Determining The Effects Of Awarding Lowest Bid Price System On The Construction Projects Of Ethiopian Southern Nation

Identifying the effects of low bid award system in construction projects can be used as benchmark to find alternative method to low bid award system in the future of construction industry. The results of questioner survey conducted to determine the effects of awarding lowest bid award system in construction projects of Ethiopian southern nation are presented in this study. Personnel from consultants, owners and contractors are among the survey`s respondent. The result of the study outlines promote transparency, avoid fraud and corruption, promoting competition amongst contractors, excessive time overrun, compromise quality and hindering profitability of contractors as the top ranked effects of low bid award system. Construction industry participants have started recognizing that accepting the least price bid does not guarantee maximum value. Achieving a value-based procurement approach is a challenge, particularly for the Pakistani public sector clients, who are limited in their ability to evaluate the competitive bids based solely on the lowest-bid award system. Persisting problems of inferior quality of constructed facilities, high incidence of claims and litigation, and frequent cost and schedule overruns have become the main features of public construction works contracts. This research was undertaken to assess the performance of public owned construction projects awarded on a lowest bidder bid awarding system.

Discrete dynamic pricing and application of network revenue management for FlixBus

We consider a real discrete pricing problem in network revenue management for FlixBus. We improve the company's current pricing policy by an intermediate optimization step using booking limits from standard deterministic linear programs. We pay special attention to computational efficiency. FlixBus' strategic decision to allow for low-cost refunds might encourage large group bookings early in the booking process. In this context, we discuss counter-intuitive findings comparing booking limits with static bid price policies. We investigate the theoretical question whether the standard deterministic linear program for network revenue management does provide an upper bound on the optimal expected revenue if customer's willingness to pay varies over time.

A Learning-Based Bidding Approach for PV-Attached BESS Power Plants

Large-scale renewable photovoltaic (PV) and battery energy storage system (BESS) units are promising to be significant electricity suppliers in the future electricity market. A bidding model is proposed for PV-integrated BESS power plants in a pool-based day-ahead (DA) electricity market, in which the uncertainty of PV generation output is considered. In the proposed model, we consider the market clearing process as the external environment, while each agent updates the bid price through the communication with the market environment for its revenue maximization. A multiagent reinforcement learning (MARL) called win-or-learn-fast policy-hill-climbing (WoLF-PHC) is used to explore optimal bid prices without any information of opponents. The case study validates the computational performance of WoLF-PHC in the proposed model, while the bidding strategy of each participated agent is thereafter analyzed.

Concentration of Bid Prices Just Above the Standard Minimum Price in Public Construction Works

In Japan, contract offices are mandated to set threshold prices for public works. A threshold price is the upper limit of the bid price, and a contractor who exceeds this threshold is disqualified. Furthermore, based on the threshold price, a minimum price is set as a price requiring investigation before acceptance. In recent years, bids and contracts for public works have generally had bid prices concentrated slightly above the standard minimum for investigation. It has been pointed out that this tendency is detrimental in terms of the motivation of engineers and social costs. In this study, we confirm that this tendency was alleviated and that the level of the winning bidder's technical evaluation score was feasible at the same time. In addition, we obtained quantitative findings on variables that affect both above. Furthermore, although it is impossible to achieve a perfect balance between alleviating the tendency of prices to concentrate slightly above the standard minimum for investigation and sufficient technical evaluation scores, elements necessary to improve the overall situation were quantitatively identified.

Bidding Models Analysis on Ship Repair Projects (Friedman and Ackoff & Sasieni Models)

The existence of a ship project carried out with a tender system by the LPSE allows all shipyard industries to bid on the project, this causes the chances of winning to become smaller, the determination of the tender price greatly determines the size of the profit that can be obtained and the percentage of the possibility of winning the project in a shipping industry. Therefore, the strategy of determining the bid price is very important. The statistical method used is multi discrete distribution, and multi normal distribution, while the bidding model uses Friedman (1956) and Ackoff & Sasieni (1968) models. The results obtained the best bid price strategy to win an auction or tender is the model that produces the lowest optimum mark-up, namely the Friedman model with multi normal distribution, while for Ackoff & Sasieni it produces a higher bid than the Friedman model except in certain company conditions.

Forecasting Price of Amazon Spot Instances Using Machine Learning

An auction-based cloud model is followed in the spot pricing mechanism, where the spot instances charge changes with time. The user is bound to pay for the time that is initially initiated. If the user terminates before the sessional hourly completion, then the customer will be billed on the entire hourly session. In case Amazon terminates the instance then the customer would not be billed for the partial hour. When the current spot price reduces to bid price without any notification the cloud provider terminates the spot instance, it is a big disadvantage to the time of the availability factor, which is highly important. Therefore, it is crucial for the bidder to forecast before engaging the bids for spot prices. This paper represents a technique to analyze and predict the spot prices for instances using machine learning. It also discusses implementation, explored factors in detail, and outcomes on numerous instances of Amazon Elastic Compute Cloud (EC2). This technique reduces efforts and errors for forecasting prices.