Time Cost Quality Trade-off Research Articles

Utilizing artificial intelligence to solving time – cost – quality trade-off problem

This study presents the Slime Mold Algorithm (SMA) to solve the time—cost—quality trade-off problem in a construction project. The proposed SMA is a flexible and efficient algorithm in exploration and exploitation to reach the best optimal solution to process the input model’s data. This paper aims to discuss and solve the optimization problem and compare the evaluation with other algorithms such as Opposition-based Multiple Objective Differential Evolution, Non-dominated sorting genetic algorithm, Multiple objective particle swarm optimization, Multiple objective differential evolution and Chaotic initialized multiple objective differential evolution (CAMODE) to verify the efficiency and potential of the proposed algorithm. According to the analysis results, the SMA model generated a diversification measure for case studies, producing superior outcomes to those of previous algorithms.

A new interval-valued fuzzy multi-objective approach for project time–cost–quality trade-off problem with activity crashing and overlapping under uncertainty

PurposeMost projects are facing delays, and accelerating the pace of project progress is a necessity. Project managers are responsible for completing the project on time with minimum cost and with maximum quality. This study provides a trade-off between time, cost, and quality objectives to optimize project scheduling.Design/methodology/approachThe current paper presents a new resource-constrained multi-mode time–cost–quality trade-off project scheduling model with lags under finish-to-start relations. To be more realistic, crashing and overlapping techniques are utilized. To handle uncertainty, which is a source of project complexity, interval-valued fuzzy sets are adopted on several parameters. In addition, a new hybrid solution approach is developed to cope with interval-valued fuzzy mathematical model that is based on different alpha-levels and compensatory methods. To find the compatible solution among conflicting objectives, an arithmetical average method is provided as a compensatory approach.FindingsThe interval-valued fuzzy sets approach proposed in this paper is denoted to be scalable, efficient, generalizable and practical in project environments. The results demonstrated that the crashing and overlapping techniques improve time–cost–quality trade-off project scheduling model. Also, interval-valued fuzzy sets can properly manage expressions of the uncertainty of projects which are realistic and practical. The proposed mathematical model is validated by solving a medium-sized dataset an adopted case study. In addition, with a sensitivity analysis approach, the solutions are compared and the model performance is confirmed.Originality/valueThis paper introduces a new continuous-based, resource-constrained, and multi-mode model with crashing and overlapping techniques simultaneously. In addition, a new hybrid compensatory solution approach is extended based on different alpha-levels to handle interval-valued fuzzy multi-objective mathematical model of project scheduling with influential uncertain parameters.

Fuzzy Multi-Mode Time–Cost–Quality Trade-Off Optimization in Construction Management of Hydraulic Structure Projects

Along with the increased use of water resources, some large water conservancy projects began construction to address power supply shortages and control flooding and drainage. As investment grows, construction cycles lengthen, external environmental impacts become bigger, and civil engineering project management becomes more complex. The real aim of the hydraulic-structure engineering project model is to manage ways of delivering the project on time while maintaining reasonable quality standards and building costs, to optimize project value. We note that the trade-off between conflicting objectives in a water conservancy project in an uncertain environment is a difficult task. To simulate the relationship between a project’s construction quality and its time limit, two new piecewise functions—a double exponential function and a quadratic function—were proposed, and then a fuzzy multi-mode discrete time–cost–quality trade-off concept for water-management projects was established. This model finds the best solution to an NP-hard problem using the particle-swarm optimization algorithm (PSO). A comparison of the calculations to previous studies validates the model and its computational approach. The optimized results of a water conservation project are provided as a conceptual framework for project planning and construction timeframes.

Trading off Time–Cost–Quality in Construction Project Scheduling Problems with Fuzzy SWARA–TOPSIS Approach

The increasing number of construction projects together with the limited resources of organizations led to tough competition for achieving project goals. Time, cost, and quality have been known as the project iron triangle. Project managers attempt to allocate the appropriate resources and make the best decisions for accomplishing projects with the shortest durations, lowest costs, and the highest quality. No study has examined the time–cost–quality trade-off problem with decision-making approaches. In this study, the fuzzy multi-criteria decision-making (MCDM) methods are exploited to choose the best mode for performing each activity. For this purpose, the SWARA method is applied to determine the importance weights of time, cost, and quality. In addition, the TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) technique is used to rank and select the best activity execution modes. The proposed model is implemented on two medium- and large-size construction projects to evaluate its efficiency. Several execution modes with fuzzy duration, cost, and quality are considered for each project activity. Finally, sensitivity analysis is conducted taking three different conditions into account: the shortest duration of the execution modes, the lowest cost of the execution modes, and the highest quality of execution modes for each activity. The solution of each trade-off is compared with the solution obtained from the fuzzy SWARA–TOPSIS method. The schedule is developed according to the best execution mode for each project activity. The obtained results in two different construction projects show significant improvements in the overall project objectives so that the projects can be completed in fewer durations and costs along with higher quality. Because of the higher importance of cost, the cost of each activity is closer to the lowest cost. The activity duration is also closer to the most likely duration, and quality is closer to the high-quality level. The application of this approach can create new opportunities for research and knowledge development in the field of construction project scheduling.

Optimization of environmental impacts of construction projects: a time–cost–quality trade-off approach

The construction industry and the implementation of civil projects are considered to be one of the causes of environmental pollution. Given the impacts and pollution created by the implementation of projects, it is necessary to identify the environmental impacts in order to reduce their effects. The purpose of this paper is to present a discrete time–cost–quality–environmental impacts trade-off problem for the construction industry with multiple execution modes for project activities in order to reduce the environmental impacts of the project implementation. A multi-objective planning model was developed in this problem to address the four objectives of minimizing time, cost, and environmental impacts and maximizing quality of the project implementation. Then, the problem was modeled as a single-objective programming model by converting the objective function to a constraint. The Leopold matrix method was used to evaluate environmental impacts. Finally, part of a rural water supply project was used as a practical example to demonstrate the applicability and efficiency of the model. The results of this study showed that the proposed method to optimize the aforementioned objectives is highly efficient, and planning and decision making in the early stages of a project to choose the method of doing the project activities will result in reducing costs, time, environmental impacts, and enhancing the quality of the project. By calculating the environmental impacts of project activities in each execution mode, project managers and stakeholders can take into account the environment around them, along with pursuing the economic, time, and quality objectives of the project, and minimize the project overall negative environmental impacts as a measurable amount. This research can help project managers choose the most appropriate method to execute their activities so that the project will ultimately be delivered with the least amount of time, cost, and environmental impact, and with the highest quality.

Optimizing temporary work and overtime in the Time Cost Quality Trade-off Problem

In spite of its significant contribution to project success, quality has been scarcely addressed in the literature on deterministic project scheduling problems. Although it is recognized that higher qualities are associated with longer processing times, no relationship between quality and resource consumption has been analytically derived to support this statement. As manufacturing projects can be accelerated using additional manpower such as overtime and temporary workers, we derive an analytical relationship between quality and manpower since overtime and overmanning negate quality. We also take into account productivity losses due to overmanning. Contrary to most previous contributions that focus on the project overall quality as an aggregation of quality levels attained at the individual activities, we impose each activity to reach a minimum quality threshold, which is consistent with project management practices. Consequently, we develop a mixed integer linear programming (MILP) to optimize temporary work and overtime so as to accelerate a project with quality and productivity considerations. The objective is to simultaneously determine for each activity the number of permanent, temporary and overtime workers over the processing periods in order to minimize the makespan, the total cost and the overall quality losses subject to individual quality constraints, precedence relationships, nonpreemption and availability of resources. Our approach is successfully applied on numerous instances based on a real project of a high speed locomotive as well as on other projects taken from the literature.

Time-Cost-Quality Trade-off Model for Optimal Pile Type Selection Using Discrete Particle Swarm Optimization Algorithm

The cost of pile foundations is part of the super structure cost, and it became necessary to reduce this cost by studying the pile types then decision-making in the selection of the optimal pile type in terms of cost and time of production and quality .So The main objective of this study is to solve the time–cost–quality trade-off (TCQT) problem by finding an optimal pile type with the target of "minimizing" cost and time while "maximizing" quality. There are many types In the world of piles but in this paper, the researcher proposed five pile types, one of them is not a traditional, and developed a model for the problem and then employed particle swarm optimization (PSO) algorithm, as one of evolutionary algorithms with the help of (Mat lab software), as a tool for decision making problem about choosing the best alternative of the traded piles, and proposes a multi objective optimization model, which aims to optimize the time, cost and quality of the pile types, and assist in selecting the most appropriate pile types. The researcher selected 10 of senior engineers to conduct interviews with them. And prepared some questions for interviews and open questionnaire. The individuals are selected from private and state sectors each one have 10 years or more experience in pile foundations work. From personal interviews and field survey the research has shown that most of the experts, engineers are not fully aware of new soft wear techniques to helps them in choosing alternatives, despite their belief in the usefulness of using modern technology and software. The Problem is multi objective optimization problem, so after running the PSO algorithm it is usual to have more than one optimal solution, for five proposed pile types, finally the researcher evaluated and discussed the output results and found out that pre-high tension spun (PHC)pile type was the optimal pile type.

Optimizing multi-mode time-cost-quality trade-off of construction project using opposition multiple objective difference evolution

The time, cost and quality are crucial, conflicting aspects to construction project management. Tradeoff optimization among project duration (time), project cost, and the project quality within the project scope is necessary to enhance overall construction project benefit. A novel optimization algorithm, Opposition-based Multiple Objective Differential Evolution (OMODE), is presented to solve the time-cost-quality tradeoff (TCQT) problem. This novel algorithm employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. A numerical high-way construction project case study is analyzed to illustrate the use of the algorithm and demonstrate its capabilities in generating non-dominated solutions. Comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previous results verify the efficiency and effectiveness of the proposed algorithm. This study is expected to provide an alternative solving methodology for the TCQT problem and help project manager plan construction methods with optimal time-cost-quality tradeoff.

Time Cost Quality Trade-off Problems: A survey exploring the assessment of quality

Abstract Time Cost Trade-off Problems have received considerable attention in the literature on deterministic project scheduling problems but integrating the quality factor to these problems dates back to the mid-nineties with the pioneering work of Babu and Suresh (1996) . Since then, to the best of our knowledge, about twenty papers have been published on this topic. The present paper analyses these Time Cost Quality Trade-off Problems in light of the usual classification for Time Cost Trade-off Problems that is based upon the number and category of resources and on the continuous or discrete type of the relationship between duration and cost or resources utilisation. In this survey, the emphasis is on the definition of project activities quality and on aggregation methods used to derive the overall project quality. We report the absence of a direct relationship between quality and resources allocated to activities and a lack of use of the lexicographic method to solve the problem.

A New Stochastic Time-Cost-Quality Trade-Off Project Scheduling Problem Considering Multiple-Execution Modes, Preemption, and Generalized Precedence Relations

Project managers always try to make the best decisions in order to complete their projects in the shortest period of time, with the least amount of costs, and with the highest degree of quality. Therefore, the process of decision-making is conducted in a triangle of time, costs, and quality. This triangle is a crucial part of management process throughout the execution of the project. However, in all problems, some unpredictable situations may be faced. In such situations, some or all parameters of the problem are expressed by uncertain variables. In this paper, a stochastic time-costquality trade off project scheduling problem (STCQTP) considering multiple-execution modes, preemption, and generalized precedence relations is developed. In order to solve the STCQTP, a hybrid solution approach based on Stochastic Chance Constraint Programming (SCCP) and Goal Programming (GP) is proposed. SCCP and GP are used to handle the uncertain nature and multiple-objectives of STCQTP, respectively. Numerical example is solved in order to illustrate the applicability of proposed model and solution approach.

Solving multi-mode time–cost–quality trade-off problems under generalized precedence relations

In this paper, we model a multi-mode time–cost–quality trade-off project scheduling problem under generalized precedence relations using mixed-integer mathematical programming. Several solution procedures, including the classical epsilon-constraint, the efficient epsilon-constraint method, dynamic self-adaptive multi-objective particle swarm optimization (DSAMOPSO), and the multi-start partial bound enumeration algorithm, are provided to solve the proposed model. Several test problems are simulated and solved with the four methods and the performance of the methods are compared according to a set of accuracy and diversity comparison metrics. Additional analyses and tests are performed on the generated Pareto fronts of the solution procedures. Computational experiments are conducted to determine the validity and the efficiency of the DSAMOPSO method. The results show that this method outperforms the other three methods. We also carry out a sensitivity analysis of the DSAMOPSO algorithm to study the effects of parameter changes on the CPU time.

A novel multi criteria decision making model for optimizing time–cost–quality trade-off problems in construction projects

The planning phase of every construction project is entangled with multiple and occasionally conflicting criteria which need to be optimized simultaneously. Multi-criterion decision-making (MCDM) approaches can aid decision-makers in selecting the most appropriate solution among numerous potential Pareto optimal solutions. An evidential reasoning (ER) approach was applied for the first time in the context of project scheduling to identify the best Pareto solution for discrete time–cost–quality trade-off problems (DTCQTPs). An exhaustive framework to synthesize the MCDM approaches with multi-objective optimization techniques was also proposed. To identify all global Pareto optimal solutions, a multi-objective genetic algorithm (MOGA) incorporating the NSGA-II procedure was developed and tested in a highway construction project case study. The Shannon’s entropy technique served to determine the relative weights of the objectives according to their contributions to the uncertainty of the results obtained. A benchmark case study of DTCQTP was solved using the proposed methodology, and the Pareto optimal solutions obtained were subsequently ranked using the ER approach. By investigating the performance of each scheduling alternative based on multiple criteria (e.g., time, cost, and quality), the proposed approach proved effective in raising the efficiently of construction project scheduling.

Hybrid multiple objective artificial bee colony with differential evolution for the time–cost–quality tradeoff problem

Time, cost, and quality are three important but often conflicting factors that must be optimally balanced during the planning and management of construction projects. Tradeoff optimization among these three factors within the project scope is necessary to maximize overall project success. In this paper, the MOABCDE-TCQT, a new hybrid multiple objective evolutionary algorithm that is based on hybridization of artificial bee colony and differential evolution, is proposed to solve time–cost–quality tradeoff problems. The proposed algorithm integrates crossover operations from differential evolution (DE) with the original artificial bee colony (ABC) in order to balance the exploration and exploitation phases of the optimization process. A numerical construction project case study demonstrates the ability of MOABCDE-generated, non-dominated solutions to assist project managers to select an appropriate plan to optimize TCQT, which is an operation that is typically difficult and time-consuming. Comparisons between the MOABCDE and four currently used algorithms, including the non-dominated sorting genetic algorithm (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC), verify the efficiency and effectiveness of the developed algorithm.

A Multi-Objective Imperialist Competitive Algorithm for solving discrete time, cost and quality trade-off problems with mode-identity and resource-constrained situations

The Discrete Time–Cost–Quality Trade-off Problem (DTCQTP) is one of the most important problems in project scheduling applications. In DTCQTP an optimal combination of construction methods is decided with the objective of minimizing cost and time while maximizing quality. Due to real world resource-constrained situations in projects, this model is expanded to mode-identity resource-constrained DTCQTP model. In this model, each activity has multiple modes to be performed. In each mode, the required resources for performing the activity are different in at least one type. In addition, each activity can be done either in a normal way or in a crashing way in each performing mode. Based on the mode-identity situation, the activities belonging to the same subset should be executed in the same mode. In this paper, we present a Multi-Objective Imperialist Competitive Algorithm (MOICA) to solve our proposed model. We explain the elements of the algorithm and solve some problems generated for this model – including large size and small size instances – by this algorithm. The performance of our proposed algorithm is evaluated by comparison with four well-known algorithms: NSGAII; PESAII-clustering; PESAII-grid based; and SPEA2. The obtained results show the effectiveness of the proposed algorithm.

A fuzzy clustering-based genetic algorithm approach for time–cost–quality trade-off problems: A case study of highway construction project

Recently government agencies have started to utilize innovative contracting methods that provide incentives for improving construction quality. These emerging contracting methods place an enormous pressure on the contractors to improve construction quality. For a general contractor, which subcontracts most tasks of a project and invites a number of bids, choosing an appropriate bid which satisfies the time, cost and quality of construction project is complex and challenging. To solve this problem involving conflicting objectives, a fuzzy clustering-based genetic algorithm (FCGA) approach is proposed in this paper. A case study of highway construction is used to demonstrate the applicability of the proposed approach. A comparative study is conducted over three test cases involving varying dimensions and complexities to test performance of the proposed FCGA against existing approaches. Results reveal that the FCGA is capable of generating better Pareto front than other existing approaches.

Extraction of decision alternatives in construction management projects: Application and adaptation of NSGA-II and MOPSO

The time-cost trade-off problem is a known bi-objective problem in the field of project management. Recently, a new parameter, the quality of the project has been added to previously considered time and cost parameters. The main specification of the time-cost trade-off problem is discretization of the decision space to limited and accountable decision variables. In this situation the efficiency of the traditional methods decrease and applying of the evolutionary algorithms is necessary. In this paper, two evolutionary algorithms that originally search the decision space in a continuous manner including: (1) multi-objective particle swarm optimization (MOPSO) and (2) nondominated sorting genetic algorithm (NSGA)-II, are considered as the optimization tools to solve two construction project management problems. These problems are both in discrete domain including two or tree objectives, separately. In this regard, some procedures has been suggested and then applied to adopt both algorithms capable in solving the problems in a discrete domain. Results show the advantages and effectiveness of the used procedures in reporting the optimal Pareto for the optimization problems. Moreover, the NSGA-II is more successful in determining optimal alternatives in both time-cost trade-off (TCTO) and time-cost-quality trade-off (TCQTO) problems than the MOPSO algorithm.

Fuzzy-multi-objective particle swarm optimization for time–cost–quality tradeoff in construction

The time–cost–quality tradeoff (TCQT) problem is to decide an optimal combination of construction methods with the objective of minimizing cost and time while maximizing quality. Searching for such an optimal combination of construction methods needs to evaluate the total cost, time and quality of the project. These performances, especially the quality, may be collected and recorded in terms of imprecise or vague data rather than precise numbers. This paper presents a fuzzy-multi-objective particle swarm optimization to solve the fuzzy TCQT problem. The time, cost and quality are described by fuzzy numbers and a fuzzy multi-attribute utility methodology incorporated with constrained fuzzy arithmetic operations is adopted to evaluate the selected construction methods. The particle swarm optimization is applied to search for the TCQT solutions by incorporating the fuzzy multi-attribute utility methodology. The proposed methodology is implemented and justified through computational analyses. The study is expected to provide an alternative methodology for solving the time–cost–quality tradeoff problem.