航海英语门式起重机外文翻译

外文原文（出自

JOURNAL OF CONSTRUCTION ENGINEEING AND MANAGEMENT

MARCH/APRIL/115-- 121

）

LOCATION OPTIMIZATION FOR A GROUP OF TOWER CRANES

ABSTRACT:

A

computerized

model

to

optimize

location

of

a

group

of

tower

cranes

is

presented. Location criteria are balanced workload, minimum likelihood of conflicts with each

other, and high efficiency of operations. Three submodels are also presented. First, the initial

location

model

classifies

tasks

into

groups

and

identifies

feasible

location

for

each

crane

according to

geometric ‘‘closeness.’’ Second, the former task groups

are adjusted to yield smooth

workloads

and

minimal

conflicts.

Finally,

a

single-tower-crane

optimization

model

is

applied

crane

by

crane

to

search

for

optimal

location

in

terms

of

minimal

hook

transportation

time.

Experimental results and the steps necessary for implementation of the model are discussed.

INTRODUCTION

On

large

construction

projects

several

cranes

generally

undertake

transportation

tasks, particularly when a single crane cannot provide overall coverage of all demand

and

supply

points,

and/or

when

its

capacity

is

exceeded

by

the

needs

of

a

tight

construction schedule. Many factors influence tower crane location. In the interests of

safety

and

efficient

operation,

cranes

should

be

located

as

far

apart

as

possible

to

avoid

interference

and

collisions,

on

the

condition

that

all

planned

tasks

can

be

performed.

However,

this

ideal

situation

is

often

difficult

to

achieve

in

practice;

constrained work space and limitations of crane capacity make it inevitable that crane

areas overlap. Subsequently, interference and collisions can occur even if crane jibs

work

at

different

levels.

Crane

position(s)

tend

to

be

determined

through

trial

and

error, based on site topography/shape and overall coverage of tasks. The alternatives

for

crane

location

can

be

complex,

so

managers

remain

confronted

by

multiple

choices and little quantitative reference.

Crane

location

models

have

evolved

over

the

past

20

years.

Warszawski

(1973)

established a time- distance formula by which quantitative evaluation of location was

possible.

Furusaka

and

Gray

(1984)

presented

a

dynamic

programming

model

with

the objective function being hire cost, but without consideration of location. Gray and

Little

(1985)

optimized

crane

location

in

irregular-shaped

buildings

while

Wijesundera and Harris (1986) designed a simulation model to reconstruct operation

times

and

equipment

cycles

when

handling

concrete.

Farrell

and

Hover

(1989)

developed

a

database

with

a

graphical

interface

to

assist

in

crane

selection

and

location. Choi and Harris (1991) introduced another model to optimize single tower

crane

location

by

calculating

total

transportation

times

incurred.

Emsley

(1992)

proposed

several

improvements

to

the

Choi

and

Harris

model.

Apart

from

these

algorithmic approaches,

rule-based systems have also evolved to assist decisions on

crane numbers and types as well as their site layout

。

Assumptions

Site

managers

were

interviewed

to

identify

their

concerns

and

observe

current

approaches to the task at hand. Further, operations were observed on 14 sites where

cranes

were

intensively

used

(four

in

China,

six

in

England,

and

four

in

Scotland).

Time

studies

were

carried

out

on

four

sites

for

six

weeks,

two

sites

for

two

weeks

each, and two for one week each. Findings suggested inter alia that full coverage of

working

area,

balanced

workload

with

no

interference,

and

ground

conditions

are

major

considerations

in

determining

group

location.

Therefore,

efforts

were

concentrated

on

these

factors

(except

ground

conditions

because

site

managers

can

specify

feasible

location

areas).

The

following

four

assumptions

were

applied

to

model development (detailed later):

1.

Geometric layout of all supply (S) and demand (D) points, together with the type

and number of cranes, are predetermined.

2.

For

each

S-D

pair,

demand

levels

for

transportation

are

known,

e.g.,

total

number of lifts, number of lifts for each batch, maximum load, unloading delays,

and so on.

3.

The duration of construction is broadly similar over the working areas.

4.

The material transported between an S-D pair is handled by one crane only.

MODEL DESCRIPTION

Three steps are involved in determining optimal positions for a crane group. First, a

location generation model produces an approximate task group for each crane. This is

then adjusted by a task assignment model. Finally, an optimization model is applied to

each tower in turn to find an exact crane location for each task group.

Initial Location Generation Model

Lift Capacity and ‘‘Feasible’’ Area

Crane

lift

capacity

is

determined

from

a

radius-load

curve

where

the

greater

the

load,

the

smaller

the

crane’s

operating

radius.

Assuming

a

load

at

supply

point

(S)

with the weight

w,

its corresponding crane radius is

r.

A crane is therefore unable to

lift a load unless it is located within a circle with radius

r

[Fig. 1(a)]. To deliver a load

from (S) to demand point (D), the crane has to be positioned within an elliptical area

(a)

FIG.1. Feasible Area of Crane Location for Task

FIG. 2. Task

Closenness

”

enclosed by two circles, shown in Fig. 1(b). This is called the feasible task area.

The size of the area is related to the distance between S and D, the weight of the

load, and crane capacity. The larger the feasible area, the more easily the task

can be handled.

Measurement of ‘‘Closeness’’ of Tasks

Three

geometric

relationships

exist

for

any

two

feasible

task

areas,

as

illustrated in Fig. 2; namely, (a) one fully enclosed by another (tasks 1 and 2);

(b)

two

areas

partly

intersected

(tasks

1

and

3);

and

(c)

two

areas

separated

(tasks 2 and 3). As indicated in cases (a) and (b), by being located in area A, a

crane

can

handle

both

tasks

1

and

2,

and

similarly,

within

B,

tasks

1

and

3.

However,

case

(c)

shows

that

tasks

2

and

3

are

so

far

from

each

other

that

a

single tower crane is unable to handle both without moving location; so more

than one crane or greater lifting capacity is required. The closeness of tasks can

be

measured

by

the

size

of

overlapping

area,

e.g.,

task

2

is

closer

to

task

1

than task 3 because the overlapping

area between tasks 1 and 2 is larger than

that for 1 and 3. This concept can be extended to measure closeness of a task to

a task group. For example, area C in Fig. 2(b) is a feasible area of a task group

consisting of three tasks, where task 5 is said to be closer to the task group than

task 4 since the overlapping area between C and D is larger than that between C

and E. If task 5 is added to the group, the feasible area of the new group would

be D, shown in Figure 2(c).

Grouping Tasks into Separated Classes

If

no

overlapping

exists

between

feasible

areas,

two

cranes

are

required

to

handle each task separately if no other

alternatives

—

such as cranes with greater lifting capacity or replanning

of site

layout

—

are

allowed.

Similarly,

three

cranes

are

required

if

there

are

three

tasks

in

which

any

two

have

no

overlapping

areas.

Generally,

tasks

whose

feasible areas are isolated must be handled by separate cranes.

These initial tasks

are assigned respectively to different (crane) task groups

as the first member of the group, then all other tasks are clustered according to

proximity to them. Obviously, tasks furthest apart are given priority as initial

tasks. When multiple choices exist, computer running time can be reduced by

selecting tasks with smaller feasible areas as initial tasks. The model provides

assistance in this respect by displaying graphical layout of tasks and a list of

the size of feasible area for each. After assigning an initial task to a group, the

model

searches

for

the

closest

remaining

task

by

checking

the

size

of

overlapping area, then places it into the task group to produce a new feasible

area

corresponding

to

the

recently

generated

task

group.

The

process

is

repeated until

there are

no tasks remaining having an overlapping area within

the present group. Thereafter, the model switches to search for the next group

from

the

pool

of

all

tasks,

the

process

being

continued

until

all

task

groups

have

been

considered.

If

a

task

fails

to

be

assigned

to

a

group,

a

message

is

produced to report which tasks are left so the user can supply more cranes or,

alternatively, change the task layout and run the model again.

Initial Crane Location

When task groups have been created, overlapping areas can be formed. Thus,

the initial locations are automatically at the geometric centers of the common

feasible

areas,

or

anywhere

specified

by

the

user

within

common

feasible

areas.

Task Assignment Model

Group location is determined by geometric ‘‘closeness.’’

However, one crane

might

be

overburdened

while

others

are

idle.

Furthermore,

cranes

can

often

interfere with each other so task assignment is applied to those tasks that can be

reached by more than one crane to minimize these possibilities.

Feasible Areas from Last Three Sets of Input

shape and size of feasible areas, illustrated in

Fig. 9.

In this case study,

from

the

data and graphic output, the user may become aware that optimal locations led by test

sets 1, 2, and 3(Fig. 3) are the best choices (balanced workload, conflict possibility,

and

efficient

operation).

Alternatively,

in

connection

with

site

conditions

such

as

availability of space for the crane position and ground conditions for the foundation,

site boundaries were restricted. Consequently, one of the cranes had to be positioned

in

the

building.

In

this

respect,

the

outcomes

resulting

from

set

4

would

be

a

good

choice

in

terms

of

a

reasonable

conflict

index

and

standard

deviation

of

workload,

provided

that

a

climbing

crane

is

available

and

the

building

structure

is

capable

of

supporting this kind

of crane. Otherwise, set

5 results

would be preferable with

the

stationary tower crane located in the elevator well, but at the cost of suffering the high

possibility of interference and unbalanced workloads

CONCLUSIONS

Overall coverage of tasks tends to be the major criterion in planning crane group