Introduction

Optimizing Estate Space Needs Through Centralization and ConsolidationThuBa T. NGUYEN, Sijie HO, Mark GOH, Robert de SOUZA

Motivation• Land for industrial estate development is scarce in Singapore.

• Need methodology for industrial warehouse clustering and freight

distribution within a cluster.

Introduce approaches to design capacity of Multi-User Distribution Centre

(MUDC) for a cluster, and to simulate and evaluate feasibility of an Estate-

wide Goods Mover System (EGMS).

Objectives• To formulate mathematical model for determining

the capacity of automated mover distribution

centre in an urban context.

• To develop simulator to evaluate the feasibility of

EGMS.

Organization

• Mathematical and simulation approaches:

o Problem definition

o Model development.

o Findings

o Summary

I. Mathematical model: Capacity Planning for Multi-Storeyed Shared Facilities II. Simulation model: Simulate Operations of EGMS

Main assumptions:

1. Raw materials (RM) & finished-goods

(FGs) are shipped first-in-first-out.

2. One type of raw material and FG for a given

user.

3. Deterministic arrivals of RM to MUDC.

4. Deterministic order quantities of RM from

the factories.

5. Only one product is stored on each rack.

6. A product can be stored only in a storey.

7. Outbound rate (number of pallets/day) of

the FGs corresponds to the outbound rate of

the raw materials requested for delivery

from MUDC to users.

Figure I.1: Storage structure on one storey of MUDC

R1 R2 R3 Rtu RT0 RT1

F1 F2 F3 Ftu

C1 C2 C3 Ctu

FT0 FT1

CT0 CT1….. ….. …..

….. ….. …..

…..…..…..

FT2

CT

…..

CT2 ..........

Planning horizon

Figure I.2: Network flow for MUDC resource planning

Problem

definition

Objective function:

Minimize construction cost which includes cost of building the floors and opening the goods lanes

Subject to:

• Constraints of building a floor,

• Constraints of opening a lane,

• Constraints of allocating a lane/ storey to each type of product and to each user,

• Constraints of delivery time and shipping time for RM and FGs respectively.

Model

development

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

0 1 2

Uniform Triangular

Normal Poisson

Delay time

(days)

Portion of RM delivered (%)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

0 1 2 3

Uniform Triangular Normal Poisson

Delay time

(days)

Percentage of finished goods shipped

(%)

Figure I.3: Pattern of delay time for delivering raw materials

Figure I.4: Pattern of delay time for shipping FG

Number

of

storeys

Required

capacity

(number of

lanes/storey)

Delay

time

(days)

Input combinations of maximum

delays (days)

RM 0 2 5 0 2 5 0 2 5

FG 0 3 5 0 3 5 0 3 5

Dis

trib

uti

on

of

inb

ou

nd

& o

utb

ou

nd

RM

ra

tes

Uniform

Min 5 5 5 22 22 22 0 0 0

Median 5 5 5 23 22 23 0 0 0

95%ile 5 5 5 23 23 23 0 1 1

Max 5 5 5 23 23 23 0 3 3

Triangular

Min 5 5 5 22 22 22 0 0 0

Median 5 5 5 23 22 22 0 0 0

95%ile 5 5 5 23 22 22 0 1 1

Max 5 5 5 23 22 22 0 2 3

Normal

Min 5 5 5 22 22 22 0 0 0

Median 5 5 5 23 23 22 0 0 0

95%ile 5 5 5 23 23 23 0 1 1

Max 5 5 5 23 23 23 0 2 2

Poisson

Min 5 5 5 22 22 22 0 0 0

Median 5 5 5 22 22 22 0 0 0

95%ile 5 5 5 23 22 22 0 1 2

Max 5 5 5 23 22 22 0 1 3

Table I.1: Sensitivity analysis on change in service

level

Findings

• Maximum number of storeys needs to be built

regardless of changing the service level.

• Same day delivery should be implemented and can

be achieved with the presence of automated MUDC.

Table II.1: Consolidated demand/supply data

(PE) manufacturing facilities

Figure II.2: Schematic layout of manufacturing

facilities in Wenya industrial estate

Figure II.3: EGMS estate simulation model for Precision

Engineering (PE) cluster using SIMIO software

Figure II.1: Schematic goods flow in EGMS model

Problem

definition

Model

development

Figure II.5:

RM count in

system

(average +

maximum)

Figure II.6:

FG count in

system

(average +

maximum)

Figure II.4: ASRS goods count (average) with

capacity reached at X = 2.1

• Marked improvement via significant reduction in the

average number of both RM and FG lingering in EGMS

estate as the number of operating AGVs increased.

• As ASRS proposed design parameter allows for 32,000

pallets space, the maximum increment in demand/supply is

capped at X=2.1.

• Linear increments in the goods count in system as the

demand/supply rate increases.

• Dedicated AGVs to single tenanted facilities to minimise

number of FGs piling up at their locations

Objective: Investigate feasibility of proposed set-up of

EGMS estate

Table II.2: Simulation results with Automated

Guided Vehicles (AGVs) in operation

Capacity Planning for Multi-Storeyed Shared Facilities:

• Systematic way to design required resource and to allocate users’ products to minimize land used.

• An automated MUDC can save up to 80% of land used.

• JIT delivery and shipment should be considered when planning required capacity of an automated MUDC

Implementation of EGMS:

• Provide useful insights for EGMS estate operational planning and supports important decision making on actual

implementation of EGMS estate

Limitations:

• Current MUDC model considers only one product; putting multiple products on a rack

and dedicated zones for certain types of products require further investigation.

• EGMS estate model analysis can be studied with more operational data and exact

designated plot

Conclusion