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This paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects.

This paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed.

The results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted.

The paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers.

This study develops a model and algorithm to solve the decentralized resource-constrained multi-project scheduling problem (DRCMPSP) and provides a suitable priority rule (PR) for coordinating global resource conflicts among multiple projects.

This study addresses the DRCMPSP, which respects the information privacy requirements of project agents; that is, there is no single manager centrally in charge of generating multi-project scheduling. Accordingly, a three-stage model was proposed for the decentralized management of multiple projects. To solve this model, a three-stage solution approach with a repeated negotiation mechanism was proposed.

The experimental results obtained using the Multi-Project Scheduling Problem LIBrary confirm that our approach outperforms existing methods, regardless of the average utilization factor (AUF). Comparative analysis revealed that delaying activities in the lower project makespan produces a lower average project delay. Furthermore, the new PR LMS performed better in problem subsets with AUF < 1 and large-scale subsets with AUF > 1.

A solution approach with a repeated-negotiation mechanism suitable for the DRCMPSP and a new PR for coordinating global resource allocation are proposed.

Project 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.

It 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.

The study provides fresh insights into modeling and resolving resource-constrained project scheduling issues, supported by real-world examples that effectively illustrate its practical significance.

The 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.

This research is aimed to mainly be applicable to expediting engineering projects, uses the method of inverse optimization and the double-layer nested genetic algorithm combined with nonlinear programming algorithm, study how to schedule the number of labor in each process at the minimum cost to achieve an extremely short construction period goal.

The method of inverse optimization is mainly used in this study. In the first phase, establish a positive optimization model, according to the existing labor constraints, aiming at the shortest construction period. In the second phase, under the condition that the expected shortest construction period is known, on the basis of the positive optimization model, the inverse optimization method is used to establish the inverse optimization model aiming at the minimum change of the number of workers, and finally the optimal labor allocation scheme that meets the conditions is obtained. Finally, use algorithm to solve and prove with a case.

The case study shows that this method can effectively achieve the extremely short duration goal of the engineering project at the minimum cost, and provide the basis for the decision-making of the engineering project.

The contribution of this paper to the existing knowledge is to carry out a preliminary study on the relatively blank field of the current engineering project with a very short construction period, and provide a path for the vast number of engineering projects with strict requirements on the construction period to achieve a very short construction period, and apply the inverse optimization method to the engineering field. Furthermore, a double-nested genetic algorithm and nonlinear programming algorithm are designed. It can effectively solve various optimization problems.

This research aims to develop an automated and optimization algorithms (OAs)-integrated 4D building information modeling (BIM) approach and a prototype and enable construction managers and practitioners to estimate the time of compound elements in building projects using the resource specification technique.

A 4D BIM estimation process was first developed by applying the resource specification and geometric information from the BIM model. A suite of OA including particle swarm optimization, ant colony, differential evolution and genetic algorithm were developed and compared in order to facilitate and automate the estimation process. The developed processes and porotypes were linked and integrated.

The OA-based automated 4D BIM estimation prototype was developed and validated through a real-life construction project. Different OAs were applied and compared, and the genetic algorithm was found as the best performing one. The prototype was successfully linked with BIM timeliner application. By using this approach, the start and finish dates of all object-based activities are developed, and the project completion time is automatically estimated.

Unlike conventional construction estimation methods which need various tools and are error prone and time-consuming, the developed method bypasses the existing time estimation tools and provides the integrated and automated process with BIM and machine learning algorithms. Furthermore, this approach integrates 4D BIM applications into construction design procedures, connected with OA automation.

The existing literature has been mainly focused on local problems but without an overall framework for studying the top-level planning of intelligent construction from a systematic perspective. The purpose of this paper is to fill this gap.

This research adopts a deductive research approach.

This research proposes a reference architecture and related business scenario framework for intelligent construction based on the existing theory and industrial practice.

The main contribution of this research is to provide a useful reference to the Chinese government and industry for formulating digital transformation strategies, as well as suggests meaningful future research directions in the construction industry.