Constraints Of Limited Resources Research Articles

Optimal multi-user offloading with resources allocation in mobile edge cloud computing

Mobile cloud computing (MCC), as a prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile device (SMD) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich central cloud. Compared to central cloud, edge cloud can provide services to nearby SMDs with lower latency. However, the edge cloud may be mobile and its resources are limited to multiple nearby users. Therefore, how to obtain an optimal offloading policy under the constraints of mobility and limited resource remains a challenging issue. In this paper, we aim to minimize the total execution cost of multiple devices by offloading the computations from SMDs onto edge clouds in edge cloud computing (ECC) system. By considering the mobility of SMDs and edge clouds, we first formulate the total cost minimization problem under the constraints of application completion deadline and connection time between SMDs and edge clouds as well as the limited computation resource of both edge clouds and SMDs. Then, by solving the minimization problem, we propose an optimal offloading selection strategy based on game model, and an edge cloud payoff competition algorithm to optimally allocate edge cloud resource to SMDs to achieve the minimum total execution cost. Experimental results show that our offloading strategy can effectively reduce energy consumption and application completion time compared with the state-of-the-art methods.

State Estimation Under Joint False Data Injection Attacks: Dealing With Constraints and Insecurity

This article is concerned with the security issue in the state estimation problem for a networked control system (NCS). A new model of joint false data injection (FDI) attack is established wherein attacks are injected to both the remote estimator and the communication channels. Such a model is general that includes most existing FDI attack models as special cases. The joint FDI attacks are subjected to limited access and/or resource constraints, and this gives rise to a few attack scenarios to be examined one by one. Our objective is to establish the so-called insecurity conditions under which there exists an attack sequence capable of driving the estimation bias to infinity while bypassing the anomaly detector. By resorting to the generalized inverse theory, necessary and sufficient conditions are derived for the insecurity under different attack scenarios. Subsequently, easy-to-implement algorithms are proposed to generate attack sequences on insecure NCSs with respect to different attack scenarios. In particular, by using a matrix splitting technique, the constraint-induced sparsity of the attack vectors is dedicatedly investigated. Finally, several numerical examples are presented to verify the effectiveness of the proposed FDI attacks.

A Discrete-Event Mathematical Model for Resource Allocation Optimization: A Case Study of Vehicle Scheduling in a Signal-Free Intersection

In industrial applications, many systems present serious productivity problems due to limited resources. Generally, the dynamics of resource allocation are inherently discrete-event driven, such as the buffer allocation in production line systems. In this paper, we develop a discrete-event mathematical model for resource allocation optimization. In this work, we consider two crucial optimization objectives, e.g., deadlock-free and efficiency, that originate from the customer’s actual requirements. The main aim is to develop a resource allocation scheme for fulfilling the production process (without deadlock) while ensuring that the cost of the process is minimized. As a case study, we consider the vehicle scheduling problem in a signal-free intersection. The intersection is divided into several disjoint spatial traffic resources, and vehicles need to occupy different traffic resources for passing through the intersection. Thus, the traffic control problem at the signal-free intersection is transformed into a scheduling problem with limited resource constraints. An online control approach is developed to schedule vehicles to go through the intersection safely and efficiently by optimizing the resource allocation order. Simulation results demonstrate the efficiency and robustness of the proposed model and optimization approach.

Multiple auxiliary classifiers GAN for controllable image generation: Application to license plate recognition

One of the main challenges in developing machine learning (ML) applications is the lack of labeled and balanced datasets. In the literature, different techniques tackle this problem via augmentation, rendering, and over-sampling. Still, these methods produce datasets that appear less natural, exhibit poor balance, and have less variation. One potential solution is to leverage the Generative Adversarial Network (GAN) which achieves remarkable results in the generation of high-fidelity natural images. However, expanding the ability of GANs' to control generated image attributes with supervisory information remains a challenge. This research aims to propose an efficient method to generate high-fidelity natural images with total control of its main attributes. Therefore, this paper proposes a novel Multiple Auxiliary Classifiers GAN (MAC-GAN) framework based on Auxiliary Classifier GAN (AC-GAN), multi-conditioning, Wasserstein distance, gradient penalty, and dynamic loss. It is therefore presented as an efficient solution for highly controllable image synthesis red that allows to enrich and re-balance datasets beyond data augmentation. Furthermore, the effectiveness of MAC-GAN images on a target ML application called Automatic License Plate Recognition (ALPR) under limited resource constraints is probed. The improvement achieved is over 5% accuracy, which is mainly due to the ability of the MAC-GAN to create a balanced dataset with controllable synthesis and produce multiple (different) images with the same attributes, thus increasing the variation of the dataset in a more elaborate way than data augmentation techniques.

Deep multi-agent reinforcement learning for resource allocation in NOMA-enabled MEC

Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) are being considered as promising technologies to address the stringent demands of the emerging fifth generation (5G) networks. This paper investigates the resource allocation problem in NOMA-enabled MEC system for multiple users, by joint optimization of power and computation resources to enhance effective throughput of the system. Because of the severe non convexity of the problem, a decentralized multi-agent reinforcement learning (MARL) scheme is proposed, where each user to base station (U2B) link acts as an agent, and collectively interacts with the network environment, in order to maximize the objective function under limited power and computation resource constraints. Simulation results demonstrate that the proposed MARL scheme results in considerable improvement in the effective throughput of the system, which is comparable to the optimal results derived from the exhaustive optimal search, with significantly low overhead.

Guest Editorial: Special Section on Distributed Intelligence Over Internet of Things

N OWADAYS, billions of devices are connected to the In-ternet, enabling Internet of Things (IoT) systems widely deployed, such as smart city, smart healthcare and intelligent plant, to capture a great quantity of sensing data. Consequently, the data transmission, processing and analysis in IoT applications bring a great pressure to the central server. Fortunately, distributed intelligence becomes one of the potential solutions. Distributed intelligence can greatly relieve server pressures via plenty of terminal devices, and these devices collaboratively perceive and handle the mass data to improve the reliability, s-calability and security of industrial IoT systems. As future IoT system will embrace more wireless sensors and devices, the high-performance computing, high-bandwidth and low-latency communication are excessively required, many new research opportunities and challenges for distributed intelligence over Internet of things have arisen. To promote the development of distributed intelligence technology, this special section (SS) focuses on various technologies and platforms regarding industrial IoT systems. This special section received nearly 50 submitted manuscripts, out of which 10 of them have been accepted after a rigorous peer review. Each manuscript is reviewed by multiple rounds of review with at least three or four reviewers, the problems to be solved and the innovation of each manuscript are mainly concerned. Then the accepted papers are summarized as follows in details. Considering the joint optimization of the offloading decision and resource allocation under limited resource constraints in collaborative edge computing networks with multiple IIoT devices and MEC servers, an improved differential evolution algorithm [7] is proposed to minimize the weighted sum of cost of energy consumption and time delay, which can effectively reduce the system delay and energy consumption. In order to improve the performance of task scheduling in cloud computing, Attiya et al. [1] propose a novel hybrid swarm intelligence method MRFOSSA, which uses a modified Manta-Ray Foraging Optimizer (MRFO) and the Salp Swarm Algorithm (SSA). MRFOSSA is superior to other methods in terms of makespan time and cloud throughput. The research goal of the paper [5] is to design an intelligent computing offloading strategy for industrial applications in order to optimize costs and mitigate energy losses. Then the paper proposes to combine a fog controller and AI-based learning techniques so that the fog controller can intelligently assign tasks to the most appropriate fog devices and find the appropriate path to the target. Considering the resource utilization efficiency under dynamic overload requests and network states in IIoT, Chen et al. [2] propose DRL-based intelligent SFC orchestration scheme and jointly optimize the VNF deployment and SFC embedment by the improved DDQN algorithm, which can improve the performance of resource utilization rate, execution cost and delay compared with other representative schemes. To solve the problem of resource allocation and energy cost in Internet of Vehicles, Kong et al. [8] design a joint computing and caching framework and formulate the problem as a reinforcement learning problem to minimize the energy cost. On this basis, the optimization algorithm based on DDPG is proposed, which can effectively decrease energy costs. To reduce the query numbers of the object model when constructing adversarial examples, Zhang et al. [10] propose generating adversarial examples with shadow model (GASM), i.e., transfering the query operations to the designed shadow model, which can achieve high attack success rates. Chen et al. [3] revise a Decentralized-Wireless-Federated-Learning algorithm (DWFL) which utilizes the superposition property of the analog scheme. It can solve the problem of single failure, limited bandwidth resource and privacy protection in wireless federated learning algorithm, which can be applied widely in wireless IoT networks. To reduce the resource consumption in CNN-based applications, Jia et al. [6] propose the CNN-based Resource Optimization APProach which utilizes model compression and computation sharing to optimize inner-model and inter-model respectively, and the comparison results show the superior performance in scalability and the decrease of resource cost. In mobile crowdsensing activities, Gao et al. [4] propose a differential Location Privacy-preserving Mechanism based on Trajectory obfuscation (LPMT) to protect the location privacy of mobile users, which includes three operations: stay points extraction, stay points obfuscation and stay points sampling. In order to mimic the task-free bottom-up visual attention process by predicting salient regions on natural images, Umer et al. [9] propose a Pseudo Knowledge Distillation (PKD) model based on knowledge distillation and pseudo labelling technique, which is computationally efficient and suitable for real-time on-device saliency prediction.

Bezier5YS and SHYqp: A general framework for generating data and for modeling symmetric and asymmetric orthotropic yield surfaces

The design of conventional yielding criteria, within the phenomenological theory of metal plasticity, is based on a pointwise interpolation of experimental data, an approach originating in the early days of plasticity when it was believed that material parameters should be explicitly linked to experimental data. Since the latter is most often a numerical set of a small size, the inherent consequence was that yield functions were, by design, conceived with a small number of parameters as well. For decades, under the constraints of limited computational resources, this approach has had remarkable successes, particularly in the numerical simulation of metal forming operations. However, with the ever increasing level of automation of the assembly lines of most manufacturers, the demand for tight tolerances has pushed the current modeling establishment to its limits. Furthermore, in the current context of climate change, the need for the reduction of energy consumption in the transportation industries has motivated a significant amount of new research into the plasticity of lighter materials such as magnesium alloys. For these, the conventional approach to yield surface modeling has failed to produce adequate models of their yielding and flow properties. This work explores, in the broader framework of data-driven plasticity, a model based on harmonics expansion with data generated by Bezier interpolation. The resulting parameter identification scheme is a quadratic problem with linear constraints (to enforce convexity). It is shown, by applications to magnesium, titanium, aluminum and steel alloys, that the plasticity of virtually any metal can be modeled within this framework with an arbitrary degree of precision. Python code at: https://github.com/stefanSCS/SHYqp

Autonomous environment perception and obstacle avoidancetechnologies of Zhurong Mars rover

<p indent="0mm">Mars surface exploration has the characteristics and constraints of harsh and complex environment, lack of prior knowledge, and strict limitation of computing resources. Autonomous environment perception and obstacle avoidance technology is the key to the ability of autonomous exploration, and also one of the key technologies in the field of deep space exploration at home and abroad. In China Tianwen-1 Mars exploration mission, the Zhurong Mars rover, China’s first Mars surface mobile exploration, has realized efficient autonomous environmental perception, obstacle avoidance planning and control under the constraints of limited resources, significantly improved the efficiency of mobile exploration, and the average moving speed is nearly 5 times higher than that of Yutu-2 lunar rover. This paper briefly introduces the sensor configuration, movement mode function and process of Zhurong Mars rover supporting autonomous perception and obstacle avoidance movement, and focuses on the breakthroughs in key technologies of efficient perception and obstacle avoidance planning, including binocular stereo vision efficient perception method based on fast parallel processing, in order to realize the efficiency and safety of autonomous perception and obstacle avoidance movement under severe resource constraints Based on the environmental modeling and obstacle avoidance planning method of trafficability analysis. Finally, the autonomous perception and obstacle avoidance mobile detection of Zhurong Mars rover during the mission is summarized, and the technical development thoughts on further enhancing the autonomous perception and obstacle avoidance ability of Mars rover in the future are put forward in the end.

Computation Bits Maximization in UAV-Enabled Mobile-Edge Computing System

In recent years, unmanned aerial vehicles (UAVs) have been widely used in various industries (e.g., search and rescue, express delivery, etc.) due to their high flexibility. In addition, the deployment of UAVs equipped with mobile-edge computing (MEC) servers to provide computing services at the edges of networks has become an emerging method. Under complex and limited resource constraints, increasing the total number of computation bits in the system becomes a challenging problem. Motivated by this, in this article, we propose an optimization framework to maximize the computation bits of the whole system by jointly optimizing the bandwidth allocation, the task offloading time allocation, and the trajectory of the UAV under the energy constraints of ground devices (GDs) and the maximal battery energy of the UAV. The formulated problem is a nonconvex and nonconcave problem that is very difficult to solve. To this end, we decompose the objective function into three suboptimization problems and adopt successive convex optimization techniques to solve them. Then, we utilize the block coordinate descent (BCD) algorithm to address the overall optimization problem. By doing so, the bandwidth allocation of GDs, task offloading time and local computing time allocation in each time slot, and the trajectory of the UAV are optimized alternately during each iteration. We conduct extensive simulations, and the results verify that the proposed solution achieves a better performance than those of other benchmark schemes.

AERO: Design Space Exploration Framework for Resource-Constrained CNN Mapping on Tile-Based Accelerators

Analog In-Memory Compute (AIMC) arrays can store weights and perform matrix-vector multiplication operations for Deep Convolutional Neural Networks (CNNs). A number of recent efforts have integrated AIMC arrays into hybrid digital-analog accelerators in a multi-layer parallel manner to achieve energy efficiency and high throughput. Multi-layer parallelism on large-scale tile-based architectures need efficient mapping support at the processing element (PE)-level ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , digital or analog processing elements) and tile-level. To find the most efficient architectures, fast and accurate design space exploration (DSE) support is required. In this paper, a novel DSE framework, AERO, is presented to characterize a CNN inference workload executing on hybrid tile-based architectures that supports multi-layer parallelism. Three characteristics can be seen in our DSE framework: (1) It presents a hierarchical Tile/PE-level mapping exploration strategy including inter-layer interaction, and allowing layer fusion/splitting configurations for PE-level mapping optimization. (2) It unlocks different Performance, Power and Area (PPA) exploration points under both sufficient and limited resource constraints, while limited resource case is not considered in prior works of multi-layer parallel architectures. The impact of weight loading and weight stationary mapping are analyzed for better insights into hybrid tile-based architectures. (3) It incorporates a detailed PPA model that supports a broad range of hybrid digital and analog units in a tile. Experimental case-studies are performed for realistic and relevant benchmarks such as MLP, CNNs (Lenet-5, Resnet-18,-34,-50 and −101).

EdgeSaver: Edge-Assisted Energy-Aware Mobile Video Streaming for User Retention Enhancement

Video streaming service is one of the most important IoT applications/services at the mobile end. To provide better services and earn more customers, the mobile video service providers have paid considerable attention to enhance end users&#x2019; Quality of Experience (QoE) in video streaming. As an important aspect of the mobile device, however, the battery power and its impacts on the mobile services were seldom concerned. According to our survey over 2000&#x002B; mobile users, the low battery power of mobile phones could cause the user to give up watching videos. To quantify the relationship between the battery power and user&#x2019;s video abandoning probability (VAP), we first extract the VAP model from the collected survey data, leveraging a reversed accumulative histogram approach. Then, referring to the quantified VAP model, we present <i>EdgeSaver</i>, an edge-assisted video transmission framework, which aims at maintaining a sustainable overall user retention rate for the service providers by reducing the power consumption of video playback at the mobile ends. Particularly, as the core component of <i>EdgeSaver</i>, a low-power video scheduler is designed to strategically select user groups, such that the most profitable outcome can be achieved under the constraints of limited edge resources. With extensive experiments using a real-world data set, we demonstrate that <i>EdgeSaver</i> can help the mobile video service provider improve the user retention rate by up to 30&#x0025; and increase the average user viewing time by 20&#x0025;.

Going . . . Going . . . Going . . . Not Yet Gone! Enhancing Small-scale Festival Survival

Festival service quality is a precursor to competitive advantage and survival, delivering a positive influence on attendee satisfaction and intentions to repatronize in future. Evidence has been built in large-scale festival settings with mixed constructs and findings reported. Less is known about small-scale festivals, which operate within more limited human and financial resource constraints. This study aims to confirm the service quality, satisfaction, and behavioral intention relationship for small-scale festivals. Five hundred and twenty-nine respondents attending four different smallscale festivals participated in an in-person or online self-administered survey. Structural equation modeling confirmed that service quality performance influences behavioral intentions directly and indirectly. Satisfaction and perceived professionalism directly influence behavioral intentions. Environment directly influences repurchase intentions but not when it is mediated by satisfaction. Focusing on delivery of service quality is essential for small-scale festival survival, given its influence on attendees' satisfaction and behavioral intentions. Ensuring festivals are delivered in good environments featuring professional staff is the key to ensuring small-scale festival survival.

A high-level synthesis approach for precisely-timed, energy-efficient embedded systems

Embedded systems continue to rapidly proliferate in diverse fields, including medical devices, autonomous vehicles, and more generally, the Internet of Things (IoT). Many embedded systems require application-specific hardware components to meet precise timing requirements within limited resource (area and energy) constraints. High-level synthesis (HLS) is an increasingly popular approach for improving the productivity of designing hardware and reducing the time/cost by using high-level languages to specify computational functionality and automatically generate hardware implementations. However, current HLS methods provide limited or no support to incorporate or utilize precise timing specifications within the synthesis and optimization process. In this paper, we present a hybrid high-level synthesis (H-HLS) framework that integrates state-based high-level synthesis (SB-HLS) with performance-driven high-level synthesis (PD-HLS) methods to enable the design and optimization of application-specific embedded systems in which timing information is explicitly and precisely defined in state-based system models. We demonstrate the results achieved by this H-HLS approach using case studies including a wearable pregnancy monitoring device, an ECG-based biometric authentication system, and a synthetic system, and compare the design space exploration results using two PD-HLS tools to show how H-HLS can provide low energy and area under timing constraints. © 2021 Elsevier Science. All rights reserved

Key emergency response technologies for abrupt air pollution accidents in China

Abrupt air pollution accidents can endanger people's health and destroy the local ecological environment. The appropriate emergency response can minimize the harmful effects of accidents and protect people's lives and property. This paper provides an overview of the key emergency response technologies for abrupt air pollution accidents around the globe with emphasis on the major achievements that China has obtained in recent years. With decades of effort, China has made significant progress in emergency monitoring technologies and equipment, source estimation technologies, pollutant dispersion simulation technologies and others. Many effective domestic emergency monitoring instruments (e.g., portable DOAS/FT-IR systems, portable FID/PID systems, portable GC-MS systems, scanning imaging remote sensing systems, and emergency monitoring vehicles) had been developed which can meet the demands for routine emergency response activities. A monitoring layout technique combining air dispersion simulation, fuzzy comprehensive evaluation, and a post-optimality analysis was proposed to identify the optimal monitoring layout scheme under the constraints of limited monitoring resources. Multiple source estimation technologies, including the forward method and the inversion method, have been established and evaluated under various scenarios. Multi-scale dynamic pollution dispersion simulation systems with high temporal and spatial resolution were further developed. A comprehensive emergency response platform integrating database support, source estimation, monitoring schemes, fast monitoring of pollutants, pollution predictions and risk assessment was developed based on the technical idea of "source identification - model simulation - environmental monitoring" dynamic interactive feedback. It is expected that the emergency response capability for abrupt air pollution accidents will gradually improve in China.

Leveraging Mixed Methods Designs for Promoting Evaluation and Evaluation Capacity Building

Abstract: Limited evaluation capacity, power dynamics, and resource constraints act as organizational barriers that inhibit evaluations in higher education contexts. Evaluation capacity promotes evaluation and minimizes the impact of these barriers. Embedding a synergistic combination of qualitative and quantitative methods within an evaluation and evaluation capacity building (ECB) initiative was our attempt to address these organizational barriers. In this practice note, we illustrate how MM designs catalyze evaluative thinking that promotes evaluation and supports ECB efforts. Our use and integration of MM strategies to build evaluation capacity and infuse evaluation into organizational culture informs higher education evaluation capacity-building initiatives.

Frugal innovation as a key to supply chain success: projection into the Moroccan context

In an economic environment characterized by rapid technological change and limited natural resource constraints, the frugal innovation approach is perfectly situated. This approach considers an economical and alternative solution especially for emerging countries by minimizing the use of funding and scarce natural resources, leading to the design of sustainable supply chain concepts capable of meeting the different consumer demands and integrating into the new global economy. This work serves to: simplify the frugal innovation approach by reviewing the separate definitions of innovation and frugality, the evolution of the approach throughout history, and comparisons of types of innovation; find the intersections and complementarities of these notions with the supply chain of the future; and seize opportunities to apply this frugal innovation approach in an emerging economy such as Morocco. The research has shown that frugal innovation can be implemented in both developing and developed countries and is one of the important factors in developing the economy and making it more competitive.

Dynamics of COVID‐19 Using SEIQR Epidemic Model

The major goal of this study is to create an optimal technique for managing COVID‐19 spread by transforming the SEIQR model into a dynamic (multistage) programming problem with continuous and discrete time‐varying transmission rates as optimizing variables. We have developed an optimal control problem for a discrete‐time, deterministic susceptible class (S), exposed class (E), infected class (I), quarantined class (Q), and recovered class (R) epidemic with a finite time horizon. The problem involves finding the minimum objective function of a controlled process subject to the constraints of limited resources. For our model, we present a new technique based on dynamic programming problem solutions that can be used to minimize infection rate and maximize recovery rate. We developed suitable conditions for obtaining monotonic solutions and proposed a dynamic programming model to obtain optimal transmission rate sequences. We explored the positivity and unique solvability nature of these implicit and explicit time‐discrete models. According to our findings, isolating the affected humans can limit the danger of COVID‐19 spreading in the future.

Can Industrial Integration Improve the Sustainability of Grain Security?

The sustainability to ensure grain security is an important issue that China’s government faces. It has become more important to ensure grain security by improving the efficiency of grain production under the constraints of limited resources. At present, industrial integration has become an important trend of economic development in China. This study explores the impact of industrial integration on the sustainability of grain security from the perspective of grain production efficiency. In this paper, the grain production efficiency was measured by stochastic frontier analysis method. The degree of industrial integration is measured by the coupling coordination method. Additionally, the effect of industrial integration on grain production efficiency is empirically studied by the panel tobit model. Empirical results show that industrial integration can promote the improvement of grain production efficiency, which reveals that accelerating industrial integration can also be conducive to grain security.

Solving the Dynamic Weapon Target Assignment Problem by an Improved Multiobjective Particle Swarm Optimization Algorithm

Dynamic weapon target assignment (DWTA) is an effective method to solve the multi-stage battlefield fire optimization problem, which can reflect the actual combat scenario better than static weapon target assignment (SWTA). In this paper, a meaningful and effective DWTA model is established, which contains two practical and conflicting objectives, namely, maximizing combat benefits and minimizing weapon costs. Moreover, the model contains limited resource constraints, feasibility constraints and fire transfer constraints. The existence of multi-objective and multi-constraint makes DWTA more complicated. To solve this problem, an improved multiobjective particle swarm optimization algorithm (IMOPSO) is proposed in this paper. Various learning strategies are adopted for the dominated and non-dominated solutions of the algorithm, so that the algorithm can learn and evolve in a targeted manner. In order to solve the problem that the algorithm is easy to fall into local optimum, this paper proposes a search strategy based on simulated binary crossover (SBX) and polynomial mutation (PM), which enables elitist information to be shared among external archive and enhances the exploratory capabilities of IMOPSO. In addition, a dynamic archive maintenance strategy is applied to improve the diversity of non-dominated solutions. Finally, this algorithm is compared with three state-of-the-art multiobjective optimization algorithms, including solving benchmark functions and DWTA model in this article. Experimental results show that IMOPSO has better convergence and distribution than the other three multiobjective optimization algorithms. IMOPSO has obvious advantages in solving multiobjective DWTA problems.

Toward Reducing Construction Project Delivery Time under Limited Resources

One of the most vital construction project aspects is to complete a project in minimum time restricted to the time–cost trade-off. Overlapping activities’ planning and their impact on the project under limited resource constraints should be considered. This study aims to develop a model for optimizing the project schedule and cost regarding overlap activities and their impacts. This study reviews previous studies on changes in past activities likely to produce additional reworking of subsequent activities. In addition, an AHP model is developed to assess the reworking time of subsequent activities based on possible changes in previous activities. In addition, five realistic construction projects are applied. Finally, an optimizing model is developed for optimizing project time and cost using overlapping techniques by using the Java program. The results indicate that the proposed model can be used by project managers easily for solving time and cost optimization problems. In addition, it can be updated to continuously improve its functionality. Finally, it can be updated later to support AI for finding better solutions.