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Advances in Manufacturing

, Volume 7, Issue 4, pp 374–388 | Cite as

Multi-objective resource optimization scheduling based on iterative double auction in cloud manufacturing

  • Zhao-Hui Liu
  • Zhong-Jie WangEmail author
  • Chen Yang
Article
  • 51 Downloads

Abstract

Cloud manufacturing is a new kind of networked manufacturing model. In this model, manufacturing resources are organized and used on demand as market-oriented services. These services are highly uncertain and focus on users. The information between service demanders and service providers is usually incomplete. These challenges make the resource scheduling more difficult. In this study, an iterative double auction mechanism is proposed based on game theory to balance the individual benefits. Resource demanders and providers act as buyers and sellers in the auction. Resource demanders offer a price according to the budget, the delivery time, preference, and the process of auction. Meanwhile, resource providers ask for a price according to the cost, maximum expected profit, optimal reservation price, and the process of auction. A honest quotation strategy is dominant for a participant in the auction. The mechanism is capable of guaranteeing the economic benefits among different participants in the market with incomplete information. Furthermore, the mechanism is helpful for preventing harmful market behaviors such as speculation, cheating, etc. Based on the iterative double auction mechanism, manufacturing resources are optimally allocated to users with consideration of multiple objectives. The auction mechanism is also incentive compatibility.

Keywords

Cloud manufacturing Resource scheduling Multi-objective optimization Iterative double auction Incentive compatibility 

List of symbols

a

Asked price

B

Bidding price set

b

Bidding price

C

Execution cost

c, cr

Number of iteration

d

Distance

e

Exponential function

F

Distribution function

f

Density function

i

Index of demander

j

Index of provider

k

Index of task submitted by a demander

l

Index of resource provided by a provider

l1

Logistics time

l2

Logistics cost

l3

Logistics load

O1

Occupied cost

O2

Occupied time

P

Relationship vector

p

Partnership between demander and provider

Q

Comprehensive quality

q

Probability of a demander adopting the strategy greater than valuation

r

Reservation price

S

Strategy space

T

Execution time

\(\bar{t}\)

Average period of each iteration

\(\hat{t}\)

Ready time

t0

Initial moment

tc

Moment of the bidding

u

Net benefit of demander

u0

Expected net benefit of a demander with bidding price b0

ub

Expected net benefit of a demander with bidding price b

û

Net benefit of provider

û0

Expected net benefit of a provider with asked price a0

ûs

Expected net benefit of a provider with asked price a

v

Transaction value

x, y

Index of subtask

Y

Maximum expected profit margin

α, γ

Subjective factor

β

Zoom factor

Φ

Finance budget

Φres

Set of subtasks that have not been matched with resources

\(\hat{\varPhi }\)

Cost of resource

Γ

Game participants

Influence of delivery time

δ

Priority vector of task

η

Quantity of candidate resources

Θ

Delivery time

Θres

Set of subtasks after a subtask along the longest past to the end subtask

θ

Delivery time of subtask

Ω

Circumference

π

Expected payment

ϑ

Resource

ρ

Influence of demander preference

τ

Probability of an event

υ

Subtask

ω

Workload of subtask

Ζ

Execution time of resource

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Copyright information

© Shanghai University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Electronics and Information EngineeringTongji UniversityShanghaiPeople’s Republic of China
  2. 2.College of Engineering Science and TechnologyShanghai Ocean UniversityShanghaiPeople’s Republic of China

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