Project Scheduling Research Articles

The golden-mean fallacy: A medium-attractiveness face predicts less funding on a crowdfunding platform

We examine the effects of facial attractiveness stereotyping in reward-based crowdfunding decisions. Our findings indicate that to a certain extent, a project creator's facial attractiveness is a valid heuristic cue that influences the evaluation of the project's future potential. Based on a large dataset, we propose a U-shaped relationship between project creators’ facial attractiveness and crowdfunding performance. The results imply that potential backers believe that projects initiated by creators whose facial attractiveness is either high or low (vs. medium) have greater future potential, leading to better crowdfunding performance. Additionally, we find that the effects of attractiveness are moderated by the narrative person used in the project introduction and by project updates on the project's progress schedule. Specifically, the U-shaped effect is stronger when attractive (vs. less attractive) project creators describe their projects in first- (vs. third-) person narration, and the effects of facial attractiveness are also enhanced by project updates. Our findings suggest that facial attractiveness is an actionable funding tool, thereby extending our understanding of the complex effect of facial attractiveness in funding.

Navigating Gender Nuances: Assessing the Impact of AI on Employee Engagement in Slovenian Entrepreneurship

Background: Our research delved into exploring various selected facets of AI-driven employee engagement, from the gender perspective, among Slovenian entrepreneurs. Methods: This research is based on a random sample of 326 large enterprises and SMEs in Slovenia, with an entrepreneur completing a questionnaire in each enterprise. Results: Findings suggest that there are no significant differences between male and female entrepreneurs in Slovenia regarding various aspects of AI-supported entrepreneurial management practice including the following: AI-supported entrepreneurial culture, AI-enhanced leadership, adopting AI to reduce employee workload, and incorporating AI tools into work processes. The widespread integration of AI into entrepreneurship marks a transition to a business landscape that values inclusivity and equity, measuring success through creativity, strategic technology deployment, and leadership qualities, rather than relying on gender-based advantages or limitations. Our research also focused on the identification of gender differences in path coefficients regarding the impact of the four previously mentioned aspects of AI on employee engagement. While both genders see the value in using AI to alleviate employee workload, the path coefficients indicate that female entrepreneurs report higher effectiveness in this area, suggesting differences in the implementation of AI-integrated strategies or tool selection. Male entrepreneurs, on the other hand, appear to integrate AI tools into their work processes more extensively, particularly in areas requiring predictive analytics and project scheduling. This suggests a more technical application of AI in their enterprises. Conclusions: These findings contribute to understanding gender-specific approaches to AI in enterprises and their subsequent effects on employee engagement.

{MFPP(R). An R package for matrix-based flexible project planning

Project planning and scheduling are essential parts of project management. While project planning and scheduling tools are already available to support traditional project management approaches, flexible project management, such as agile, extreme and hybrid project planning, are only somewhat supported by computer algorithms. To the best of our knowledge, no existing R package for project planning and scheduling can support project planning and scheduling for flexible projects. In this paper, the goal is to fill this gap; to this end, the R package mfpp for matrix-based flexible project planning/scheduling is introduced and described. This package includes a comprehensive set of tools for project managers to schedule both traditional and flexible project plans. The use of the package is illustrated through examples.

A study of comprehensive resource scheduling under public health events: An improved heuristic quantum algorithm

This study explores the impact of public health events, multi-modal projects, multi-project environments, and multi-capacity resource constraints on project scheduling. It describes the comprehensive resource-constrained project scheduling problem (MCMRCMPSP) specifically for public health events, and proposes two approaches for modelling and solving the problem. The objective is to enhance the practical relevance of project scheduling and enrich the problem itself. To improve efficiency and the algorithm for scheduling problems, an enhanced quantum algorithm based on the quantum particle swarm algorithm (QPSO) is proposed. The enhancements include Gaussian variation and a tournament selection strategy. Furthermore, the article integrates multiple heuristic rules with the algorithm to minimize illogical computations, improve computational efficiency, and enhance solution quality. The proposed algorithm’s effectiveness is validated through performance tests and practical application experiments. The results show that the algorithm has superior convergence performance and solution accuracy compared with the traditional QPSO, particle swarm algorithm (PSO), genetic algorithm, ant colony algorithm, and cuckoo algorithm. Thus, the algorithm provides a targeted resource scheduling plan for real-world cases. This research contributes to the field of project scheduling problems and proposes a new solution.

Research and design of urban flood control and drainage dispatching system

In order to solve the problem of frequent flood disasters in urban areas, water conservancy models such as hydrological forecast, flood simulation and operation decision-making were studied and constructed for the waterway-region-urban flood control and drainage engineering facilities, and urban flood control and drainage operation system was designed with the model as the core. The overall structure of the system is "five horizontal and two vertical". The "five horizontal" bottom up are respectively the intelligent perception layer, cloud infrastructure layer, data layer, service layer and application layer, and the "two vertical" are respectively the security system and the standard specification system. On this basis, the function customization of the system is completed to achieve comprehensive display, early warning and forecast, flood simulation, risk assessment, project scheduling and intelligent decision-making functions.

Scheduling Analysis on the Heavy Rehabilitation Construction Project of the Dr. R. Koesma Tuban Hospital Building Using the PERT Method

Project scheduling is a key factor in achieving successful implementation as planned. Constraints such as limited resources, improper reporting, and high costs often become challenges in project management. Delays in project completion are very detrimental both in terms of cost and time. Just like what happened in the Rehabilitation and Heavy Construction Project of the Outpatient and Administration Building at Dr. R. Koesma Regional General Hospital in Tuban Regency, there was a delay of 10 days in its implementation. This research aims to determine the duration of the rehabilitation of the dr. R. Koesma Hospital building in Tuban Regency using the PERT method to improve time efficiency, reduce the risk of delays, and ensure overall project success. The research method used is combining two approaches, PERT method (Program Evaluation and Review Technique) to calculate the expected duration estimation of each work, and Microsoft Project 2016 to compile a project schedule that allows identification of critical activities and work network analysis. The results of the analysis using PERT method and Microsoft Project 2016 showed that the project was estimated to be completed in 115 days, faster than the planned 133 days. The probability that the project will be completed within the planned target duration T(d) = 133 days is 99.9%. Hence, there is a high probability that the project will be completed on time.

Evaluating the Delivery and Oversight of State-Funded Local Bridge Projects

State-funded local bridge projects face several challenges including but not limited to constrained budgets, increasing burden from state departments of transportation (DOT) staff, and the lack of expertise of some local personnel. By streamlining the delivery and oversight of state-funded projects through delegation, a collaborative and proactive approach can be achieved, ensuring the successful maintenance and development of local low-risk bridges, which are not on the National Highway System and have minimal to no environmental, right of way, utility, or railroad impacts. This paper offers a practical approach to evaluating programs focused on progressing project delivery by delegating specific tasks and responsibilities to local governments. Taken as a study case, Wisconsin Department of Transportation’s low-risk bridge pilot program was evaluated using both a quantitative methodology: data analysis using a data-driven statistical approach involving analysis of pilot and control group data, and an evaluation of the performance metrics developed by the state DOT; and a qualitative approach: a survey of local sponsors and stakeholders about the pilot project. The survey corroborated the findings derived from the data analysis: improved performance in project cost and schedule for pilot projects, highlighting consistency and reliability in the evaluation process. The integration of both methodologies resulted in valuable insights and experiences, providing valuable guidance to other states conducting similar program evaluations.

Artificial Intelligence in Project Management: Enhancing Efficiency and Decision-Making

This thesis explores the integration of Artificial Intelligence (AI) in project management practices to improve efficiency and decision-making processes. As organizations increasingly rely on project management methodologies to execute tasks, deliverables, and achieve objectives, the role of AI in enhancing these processes becomes pivotal. Through an examination of existing literature, case studies, and theoretical frameworks, this thesis investigates the potential benefits, challenges, and implications of incorporating AI technologies in project management. It aims to provide insights into how AI can optimize project planning, scheduling, resource allocation, risk management, and stakeholder communication. Additionally, the thesis explores the ethical considerations and societal impacts associated with the adoption of AI in project management. By analyzing real-world applications and theoretical perspectives, this research contributes to the understanding of how AI can be effectively utilized to streamline project management practices and drive organizational success in diverse industries.

The Continuous Time-Resource Trade-off Scheduling Problem with Time Windows

We introduce a variant of the cumulative scheduling problem (CuSP) characterized by continuous modes, time windows, and a criterion that involves safety margin maximization. The study of this variant is motivated by the Geospatial based Environment for Optimisation Systems Addressing Fire Emergencies Horizon 2020 Project, which is devoted to the design of evacuation plans in the face of natural disasters and more specifically, wildfire. People and goods have to be transferred from endangered places to safe places, and evacuation planning consists of scheduling evacuee moves along precomputed paths under arc capacities and deadlines. The resulting model is relevant in other contexts, such as project or industrial process scheduling. We consider here several formulations of the continuous time-resource trade-off scheduling problem (CTRTP-TW) with a safety maximization objective. We establish a complete complexity characterization distinguishing polynomial and NP-hard special cases depending on key parameters. We show that the problem with fixed sequencing (i.e., with predetermined overlap or precedence relations between activities) is convex. We then show that the preemptive variant is polynomial, and we propose lower and upper bounds based on this relaxation. A flow-based mixed-integer linear programming formulation is presented, from which a branch-and-cut exact method and an insertion heuristic are derived. An exact dedicated branch-and-bound algorithm is also designed. Extensive computational experiments are carried out to compare the different approaches on evacuation planning instances and on general CTRTP-TW instances. The experiments also show the interest of the continuous model compared with a previously proposed discrete approximation. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work was funded by the Horizon 2020 Marie Skłodowska-Curie Research and Innovation Staff Exchange European Project 691161 GEO-SAFE (Geospatial based Environment for Optimisation Systems Addressing Fire Emergencie). This work has also been supported by ANITI, the Artificial and Natural Intelligence Toulouse Institute. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0142 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0142 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

A System Dynamics Approach to Feedback Processes in Project Scheduling

Projects, as catalysts for proactive transformation, offer a temporary and adaptable framework that effectively handles complexities (or uncertainties) within a competitive corporate landscape. Hence, the use of an effective project management framework, such as Dynamic Project Scheduling (DPS), is a method to handle intricacies in order to accomplish organizational objectives. DPS refers to a triangle interaction involving baseline scheduling, schedule risk analysis, and project control while supporting schedule adjustment in response to changes and uncertainties. However, there is a lack of information regarding studies that have investigated the feedback mechanisms among DPS components. This study was designed to examine the counterintuitive relationships between these components using system dynamics. The quantities within the DPS system were identified and defined. A casual loop diagram was used to illustrate the interactions among these quantities. Subsequently, a Stock and Flow Diagram (SFD) was created to identify the inputs, states, and flow mechanisms within the DPS. Using the SFD, a system dynamics expression was generated which was then employed to compute the rate of change of the Budgeted Cost of Work Remaining (BCWR) for two projects at different time intervals. The results properly indicated the period of idleness during project execution. The use of BCWR rather than schedule variance provides a more effective visual representation for evaluating performance and tracking progress. The BCWR and planned value exhibit contrasting trends, highlighting the importance of earned value quantities in project control. The use of system dynamics in project management can enhance the planning and scheduling phase, allow project managers to monitor pertinent performance measures, and optimize project outcomes through informed decisions.

Building Information Modeling (BIM) for Construction Project Schedule Management: A Review

Nowadays, the implementation and application of the BIM process in construction project management is a pressing need. This aligns with the global development trends in the construction sector and project information management in general. Numerous scholars and companies are actively engaged in learning, understanding, and investigating various aspects of BIM to stay up-to-date and meet the inevitable developmental requirements. This study focuses on the role and application of BIM, intending to identify limitations that hinder its fulfillment of expectations in project schedule management. In addition, it explores studies that show how other countries have effectively employed BIM in project management and progress tracking throughout the project lifecycle. The study aims to address three main objectives: (a) comprehensively examine and provide evidence related to the concept of BIM in project schedule management, (b) present the benefits of applying BIM in comparison to traditional methods in project management and operation, and (c) identify limitations stemming from various factors that may pose challenges in the application of BIM in project schedule management.

Emerge of scaling in project schedules

Emerge of scaling in project schedules

Accelerating the middle – rise building construction duration using BIM 4D, a case study in central Jakarta

Construction planning involves scheduling and sequencing of project activities for proper execution. Research was conducted to obtain and enhance construction project scheduling, which was good and in accordance with the targets achieved. The results of the project performance analysis in the 9th month using the Earned Value Method resulted in SPI <1 and SV <0, indicating that the project experienced delays. Scheduling using the PDM based on the actual duration of the project shows that the project will be completed in 511 days. The project duration can be accelerated to 429 days by combining overlap and overtime methods. The scheduling results were simulated using BIM 4D to generate a visualization of the scheduling to improve the project monitoring process.

Optimizing Software Effort Estimation Models Using Enhancement of Firefly Algorithm

Abstract: Estimating the amount of work required in order to create software development is regarded as essential to the software development life cycle and to the control of project costs, schedules, and quality. As a result, precise estimation plays a critical role in project success and risk mitigation. Software effort estimation has drawn a lot of interest from scholars recently and presented a problem to the software business. Three COCOMO-based models' parameters are to be optimized using a metaheuristic method called improvement of Firefly Algorithm, which is proposed in this research. The basic COCOMO model and the other two models that have suggested in literature as expansions of basic COCOMO model are among these models. Root Mean Square Error (RMSE) is one of the evaluation measures used to assess the created estimate models.

A study on time schedules for construction projects in Hanoi, Vietnam

With its position as the capital, Hanoi is the political center as well as the second largest economic center of the country. Therefore, the city is always allocated a large budget in construction investment to create material facilities for political tasks and economic and social development. During the implementation of construction projects, a number of difficulties and limitations have appeared. In which, projects are delayed in construction and disbursement, reducing investment efficiency and not meeting the expectations of the government and people. From this fact, the authors have conducted a study to evaluate the causes affecting the time schedule of construction projects in Hanoi. The method F-APH (Fuzzy Analytic Hierarchy Process) was used to analyze data objectively and accurately about the causes affecting the time schedule. From there, these causes are classified into groups of subjective causes (from within the project) and groups of objective causes (from outside the project). The results show that subjective causes, originating from project participants, have a stronger influence than objective causes. A number of specific proposals to the actors involved in construction projects are made to eliminate or limit the impact of the causes of construction progress.

Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint

Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated automatically by employing machine learning (ML) and building information modeling (BIM). The proposed solution should utilize building information modeling (BIM) international foundation class (IFC) 3D files of previous projects to train the ML model. The training schedules (the dependent variable) are intended to be prepared by an experienced scheduler, and the 3D BIM files should be used as the source of the scheduled activities. Using the ML model can enhance the generalization of model application to different construction projects. Furthermore, the cost and required resources for each activity could be generated. Accordingly, unlike other solutions, the proposed solution could sequence activities based on an ML model instead of manually developed constraint matrices. The proposed solution is intended to generate the duration, cost, and required resources for each activity.

Project Scheduling Problems with Constraints on Personnel Scheduling

Project Scheduling Problems with Constraints on Personnel Scheduling

Compound Matrix-Based Project Database (CMPD)

The impact of projects is vital, from business operations to research to the national economy. Therefore, management science and operation research have extensively studied project scheduling and resource allocation for over six decades. Project databases were proposed to test algorithms, including simulated or real, single or multiprojects, and single-mode or multi-mode projects. However, the dozens of project databases are extremely heterogeneous regarding the file structure and the features of the modeled projects. Furthermore, the efficiency and performance of project scheduling and resource allocation algorithms are susceptible to the characteristics of projects. Therefore, the proposed Compound Matrix-Based Project Database (CMPD) collects and consolidates the most frequently used project databases. The proposed Unified Matrix-Based Project-Planning Model (UMP) sparse matrix-based model enables the addition of new features to existing project structures, such as completion priorities, structural flexibility, and quality parameters, to broaden the scope of considered projects and to take account of flexible approaches, such as agile, extreme, and hybrid projects.

Integrating Artificial Intelligence in Construction Management: Improving Project Efficiency and Cost-effectiveness

The construction industry faces challenges such as project complexity, delays, and communication issues. Leveraging AI, particularly through data analysis, predictive analytics, and machine learning, addresses these challenges by optimizing project planning, scheduling, and risk management. This paper outlines strategies for AI integration, including data collection, machine learning algorithms, and cloud computing. Case studies highlight successful implementations, showcasing benefits such as increased efficiency, cost savings, and improved safety. However, challenges like data security and workforce acceptance must be considered. The abstract concludes by discussing future trends and encouraging the construction industry to embrace AI for enhanced project outcomes.

Multi-objective planning for time-cost trade-offs in multi-project parallel environment

PurposeProject managers bear the responsibility of selecting and developing resource scheduling methods that align with project requirements and organizational circumstances. This study focuses on resource-constrained project scheduling in multi-project environments. The research simplifies the problem by adopting a single-project perspective using gain coefficients.Design/methodology/approachIt employs uncertainty theory and multi-objective programming to construct a model. The optimal solution is identified using Matlab, while LINGO determines satisfactory alternatives. By combining these methods and considering actual construction project situations, a compromise solution closely approximating the optimal one is derived.FindingsThe study provides fresh insights into modeling and resolving resource-constrained project scheduling issues, supported by real-world examples that effectively illustrate its practical significance.Originality/valueThe research highlights three main contributions: effective resource utilization, project prioritization and conflict management, and addressing uncertainty. It offers decision support for project managers to balance resource allocation, resolve conflicts, and adapt to changing project demands.