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

Optimized Resource-Constrained Method for Project Schedule Compression

Construction projects are unique and can be executed in an accelerated manner to meet market conditions. Accordingly, contractors need to compress project durations to meet client changing needs and related contractual obligations and recover from delays experienced during project execution. This acceleration requires resource planning techniques such as resource leveling and allocation. Various optimization methods have been proposed for the resource-constrained schedule compression and resource allocation and leveling individually. However, in real-world construction projects, contractors need to consider these aspects concurrently. For this purpose, this study proposes an integrated method that allows for joint consideration of the above two aspects. The method aims to optimize project duration and costs through the resources and cost of the execution modes assigned to project activities. It accounts for project cost and resource-leveling based on costs and resources imbedded in these modes of execution. The method's objective is to minimize the project duration and cost, including direct cost, indirect cost, and delay penalty, and strike a balance between the cost of acquiring and releasing resources on the one hand and the cost of activity splitting on the other hand. The novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting. The proposed method utilizes the fuzzy set theory (FSs) for modeling uncertainty associated with the duration and cost of project activities and genetic algorithm (GA) for scheduling optimization. The method has five main modules that support two different optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; sensitivity IV analysis module; and decision-support module. The two optimization methods use the genetic algorithm as an optimization engine to find a set of non-dominated solutions. One optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the other uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization method is coded in python as a stand-alone automated computerized tool to facilitate the needed iterative rescheduling of project activities and project schedule optimization. The developed method is applied to a numerical example to demonstrate its use and to illustrate its capabilities. Since the adopted numerical example is not a resource-constrained optimization example, the proposed optimization methods are validated through a multi-layered comparative analysis that involves performance evaluation, statistical comparisons, and performance stability evaluation. The performance evaluation results demonstrated the superiority of the NSGA-II against the dynamic weighted optimization genetic algorithm in finding better solutions. Moreover, statistical comparisons, which considered solutions’ mean, and best values, revealed that both optimization methods could solve the multi-objective time-cost optimization problem. However, the solutions’ range values indicated that the NSGA-II was better in exploring the search space before converging to a global optimum; NSGA-II had a trade-off between exploration (exploring the new search space) and exploitation (using already detected points to search the optimum). Finally, the coefficient of variation test revealed that the NSGA-II performance was more stable than that of the dynamic weighted optimization genetic algorithm. It is expected that the developed method can assist contractors in preparation for efficient schedule compression, which optimizes schedule and ensures efficient utilization of their resources

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities

Cash flow optimization for construction engineering portfolios

One of the main issues in construction projects is finance; proper cash-flow management is necessary to insure that a construction project finishes within time, on budget, and yielding a satisfying profit. Poor financial management might put the contractor, or the owner, in a situation where they are unable to finance the project due to insufficient liquidity, or where they are engaged in excessive loans to finance the project, decreasing the profit, and even creating unsettled debts. Engagement with a portfolio of large construction projects, like infrastructure projects, makes attention to finance more critical, due to large budgets and long project durations, which also requires attention to the time value of money when the project spans over many years and the work environment has a high inflation rate. This thesis aims at the analysis and optimization of the cash-flow request for large engineering portfolios from the contractor\u27s point of view. A computational model, with a friendly user interface, was created to achieve that. The user is able to create a portfolio of projects, and create activities in them with different relationship types, lags, constraints, and costs, as similar to commercial scheduling software. Parameters necessary for the renumeration are also considered, which include the down payment percentage, duration between invoices, duration for payment, retention percentage, etc. The model takes into consideration the time value of money, calculated with an interest rate assigned to the projects by the user; this could be the inflation rate or the (Minimum Attractive Rate of Return) MARR of the contractor. Optimization is done with the objective of maximizing the Net Present Value (NPV) for the projects as a whole, discounted at the start of the portfolio. The variables for the optimization are lags that are assigned for each activity, which, after rescheduling, delays the activities after their early start with the value of those lags, and thus creates a modified cash flow for the project. Optimization of those variables, within scheduling constraints results in a near-optimum NPV. Verification of the model was done using sets of portfolios, and the validation was done using an actual construction portfolio from real life. The results were satisfactory and matched initial expectations. The NPV was successfully optimized to a near optimum. A sensitivity analysis of the model was conducted and it showed that the model behaves as expected for different inputs. A time test was performed, taking into consideration the effect of the size and complexity of a portfolio on the calculation time for the model, and it showed that the speed was satisfactory, though it should be improved. Overall, the conclusion is that the model delivers its goal of maximizing the Net Present Value of a large portfolio as a whole

Time-cost-quality trade-off analysis for construction projects

The main objective of construction projects is to finish the project according to an available budget, within a planned schedule, and achieving a pre-specified extent of quality. Therefore, time, cost, and quality are considered the most important attributes of construction projects. The purpose of this study is to incorporate quality into the traditional two-dimensional time-cost trade-off (TCT) in order to develop an advanced three-dimensional time-cost-quality trade-off (TCQT) approach. Time, cost, and quality of construction projects are interrelated and have impacts on each other. It is a challenging task to strike a balance among these three conflicting objectives of construction projects since no one solution can be optimal for the three objectives. The overall performance of a project regarding time, cost, and quality is determined by the duration, cost, and quality of its activities. These attributes of each activity depend on the execution option by which the activity’s work is completed. It is required to develop an approach that is capable of finding an optimal or near optimal set of execution options for the project’s activities in order to minimize the project’s total cost and total duration, while its overall quality is maximized. For the aforementioned purpose, three various Microsoft Excel based TCQT models have been developed as follows: • First, a simplified model is developed with the objective of optimizing the total duration, cost, and quality of simple construction projects utilizing the GA-based Excel add in Evolver. • Second, a stochastic model is developed with the objective of optimizing the total duration, cost, and quality of construction projects applying the PERT approach in order to consider uncertainty associated with the performance of execution options and the whole project. • Third, an advanced multi objective optimization model is developed utilizing a self-developed optimization tool having the following capabilities: 1. Selecting an appropriate execution option for each activity within a considered project to optimize the objectives of time, cost, and quality. 2. Considering the discrete nature of duration, cost, and quality of various options for executing each activity. 3. Applying three various optimization approaches, which are the Goal Programming (GP), the Modified Adaptive Weight Approach (MAWA), and the Non-dominated Sorting Genetic Algorithms (NSGAII). 4. Analyzing both TCT and TCQT problems. 5. Considering finish-to-finish, start-to-start, and start-to-finish dependency relationships in addition to the traditional finish-to-start relationships among activities. 6. Considering any number of successors and predecessors for activities. 7. User-friendly input and output interfaces to be used for large-scale projects. To validate the developed models and demonstrate their efficiency, they were applied to case studies introduced in literature. Results obtained by the developed models demonstrated their effectiveness and efficiency in analyzing both TCT and TCQT problems

Distribution network reconfiguration using time-varying acceleration coefficient assisted binary particle swarm optimization

The particle swarm optimization (PSO) algorithm is widely used to solve a variety of complicated engineering problems. However, PSO may suffer from an effective balance between local and global search ability in the solution search process. This study proposes a new acceleration coefficient for the PSO algorithm to overcome this issue. The proposed coefficient is implemented on the distribution network reconfiguration (DNR) problem to reduce power loss. The lowest power loss is obtained while problem constraints (maintain radial structure, voltage limits, and power flow balance) are satisfied with the proposed method. The validity of the proposed acceleration coefficient-based binary particle swarm optimization (BPSO) in handling the DNR problem is examined through simulation studies on IEEE 33-bus, P&G 69-bus, and 84-bus Taiwan Power Company (TPC) practical distribution networks. Furthermore, the DNR problem is evaluated regarding energy cost and environmental issues. Finally, the average computational times of the different acceleration coefficient-based PSO methods are compared. The solution speed of the proposed acceleration coefficient-based method is faster than the other methods in the DNR problem

An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution

Nowadays, data caching is being used as a high-speed data storage layer in mobile edge computing networks employing flow control methodologies at an exponential rate. This study shows how to discover the best architecture for backhaul networks with caching capability using a distributed offloading technique. This article used a continuous power flow analysis to achieve the optimum load constraints, wherein the power of macro base stations with various caching capacities is supplied by either an intelligent grid network or renewable energy systems. This work proposes ubiquitous connectivity between users at the cell edge and offloading the macro cells so as to provide features the macro cell itself cannot cope with, such as extreme changes in the required user data rate and energy efficiency. The offloading framework is then reformed into a neural weighted framework that considers convergence and Lyapunov instability requirements of mobile-edge computing under Karush Kuhn Tucker optimization restrictions in order to get accurate solutions. The cell-layer performance is analyzed in the boundary and in the center point of the cells. The analytical and simulation results show that the suggested method outperforms other energy-saving techniques. Also, compared to other solutions studied in the literature, the proposed approach shows a two to three times increase in both the throughput of the cell edge users and the aggregate throughput per cluster

Modeling and Solving Flow Shop Scheduling Problem Considering Worker Resource

In this paper, an uninterrupted hybrid flow scheduling problem is modeled under uncertainty conditions. Due to the uncertainty of processing time in workshops, fuzzy programming method has been used to control the parameters of processing time and preparation time. In the proposed model, there are several jobs that must be processed by machines and workers, respectively. The main purpose of the proposed model is to determine the correct sequence of operations and assign operations to each machine and each worker at each stage, so that the total completion time (Cmax) is minimized. Also this paper, fuzzy programming method is used for control unspecified parameter has been used from GAMS software to solve sample problems. The results of problem solving in small and medium dimensions show that with increasing uncertainty, the amount of processing time and consequently the completion time increases. Increases from the whole work. On the other hand, with the increase in the number of machines and workers in each stage due to the high efficiency of the machines, the completion time of all works has decreased. Innovations in this paper include uninterrupted hybrid flow storage scheduling with respect to fuzzy processing time and preparation time in addition to payment time. The allocation of workers and machines to jobs is another innovation of this article

The AddACO: A bio-inspired modified version of the ant colony optimization algorithm to solve travel salesman problems

The Travel Salesman Problem (TSP) consists in finding the minimal-length closed tour that connects the entire group of nodes of a given graph. We propose to solve such a combinatorial optimization problem with the AddACO algorithm: it is a version of the Ant Colony Optimization method that is characterized by a modified probabilistic law at the basis of the exploratory movement of the artificial insects. In particular, the ant decisional rule is here set to amount in a linear convex combination of competing behavioral stimuli and has therefore an additive form (hence the name of our algorithm), rather than the canonical multiplicative one. The AddACO intends to address two conceptual shortcomings that characterize classical ACO methods: (i) the population of artificial insects is in principle allowed to simultaneously minimize/maximize all migratory guidance cues (which is in implausible from a biological/ecological point of view) and (ii) a given edge of the graph has a null probability to be explored if at least one of the movement trait is therein equal to zero, i.e., regardless the intensity of the others (this in principle reduces the exploratory potential of the ant colony). Three possible variants of our method are then specified: the AddACO-V1, which includes pheromone trail and visibility as insect decisional variables, and the AddACO-V2 and the AddACO-V3, which in turn add random effects and inertia, respectively, to the two classical migratory stimuli. The three versions of our algorithm are tested on benchmark middle-scale TPS instances, in order to assess their performance and to find their optimal parameter setting. The best performing variant is finally applied to large-scale TSPs, compared to the naive Ant-Cycle Ant System, proposed by Dorigo and colleagues, and evaluated in terms of quality of the solutions, computational time, and convergence speed. The aim is in fact to show that the proposed transition probability, as long as its conceptual advantages, is competitive from a performance perspective, i.e., if it does not reduce the exploratory capacity of the ant population w.r.t. the canonical one (at least in the case of selected TSPs). A theoretical study of the asymptotic behavior of the AddACO is given in the appendix of the work, whose conclusive section contains some hints for further improvements of our algorithm, also in the perspective of its application to other optimization problems