Bidding Strategy in Construction Public Procurement: A Contractor’s Perspective

Cloud providers are auctioning their excess capacity using dynamically priced virtual instances. These spot instances provide significant savings compared to on-demand or fixed price instances. The users willing to use these resources are asked to provide a maximum bid price per hour, and the cloud provider runs the instances as long as the market price is below the user's bid price. By using such resources, the users are exposed explicitly to failures, and need to adapt their applications to provide some level of fault tolerance. In this paper, we expose the effect of bidding in the case of virtual HPC clusters composed of spot instances. We describe the interesting effect of uniform versus non-uniform bidding in terms of both the failure rate and the failure model. We propose an initial attempt to deal with the problem of predicting the runtime of a parallel application under various bidding strategies and various system parameters. We describe the relationship between bidding strategies and programming models, and we build a preliminary optimization model that uses real price traces from Amazon Web Services as inputs, as well as instrumented values related to the processing and network capacities of cluster instances on the EC2 services. Our results show preliminary insights into the relationship between non-uniform bidding and application scaling strategies.

Assessing the economic impact of public investment in Malaysia: a case study on MyRapid Transit project using a dynamic computable general equilibrium model

Balancing competing policy demands: the case of sustainable public sector food procurement

Objectives: In this work, the complex debate on risk perception is combined with its assessment, but from the standpoint of behavioral choice theories. Method: Traditional methods of research are not able to fully reveal the role of the individual characteristics of criminal risk subjects. The authors looked into socio-perceptual mechanisms of public procurement participants and resorted to the ‘red flags’ and ‘crime hotspots’ methods to clarify regularities at the individual level of deviant behavior in order to minimize them and to develop coping strategies Findings: Criminal risks of the national public procurement system have been audited; typical criminal risks arising in all procurement stages both on the part of customers and on the part of other parties have been theoretically diagnosed. The original hypothesis of an exclusively deliberate choice of criminal behavior has been confirmed in part. Improvements: The findings presented in the paper can help increase the detection of crimes in the area of public procurement, enhance the efficiency of investigations and minimize the scope for criminal abuse.

Despite the roles which small and medium construction enterprises (SMCEs) play in the Nigeria construction industry and the economy at large, extant research revealed that their participation in public procurement is poor as large firms have dominated the public procurement market. Previous studies have identified barriers responsible for their low participation and also suggested improvement measures. However, improvement measures best suited to dismantle/mitigate each barrier is not known. This study thus, assessed measures for improving SMCEs’ participation in Nigeria public procurement. The study collected data from 100 SMCEs in Nigeria, via a questionnaire survey. Mean/relative importance index was used for ranking the measures and ANOVA for checking difference between the studied measures. The result of the study revealed that E-procurement is the best measure for improving SMCEs’ participation in public procurement as it is best in dismantling barriers related to “problematic procurement procedure”, “lack of awareness of public procurement” and “legal issues/corruption”. Training which is suitable for eliminating barriers related to “lack of skilled employees and partners”, emerged as the second best followed by Division of Contract into lots that is best suitable for tackling barriers related to “lack of resources” which is contrary to previous researchers’ belief that it is the best measure. The study concludes that E-procurement, training and division of contract into lots are all suitable measures for improving SMCEs’ participation in Nigeria public procurement in their other of priority. The study’s findings show that it is desirous that the measures are applied together as none of these measures is singularly sufficient in dismantling all barriers to SMCEs’ participation in public procurement.

Public procurement in the construction sector has been an historic problem in Northern Ireland (NI), to the extent that a public inquiry was held on the matter, including investigating why legal challenge are prevalent, and its findings reported in 2010; a 2019 NI Audit Office report into several major construction projects in NI highlighted legal challenges contributing to project delay, suggesting that perhaps there have not been improvements in relation to legal challenges since the public inquiry. This paper aims to ascertain the frequency of public procurement legal challenges in the construction sector in NI, not just the frequency of court cases, but any challenge to public procurement that delays projects. A multi-phase, mixed methods approach was adopted including a literature review, interviews and questionnaire surveys of procuring and contracting organisations to provide details of all forms of legal challenges, to understand why they challenge procurement decisions and to ascertain views on how public procurement is managed in Northern Ireland. The data reveal that approximately 40 out of 1488 procurement exercises in the last 7 years have had some form of legal challenge. The results also indicate that legal challenges are not as prevalent as the contracting side of the construction industry in NI believes them to be and suggest that the legacy from the public inquiry era around 2008–2012 continues to cloud perceptions of public procurement in NI. The analysis also reveals that the challenging political environment, issues with quality assessment and abnormally low tenders have been the main contributory factors to the rise in legal challenges to public procurement in construction in Northern Ireland.

A “low-balling strategy” by bidding contractors has increasingly been recognized as an important issue in public infrastructure procurement. Public works contracts are often imperfect and renegotiated after the contract award. Given the expectation for ex post adjustments, bidders seem motivated to take advantage of the low-balling strategy. This paper analyzes the endogeneity between the bid strategy and ex post adjustments. Using procurement data on rural road projects in Nepal, it shows that the bid strategy and adjustments are determined endogenously in the system. Anticipating cost and time overruns, firms would likely undercut normal bid prices. Then, ex post contract adjustments actually happen, because of their too aggressive bids.

Outdated tendering system is a significant obstacle in the momentum of public sector development in Pakistan. This study aims to examine various undiscovered part of public tendering through a detailed survey from key professionals, experts, and decision-makers of public projects. Furthermore, research covers the present status of public tendering in Pakistan and provides recommendations as per experts’ opinion. This paper exhaustively highlights how the classical customs of the public tendering in Pakistan could track the old-fashioned sector to an upright path. Intensive interviews and questionnaire surveys were carried out throughout the country for data compilation. The one-way ANOVA test was performed to verify the perception of various participants and to reject the null hypothesis. The study revealed various interesting facts of present-day situations of public tendering. Various pitfalls in public tendering were underlined in the speculation of experts. The study concluded that public tendering in Pakistan is crowded with severe threats that may be alarming for the future of the industry. Un-discovering of alarming facts about public tendering in Pakistan opens the directions for several researchers in terms of exploring project case studies further. The research is an eye-opener for policymakers, experts, decision-makers and governmental bodies to regulate the public tendering system accordingly.

When carrying out construction work, identifying the best contractor is a critical component of the project life cycle in the construction industry. The investor must use effective and efficient strategies to create a competitive bidding environment in public projects. The research presented in this paper was conducted to demonstrate the competitive nature of public procurements, where contractors compete to present the best bid and win the contract. To award the contract, the best offer must be selected. Based on different strategies and multi-criteria decision-making approaches this study proposes a method for identifying the most suitable strategy out of eight bidding strategies on four different lots, resulting in the most suitable one for landslide rehabilitation in the Brčko district. The results reveal the optimal approach to follow to minimize time and financial losses in the case of landslide rehabilitation during periods of market instability. Such research findings validate the efficiency of the bidding strategies-based decision-making support. The proposed method allows for compromise on both the completion date and the lowest bid made by the winning contractor.

PurposeWith the surge in public procurement, especially in developing countries, ensuring fair competition in procurement has assumed paramount importance. Academic endeavors in the domain of competition issues have often lacked the views of field-level functionaries. This study aims to involve a large number of expert practitioners in India to identify the significant contemporary competition risks in public procurement from the procurer and supplier sides and develop a model depicting the hierarchy of competition-restrictive actions (CRAs) in procurement based on their mutual interactions.Design/methodology/approachThe significant CRAs along the procurement cycle are identified through literature survey, interactive workshops and expert interviews. A questionnaire survey covering 143 respondents from 12 public organizations is used to evaluate their impact. Considering the complex causal interactions involved, interpretive structural modeling followed by MICMAC (Iimpact matrix cross-reference multiplication applied to a classification analysis is used to develop a hierarchical model of competition risks in procurement.FindingsTailor-made contracts, splitting of a project below competition thresholds, restrictive selection criteria and awarding the contract on nomination emerge as CRAs with the highest driving power. Horizontal collusion among vendors strongly depends on practices followed in the procuring organization.Research limitations/implicationsThe survey data and the experts’ opinions emanate from practitioners in India, which is a limitation. However, with necessary contextual calibrations, the study is of high functional utility to policymakers and practitioners.Social implicationsThe research facilitates a comprehensive understanding to procurement managers/policymakers of the CRAs along the procurement cycle and their interdependencies. It offers valuable insights for improving competition, which is foundational for optimal procurement outcomes.Originality/valueThe study enriches the public procurement domain knowledge by identifying and assessing the significant contemporary CRAs, examining their mutual interactions and developing an interpretive structural model. Although contributing to the body of knowledge, the study is unique in being grounded in field realities.

The operation of power-to-X systems requires measures to control the cost and sustainability of electricity purchased from spot markets. This study investigated different bidding strategies for the day-ahead market with a special focus on Sweden. A price independent order (PIO) strategy was developed assisted by forecasting electricity prices with an artificial neural network. For comparison, a price dependent order (PDO) with fixed bid price was used. The bidding strategies were used to simulate H2 production with both alkaline and proton exchange membrane electrolysers in different years and technological scenarios. Results showed that using PIO to control H2 production helped to avoid the purchase of expensive and carbon intense electricity during peak loads, but it also reduced the total number of operating hours compared to PDO. For this reason, under optimal conditions for both bidding strategies, PDO resulted in an average of 10.9% lower levelised cost of H2, and more attractive cash flows and net present values than PIO. Nevertheless, PIO showed to be a useful strategy to control costs in years with unexpected hourly price behaviour such as 2018. Furthermore, PIO could be successfully demonstrated in a practical case study to fulfil the on-demand requirement of an industrial captive customer.

Real-time bidding (RTB) is one of the most striking advances in online advertising, where the websites can sell each ad impression through a public auction, and the advertisers can participate in bidding the impression based on its estimated value. In RTB, the bidding strategy is an essential component for advertisers to maximize their revenues (e.g., clicks and conversions). However, most existing bidding strategies may not work well when the RTB environment changes dramatically between the historical and the new ad delivery periods since they regard the bidding decision as $\boldsymbol {a}$ static optimization problem and derive the bidding function only based on historical data. Thus, the latest research suggests using the reinforcement learning (RL) framework to learn the optimal bidding strategy suitable for the highly dynamic RTB environment. In this paper, we focus on using model-free reinforcement learning to optimize the bidding strategy. Specifically, we divide an ad delivery period into several time slots. The bidding agent decides each impression's bidding price depending on its estimated value and the bidding factor of its arriving time slot. Therefore, the bidding strategy is simplified to solve each time slot's optimal bidding factor, which can adapt dynamically to the RTB environment. We exploit the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to learn each time slot's optimal bidding factor. Finally, the empirical study on a public dataset demonstrates the superior performance and high efficiency of the proposed bidding strategy compared with other state-of-the-art baselines.

Revisiting the Concept of Undertaking from a Public Procurement Law Perspective A Discussion on Easypay and Finance Engineering

Globally, sustainable procurement is seen as a crucial component of public sector sustainability initiatives. This research investigates the internal and external barriers to the implementation of sustainable procurement in China’s public sector. Primary data for this research were gathered using survey techniques from 287 respondents who were procurement officials at higher education institutions that are part of the public sector. Twelve hypotheses were developed based on the conceptual model and literature review and evaluated using SEM-AMOS in order to look at the correlations between the variables. The study’s findings demonstrate that employee competency, employee motivation, training programs, financial aspects, management commitment, government rules and regulations, unavailability of green products, supplier capacity, and third-party pressure statistically influence the sustainability of public procurement. At the same time, green practices, the ineffectiveness of green products, and prices of green items had statistically no significant influence on sustainability in public procurement. This is a crucial effort to identify the factors that influence sustainable purchasing in China’s public colleges. This study draws interest from stakeholders and adds to the scant body of knowledge on sustainability in developing nations. In addition, this research is crucial for creating a roadmap for China’s long-term sustainable development in the area of procurement.

With widespread use of the Internet, Internet auctions (e-auctions) become more popular in order to trade increasing number of goods as Internet provides both almost perfect market information and an infrastructure for executing auctions at lower administrative costs. The ascending-bid, second-price auction is the most widely used e-auction format. The aim of this study is to present a dynamic model of an e-auction so as to investigate how the welfare of buyers is affected by different ascending-bid types. This problem has been studied theoretically in economics in various static auction mechanisms where perfect rationality of participants is assumed. To overcome the limitations of this approach, the new agent based modeling methodology in which researchers use simulations to investigate the behavior and interactions of autonomous, heterogeneous, bounded rational adaptive population of agents in the social and economical environments, has been emerged. In this paper we adapt the bottom-up agent based modeling methodology to investigate the behavior of participants in electronic markets. On the other hand, since observing the biding strategies of individuals is almost impossible in laboratory or field experiments, we developed a simulation model to understand the welfare effects of different bidding strategies. To some extend sensitivity of the auction outcome on auction rules and market design parameters are also investigated. In our experiments the strategies where the agents update their bid increments in proportion to the differences between their reservation price and current bid are found to be the winning strategies where the duration of the auction is shorter. As the duration of the auction increases all the strategies converged to the same average payoffs.