Shubhashis Kumar Shil

Winner Determination in Combinatorial Reverse Auctions

Since commercially efficient, combinatorial auctions are getting more interest than traditional auctions. However, winner determination problem is still one of the main challenges of combinatorial auctions. In this paper, we propose a new method based on genetic algorithms to address two important issues in the context of combinatorial reverse auctions: determining the winner(s) in a reasonable processing time and reducing the procurement cost. Indeed, not much work has been done using genetic algorithms to determine the winner(s) specifically for combinatorial reverse auctions. To evaluate the performance of our method, we conducted several experiments comparing our proposed method with another method related to determining winner(s) in combinatorial reverse auctions. The experiment results clearly demonstrate the superiority of our method in terms of processing time and procurement cost.

An approach to solve winner determination in combinatorial reverse auctions using genetic algorithms

Nowadays, winner determination problem is one of the main challenges in the domain of real-time applications such as combinatorial reverse auctions. To determine the winner(s) in combinatorial reverse auctions, in our previous work, we have proposed a Genetic Algorithm (GA)-based method and have demonstrated its superiority in terms of processing time and procurement cost. One of the main drawbacks of traditional GA-based solutions is their inconsistency in different runs. In this paper, we perform a statistical-based experiment that reveals that our proposed method is not affected by the inconsistency issue. In addition, we show two other features of our GA-based method: (1) the quality of the solution improves over generations, and (2) the any-time behavior.

Winner Determination in Multi-attribute Combinatorial Reverse Auctions

Winner(s) determination in online reverse auctions is a very appealing e-commerce application. This is a combinatorial optimization problem where the goal is to find an optimal solution meeting a set of requirements and minimizing a given procurement cost. This problem is hard to tackle especially when multiple attributes of instances of items are considered together with additional constraints, such as seller’s stocks and discount rate. The challenge here is to determine the optimal solution in a reasonable computation time. Solving this problem with a systematic method will guarantee the optimality of the returned solution but comes with an exponential time cost. On the other hand, approximation techniques such as evolutionary algorithms are faster but trade the quality of the solution returned for the running time. In this paper, we conduct a comparative study of several exact and evolutionary techniques that have been proposed to solve various instances of the combinatorial reverse auction problem. In particular, we show that a recent method based on genetic algorithms outperforms some other methods in terms of time efficiency while returning a near to optimal solution in most of the cases.

Constraint and Qualitative Preference Specification in Multi-Attribute Reverse Auctions

In the context of Multi-Attribute and Reverse Auctions MARAs, two significant problems need to be addressed: 1 specifying precisely the buyers requirements about the attributes of the auctioned product, and 2 determining the winner accordingly. Buyers are more comfortable in expressing their preferences qualitatively, and there should be an option to allow them describes their constraints. Both constraints and preferences may be non-conditional and conditional. However for the sake of efficiency, it is more suitable for MARAs to process quantitative requirements. Hence, there is a remaining challenge to provide the buyers with more facilities and comfort, and at the same time to keep the auctions efficient. To meet this challenge, we develop a MARA system based on MAUT. The proposed system takes advantage of the efficiency of MAUT by transforming the qualitative requirements into quantitative ones. Another benefit of our system is the complete automation of the bid evaluation since it is a really difficult task for buyers to determine quantitatively all the weights and utility functions of attributes, especially when there is a large number of attributes. The weights and utility functions are produced based on the qualitative preferences. Our MARA looks for the outcome that satisfies all the constraints and best satisfies the preferences. We demonstrate the feasibility of our system through a 10-attribute reverse auction involving many constraints and qualitative preferences.

Winner Determination in Multi-Objective Combinatorial Reverse Auctions

This study introduces a new type of Combinatorial Reverse Auction (CRA), products with multi-units, multi-attributes and multi-objectives, which are subject to buyer and seller constraints. In this advanced CRA, buyers may maximize some attributes and minimize some others. To address the Winner Determination (WD) problem in the presence of multiple conflicting objectives, we propose an optimization approach based on genetic algorithms. To improve the quality of the winning solution, we incorporate our own variants of the diversity and elitism strategies. We illustrate the WD process based on a real case study. Afterwards, we validate the proposed approach through artificial datasets by generating large instances of our multi-objective CRA problem. The experimental results demonstrate on one hand the performance of our WD method in terms of three quality metrics, and on the other hand, its significant superiority to well-known heuristic and exact WD techniques that have been defined for simpler CRAs.

Considering Multiple Instances of Items in Combinatorial Reverse Auctions

Winner determination in combinatorial reverse auctions is very important in e-commerce especially when multiple instances of items are considered. However, this is a very challenging problem in terms of processing time and quality of the solution returned. In this paper, we tackle this problem using genetic algorithms. Using a modification of the crossover operator as well as two routines for consistency checking, our proposed method is capable of finding the winners with a minimum procurement cost and in an efficient processing time. In order to assess the performance of our GA-based method, we conducted several experiments on generated instances. The results clearly demonstrate the good time performance of our method as well as the quality of the solution returned.

Combinatorial Reverse Electricity Auctions

Utility companies can organize e-auctions to procure electricity from other suppliers during peak load periods. For this purpose, we develop an efficient Combinatorial Reverse Auction (CRA) to purchase power from diverse sources, residents and plants. Our auction is different from what has been implemented in the electricity markets. In our CRA, which is subject to trading constraints, an item denotes a time slot that has two conflicting attributes, energy volume and its price. To ensure the security of energy, we design our auction with two bidding rounds: the first one is for variable-energy suppliers and the second one for other sources, like controllable load and renewable energy. Determining the winner of CRAs is a computational hard problem. We view this problem as an optimization of resource allocation that we solve with multi-objective genetic algorithms to find the best solution. The latter represents the best combination of suppliers that lowers the price and increases the energy.

Multi-Objective Optimization in Multi-Attribute and Multi-Unit Combinatorial Reverse Auctions

This study introduces an advanced Combinatorial Reverse Auction (CRA), multi-units, multiattributes and multi-objective, which is subject to buyer and seller trading constraints. Conflicting objectives may occur since the buyer can maximize some attributes and minimize some others. To address the Winner Determination (WD) problem for this type of CRAs, we propose an optimization approach based on genetic algorithms that we integrate with our variants of diversity and elitism strategies to improve the solution quality. Moreover, by maximizing the buyer’s revenue, our approach is able to return the best solution for our complex WD problem. We conduct a case study as well as simulated testing to illustrate the importance of the diversity and elitism schemes. We also validate the proposed WD method through simulated experiments by generating large instances of our CRA problem. The experimental results demonstrate on one hand the performance of our WD method in terms of several quality measures, like solution ...