An improved ant colony algorithm for winner determination in multi-attribute combinatorial reverse auction

Qian, Xin; Huang, Min; Gao, Taiguang; Wang, Xingwei

doi:10.1109/cec.2014.6900493

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…Table 8 provides the processing time of IAC, EAB and GAMICRA for much simpler combinatorial reverse auctions. The first two were taken directly from [24] and the last one from [15]. As we can see WD is significantly superior to all of them.…”

Section: Comparison With Heuristic Algorithmsmentioning

confidence: 97%

Meeting peak electricity demand through combinatorial reverse auctioning of renewable energy

Shil

Sadaoui

2017

J. Mod. Power Syst. Clean Energy

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The option of organizing E-auctions to purchase electricity required for anticipated peak load period is a new one for utility companies. To meet the extra demand load, we develop electricity combinatorial reverse auction (CRA) for the purpose of procuring power from diverse energy sources. In this new, smart electricity market, suppliers of different scales can participate, and homeowners may even take an active role. In our CRA, an item, which is subject to several trading constraints, denotes a time slot that has two conflicting attributes, electricity quantity and price. To secure electricity, we design our auction with two bidding rounds: round one is exclusively for variable energy, and round two allows storage and nonintermittent renewable energy to bid on the remaining items. Our electricity auction leads to a complex winner determination (WD) task that we represent as a resource procurement optimization problem. We solve this problem using multi-objective genetic algorithms in order to find the trade-off solution that best lowers the price and increases the quantity. This solution consists of multiple winning suppliers, their prices, quantities and schedules. We validate our WD approach based on large-scale simulated datasets. We first assess the time-efficiency of our WD method, and we then compare it to well-known heuristic and exact WD techniques. In order to gain an exact idea about the accuracy of WD, we implement two famous exact algorithms for our constrained combinatorial procurement problem.

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Section: Comparison With Heuristic Algorithmsmentioning

confidence: 97%

Meeting peak electricity demand through combinatorial reverse auctioning of renewable energy

Shil

Sadaoui

2017

J. Mod. Power Syst. Clean Energy

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“…. , S N }, and the element of L is the side of any two points in S [43]- [45]. In this paper, the undirected graph G V = {V , L} of DPCMP satisfies the assumptions: if there is no intersection between two groups, there is an side between the two groups.…”

Section: A Construct Undirected Graphmentioning

confidence: 99%

Doctor-Patient Combined Matching Problem and its Solving Algorithms

Xing

Tan

et al. 2019

IEEE Access

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Difficult to see a doctor'' and prominent contradictions between doctors and patients in large public hospitals have become an urgent research topic in China. In this paper, we propose a multi-patient treatment mode (MTM) to improve medical efficiency and patient satisfaction. Based on the MTM, a problem named the doctor-patient combined matching problem (DPCMP) is proposed and can be described by a two-stage process: (1) The patients are grouped according to similar disease symptoms. (2) How to capture an optimal matching scheme form the grouped patients? Therefore, to solve the aforementioned problem, a mathematical model of the DPCMP is constructed, and several improved ant colony optimization algorithms are designed. Finally, certain examples verify the effectiveness and good performances of the proposed methods.INDEX TERMS Doctor-patient contradictions, patient satisfaction, multi-patient treatment mode, doctor-patient combined matching problem, medical efficiency, ant colony optimization algorithm, local search operator.

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“…They found solutions of very large problems, which are comparable or even better than any other solution found by other heuristics. An improved ant colony (IAC) algorithm considering the dynamic transition strategy and the Max-Min pheromone strategy was proposed for the allocation problem (17) . The study of approximate optimal solution to the WDP problem in combinatorial auctions can help the auctioneer to make a quick and efficient decision to increase the profits for the auctioneer.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Programming based Allocation of Virtual Machine Instances in Clouds

Geng

Zhu²,

Jiang

et al. 2016

The Proceedings of the 4th International Conference on Industrial Application Engineering 2016

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Auction-based mechanism for dynamic virtual machine provisioning and allocation (VMPA) can help cloud providers configure the resources efficiently based on the user demand and yield high revenue. The existing auction-based mechanisms mostly use greedy method to allocate the VM resources, which give priority to the users with high bid density. However, this kind of local optimal selection does not always bring the overall optimal solution. We propose dynamic programming based mechanism for solving VMPA problem (DP-VMPA), which takes the maximum social welfare as the objective function, and uses the combinatorial auction-based dynamic programming (CA-DP) allocation algorithm to solve the Winner Determination Problem (WDP). Finally, the Vickrey-Clarke-Groves (VCG) mechanism is used to decide the payment of each user. We perform simulation experiments to compare our proposed mechanism with CA-PROVISION mechanism and show that the DP-VMPA mechanism can allocate VM resources more effectively, and bring higher profits to the auctioneer.

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An improved ant colony algorithm for winner determination in multi-attribute combinatorial reverse auction

Cited by 9 publications

References 14 publications

Meeting peak electricity demand through combinatorial reverse auctioning of renewable energy

Meeting peak electricity demand through combinatorial reverse auctioning of renewable energy

Doctor-Patient Combined Matching Problem and its Solving Algorithms

Dynamic Programming based Allocation of Virtual Machine Instances in Clouds

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