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

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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.

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Abbreviations

a :

Asked price

B :

Bidding price set

b :

Bidding price

C :

Execution cost

c, c r :

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

l 1 :

Logistics time

l 2 :

Logistics cost

l 3 :

Logistics load

O 1 :

Occupied cost

O 2 :

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

t 0 :

Initial moment

t c :

Moment of the bidding

u :

Net benefit of demander

u 0 :

Expected net benefit of a demander with bidding price b0

u b :

Expected net benefit of a demander with bidding price b

û :

Net benefit of provider

û 0 :

Expected net benefit of a provider with asked price a0

û s :

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

Φ res :

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

Θ res :

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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Authors and Affiliations

  1. College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, People’s Republic of China

    Zhao-Hui Liu & Zhong-Jie Wang

  2. College of Engineering Science and Technology, Shanghai Ocean University, Shanghai, 201306, People’s Republic of China

    Chen Yang

Authors
  1. Zhao-Hui Liu
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  2. Zhong-Jie Wang
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  3. Chen Yang
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Correspondence to Zhong-Jie Wang.

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Liu, ZH., Wang, ZJ. & Yang, C. Multi-objective resource optimization scheduling based on iterative double auction in cloud manufacturing. Adv. Manuf. 7, 374–388 (2019). https://doi.org/10.1007/s40436-019-00281-2

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  • DOI: https://doi.org/10.1007/s40436-019-00281-2

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