Probabilistic Scheduling Based On Hybrid Bayesian Network–Program Evaluation Review Technique,

Project scheduling based on probabilistic methods commonly uses the Program Evaluation Review Technique (PERT). However, practitioners do not widely utilize PERT-based scheduling due to the difficulty in obtaining historical data for similar projects. PERT has several drawbacks, such as the inability to update activity dura- tions in real time. In reality, changes in project conditions related to resources have a highly dynamic nature. The availability of materials, fluctuating labor productiv- ity, and equipment significantly determine the project completion time. This research aims to propose a probabilistic scheduling model based on the Hybrid Bayesian Network-PERT. This model combines PERT with Bayesian Network (BN). BN is used to accommodate real-time changes in resource conditions. The modeling of BN diagrams and variables is obtained through an in-depth literature review, direct field observations, and distributing questionnaires to experts in project scheduling. The model is validated by applying the proposed model to a 60 m concrete bridge construction project in Indonesia. The simulation results of the proposed model are then compared with the case study project to assess the model’s accuracy. The result of the study shows that the proposed hybrid Bayesian-PERT model is accurate and can eliminate the weaknesses of the PERT method. Besides being able to provide an accurate prediction of project completion time (93.4%), this model can also be updated in real-time according to the actual condition of the projec

Current state of existing project risk modeling and analysis methods with focus on fuzzy risk assessment – Literature Review

Risk modeling and analysis is one of the most important stages in project success. There are many approaches for risk assessment and an investigation of existing methods helps in developing new models . This paper is an extensive literature survey in risk modeling and analysis methods with main focus on fuzzy risk assessment

Optimizing Rehabilitation and Maintenance of Hospitals

Hospitals are one of the core elements of a health care system that provide medical service to the patients. Hospital facility management is a complex issue as it involves the management of several complex systems that have a direct impact on the delivery of health care issues. This research focuses on two vital aspects of hospital facility management, (1) level of service provided by the hospital and (2) technical aspects of mission critical hospital subsystems. This study proposes two models in order to maintain and improve the level of service delivered to the patients. The first model operates at the macro-level and undertakes the Network-level Hospital Rehabilitation Trade off model (NEHIR). The model optimizes the scheduling of rehabilitation works through the use of genetic algorithm optimization engine. The model features through five modules, (1) Database module that stores the hospitals data, (2) Backward Markov chain module that estimates the transition probability matrix, (3) Deterioration prediction module that predict the future condition of the asset, (4) Rehabilitation Cost optimization and (5) Multi-objective rehabilitation schedule optimization that conducts a tradeoff between the modified rehabilitation cost and the number of unserved patients. The second model operates at the micro-level and undertakes the Hospital-level Reliability Centered Maintenance model (HOREM). The model optimizes the maintenance tasks for critical subsystems and optimize the allocation of maintenance budget among the hospital subsystems. HOREM model is consisted of five modules as follows, (1) Reliability Centered Maintenance module that was used to define the components, functions, functional failure, failure modes, failure consequence and maintenance type for subsystems components, (2) fuzzy logic system module for determining the probability of failure of different replacement/restoration intervals, (3) Monte-Carlo simulation module determining the probability of failure of different inspection intervals, (4) Multi-objective maintenance optimization module that tradeoff between the downtime and maintenance costs and (5) Systems Integration optimization module that optimize the top management maintenance budget on hospitals subsystems. Two case studies were considered for verification and validation. The first case study is comprised of four hospitals was used for NEHIR model validation. The results of NEHIR model showed 8% decrease in number of unserved patients and 20% saving in rehabilitation costs. The second case study was one hospital that was used for validating HOREM model. The results of HOREM model showed 17% reduction in maintenance costs compared to traditional methods for the same downtime

A risk mitigation framework for construction / asset management of real estate and infrastructure projects

The increasing demand on residential, office, retail, and services buildings as well as hotels and recreation has been encouraging investors from both private and public sectors to develop new communities and cities to meet the mixed demand in one location. These projects are huge in size, include several diversified functions, and are usually implemented over many years. The real estate projects’ master schedules are usually initiated at an early stage of development. The decision to start investing in infrastructure systems, that can ultimately serve fully occupied community or city, is usually taken during the early development stage. This applies to all services such as water, electricity, sewage, telecom, natural gas, roads, urban landscape and cooling and heating. Following the feasibility phase and its generated implementation schedule, the construction of the infrastructure system starts together with a number of real estate projects of different portfolios (retail, residential, commercial,…etc.). The development of the remaining real estate projects continues parallel to customer occupancy of the completed projects. The occurrence of unforeseen risk events, post completing the construction of infrastructure system, may force decision makers to react by relaxing the implementation of the remaining unconstructed projects within their developed communities. This occurs through postponing the unconstructed project and keeping the original feasibility-based sequence of projects unchanged. Decision makers may also change the sequence of implementing their projects where they may prioritize either certain portfolio or location zone above the other, depending on changes in the market demand conditions. The change may adversely impact the original planned profit in the original feasibility. The profit may be generated from either real estate portfolios and/or their serving Infrastructure system. The negative impact may occur due to possible delayed occupancy of the completed real estate projects which in turn reduces the services demand. This finally results in underutilization of the early implemented Infrastructure system. This research aims at developing a dynamic decision support prototype system to quantify impacts of unforeseen risks on the profitability of real estate projects as well as its infrastructure system in the cases of changing projects’ implementation schedules. It is also aimed to support decision makers with scheduled portfolio mix that maximizes their Expected Gross Profit (EGP) of real estate projects and their infrastructure system. The provided schedules can be either based on location zone or portfolio type to meet certain marketing conditions or even to respect certain relations between neighbor projects’ implementation constraints. In order to achieve the research objectives, a Risk Impact Mitigation (RIM) decision support system is developed. RIM consists mainly of four models, Real Estate Scheduling Optimization Model RESOM, Sustainable Landscape Optimization Model SLOM, District Cooling Optimization Model DCOM and Water Simulation Optimization Model WSOM. Integrated with the three Infrastructure specialized models SLOM, DCOM, WSOM, RESOM provides EGP values for individual Infrastructure systems. The three infrastructure models provide the demand profile that relate to a RESOM generated implementation schedule. RESOM then uses these profiles for calculating the profits using the projects’ capital expenditure and financial expenses. The three models included in this research (SLOM, DCOM and WSOM) relate to the urban landscape, district cooling and water systems respectively. RIM is applied on a large scale real estate development in Egypt. The development was subjected to difficult political and financial circumstances that were not forecasted while preparing original feasibility studies. RIM is validated using a questionnaire process. The questionnaire is distributed to 31 experts of different academic and professional background. RIM’s models provided expected results for different real life cases tested by experts as part of the validation process. The validation process indicated that RIM’s results are consistent, in compliance with expected results and is extremely useful and novel in supporting real estate decision makers in mitigating risk impacts on their profits. The validation process also indicated promising benefits and potential need for developed commercial version for future application within the industry

Optimal Cultivation Pattern to Increase Revenue and Reduce Water Use: Application of Linear Programming to Arjan Plain in Fars Province

Because the available water resources of the Arjan plain region in Iran do not fully meet the watering requirements for plants in farmlands, the crops suffer from water stress, a situation that causes them to wilt. The aim of this study is to develop a water resources planning model that helps decision-makers determine an appropriate cultivation pattern, optimize the exploitation from surface water resources, and specify the method of allocating water across different farm crops to minimize the detrimental effects of water shortage. Through investigating various models of water resources planning and properties along with the governing conditions for each of these models, the linear programming model was selected as a suitable option due to its simplicity and practical applicability to water resource allocation planning. The model was run for a five-year period by considering gradual variations through the determination of the most appropriate exploitation pattern from the available water resources (surface and groundwater). Results reveal that the negative water balance can be improved gradually as positive, where it will reach +20 million m3 per year in 2040 from the current deficit of 236 million m3 with an 8% increased net profit

Optimal Cultivation Pattern to Increase Revenue and Reduce Water Use: Application of Linear Programming to Arjan Plain in Fars Province

Because the available water resources of the Arjan plain region in Iran do not fully meet the watering requirements for plants in farmlands, the crops suffer from water stress, a situation that causes them to wilt. The aim of this study is to develop a water resources planning model that helps decision-makers determine an appropriate cultivation pattern, optimize the exploitation from surface water resources, and specify the method of allocating water across different farm crops to minimize the detrimental effects of water shortage. Through investigating various models of water resources planning and properties along with the governing conditions for each of these models, the linear programming model was selected as a suitable option due to its simplicity and practical applicability to water resource allocation planning. The model was run for a five-year period by considering gradual variations through the determination of the most appropriate exploitation pattern from the available water resources (surface and groundwater). Results reveal that the negative water balance can be improved gradually as positive, where it will reach +20 million m3 per year in 2040 from the current deficit of 236 million m3 with an 8% increased net profit

Strategic development in the petrochemical industry

Imperial Users onl

On being balanced in an unbalanced world

This paper examines the case of a procurement auction for a single project, in which the breakdown of the winning bid into its component items determines the value of payments subsequently made to bidder as the work progresses. Unbalanced bidding, or bid skewing, involves the uneven distribution of mark-up among the component items in such a way as to attempt to derive increased benefit to the unbalancer but without involving any change in the total bid. One form of unbalanced bidding for example, termed Front Loading (FL), is thought to be widespread in practice. This involves overpricing the work items that occur early in the project and underpricing the work items that occur later in the project in order to enhance the bidder's cash flow. Naturally, auctioners attempt to protect themselves from the effects of unbalancing—typically reserving the right to reject a bid that has been detected as unbalanced. As a result, models have been developed to both unbalance bids and detect unbalanced bids but virtually nothing is known of their use, success or otherwise. This is of particular concern for the detection methods as, without testing, there is no way of knowing the extent to which unbalanced bids are remaining undetected or balanced bids are being falsely detected as unbalanced. This paper reports on a simulation study aimed at demonstrating the likely effects of unbalanced bid detection models in a deterministic environment involving FL unbalancing in a Texas DOT detection setting, in which bids are deemed to be unbalanced if an item exceeds a maximum (or fails to reach a minimum) ‘cut-off’ value determined by the Texas method. A proportion of bids are automatically and maximally unbalanced over a long series of simulated contract projects and the profits and detection rates of both the balancers and unbalancers are compared. The results show that, as expected, the balanced bids are often incorrectly detected as unbalanced, with the rate of (mis)detection increasing with the proportion of FL bidders in the auction. It is also shown that, while the profit for balanced bidders remains the same irrespective of the number of FL bidders involved, the FL bidder's profit increases with the greater proportion of FL bidders present in the auction. Sensitivity tests show the results to be generally robust, with (mis)detection rates increasing further when there are fewer bidders in the auction and when more data are averaged to determine the baseline value, but being smaller or larger with increased cut-off values and increased cost and estimate variability depending on the number of FL bidders involved. The FL bidder's expected benefit from unbalancing, on the other hand, increases, when there are fewer bidders in the auction. It also increases when the cut-off rate and discount rate is increased, when there is less variability in the costs and their estimates, and when less data are used in setting the baseline values

Optimizing Cash-Flow-at-Risk in Construction Projects: A Cost Reduction Approach

Project managers normally are facing with difficulties behind management of project cash flow, which requires distinguished methods and appropriate tools to manage negative cash flows. Cash-Flow-at-Risk (CFaR) model is an efficient approach to predict cash flow trend. In this study, all risk factors affecting project management environment have incorporated to predict an accurate project cash flow. Then, a response surface method (RSM) is applied to determine optimal level of risk. The results have successfully implemented through a case study to demonstrate the applicability of the proposed method