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Combinatorial procurement auctions are increasingly being employed in the private and public sector as an alternative to simultaneous single contract auctions. This mechanism has the advantage that it enables suppliers to express synergies across bundles of public contracts. This mitigates the exposure problem and also has the potential to both lower the price paid by the procuring authority and to enhance efficiency. This paper provides stylized facts of recently performed combinatorial public procurements in various markets in Sweden.

This paper is an empirical analysis of first-price sealed-bid procurement auctions in Sweden, with and without combinatorial bidding. The data comprises procurement auctions of identical contracts (road resurfacing) with identical bidders conducted under the same time period (2009-2011) in two different regions in Sweden. Given the comparison of the suppliersʼ offered price per tons of asphalt, we cannot reject the hypothesis of identical distribution of standalone bids generated in both types of auction. The distribution of package bids within the combinatorial format is significantly lower than the distribution of standalone bids within the non-combinatorial format, suggesting substantial cost reduction of allowing package bidding. Also, within the combinatorial format, our analysis of data indicates higher costs when packages are predetermined by the purchaser rather than chosen freely by the suppliers.

The purpose of this paper is to improve the relief procurement process as one of the most important elements of humanitarian logistics. For doing so, a novel two-round decision model is developed to capture the dynamic nature of the relief procurement process by allowing demand updating. The model accounts for the supply priority of items at response phase as well.

A mixed procurement/supply policy is developed through a mathematical model, which includes spot market procurement and a novel procurement auction mechanism combining the concepts of multi-attribute and combinatorial reverse auctions. The model is of bi-objective mixed-integer non-linear programming type, which is solved through the weighted augmented e-constraint method. A case study is also provided to illustrate the applicability of the model.

This study demonstrates the ability of proposed approach to model post-disaster procurement which considers the dynamic environment of the relief logistics. The sensitivity analyses provide useful managerial insights for decision makers by studying the impacts of critical parameters on the solutions.

This paper proposes a novel reverse auction framework for relief procurement in the form of a multi-attribute combinatorial auction. Also, to deal with dynamic environment in the post-disaster procurement, a novel two-period programming model with demand updating is proposed. Finally, by considering the priority of relief items and model’s applicability in the setting of relief logistics, post-disaster horizon is divided into three periods and a mixed procurement strategy is developed to determine an appropriate supply policy for each period.

This paper aims to report work on achieving semantic interoperability in electronic auctions. In particular, it considers the advantages and drawbacks of using hard‐coding and using semantic messages in the communication between the auction system and the participants of the auction.

It is demonstrated that although XML‐documents are commonly used for information exchange they do not provide any means of talking about the semantics (i.e. meaning) data. It is also shown that by expressing exchanged documents by resource description framework (RDF) the semantics of the messages can be captured in the message.

It is recognized that hard‐coding is proven to be a valuable and powerful way for an exchange of structured and persistent business documents (messages). However, if we use hard‐coding in the case of non‐persistent documents and non‐static markets we will encounter problems in deploying new auction policies and extending the system by new participants.

The introduction of the RDF‐technology in message exchange is challenging as it incorporates Semantic web technologies into many parts of the auction system, e.g. on data stores and query languages. The introduction of this technology is also an investment. The investment on new Semantic web technology includes a variety of costs including software, hardware and training costs.

By automating electronic auctions both buyers and sellers can benefit as they can achieve cost reductions and shorten the duration of the auction processes. Also new auction formats can be easily deployed.

This paper aims to present a concise review on the variant state-of-the-art dynamic task allocation strategies. It presents a thorough discussion about the existing dynamic task allocation strategies mainly with respect to the problem application, constraints, objective functions and uncertainty handling methods.

This paper briefs the introduction of multi-robot dynamic task allocation problem and discloses the challenges that exist in real-world dynamic task allocation problems. Numerous task allocation strategies are discussed in this paper, and it establishes the characteristics features between them in a qualitative manner. This paper also exhibits the existing research gaps and conducive future research directions in dynamic task allocation for multiple mobile robot systems.

This paper concerns the objective functions, robustness, task allocation time, completion time, and task reallocation feature for performance analysis of different task allocation strategies. It prescribes suitable real-world applications for variant task allocation strategies and identifies the challenges to be resolved in multi-robot task allocation strategies.

This paper provides a comprehensive review of dynamic task allocation strategies and incites the salient research directions to the researchers in multi-robot dynamic task allocation problems. This paper aims to summarize the latest approaches in the application of exploration problems.

Supplier selection is a complex and strategic activity needed in every organization, involving many stakeholders and different attributes as price, delivery performance, and product quality. Globalization, in the last decades, increased the competitiveness between vendors, enhancing the use of decision models to support the best choice based on optimizations and bidding variations due to specific needs. This chapter presents three models of multi-dimensional auctions to improve an international humanitarian NGO process procurement efficiency by reducing procurement costs and the decision-making process time. These models have the advantage to be easily implementable in typically complex environments where there is a large number of categories, suppliers, and other features.

The first proposed model uses combinatorial auctions and is suited for procurement, where suppliers can benefit from cost complementarity. The second one uses volume discount auctions and is suited for volumetric purchases, where discounts for large quantities are common. The third one is a multi-attribute model, which computes the best possible solution considering several criteria and can be used in case of complex purchases that involve various categories and trade-offs and are subject to spot prices.

Several design considerations for this type of auctions are reviewed, as well as the mathematical formulation to determine the best alternative (i.e., winner) that can be solved using simple tools like Microsoft Excel. The models are optimized by a mixed-integer programming, and the multi-attribute one is developed using multi-criteria decision analysis (MCDA). All three models developed in this research showed superior results compared to the baseline, being between 9% and 20% more efficient than a regular supplier selection (singly choosing the lowest price) and improving the bidding compliance.

Realizing value from information and communication technology (ICT) in procurement in developing countries is complex due to diverse stakeholders and intertwined procurement processes. The purpose of this paper is to examine the experience of the Government of Jamaica in leveraging ICTs as an intervention to transform its procurement operations and combat corruption.

The study examines conversations with employees in the Government of Jamaica to understand key milestones in its procurement history. Based on the view that the intervention context is an ecosystem where multiple and inconsistent views of the e-procurement system evolve over time, the study analyzes milestones to reveal key actions that contributed either to the initial success of or introduced challenges to the e-procurement system.

The findings suggest that inducing positive sentiments about the intervention through transparency will overcome a long history of negative sentiments about the initiatives of government bodies in general. Furthermore, positive sentiments may not be directly related to the e-procurement system.

The study offers important insights that government bodies in similar contexts can apply to guide initiatives for transforming procurement operations. For instance, training should emphasize not only the technical aspects of the system from the perspective of different stakeholders but also their job descriptions. Future research may examine other initiatives in developing countries to compare the role of sentiments over time.

The study adopts a unique approach to understand the experience of a developing country in harnessing ICTs to transform procurement operations.

This paper studied a bid generation problem in combinatorial transportation auctions that considered in-vehicle consolidations. The purpose of this paper seeks to establish mixed integer programming to the most profitable transportation task packages.

The authors proposes a mathematical model to identify the most profitable transportation task packages under vehicle capacity, flow balance and in-vehicle consolidation operational constraints, after which a two-phase heuristic algorithm was designed to solve the proposed model. In the first phase, a method was defined to compute bundle synergy, which was then combined with particle swarm optimization (PSO) to determine a satisfactory task package, and in the second phase, the PSO was adopted to program vehicle routings that considered in-vehicle consolidation.

Three numerical examples were given to analyze the effects of the proposed model and method, with the first two small-scale examples coming from the same data base and the third being a larger scale example. The results showed that: (1) the proposed model was able to find a satisfactory solution for the three numerical examples; (2) the computation time was significantly shorter than the accurate algorithm and (3) considering in-vehicle consolidations operations could increase the carrier profits.

The highlights of this paper are summarized as following: (1) it considers in-vehicle consolidation when generating bids to maximize profits; (2) it simultaneously identifies the most valuable lane packages and reconstructs vehicle routes and (3) proposes a simple but effective synergy-based method to solve the model.

In private values settings, the Wickrey-Clarke-Groves (VCG) mechanism leads to efficient auction outcomes, while the theoretical properties of the Simultaneous Ascending (SA) auction are not well understood. This leads us to compare the properties of an SA and a VCG auction in an experimental setting with private values for multiple objects having complementarities. Statistically, we find little to distinguish the two auctions with both auction forms achieving more than 98% efficiency and extracting roughly 95% of the available surplus. Finally, in contrast to experimental results in single object VCG settings, the theoretical prediction of demand revelation in the multiple object VCG auction is largely supported in our experiments.

The purpose of this paper is to study a transportation service procurement bid construction problem from a less than a full truckload perspective. It seeks to establish stochastic mixed integer programming to allow for the proper bundle of loads to be chosen based on price, which could improve the likelihood that carrier can earn its maximum utility.

The authors proposes a bi-level programming that integrates the bid selection and winner determination and a discrete particle swarm optimization (PSO) solution algorithm is then developed, and a numerical simulation is used to make model and algorithm analysis.

The algorithm comparison shows that although GA could find a little more Pareto solutions than PSO, it takes a longer time and the quality of these solutions is not dominant. The model analysis shows that compared with traditional approach, our model could promote the likelihood of winning bids and the decision effectiveness of the whole system because it considers the reaction of the shipper.

The highlights of this paper are considering the likelihood of winning the business and describing the conflicting and cooperative relationship between the carrier and the shipper by using a stochastic mixed integer programming, which has been rarely examined in previous research.