A Min-max Regret Approach to Unbalanced Bidding in Construction

Abbas Afshar,Helia Amiri 대한토목학회 2010 KSCE JOURNAL OF CIVIL ENGINEERING Vol.14 No.5

Unbalanced bidding is a method to benefit from uneven markup distribution among different items of project. As bidding process is applied for a project which is going to be done in future, most of parameters cannot be estimated accurately. Therefore, there are some uncertainties in bidding phase. Uncertainties in quantities of works play an important role for unit price determination in an unbalanced bidding model. Therefore, in this paper these uncertainties are considered, applying Minimize Maximum Regret (MMR)and Minimize Total Regret (MTR) approach in a discrete area where each scenario represents a set of possible quantities of works. Allocating interval scenario case for quantities of works seems to be more appropriate than discrete one. Thus, the unbalanced bidding model with interval quantities of works using MMR is proposed. To define manageable number of scenarios resulting from possible combinations of different unit prices in formulating the Min Max Regret (MMR) model as interval scenario case, a relaxation procedure is employed. In this approach, instead of considering all possible objective functions, a model which is called “Candidate Maximum Regret (CMR)” is applied to determine worst case scenarios. Models are applied to a hypothetical case example and the results are compared.

Multi Objective Calibration of Large Scaled Water Quality Model Using a Hybrid Particle Swarm Optimization and Neural Network Algorithm

Abbas Afshar,Hamideh Kazemi 대한토목학회 2012 KSCE Journal of Civil Engineering Vol.16 No.6

Large scaled simulation models, especially the water quality simulation models, are so complicated that makes calibration processes huge tasks; in order to attain optimum solution, lots of parameters must be calibrated, simultaneously. Methods based on evolutionary algorithm developed new horizons in calibration procedure. Hybrid algorithms are of the newest. In hybrid algorithms,one of the modules is applied as a simulator and the other one takes role as an optimization module. In this article, overcoming these challenges, hybrid ANN-PSO algorithm is applied in calibration process of water quality model CE-QUAL-W2. Here, Particle Swarm Optimization (PSO) provides simulation (CE-QUAL-W2) model with sets of parameters to simulate model. Using these results, Neural Network (estimator) is trained. In the next step, simulator would be replaced with estimator and Artificial Neural Network (ANN) would estimate simulator’s behavior in a way less time. The first goal is to calibrate thermal parameter; going forward through this process needs water surface elevation parameter to be calibrated, too. As a result, the proposed model will become multi-objective one, applied in Karkheh reservoir in Iran during 6 month simulation period. The proposed approach overcomes the high computational efforts required if a conventional calibration search technique was used, while retaining the quality of the final calibration results. Estimator (ANN) embedded in optimization algorithm (PSO) in calibration process,undoubtedly, reduced run time while the answers have reliable quality.

A Multi-intelligent Agent Architecture for Knowledge Extraction : Novel Approaches for Automatic Production Rules Extraction

Mohammed Abbas Kadhim,M. Afshar Alam,Harleen Kaur 보안공학연구지원센터 2014 International Journal of Multimedia and Ubiquitous Vol.9 No.2

GA-Based Multi-Objective Optimization of Finance-Based Construction Project Scheduling

Habib Fathi,Abbas Afshar 대한토목학회 2010 KSCE JOURNAL OF CIVIL ENGINEERING Vol.14 No.5

From a financial management perspective, the profitability of a construction project is connected to the cash requirements of the project and the ability of a company to procure cash at the right time. Line of credit as a bank credit agreement provides an alternative way of managing the necessary capital and cash flow for the project. Today’s highly competitive business environment necessitates comprehensive scheduling with respect to cash providing provisions and restrictions. This paper presents a multi-objective elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) based optimization model for finance-based scheduling which facilitates the decision making process of the most appropriate line of credit option for cash procurement. Finance-based scheduling modifies the initial schedule of the project so that its maximum negative cash flow is limited to a specific credit limit. Furthermore, this paper suggests several improvements to basic NSGA-II and demonstrates how they significantly enhance the efficiency of the model in searching for non-dominated solutions. The proposed model is validated by a designed benchmark problem, and its performance and merits are illustrated through its application to a case example. It is shown that the model can effectively approach to the optimal Pareto set and maintain diversity in solutions.

Simulation-Optimization Model for Non-point Source Pollution Management in Watersheds: Application of Cooperative Game Theory

Mohammad J. Emami Skardi,Abbas Afshar,Samuel Sandoval Solis 대한토목학회 2013 KSCE JOURNAL OF CIVIL ENGINEERING Vol.17 No.6

A new cooperative watershed management methodology is designed for developing an equitable and efficient Best Management Practice cost allocation among landowners in a watershed. The approach intends to control the total sediment yield in the watershed,considering landowners’ conflicting interests. Wet detention ponds, are considered as the only available options to the landowners. The quality of the storm water is evaluated by the Total Suspended Solid loading from the watersheds. The proposed methodology combines a watershed simulation model, named Soil Water Accounting Tool (SWAT), with an Ant Colony Optimization (ACO)module and the cooperative game theory approach. Integration of SWAT and ACO modules provide the best set of designs for any constraints on target sediment removal set forth by non-cooperative and cooperative behaviors of the stakeholders to participate in the coalition to minimize the total cost of management practice. Nash Bargaining Theory is used to investigate how the maximum saving on cost of the participating players in a coalition can be fairly allocated. The proposed method is illustrated by a hypothetical example. The results demonstrate the applicability of the methodology. For the hypothetical case example, the proposed methodology with grand coalition leads to approximately 48 percent cost saving.

Detection of Epstein-Barr Virus and Cytomegalovirus in Gastric Cancers in Kerman, Iran

Leila, Zaruni,Arabzadeh, Seyed Alimohammad,Afshar, Reza Malekpour,Afshar, Abbas Aghaei,Mollaei, Hamid Reza Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.5

Gastric cancer (GC) is a multifactorial disease with different factors having roles in its genesis. Helicobacter pylori and Epstein-Barr virus (EBV) are known infectious agents that could contribute. In addition, there is evidence of a relationship with cytomegalovirus (CMV). Since data on CMV prevalence in gastric cancer are limited, we here evaluated the frequency of EBV and CMV in Iranian patients. Ninety paraffin blocks of GC tissues from patients in Kerman were evaluated for the presence of EBV and CMV genomes by real-time polymerase chain reaction. EBV was detected in 10 cases (11.1%) and CMV in seven. One out of 17 female patients (5.88%) and nine out of 73 male patients (12.3%) were positive for EBV, while one out of 17 female patients (5.88%) and six out of 73 male patients (8.22%) were positive for CMV. The mean age for EBV-positive patients was $60.5{\pm}14.9years$ and the mean age for CMV-positive patients was $67.9{\pm}12.3years$. This study shows that the frequency of EBV-associated GC is high in Kerman. It also indicates that further studies of associations between GC and CMV are warranted, covering larger samples and populations from different areas of the world.

Multi-objective Optimization Response Modeling to Contaminated Water Distribution Networks: Pressure Driven versus Demand Driven Analysis

Seyyed Nasser Bashi-Azghadi,Mohammad Hadi Afshar,Abbas Afshar 대한토목학회 2017 KSCE JOURNAL OF CIVIL ENGINEERING Vol.21 No.6

Implementation of management strategies following contamination detection in water distribution networks may extensively change operational mode of nominated valves and hydrants. The commonly used demand driven network solvers may fail to realistically represent system’s performances of new topology due to possible pressure-deficient condition. Realizing their drawbacks, this paper integrates a Pressure Driven Network Solver (PDNS) with multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II) in a simulation-optimization scheme. It is illustrated that the two commonly used objective functions, namely minimization of consumed contamination mass and number of polluted nodes, may be in conflict when an operational strategy is implemented. A trade-off is developed to help decision-maker compromise between restraining spatial spread of contaminant and its risk to public health. Decision variables in this optimization model are valve closure and hydrant opening. Each trial solution developed by the NSGA-II addresses a new system topology by changing operational modes of the nominated valves and hydrants. The PDNS determines the nodal pressures and refines the nodal withdraw for trial solution. To illustrate the performance of the proposed methodology, Net3 from EPANET 2 is employed. The results show that the pressure-driven analysis is more realistic and appropriate in comparison with demand-driven analysis in operational conditions.