GENETIC ALGORITHM OPTIMIZATION OF MULTIPLE RESOURCES FOR MULTI-PROJECTS

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Sarah Fotouh ◽  
A. Samer Ezeldin
Keyword(s):  
Genetic Algorithm ◽  
Construction Projects ◽  
Project Managers ◽  
Algorithm Optimization ◽  
Multiple Resources ◽  
Proposed Model ◽  
The Cost ◽  
User Friendly ◽  
Critical Resources ◽  
Available Resources

Optimization of resources is very important in all construction projects. Project managers have to face problems regarding management of cost, time and available resources for single projects. This is more challenging when managing multiple projects. Most of the recent studies focused on optimization of resources for a single project, or a single resource. This paper presents a numerical model of multiple resources optimization for multiple projects using Genetic Algorithm. Most of the companies in the construction industry optimize the resources for single projects only. However, with the presence of several mega projects in several developing countries running at the same time, there is a need for a model to enhance the efficiency of available resources, and decreases the fluctuation as much as possible and try to maximize the use of the available pool of resources. The proposed model is user friendly, and it can optimize up to nine resources in three different projects running at the same time. The model is used on the identified critical resources. It calculates the cost of each resource, minimize the cost of extra resources as much as possible and generate the schedule of each project within a selected overall program.

COST OPTIMIZATION OF MULTIPLE RESOURCES ALLOCATION FOR MULTI-PROJECTS

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Sarah Aboul Fotouh ◽  
A. Samer Ezeldin
Keyword(s):  
Genetic Algorithm ◽  
Construction Industry ◽  
Cost Optimization ◽  
Project Managers ◽  
Resources Allocation ◽  
Total Cost ◽  
Multiple Resources ◽  
The Cost ◽  
Critical Resources ◽  
Available Resources

As the construction industry grows year by year, optimization of resources is becoming essential to reduce their required number, their costs and as a consequence the total cost of the project. Project managers have to face problems regarding management of cost, time, and available resources for single projects. What is more challenging is to optimize the available resources for multiple projects, which would result in appreciable savings. Most of the companies in the construction industry commonly optimize the resources for single project. However, with the presence of several mega projects in many developing countries running at the same time, there is a need for a model to enhance the efficiency of available resources among multiple simultaneous projects. This paper discusses a numerical model of cost optimization and allocation of up to nine resources for up to three projects for a given company, taking into consideration the transportation of resources from one project to another and the cost of unused resources. The model was developed using a genetic algorithm, and it is used on the identified critical resources. It calculates the cost of each resource, minimizes the cost of extra resources, cost of unused resources, and generates the schedule of each project within a selected overall program.

A novel model for risk management of outsourced construction projects using decision-making methods: a case study

2020 ◽  
Vol 10 (2) ◽  
pp. 97-123 ◽  
Author(s):  
Amin Mahmoudi ◽  
Mehdi Abbasi ◽  
Xiaopeng Deng ◽  
Muhammad Ikram ◽  
Salman Yeganeh
Keyword(s):  
Decision Making ◽  
Construction Projects ◽  
Project Managers ◽  
Analytic Hierarchy ◽  
Suitable Alternative ◽  
Content Type ◽  
Proposed Model ◽  
The Cost ◽  
First Time

PurposeSelecting a suitable contract to outsource construction projects is an ongoing concern for project managers and organizational directors. This study aims to propose a comprehensive model to manage the risks of outsourced construction project contracts.Design/methodology/approachTo employ the proposed model, firstly, the types of contracts and risks in the organization should be identified, then, to prioritize the contracts, the identified risks are considered as criteria. After receiving the experts' opinions, the best–worst method (BWM) integrated with grey relation analysis (GRA) method was used to prioritize the contracts. BWM and GRA are multi-criteria decision-making methods with different approaches and applications. In the current study, BWM has been employed to calculate the weights of criteria because it has better performance than other methods such as the analytic hierarchy process (AHP). After calculating the weights of criteria, the GRA method has been utilized for ranking the alternatives.FindingsAccording to the results obtained from the case study, the cost plus award fee contract is the most suitable alternative for outsourcing construction projects. The proposed methodology can be practically applied through different types of the projects such as construction or “engineering, procurement and construction”.Originality/valueTo the best of our knowledge, this is the first time a conceptual model has been proposed to select an appropriate contract for construction projects. Also, for the first time, the BWM integrated with GRA method has been used to prioritize project contracts based on the potential risks. The proposed model can contribute to project managers for selecting a suitable contract with the least risk in construction projects.

scholarly journals Genetic Algorithm Optimization Model for Determining the Probability of Failure on Demand of the Safety Instrumented System

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Ahmed H. Aburawwash ◽  
Moustafa Mohammed Eissa ◽  
Azza F. Barakat ◽  
Hossam M. Hafez
Keyword(s):  
Genetic Algorithm ◽  
Accurate Determination ◽  
Probability Of Failure ◽  
System Structure ◽  
Control Element ◽  
Algorithm Optimization ◽  
On Demand ◽  
Uncertainty Sources ◽  
Iec 61508 ◽  
Proposed Model

A more accurate determination for the Probability of Failure on Demand (PFD) of the Safety Instrumented System (SIS) contributes to more SIS realiability, thereby ensuring more safety and lower cost. IEC 61508 and ISA TR.84.02 provide the PFD detemination formulas. However, these formulas suffer from an uncertaity issue due to the inclusion of uncertainty sources, which, including high redundant systems architectures, cannot be assessed, have perfect proof test assumption, and are neglegted in partial stroke testing (PST) of impact on the system PFD. On the other hand, determining the values of PFD variables to achieve the target risk reduction involves daunting efforts and consumes time. This paper proposes a new approach for system PFD determination and PFD variables optimization that contributes to reduce the uncertainty problem. A higher redundant system can be assessed by generalizing the PFD formula into KooN architecture without neglecting the diagnostic coverage factor (DC) and common cause failures (CCF). In order to simulate the proof test effectiveness, the Proof Test Coverage (PTC) factor has been incorporated into the formula. Additionally, the system PFD value has been improved by incorporating PST for the final control element into the formula. The new developed formula is modelled using the Genetic Algorithm (GA) artificial technique. The GA model saves time and effort to examine system PFD and estimate near optimal values for PFD variables. The proposed model has been applicated on SIS design for crude oil test separator using MATLAB. The comparison between the proposed model and PFD formulas provided by IEC 61508 and ISA TR.84.02 showed that the proposed GA model can assess any system structure and simulate industrial reality. Furthermore, the cost and associated implementation testing activities are reduced.

scholarly journals Method of Assessing the Risk of Implementing Railway Investments in Terms of the Cost of Their Implementation

2021 ◽  
Vol 13 (23) ◽  
pp. 13085
Author(s):  
Jan Kowalski ◽  
Mieczysław Połoński ◽  
Marzena Lendo-Siwicka ◽  
Roman Trach ◽  
Grzegorz Wrzesiński
Keyword(s):  
Construction Projects ◽  
Original Method ◽  
Project Managers ◽  
Simple Method ◽  
Construction Companies ◽  
Risk Forecasting ◽  
Specific Object ◽  
Effective Cost ◽  
The Cost ◽  
The Impact

Exceeding the approved budget is often an integral part of the implementation of construction projects, especially those where unforeseen threats may occur. Therefore, each construction investment should contain elements of risk forecasting, mainly in terms of the cost of its implementation. Only a small number of institutions apply effective cost control methods, taking into account the specifics of a given industry. Especially small construction companies that participate in the structure of the implementation of large construction projects as subcontractors. The article presents a method by which it is possible to determine, with certain probability, the final cost of railway construction investments carried out in Poland. The method was based on a reliable database of risk factors published in sources. In this article, the main presumptions of the original method are presented, which take into account the impact of potential, previously recognized, risks specific to railway investments, and enable project managers to relate them to the conditions where the implementation of a specific object is planned. The authors assumed that such a relatively simple method, supported by a suitable computational program, would encourage teams that plan to implement railway projects to use it and increase the credibility of their schedules.

scholarly journals Optimization of Earthwork Allocation Path as Vehicle Route Problem Based on Genetic Algorithm

2020 ◽  
Vol 165 ◽  
pp. 04057
Author(s):  
Naifu Deng ◽  
Xuyang Li ◽  
Yanmin Su
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Construction Projects ◽  
Vehicle Route Problem ◽  
Vehicle Route ◽  
Route Problem ◽  
Modified Genetic Algorithm ◽  
Global Optimal ◽  
The Cost ◽  
Engineering Projects

In civil engineering, earthwork, prior to the construction of most engineering projects, is a lengthy and time-consuming work involving iterative processes. The cost of many AEC (Architecture, Engineering and Construction) projects is highly dependent on the efficiency of earthworks (e.g. road, embankment, railway and slope engineering). Therefore, designing proper earthwork planning is of importance. This paper simplifies the earthwork allocation problem to Vehicle Route Problem (VRP) which is commonly discussed in the field of transportation and logistics. An optimization model for the earthwork allocation path based on the modified Genetic Algorithm with a self-adaptive mechanism is developed to work out the global optimal hauling path for earthwork. The research results also instruct the initial topographic shaping of the Winter Olympic Skiing Courses Project. Furthermore, this optimization model is highly compatible with other evolutionary algorithms due to its flexibility, therefore, further improvement in this model is feasible and practical.

Cost Performance Estimation in Construction Projects Using Fuzzy Time Series

2015 ◽  
Vol 6 (1) ◽  
pp. 66-75 ◽  
Author(s):  
Mostafa Salari ◽  
Nooshin Yousefi ◽  
Mohhmad Mahdi Asgary
Keyword(s):  
Time Series ◽  
Construction Projects ◽  
Project Managers ◽  
Performance Estimation ◽  
Fuzzy Time Series ◽  
Linguistic Terms ◽  
Cost Performance ◽  
Times Series ◽  
Future Performance ◽  
Proposed Model

In this paper, the authors develop a model to estimate future performance of construction projects. For the purpose of estimation, fuzzy times series models are used as an effective approach in estimation process. Furthermore, linguistic terms are applied to interpret the fuzzy-based results. The proposed model can assists project managers to develop their knowledge concerning the future aspects of project cost performance. It also provides the early warning of weak upcoming performance of project and extends the feasible time for corrective actions. Eventually, a small example has been provided to illustrate how the new model can be implemented in reality.

Overall multiobjective optimization of construction projects scheduling using particle swarm

2016 ◽  
Vol 23 (3) ◽  
pp. 265-282 ◽  
Author(s):  
Emad Elbeltagi ◽  
Mohammed Ammar ◽  
Haytham Sanad ◽  
Moustafa Kassab
Keyword(s):  
Cash Flow ◽  
Construction Projects ◽  
Real Life ◽  
Particle Swarm ◽  
Project Managers ◽  
Time Cost ◽  
Swarm Optimization ◽  
Content Type ◽  
Proposed Model ◽  
Optimized Schedule

Purpose – Developing an optimized project schedule that considers all decision criteria represents a challenge for project managers. The purpose of this paper is to provide a multi-objectives overall optimization model for project scheduling considering time, cost, resources, and cash flow. This development aims to overcome the limitations of optimizing each objective at once resulting of non-overall optimized schedule. Design/methodology/approach – In this paper, a multi-objectives overall optimization model for project scheduling is developed using particle swarm optimization with a new evolutionary strategy based on the compromise solution of the Pareto-front. This model optimizes the most important decisions that affect a given project including: time, cost, resources, and cash flow. The study assumes each activity has different execution methods accompanied by different time, cost, cost distribution pattern, and multiple resource utilization schemes. Findings – Applying the developed model to schedule a real-life case study project proves that the proposed model is valid in modeling real-life construction projects and gives important results for schedulers and project managers. The proposed model is expected to help construction managers and decision makers in successfully completing the project on time and reduced budget by utilizing the available information and resources. Originality/value – The paper presented a novel model that has four main characteristics: it produces an optimized schedule considering time, cost, resources, and cash flow simultaneously; it incorporates a powerful particle swarm optimization technique to search for the optimum schedule; it applies multi-objectives optimization rather than single-objective and it uses a unique Pareto-compromise solution to drive the fitness calculations of the evolutionary process.

scholarly journals PROPOSING A NEURAL NETWORK MODEL TO PREDICT TIME AND COST CLAIMS IN CONSTRUCTION PROJECTS

2016 ◽  
Vol 22 (7) ◽  
pp. 967-978 ◽  
Author(s):  
Vahidreza YOUSEFI ◽  
Siamak HAJI YAKHCHALI ◽  
Mostafa KHANZADI ◽  
Ehsan MEHRABANFAR ◽  
Jonas ŠAPARAUSKAS
Keyword(s):  
Neural Network ◽  
Case Studies ◽  
Construction Industry ◽  
Network Model ◽  
Neural Network Model ◽  
Construction Management ◽  
Construction Projects ◽  
New Model ◽  
Proposed Model ◽  
The Cost

Despite broad improvements in construction management, claims still are an inseparable part of many con-struction projects. Due to huge cases of claim in construction industry, this study argues that claim management is a significant factor in construction projects success. In this study, the most possible causes of these emerging claims are identified and statistically ranked by Probability-Impact Matrix. Subsequently, by classifying claims in different cases, the most important ones are ranked in order to achieve a better understanding of claim management in each project. In this regard, a new index is defined, being able to be applied in a variety of projects with different time and cost values, to calculate the amount of possible claims in each project along with related ratios with respect to the cost and time of each claim. This study introduces a new model to predict the frequency of claims in construction projects. By using the proposed model, the rate of possible claims in each project can be obtained. This model is validated by applying it into fitting case studies in Iran construction industry.

scholarly journals A Conceptual Model for a Safety-Based Theory of Lean Construction

Buildings ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 23 ◽  
Author(s):  
Soheila Moaveni ◽  
Seyed Banihashemi ◽  
Mohammad Mojtahedi
Keyword(s):  
Conceptual Model ◽  
Construction Projects ◽  
Project Managers ◽  
Lean Construction ◽  
Project Delivery ◽  
New Approach ◽  
Factors Affecting ◽  
Work Related ◽  
Proposed Model ◽  
Optimal Value

The construction industry is one of the most fatal industries, so it is important to pay more attention to safety solutions. Even though work-related accidents are known as a major waste in construction projects, little attention has been paid so far to incorporating safety into the lean construction framework. In this research, lean construction theory is reviewed through the lens of safety. That being so, the identified challenges in previous research on improving safety in construction projects are categorized, and those related to the concept of lean project delivery are introduced. Then, the principles of the lean construction framework are explained, and the relevant changes for incorporating safety into the framework are introduced and discussed. The proposed model includes a new approach to the Transformation-Flow-Value framework, in order to pay particular attention to safety in construction projects as one of the factors affecting the success of projects, and achieving optimal value for stakeholders. It is expected that this hybrid model would further enrich the lean construction framework. The careful attention of project executives to this model may improve the safety situation in construction projects. The conceptual model presented in this study can be used in the decision making process for project managers as well as research into optimization of safety costs, and eliminating waste (including models for optimizing the movement of machinery, controlling and reducing rework, and designing the site layout).

scholarly journals Bicriteria problem of discrete optimization in planning a multiunit construction project

2018 ◽  
Vol 174 ◽  
pp. 04008
Author(s):  
Michał Podolski
Keyword(s):  
Discrete Optimization ◽  
Construction Projects ◽  
Flow Shop ◽  
Discrete Optimization Problem ◽  
Early Completion ◽  
Proposed Model ◽  
Construction Contractor ◽  
Flow Shop Problem ◽  
Bicriteria Problem ◽  
The Cost

The paper describes the bicriteria discrete optimization problem, that may occur during the scheduling of multiunit construction projects. The multiunit project involves the construction of many civil structures with the same sets of activities needed, but different in size. In the project the deadlines of activities in units are adopted. The missing of them by the contractor causes the payment of the disincentive penalty. The early completion of the activities in units is rewarded extra income for the construction contractor i.e. a incentive bonus. Changing the order of the execution of the units changes the value of the objective functions: the duration of the project and the cost (the sum of the disincentive penalties and incentive bonuses). The proposed model of the project is the bicriteria NP-hard flow shop problem with constraints characteristic for construction projects. The paper presents the method of determining the set of Paretooptimal solutions for small projects. The computational example of the model of the project is also included in the paper.

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