Time-cost Optimization Problem Research Articles

Smart Contract-Based Right-of-Way Trade with Game Theory in Blockchain-Enabled Internet of Vehicles

The Internet of Vehicles (IoV) has a significant impact on improving traffic efficiency and driving safety. In this paper, we propose an intelligent transportation credit system based on blockchain, and design a crossroad passing smart contract that allows time-sensitive convoys to pass a crossroad earlier by paying traffic tokens for right-of-way trade. Second, this paper formulates the time–cost optimization problem under the premise of protecting the privacy of preceding convoys. Based on game theory with incomplete information, two right-of-way optimal bid strategies are given. Both theoretical analysis and simulation prove that the strategy proposed in this paper effectively reduces the waiting time of time-sensitive vehicles at a crossroad and increases the trade success probability, which, in turn, improves the expected total profit of convoys and achieves Pareto improvement.

Solving time cost optimization problem with adaptive multi-verse optimizer

Solving time cost optimization problem with adaptive multi-verse optimizer

Modified dynamic opposite learning assisted TLBO for solving Time-Cost optimization in generalized construction projects

This study proposes an improved version of teaching learning-based optimization (TLBO) with a modified dynamic-opposition learning (MDOL) strategy, called modified dynamic oppositional learning TLBO (MDOLTLBO) to narrow the gap between research and practice for generalized time–cost optimization. Instead of opposite numbers, this study employs modified dynamic opposite points for the population initialization and generation jumping. This novel feature of the proposed algorithm is intended to diminish the randomness of the initial population and minimize the search effort required to achieve the optimal set of time–cost alternatives in the search space. An iterative-based varying weighting factor for MDOL is introduced to adjust the range of the search space with the aim of balancing the dynamicity and diversity of the algorithm. Hence, the MDOLTLBO algorithm can identify diversified and high-quality solutions based on non-dominated solutions. Four different case studies of real construction projects, ranging from 29 to 290 activities, were investigated to demonstrate the applicability of the proposed model. The experimental findings verify that the proposed algorithm is efficient, effective, and highly satisfying. Hence, the primary contribution of this study is MDOL approach that can successfully achieve high-quality solutions and good convergence speed for large-scale discrete time–cost optimization problems with generalized logical relationships.

Optimizing of Discrete Time-Cost in Construction Projects Using New Adaptive Weight Formulations

In this paper, four new adaptive weight formulations are developed to enhance the performance of traditional Modified Adaptive Weight Approach (MAWA), which is the simplest way for solving the multi-objective optimization of discrete time-cost trade-off problem (DTCTP). Several numerical experiments are conducted to validate the newly proposed formulations. In addition, three meta-heuristic-based optimization algorithms are utilized to examine the variations within the results of the test instances. The proposed model provides several ways to solve DTCTP, as it includes different formulations and optimization algorithms. So, this model performs well for the medium and large-scale time-cost optimization problems on contrary to the traditional MAWA.

Color Revolution: A Novel Operator for Imperialist Competitive Algorithm in Solving Cloud Computing Service Composition Problem

In this paper, a novel high-performance and low-cost operator is proposed for the imperialist competitive algorithm (ICA). The operator, inspired by a sociopolitical movement called the color revolution that has recently arisen in some countries, is referred to as the color revolution operator (CRO). The improved ICA with CRO, denoted as ICACRO, is significantly more efficient than the ICA. On the other hand, cloud computing service composition is a high-dimensional optimization problem that has become more prominent in recent years due to the unprecedented increase in both the number of services in the service pool and the number of service providers. In this study, two different types of ICACRO, one that applies the CRO to all countries of the world (ICACRO-C) and one that applies the CRO solely to imperialist countries (ICACRO-I), were used for service time-cost optimization in cloud computing service composition. The ICACRO was evaluated using a large-scale dataset and five service time-cost optimization problems with different difficulty levels. Compared to the basic ICA and niching PSO, the experimental and statistical tests demonstrate that the ability of the ICACRO to approach an optimal solution is considerably higher and that the ICACRO can be considered an efficient and scalable approach. Furthermore, the ICACRO-C is stronger than the ICACRO-I in terms of the solution quality with respect to execution time. However, the differences are negligible when solving large-scale problems.

New adaptive weight formulations for time-cost optimization

Modified Adaptive Weight Approach (MAWA) is one of the simplest methods used for solving time-cost optimization problems. This type of optimization problem, categorized as multi-objective optimization, can be solved with metaheuristic algorithms. Here, metaheuristic algorithms evaluate a randomly generated solution set, referred to as population, within the boundary condition of a solution space. The weight factor values determined by MAWA are applied to all solutions in the population without distinguishing the solutions in that population. However, the potential solutions in the population indicate different fitness properties concerning solution space. In this paper to solve time-cost optimization problems, new adaptive weight formulations are proposed. In contrast to MAWA, the novelty of this study’s formulations is to adaptively detect the weight factor value, depending on the solution‘s fitness in the population. The results obtained from the numerical experiments examined in this study show that the proposed formulations can improve the performance of MAWA and can find identical or slightly different Pareto results for investigated multi-objective optimization problems.

Developing an optimization financing cost-scheduling trade-off model in construction project

Time-cost trade-off analysis is one of the most challenging tasks of construction project planners. Project planners face complicated multivariate time-cost optimization problems that require simultaneous minimization of total project duration and total project cost while considering issues related to the optimal present value of profit. In addition, given the complexity of construction projects in recent years, clever management has become increasingly important in cases of project financing and scheduling. There are choices and limits that make it difficult for project planners to develop a proper financing plan considering project time status. Therefore, the methods of financing affect the project plan. Parameters such as interest rate, contract terms, and financing options (i.e., types of the loan) in the project construction period can change the final profit value of the project. Hence, a skilled planner should consider various effective parameters for scheduling projects. This study presents a hybrid meta-heuristic algorithm to solve a multi-objective optimization problem in construction project planning and financing. The main objective of the model is to present a Pareto chart that shows the trade-off between duration of the project and the financing cost by considering different durations for each activity of the project and various financing options. The model is first validated in a simple case study and then applied to a large-scale case study. The model shows the optimal solutions which have time and financing cost. For each optimal solution, the model will create a cash flow containing project (direct and indirect cost and payments) and financing flows (borrowed money and repayments of principal and interest) based on a work schedule. Finally, a sensitivity analysis is performed to examine the effects of changes in the various parameters of the project cash flow on the ultimate results. It is shown that the proposed model is superior to the existing models in finding better project planning solutions with lower total project duration and lower total project financing cost in the proposed construction project financing problems. In addition, the proposed model improves the solutions through generations and provides optimal solutions in acceptable processing time.

DEVELOPING NEW CHARGED SYSTEM SEARCH-BASED ALGORITHM: APPLICATION IN THE TIME-COST TRADE-OFF PROBLEMS

The trade-off between the total cost and project duration is one of the most important parameters of construction project planning. There are various methods to optimize time-cost trade-off problems. Mathematical programming models as one type of them cannot solve large and complex networks effectively. On the other hand, although the meta-heuristics algorithms in many cases can find a complete set of solutions but to optimize the time-cost trade-off problems in very massive construction projects they need to spend a lot of time, so existence a powerful algorithm with higher convergence rate is necessary. In this paper new procedures MAWA-CSS and SMOCSS are introduced to generalize the well-known CSS algorithm for solving TCTP optimization problem and all multi-objective optimization problems in discrete and continuous search space. The overall structure of SMOCSS algorithm is similar to the MOPSO and to determine the Charge magnitude of particles a new simple method is introduced. The proposed method is examined for different test functions and the results are compared to the results of two well-known multi- objective algorithms (NSGA-II and MOPSO). In addition, two example of time-cost optimization problem (Feng and Zheng network with 18 and 7 activities respectively) are used to evaluate the performance of the proposed algorithms. The results indicate that the SMOCSS algorithm has the ability to find out the optimal solution and define the Pareto front as well in reasonable time. Hence the proposed approach in this paper is much adaptive and suitable for tackling TCTP, which is useful and beneficial for decision-making on the trade-off between project duration and total cost.

Time–Cost Optimization Model Considering Float-Consumption Impact

AbstractThe main objectives of construction projects are completing the project on time and within budget. Typically, there is a trade-off between time and cost. Time loss is costly and time savings can provide benefits to all the parties involved in the project. Time–cost optimization is essential for construction projects. The objective of time–cost optimization is to determine the optimum project duration corresponding to the minimum total cost. This is accomplished by shortening the duration of critical activities to reduce the overall project duration. Time–cost optimization techniques result in reducing the available total float for noncritical activities, and thus, reduce the schedule flexibility. This paper presents a nonlinear-integer programming model that is developed to solve the time–cost optimization problem taking into account the impact of total float loss. The model uses What’sBest solver to find an efficient solution to the optimization problem while incorporating the total float-loss co...

Self-adaptive ant colony optimization for construction time-cost optimization

Purpose – This research aims to propose self-adaptive ant colony optimization (SACO) with changing parameters for solving time-cost optimization (TCO) problems to assist the relevant construction management firm with their technological tool. Design/methodology/approach – A SACO with changing parameters based on information entropy has been employed to model TCO problem, which overcomes the intrinsic weakness of premature convergence of the basic ant colony optimization by adjusting parameters according to mean information entropy of the ant system. A computer simulation with Matlab 7.0 based on a prototype example has been carried out on the basis of SACO for TCO problem. Findings – The test results show that the SACO for TCO model can generate a better cost under the same duration and achieve a better Pareto front than other models. Therefore, the SACO can be regarded as a useful approach for solving construction project TCO problems. Research limitations/implications – Further research on selection parameters should be conducted to further improve the robustness of the SACO for TCO model. Practical implications – The modelling results can help the construction management to good result of TCO problems in construction sites. Originality/value – A new approach to study the TCO model is proposed based on SACO.

A Multi-Objective PSO Algorithm Based Niche Technique for Service-Workflows Scheduling

Time-cost optimization problem in service-workflows is a widely existing problem hard to be solved in practical systems. In this paper, an Niche Technique multi-objective PSO method is proposed. Generating initial optimal solutions with single-objective. External Population and Niche Technique and Meshing hybrid method is introduced to obtain an evenly distributed Pareto set. Experimental results prove that the proposed algorithm is efficient and effective. For various characteristic instances, more evenly distributed Pareto Sets are obtained with high quality.

Genetic algorithm for project time-cost optimization in fuzzy environment

Purpose: The aim of this research is to develop a more realistic approach to solve project time-cost optimization problem under uncertain conditions, with fuzzy time periods. Design/methodology/approach: Deterministic models for time-cost optimization are never efficient considering various uncertainty factors. To make such problems realistic, triangular fuzzy numbers and the concept of a-cut method in fuzzy logic theory are employed to model the problem. Because of NP-hard nature of the project scheduling problem, Genetic Algorithm (GA) has been used as a searching tool. Finally, Dev-C++ 4.9.9.2 has been used to code this solver. Findings: The solution has been performed under different combinations of GA parameters and after result analysis optimum values of those parameters have been found for the best solution. Research limitations/implications: For demonstration of the application of the developed algorithm, a project on new product (Pre-paid electric meter, a project under government finance) launching has been chosen as a real case. The algorithm is developed under some assumptions. Practical implications: The proposed model leads decision makers to choose the desired solution under different risk levels. Originality/value: Reports reveal that project optimization problems have never been solved under multiple uncertainty conditions. Here, the function has been optimized using Genetic Algorithm search technique, with varied level of risks and fuzzy time periods.

AN ANT COLONY SYSTEM BASED DECISION SUPPORT SYSTEM FOR CONSTRUCTION TIME-COST OPTIMIZATION

Time and cost are the two most important factors to be considered in every construction project. In order to maximize the profit, both the client and contractor would strive to minimize the project duration and cost concurrently. In the past, most of the research studies related to construction time and cost assumed time to be constant, leaving the analyses based purely on a single objective of cost. Acknowledging this limitation, an evolutionary-based optimization algorithm known as an ant colony system is applied in this study to solve the multi-objective time-cost optimization problems. In this paper, a model is developed using Visual Basic for Application™ which is integrated with Microsoft Project™. Through a test study, the performance of the proposed model is compared against other analytical methods previously used for time-cost modeling. The results show that the model based on the ant colony system techniques can generate better solutions without utilizing excessive computational resources. The model, therefore, provides an efficient means to support planners and managers in making better time-cost decisions efficiently.

STUDY AND APPLY ACO ALGORITHM IN TIME-COST OPTIMIZATION OF CONSTRUCTION PROJECT

Time-cost optimization problem is one of the most important aspects of construction project management. In order to maximize the return, construction planners would strive to optimize the project duration and cost concurrently. Over the years, many researches have been conducted to model the time-cost relationships; the modeling techniques range from the heuristic method and mathematical approach to genetic algorithm. In this paper, an evolutionary-based optimization algorithm known as ant colony optimization (ACO) is applied to solve the multi-objective time-cost problem. By incorporating with the modified adaptive weight approach (MAWA), the proposed model will find out the most feasible solutions. The concept of the ACO-TCO model is developed by a computer program in the Visual Basic platforms. An example was analyzed to illustrate the capabilities of the proposed model and to compare against GA-based TCO model. The results indicate that ant colony system approach is able to generate better solutions without making the most of computational resources which can provide a useful means to support construction planners and managers in efficiently making better time-cost decisions.

Nondominated Archiving Multicolony Ant Algorithm in Time–Cost Trade-Off Optimization

Time–cost trade-off analysis is addressed as an important aspect of any construction project planning and control. Nonexistence of a unique solution makes the time–cost trade-off problems very difficult to tackle. As a combinatorial optimization problem one may apply heuristics or mathematical programming techniques to solve time–cost trade-off problems. In this paper, a new multicolony ant algorithm is developed and used to solve the time–cost multiobjective optimization problem. Pareto archiving together with innovative solution exchange strategy are introduced which are highly efficient in developing the Pareto front and set of nondominated solutions in a time–cost optimization problem. An 18-activity time–cost problem is used to evaluate the performance of the proposed algorithm. Results show that the proposed algorithm outperforms the well-known weighted method to develop the nondominated solutions in a combinatorial optimization problem. The paper is more relevant to researchers who are interested i...

Optimizing Construction Time and Cost Using Ant Colony Optimization Approach

Time and cost are the most important factors to be considered in every construction project. In order to maximize the return, both the client and contractor would strive to optimize the project duration and cost concurrently. Over the years, many research studies have been conducted to model the time–cost relationships, and the modeling techniques range from the heuristic methods and mathematical approaches to genetic algorithms. Despite that, previous studies often assumed the time being constant leaving the analyses based purely on a single objective—cost. Acknowledging the significance of time–cost optimization, an evolutionary-based optimization algorithm known as ant colony optimization is applied to solve the multiobjective time–cost optimization problems. In this paper, the basic mechanism of the proposed model is unveiled. Having developed a program in the Visual Basic platform, tests are conducted to compare the performance of the proposed model against other analytical methods previously used fo...

Applying Pareto Ranking and Niche Formation to Genetic Algorithm-Based Multiobjective Time–Cost Optimization

Time–cost optimization (TCO) is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Although the TCO problem has been extensively examined, many research studies only focused on minimizing the total cost for an early completion. This does not necessarily convey any reward to the contractor. However, with the increasing popularity of alternative project delivery systems, clients and contractors are more concerned about the combined benefits and opportunities of early completion as well as cost savings. In this paper, a genetic algorithms ( GAs ) -driven multiobjective model for TCO is proposed. The model integrates the adaptive weight to balance the priority of each objective according to the performance of the previous “generation.” In addition, the model incorporates Pareto ranking as a selection criterion and the niche formation techniques to improve popularity diversity. Based on the pr...

Using Improved Genetic Algorithms to Facilitate Time-Cost Optimization

Time-cost optimization problems in construction projects are characterized by the constraints on the time and cost requirements. Such problems are difficult to solve because they do not have unique solutions. Typically, if a project is running behind the scheduled plan, one option is to compress some activities on the critical path so that the target completion time can be met. As combinatorial optimization problems, time-cost optimization problems are suitable for applying genetic algorithms (GAs). However, basic GAs may involve very large computational costs. This paper presents several improvements to basic GAs and demonstrates how these improved GAs reduce computational costs and significantly increase the efficiency in searching for optimal solutions.