Time-Cost Tradeoff and Resource-Scheduling Problems in Construction: A State-of-the-Art Review

Duration, cost, and resources are defined as constraints in projects. Consequently, Construction manager needs to balance between theses constraints to ensure that project objectives are met. Choosing the best alternative of each activity is one of the most significant problems in construction management to minimize project duration, project cost and also satisfies resources constraints as well as smoothing resources. Advanced computer technologies could empower construction engineers and project managers to make right, fast and applicable decisions based on accurate data that can be studied, optimized, and quantified with great accuracy. This article strives to find the recent improvements of resource-scheduling problems and time-cost trade off and the interacting between them which can be used in innovating new approaches in construction management. To achieve this goal, a state-of-the-art review, is conducted as a literature sample including articles implying three areas of research; time-cost trade off, constrained resources and unconstrained resources. A content analysis is made to clarify contributions and gaps of knowledge to help suggesting and specifying opportunities for future research

The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review

Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe

A novel Multiple Objective Symbiotic Organisms Search (MOSOS) for time–cost–labor utilization tradeoff problem

Multiple work shifts are commonly utilized in construction projects to meet project requirements. Nevertheless, evening and night shifts raise the risk of adverse events and thus must be used to the minimum extent feasible. Tradeoff optimization among project duration (time), project cost, and the utilization of evening and night work shifts while maintaining with all job logic and resource availability constraints is necessary to enhance overall construction project success. In this study, a novel approach called “Multiple Objective Symbiotic Organisms Search” (MOSOS) to solve multiple work shifts problem is introduced. The MOSOS algorithm is new meta-heuristic based multi-objective optimization techniques inspired by the symbiotic interaction strategies that organisms use to survive in the ecosystem. A numerical case study of construction projects were studied and the performance of MOSOS is evaluated in comparison with other widely used algorithms which includes non-dominated sorting genetic algorithm II (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC). The numerical results demonstrate MOSOS approach is a powerful search and optimization technique in finding optimization of work shift schedules that is it can assist project managers in selecting appropriate plan for project

Fuzzy-multi-mode Resource-constrained Discrete Time-cost-resource Optimization in Project Scheduling Using ENSCBO

Construction companies are required to employ effective methods of project planning and scheduling in today's competitive environment. Time and cost are critical factors in project success, and they can vary based on the type and amount of resources used for activities, such as labor, tools, and materials. In addition, resource leveling strategies that are used to limit fluctuations in a project's resource consumption also affect project time and cost. The multi-mode resource-constrained discrete-time–cost-resource optimization (MRC-DTCRO) is an optimization tool that is developed for scheduling of a set of activities involving multiple execution modes with the aim of minimizing time, cost, and resource moment. Moreover, uncertainty in cost should be accounted for in project planning because activities are exposed to risks that can cause delays and budget overruns. This paper presents a fuzzy-multi-mode resource-constrained discrete-time–cost-resource optimization (F-MRC-DTCRO) model for the time-cost-resource moment tradeoff in a fuzzy environment while satisfying all the project constraints. In the proposed model, fuzzy numbers are used to characterize the uncertainty of direct cost of activities. Using this model, different risk acceptance levels of the decision maker can be addressed in the optimization process. A newly developed multi-objective optimization algorithm called ENSCBO is used to search non-dominated solutions to the fuzzy multi-objective model. Finally, the developed model is applied to solve a benchmark test problem. The results indicate that incorporating the fuzzy structure of uncertainty in costs to previously developed MRC-DTCRO models facilitates the decision-making process and provides more realistic solutions

Risk-Based Decision Making Support for Construction Corporate Resource Management

Competitive bidding typically challenges contractors to stay in business by reducing contingency and limiting profit margin, which imposes more prudent resource utilization and allocation decisions during both planning and construction phases of projects. Many of these decisions must be made considering uncertainties that affect resource production and construction performance through several factors such as weather, managerial practices, job-type, and market conditions, etc. Construction decision makers will therefore have varied approaches to deal with these uncertainties based on their risk utility or perception. This research presents the development of a model for investigating the impact of risk-based approaches on construction network outcomes. The current study contributes to development of a model that enables corporate managers to understand the impact of different resource utilization and sharing policies on the overall outcome of their project and to select among optimum planning solutions that satisfy their profit margin and capital limitations. This research also enables corporate decision makers to have more realistic estimates for the profitability of their company, and understand consequences of their decisions in short and long term. Findings of this research provide decision makers with different solutions for profitability of their corporation based on non-dominated optimal time-cost trade-offs, and also broader perspective on how overall time and budget limitations, as well as risk perceptions, can affect the decision-making process. The model is verified and the results are validated through acquiring data from actual large scale construction projects in South Florida

An integer linear programming model including time, cost, quality, and safety

Time, cost, quality and safety, are the four critical elements that contribute to project success. Traditionally, literature has focused on analysing only time and cost. Subsequently, other multi-objective optimization methods developed to optimize time, cost, quality or safety have been developed. New types of contracting methods designed by governments, included maximizing construction quality while minimizing its time and cost. Recently, due to the fact that the construction industry suffers from more accidents of greater severity than other industrial sectors, safety has become one of the four critical elements that contribute to project success. The project scheduling literature largely concentrates on the generation of a precedence and resource feasible schedule that optimizes the scheduling objective and that should serve as a baseline schedule for executing the project. However, these models do not allow to analyse alternative work plans that consider the trade-offs between time, cost, quality, and safety. In this paper, an integer linear programming problem is applied to a decision-CPM network in order to obtain an overall optimum including time, cost, quality and safety in a road building project. Using this type of model, the effects of alternative methods of performing the tasks can be considered and a greater degree of interaction between the planning and scheduling phases of a project is obtained

Simulation and optimization model for the construction of electrical substations

One of the most complex construction projects is electrical substations. An electrical substation is an auxiliary station of an electricity generation, transmission and distribution system where voltage is transformed from high to low or the reverse using transformers. Construction of electrical substation includes civil works and electromechanical works. The scope of civil works includes construction of several buildings/components divided into parallel and overlapped working phases that require variety of resources and are generally quite costly and consume a considerable amount of time. Therefore, construction of substations faces complicated time-cost-resource optimization problems. On another hand, the construction industry is turning out to be progressively competitive throughout the years, whereby the need to persistently discover approaches to enhance construction performance. To address the previously stated afflictions, this dissertation makes the underlying strides and introduces a simulation and optimization model for the execution processes of civil works for an electrical substation based on database excel file for input data entry. The input data include bill of quantities, maximum available resources, production rates, unit cost of resources and indirect cost. The model is built on Anylogic software using discrete event simulation method. The model is divided into three zones working in parallel to each other. Each zone includes a group of buildings related to the same construction area. Each zone-model describes the execution process schedule for each building in the zone, the time consumed, percentage of utilization of equipment and manpower crews, amount of materials consumed and total direct and indirect cost. The model is then optimized to mainly minimize the project duration using parameter variation experiment and genetic algorithm java code implemented using Anylogic platform. The model used allocated resource parameters as decision variables and available resources as constraints. The model is verified on real case studies in Egypt and sensitivity analysis studies are incorporated. The model is also validated using a real case study and proves its efficiency by attaining a reduction in model time units between simulation and optimization experiments of 10.25% and reduction in total cost of 4.7%. Also, by comparing the optimization results by the actual data of the case study, the model attains a reduction in time and cost by 13.6% and 6.3% respectively. An analysis to determine the effect of each resource on reduction in cost is also presented

Finding Optimum Resource Allocation to Optimizing Construction Project Time/Cost through Combination of Artificial Agents CPM and GA

In order to plan a construction project, computer simulations are frequently used to predict the performance of the operations through simulating the process flows and resource selection procedure. However, for finding the optimum resource allocation of the construction activities, all possible combinations must be tested through simulation study. If the number of activities and allocated resources are high, the numbers of these combinations become too large, then this process would not be economical task to do. Therefore, simulation analysis is no longer considered through an optimization technique. Using of Genetic Algorithms (GA) is one of the simple and widely used tools for optimizing heavy intensive engineering problems which can covers various areas of research. With keeping this in mind, this study presented a new hybrid model which integrated agent based modeling with CPM and GA to find out the best resource allocation combination for the construction project’s activities. Based on the results obtained, this new hybrid model can eectively find the optimum resource allocation with respect to time, cost, or any combination of time-cost

Multi-Objective Multi-Project Construction Scheduling Optimization

In construction industry, contractors usually manage and execute multiple projects simultaneously within their portfolio. This involves sharing of limited resources such as funds, equipment, manpower, and others among different projects, which increases the complexity of the scheduling process. The allocation of scarce resources then becomes a major objective of the problem and several compromises should be made to solve the problem to the desired level of optimality. In such cases, contractors are generally concerned with optimizing a number of different objectives, often conflicting among each other. Thus, the main objective of this research is to develop a multi-objective scheduling optimization model for multiple construction projects considering both financial and resource aspects under a single platform. The model aims to help contractors in devising schedules that obtain optimal/near optimal tradeoffs between different projects’ objectives, namely: duration of multiple projects, total cost, financing cost, maximum required credit, profit, and resource fluctuations. Moreover, the model offers the flexibility in selecting the desired set of objectives to be optimized together. Three management models are built in order to achieve the main objective which involves the development of: (1) a scheduling model that establishes optimal/near optimal schedules for construction projects; (2) a resource model to calculate the resource fluctuations and maximum daily resource demand; and (3) a cash flow model to calculate projects’ financial parameters. The three management models are linked with the designed optimization model, which consequently performs operations of the elitist non-dominated sorting genetic algorithm (NSGA-II) technique, in three main phases: (1) population initialization; (2) fitness evaluation; and (3) generation evolution. The optimization model is implemented and tested using different case studies of different project sizes obtained from literature. Finally, an automated tool using C# language is built with a friendly graphical user interface to facilitate solving multi-objective scheduling optimization problems for contractors and practitioners