Mohammad A. Ammar

Fuzzy-Based Life-Cycle Cost Model for Decision Making under Subjectivity

AbstractDecision support models are needed to facilitate long-term planning and priority setting among competing alternatives. Life-cycle cost is the most frequently used economic model that considers all cost elements and related factors throughout the service life of the alternatives being considered. These cost elements and related factors are usually associated with uncertainty and subjectivity. As such, it is important to model the uncertainty arising from the assumed data over the service life of competing alternatives. Probabilistic techniques, such as Monte Carlo simulation, are commonly used to deal with such uncertainty or vagueness. However, they have been criticized for their complexity and amount of data required. This paper presents a fuzzy-based life-cycle cost model that accounts for uncertainty in a manner that disadvantages commonly encountered in probabilistic models are alleviated. The developed model utilized fuzzy set theory and interval mathematics to model vague, imprecise, qualita...

Resource constrained project scheduling using simulation

Resource allocation is one of the most important issues of construction management. Two problems of resource allocation are of concern: resource levelling and resource scheduling. Traditionally, the resource scheduling problem is solved using either heuristic methods or optimization techniques. When heuristic methods are used, resource scheduling is treated as a subsequent problem for the CPM analysis. In this paper, the resource scheduling problem is handled using simulation, where logic dependence and resource availability limits are considered simultaneously during the time scheduling process. Simulation is applied to the resource scheduling problem at the project level. A simulation system called SIRBUS is used to schedule construction projects under resource constraints. Constant resource demand of activities is assumed, and the activity once started cannot be interrupted. Six example projects previously solved by different heuristic methods are re-solved using simulation. The results are compared with the latest heuristic models: current float technique and ranked positional weight method. In addition to the advantage that resource availability is considered during time scheduling as a starting point, which is an apparent feature of simulation, SIRBUS gives good results compared with existing heuristic methods.

Float analysis of non-serial repetitive activities

Floats and critical path(s) are important issues in construction management practice. In critical path method, activities not on the critical path(s) must have float. Float measures the amount of time an activity can be delayed before it becomes critical. Consecutive repetitive activities have production rates, which may vary considerably from one activity to another. This creates a different situation (from traditional non‐repetitive activities) such that repetitive activities may have rate float, in addition to time float. In this paper, a proposed model for determining different types of floats for non‐serial repetitive activities is developed. The traditional concept of time float is extended to repetitive activities. Rate float, which is an inherent property of repetitive activities, is also determined. Float analysis is performed in a very similar way to Critical Path Method (CPM) analysis, without the need for graphical aids. The analysis is based on a repetitive scheduling method, which utilizes a traditional CPM network of a typical unit, in which overlapping activities are used to model repetitive activities. A constant activity production rate is assumed and resource continuity is maintained. The method for determining time floats (total and free) and the rate float of non‐critical activities and of non‐controlling segments of controlling activities is described in detail. The proposed model was automated by a macro‐program, which has been coded on a commercial scheduling software to facilitate float determination.

Optimum analysis of pavement maintenance using multi-objective genetic algorithms

Abstract Road network expansion in Egypt is considered as a vital issue for the development of the country. This is done while upgrading current road networks to increase the safety and efficiency. A pavement management system (PMS) is a set of tools or methods that assist decision makers in finding optimum strategies for providing and maintaining pavements in a serviceable condition over a given period of time. A multi-objective optimization problem for pavement maintenance and rehabilitation strategies on network level is discussed in this paper. A two-objective optimization model considers minimum action costs and maximum condition for used road network. In the proposed approach, Markov-chain models are used for predicting the performance of road pavement and to calculate the expected decline at different periods of time. A genetic-algorithm-based procedure is developed for solving the multi-objective optimization problem. The model searched for the optimum maintenance actions at adequate time to be implemented on an appropriate pavement. Based on the computing results, the Pareto optimal solutions of the two-objective optimization functions are obtained. From the optimal solutions represented by cost and condition, a decision maker can easily obtain the information of the maintenance and rehabilitation planning with minimum action costs and maximum condition. The developed model has been implemented on a network of roads and showed its ability to derive the optimal solution.

Decision support model for selection of rehabilitation methods of water mains

The deteriorating condition of water mains in Canada and US calls for rehabilitation strategies that accounts mainly for budget and level of service constraints. These water mains have received ‘D’ grade in the two countries. Decision support models can assist decision makers regarding when to rehabilitate and whether to repair, renovate or replace section(s) of water mains. The literature indicates that decision models should account for life cycle cost, uncertainty, long-term planning, targeted levels of service and budget constraints. The objectives of this paper are to: identify and group rehabilitation methods, present decision support model to rank and select most suitable rehabilitation method(s), and study the impact of rehabilitation methods on the functional and structural performance of water mains. The developed decision support model accounts for life cycle cost of each competing scenario along with the associated uncertainty. The model, unlike available models, can effectively account for vagueness, qualitative assessments and human judgment associated with input data. A case study of a water main network was analysed in order to demonstrate the use of the developed model and to illustrate its essential features. The results obtained indicate that the model can support the generation of well-informed decisions in a timely manner.

INVESTIGATION AND MODELING OF CRITICAL SUCCESS FACTORS IN CONSTRUCTION ORGANIZATIONS

Profit and success are considered the main drivers of any organization. Achieving this success is based on many factors which have a direct effect on the performance of these organizations. Predicting construction organizations performance helps define the weak organization points in order to improve its performance and increase the profit. In construction organizations, it is more difficult to achieve or maintain a scientific strategy to measure their current success due to the diversity and complexity of construction organizations. Previous studies used questionnaires and interviews with technical and professional persons. However, most of these studies concentrated on the critical success factors on project level. The scope of this study is to investigate the most significant organizational success factors with focus on construction organizations. This paper aims at determining most significant (i.e. critical) success factors, and to develop a model to predict the company performance based on these critical success factors. The potential success factors were surveyed from the literature study. A questionnaire was prepared for evaluating the effect of those potential success factors on organizational performance. The data collected were analyzed using Artificial Neural Networks (ANNs). Neuro-Shell software was used to rank the potential success factors utilizing the data obtained from different construction organizations. The critical success factors were used in-turn to develop a NN prediction performance model of construction organizations. The model can be used to predict the performance of a construction organization based on estimated values of its success factors.

A Framework for Performance Assessment of Organizations in the Construction Industry

Competition and customer needs forced construction companies (organizations) to measure their performance beyond the financial aspects. Success, the main driver of any organization, depends mainly on a variety of factors that impact organizations performance. Predicting the performance of a construction organization helps in identifying the weak points and in searching solutions to improve its performance and increase profit. Due to the diversity and complexity of construction organizations, it is intricate to adopt a scientific strategy that measures their success. Previous research works showed a lack of attention towards modeling organizations non-financial performance while focusing on measures of project success. Therefore, the objective of the present research is to identify and study the success factors and to develop performance prediction model(s) for construction organizations. The potential success factors are collected from literature and practitioners through a questionnaire that is prepared and sent to evaluate the eect of these potential success factors on organizational performance. The collected data are analyzed using artificial neural network (ANN) to determine the most significant (critical) success factors. Two performance prediction models are developed using ANN and regression analysis, which show robust results when verified and tested. The analysis shows that the developed models are sensitive to the identified critical success factors.

Fuzzy- versus Simulation-Based Life-Cycle Cost for Sewer Rehabilitation Alternatives

AbstractThe ample framework to maintain infrastructure remains the continuous challenge in efficiently carrying out municipal duties. In North America, aging municipal infrastructure has reached the breaking point. This results in a large excess of infrastructure rehabilitation activities and cost escalation. Life-cycle cost (LCC) analysis, which can effectively deal with data vagueness and judgmental appraisal, is essential to evaluate various alternatives of infrastructure rehabilitation, particularly for sewers. Therefore, the objective of the present research is to develop LCC models using fuzzy and simulation approaches that deal with vague, imprecise, qualitative, linguistic, or incomplete data. Deterioration and cost data are collected for two sewer materials—PVC and concrete—with diameter ranges from 150 to 600 mm. The developed LCC models, with the help of an automated Microsoft Excel–based program, are utilized to select the best rehabilitation alternatives/scenarios. Results show that open-tren...

Learning curve modelling of gas pipeline construction in Egypt

It is commonly accepted that production rates for repetitive tasks will improve with acquired experience and learning. Several mathematical models, or learning curves, have been proposed to investigate improvement in productivity as a function of the number of units produced. Deciding the best-fit learning curve model for construction activities is a managerial challenge. In this paper, the best-fit learning curve model for describing past performance of gas pipeline construction in Egypt is investigated. Data were collected from eight real-life projects, which are constructed in different types of land, under various weather conditions with different sizes, lengths, and pipe diameters. Only labour-intensive activities are considered in the present work. Cumulative average data is used to represent collected data in order to ensure curve smoothness as well as to avoid scattered data. The commercial Statistical Package for Social Science (SPSS) is used to determine Pearsons coefficient of correlation. The cubic model is found to be the best fitting curve to describe welding activities in gas pipeline construction activities in Egypt. This work helps both academics and practitioners in deciding the best-fit learning curve model(s) for gas pipeline construction. This will aid in making realistic estimates of the time and cost of repetitive projects.

Efficient modeling of time-cost trade-off problem by eliminating redundant paths

Abstract Time-cost trade-off (TCTO) is a traditional decision-making problem in construction management. Numerous optimization techniques have been developed to solve such problem; however, they are not applicable to large-sized projects. Although heuristic methods can handle large-sized problems, they do not guarantee optimum solution. In a project network, the proper sequence of activities can be maintained by allowing a precedence constraint for each path. In practice, only small number of paths are dominant while others are redundant. In this paper, an efficient optimization model is developed to minimize project direct cost where redundant paths are eliminated, precise activity discrete time-cost relationship is used and overlap between activities is permitted. The optimization model is formulated in the standard form of zero-one programming. The model requires as inputs: discrete utility data of activities, precedence relationships and overlap between consecutive activities. Details of model formulation is illustrated by an example project and then is validated by a real-life project. Based on the obtained results, the developed model is compared with traditional optimization models. The developed model guarantees optimal solution and can be applied efficiently to large-sized projects. The proposed approach provides both academicians and practitioners with an efficient technique that produces optimal solution based on realistic assumptions.