of Layout Alternative Based on Qualitative Relationships Pair of CentersProximity RelationshipMeasureImportanceDesign Evaluation Center 1Center 2ImportanceDesired ProximityReasonBetweenDistance MetricWeightDistance SatisfactionContribution MPAImportantNearFlowI/OAisle42400 MPEVery important NearFlowI/OAisle164116 MPIn1VitalAdjacentFlowBoundariesRectilinear01Feasible ACImportant NearFlowI/OAisle4100.451.8 BCVery important Not farInfrastructureCentroidRectilinear162200 BDVery important Not farInfrastructureCentroidRectilinear163700 BJImportantNearFlowI/OAisle480.853.4 CDVery important Not farInfrastructureCentroidRectilinear16150.11.6 CEImportantNearFlowI/OAisle4614 DGVery important Very FarSafetyCentroidEuclidean1635116 EF Very important NearFlowI/OAisle162200 EGDesirableNot farOrganizationBoundariesAisle1811 EPFCritical Very near Flow + OrgI/OAisle6480.212.8 FGImportantFarNoiseCentroidEuclidean43214 FHVery important NearFlowI/OAisle1670.9515.2 GHImportantNot farProcessBoundariesAisle41800 GI Very important NearFlowI/OAisle161600 GOUTCritical AdjacentFlowBoundariesRectilinear640164 HIVitalAdjacentInfra + Org + FlowBoundariesRectilinear01Feasible I PFCritical NearFlowI/OAisle64100.4528.8 PFOUTVitalAdjacentFlowBoundariesRectilinear01Feasible Maximum possible value:345Design value:168.6 Design score:48.9%
B F
J MP
G
PF H1 A1
A2
C1 C2 C3 C4 D7
D5D4 D3D2 D1
D6
E1 E2 E3 E4
H2 H3 H5 H4 H6 H7 H8 H9
I1 I2 I3 I4 I5 I6
I11 I12 I13 I14 I15 I16 I7 I8 I9I10
B J
C1
I3 I7
I5 I10
I8
I4 I9
I1 I2
16
G
MP/PF
A1
A2 C2
E1 E2
MP/PF
E3 F E4
I11 I12
I13
H1 H2
H9 H3
H4
H5 H6 H7 H8
C3 C4
D 7 D 6 D 5 D 4 D 3 D 2 D 1
ABCD.1 ABCD.2
ABCD.5 ABCD.3 ABCD.4
ABCD.7 ABCD.6
ABCD.8 ABCD.9 ABCD.10 ABCD.11 ABCD.12 ABCD.13
E.3 E.4 E.1 E.2
FGJ.2 FGJ.1
HI.1 HI.2
HI.3 HI.4
HI.5 HI.6
HI.7 HI.8
HI.9 HI10
HI11 HI12
HI13 HI14
HI15 HI16
HI17
HI19 HI20
MP/PF MP
PF
Estimated loaded travel: 91,017 Estimated average loaded travel per flow: 29.5
Estimated loaded travel: 33,680 Estimated average loaded travel per flow: 10.9
Estimated loaded travel: 23,285 Estimated average loaded travel per flow: 7,4
FIGuRE 5�9 Processor layouts of alternatives 1, 2 with spatial flexibility and 3 with added processing flexibility.
TABLE 5�9 Elemental Process and Specialized Processor Specifications
Operation Type A B C D E F G H I J
Unit time (minutes) 0.6 0.5 0.9 5 0.3 0.2 0.1 5 1.5 0.7
Number of processors 2 1 4 7 4 1 1 9 13 1
Processor size 3*2 2*9 2*7 1*2 2*5 6*5 2*13 2*2 1*1 3*13
Expected net utilization (%) 93 68 90 88 80 31 55 91 86 96
A period is set to a 20-hour workday.
Processor efficiency is estimated at 80%.
As in Section 5.6, an engineer was again asked to generate an alternative layout exploiting this knowl-edge and the potential for spatially dispersing identical processors instead of grouping them in a single functional center. He was allowed to use flexible centers responsible for both inbound materials and out-bound products. He generated alternative layout 2 shown in Figure 5.9. First, given the relatively small size of the case, he has decided not to create centers and has, rather, developed the design directly at the pro-cessor level. Second, he has indeed exploited flexibility allowed to disperse propro-cessors. He has strictly separated groups of processors of types H and I. He has contiguously laid out processors of types C and E, yet has oriented them so as to better enable efficient travel for distinct products. Third, the spatial disper-sion exploited is not extreme. In fact, it is limited to a fraction of the overall design.
Assume now that there exist flexible processors capable of performing multiple processes. In fact, here assume there are three flexible processor types respectively termed ABCD, FGJ, and HI capable of per-forming the processes embedded in their identifier. In order for the example to focus strictly on exhibiting the impact of flexibility, first the processing times are identical as in Table 5.9 and, second, the flexible processors have space requirements such that the overall space they jointly need is the same as the original specialized processors. The engineer was again required to generate an alternative layout exploiting this flexibility as well as spatial dispersion. He has designed the significantly different alternative layout 3 in the lower part of Figure 5.9.
The design scores provided under the layouts of Figure 5.9 illustrate vividly the potential of exploiting spatial dispersion and flexibility. Alternative 1 is used as a comparative basis. It has an estimated loaded travel score of 91,017. Alternative 2, exploiting spatial dispersion, has an estimated loaded travel score of 33,680, slicing 63% off alternative 1’s travel. Alternative 3 reduces further the estimated loaded travel score to 23,285, which slices 31% off alternative 2’s travel.
The scores have been estimated by assuming that the factory operating team will favor the products with a high number of equivalent trips when assigning products to processors. Heuristically, the engineer has first assigned the best paths to products P3, P6, and so on, taking into consideration processor avail-ability and processing times. For example, in alternative 2, product P3 getting out of center G is given priority for routing to processors I1 to I9 and then to the nearby MP/FP center.
When locating facilities around the world or processors within a facility, exploiting flexibility leads to what are known as location-allocation problems (Francis et al. 1992). The most well-known illustration is the case where distribution centers have to be located to serve a wide area market subdivided as a set of market zones or clients. There are a limited number of potential discrete locations considered for the dis-tribution centers. Each disdis-tribution center is flexible, yet has a limited throughput and storage capacity which can be either a constraint or a decision variable.
The assignment of market zones to specific distribution centers is not fixed a priori. The unit cost of designating a zone through a distribution center located at a given discrete location is precomputed for each potential combination, given the service requirements of each market zone (e.g., 24-h service). The goal is to determine the number of distribution centers to be implemented, the location and capacity of each implemented center, and the assignments of zones to centers. This can be done for single product cases and for multiple product cases. The same logic applies for flexible factories aimed to be spread around a wide market area so as to serve its production to order needs.