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In repetitive projects, repetition offers more possibilities for activity scheduling at the sub-activity level. However, existing resource-constrained repetitive scheduling problem (RCRSP) models assume that there is only one sequence in performing the sub-activities of each activity, resulting in an inefficient resource allocation. This paper proposes a novel repetitive scheduling model for solving RCRSP with soft logic.

In this paper, a constraint programming model is developed to solve the RCRSP using soft logic, aiming at the possible relationship between parallel execution, orderly execution or partial parallel and partial orderly execution of different sub activities of the same activity in repetitive projects. The proposed model integrated crew assignment strategies and allowed continuous or fragmented execution.

When solving RCRSP, it is necessary to take soft logic into account. If managers only consider the fixed logic between sub-activities, they are likely to develop a delayed schedule. The practicality and effectiveness of the model were verified by a housing project based on eight different scenarios. The results showed that the constraint programming model outperformed its equivalent mathematical model in terms of solving speed and solution quality.

Available studies assume a fixed logic between sub-activities of the same activity in repetitive projects. However, there is no fixed construction sequence between sub-activities for some projects, e.g. hotel renovation projects. Therefore, this paper considers the soft logic relationship between sub-activities and investigates how to make the objective optimal without violating the resource availability constraint.

The aggressive competition in the real estate market forces real estate developers to tackle the challenge of selecting the best project construction phasing alternative. The real estate industry is characterized by high costs, high profit and high risks. The schedules of real estate projects are also characterized by having large number of repetitive activities that are executed over a long duration. The repetitiveness, long duration of execution, the high amounts of money involved and the high risk made it desirable to leverage the impact of changes in phasing plans on net present value of amounts incurred and received over the long execution and selling duration. This also changes the project progress, and delivery time as well as their respective impact on customer degree of satisfaction. This research addresses the problem of selecting the best phasing alternative for real estate development projects while maximizing customer satisfaction and project profit.

The research proposes a model that generates all construction phasing alternatives and performs decision-making to rank all possible phasing alternatives. The proposed model consists of five modules: (1) Phasing Sequencing module, (2) Customer Satisfaction module, (3) Cash-In calculation module, (4) Cost Estimation module and (5) Decision-making module. A case study was presented to demonstrate the practicality of the model.

The proposed model satisfies the real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model generates all construction phasing alternatives and performs multi-criteria decision making to rank all possible phasing alternatives. It quantifies the score of the two previously mentioned criteria and ranks all solutions according to their overall score.

The research proposes a model that assist real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model can be used to conclude general guidelines and common successful practices to be used by real estate developers when deciding the construction phasing plan. In this study the model is based on business models where all the project units are sold, rental cases are not considered. Also, the budget limitations that might exist when phasing is not considered in the model computations.

The model can be used as a complete platform that can hold all real estate project data, process revenues and cost information for estimating profit, plotting cash flow profiles, quantifying the degree of customer satisfaction attributable to each phasing alternative and providing recommendation showing the best one. The model can be used to conclude general guidelines and common successful practices to be used by real estate developers when tackling the challenge of selecting construction phasing plans.

The present work seeks to determine the optimal delivery schedule of equipment at a project site in the backdrop of limited storage space, at a minimum cost, and without disturbing the overall project schedule. In addition, the optimized delivery schedule helps in minimizing the fluctuating requirements of space at the project site across the entire project lifespan.

The study is carried out at a Steel plant operating in a constrained space but undergoing a production capacity expansion. The problem motivated us to explore the possibility of postponing the delivery dates of certain equipment closer to the erection dates without compromising on the project schedule. Given the versatility of linear programming models in dealing with such schedule optimization problems, the authors formulated the above problem as a Zero-One Integer Linear Programming problem.

The model is implemented for all the new equipment arriving for two major units – the Hot Strip Mill (HSM) and the Blast Furnace (BF). It generates an optimized delivery schedule by delaying the delivery of some equipment by a certain number of periods, without compromising the overall project schedule and at a minimum storage cost. The average space utilization increases by 25.85 and 14.79% in HSM and BF units respectively. The fluctuations in space requirements are reduced substantially in both units.

The study shows a timeline in the form of a Gantt chart for the delivery of equipment, storage of equipment across different periods, and the number of periods for which the delivery of certain equipment needs to be postponed. The study uses linearly increasing storage costs with the increase in the number of periods for storage of the equipment in the temporary shed.

1. Determined the optimal delivery schedule of the equipment in a project environment in the backdrop of limited storage space in the project site.

2. Formulated the above problem as a Zero-One Integer Linear Programming (ILP) problem.

3. The average space utilization has increased by 25.85 and 14.79% in HSM and BF units respectively.

4. The optimized delivery schedule helps in reducing the fluctuations in space requirements substantially across the entire lifespan of the project.

5. The timeline of delivery of equipment, storage of equipment across different periods and periods of postponement of the equipment are shown in the form of a Gantt Chart.

Determined the optimal delivery schedule of the equipment in a project environment in the backdrop of limited storage space in the project site.

Formulated the above problem as a Zero-One Integer Linear Programming (ILP) problem.

The average space utilization has increased by 25.85 and 14.79% in HSM and BF units respectively.

The optimized delivery schedule helps in reducing the fluctuations in space requirements substantially across the entire lifespan of the project.

The timeline of delivery of equipment, storage of equipment across different periods and periods of postponement of the equipment are shown in the form of a Gantt Chart.

Work crew productivity and the application of limited resources are necessary elements in construction duration delay analysis. This study thus proposes a method to analyze construction delays and resource reallocation based on work crew productivity and resource constraints. The study also presents an economic feasibility analysis that maximizes economic effect by reducing construction duration, the cost of resource reallocation, delay liquidated damages (DLDs) and incentives for reducing contractual duration.

The proposed method involved three steps. First, work crew characteristics such as productivity, unit price and workload helped analyze delay information, including delay duration, reducible duration and daily reduced cost. Next, a goal programming method assessed resource reallocation based on the priority (as determined by decision-makers) of each constraint condition, such as the available number of workers, cost, goal workload and statutory working hours. Lastly, the level of reallocation was analyzed based on the results of the economic feasibility analysis and decision-makers’ delay attitudes.

A case study was performed to test the proposed method's applicability. Its involved sensitivity analysis indicated proposing to decision-makers a scenario based on the prioritization of economic feasibility. The proposed method's applicability proved high for decision-makers, as they can determine whether to reduce construction duration per the proposed data.

The proposed method's main contribution is the reallocation of resources to reduce construction duration based on work crew productivity and the prioritization of limited resources. The proposed method can analyze the differences in productivity between the plan and actual progress, as well as calculate the necessary number of workers. Decision-makers can then reduce the appropriate level of contractual duration based on their own delay attitude, constraint condition prioritization and results from daily economic feasibility analyses.

The purpose of this study is to automatically generate a construction schedule by extracting data from the BIM (Building Information Modeling) model and combining an ontology constraint rule and a genetic algorithm (GA).

This study developed a feasible multi-phase framework to generate the construction schedule automatically through extracting information from the BIM, utilizing the ontology constraint rule to demonstrate the relationships between all the components and finally using the GA to generate the construction schedule.

To present the functionality of the framework, a prototype case is adopted to show the whole procedure, and the results show that the scheme designed in this study can quickly generate the schedule and ensure that it can satisfy the requirements of logical constraints and time parameter constraints.

A proper utilization of conceptual framework can contribute to the automatic generation of construction schedules and significantly reduce manual errors in the Architectural, Engineering, and Construction (AEC) industry. Moreover, a scheme of BIM-based ontology and GA for construction schedule generation may reduce additional manual work and improve schedule management performance.

The hybrid approach combines the ontology constraint rule and GA proposed in this study, and it is an effective attempt to generate the construction schedule, which provides a direct indicator for the schedule control of the project.

In this study, the data application process of the BIM model is divided into four modules: extraction, processing, optimization, and output. The key technologies including secondary development, ontology theory, and GA are introduced to develop a multi-phase framework for the automatic generation of the construction schedule and to realize the schedule prediction under logical constraints and duration interference.

Exact solution of time–cost trade-off problem (TCTP) by the state-of-the-art meta-heuristic algorithms can be obtained for small- and medium-scale problems, while satisfactory results cannot be obtained for large construction projects. In this study, a hybrid heuristic meta-heuristic algorithm that adapts the search domain is developed to solve the large-scale discrete TCTP more efficiently.

Minimum cost slope–based heuristic network analysis algorithm (NAA), which eliminates the unfeasible search domain, is embedded into differential evolution meta-heuristic algorithm. Heuristic NAA narrows the search domain at the initial phase of the optimization. Moreover, activities with float durations higher than the predetermined threshold value are eliminated and then the meta-heuristic algorithm starts and searches the global optimum through the narrowed search space. However, narrowing the search space may increase the probability of obtaining a local optimum. Therefore, adaptive search domain approach is employed to make reintroduction of the eliminated activities to the design variable set possible, which reduces the possibility of converging into local minima.

The developed algorithm is compared with plain meta-heuristic algorithm with two separate analyses. In the first analysis, both algorithms have the same computational demand, and in the latter analysis, the meta-heuristic algorithm has fivefold computational demand. The tests on case study problems reveal that the developed algorithm presents lower total project costs according to the dependent t-test for paired samples with α = 0.0005.

In this study, TCTP is solved without considering quality or restrictions on the resources.

The proposed method enables to adapt the number of parameters, that is, the search domain and provides the opportunity of obtaining significant improvements on the meta-heuristic algorithms for other engineering optimization problems, which is the theoretical contribution of this study. The proposed approach reduces the total construction cost of the large-scale projects, which can be the practical benefit of this study.

The occurrence of COVID-19 has impacted the wide-reaching dimensions of manufacturing, materials, procurement, management, etc., and has loaded disruptions in the wide range of supply chain (SC) activities. The impact of COVID-19 has twisted supplier performance and influenced stakeholders’ thinking towards selecting supplier sources and making strategic sourcing decision for convinced arrangement of construction management (CM) resources. Nowadays, suppliers are intently evaluated by stakeholders in post-COVID-19 phase to induce agile availability of CM resources. Accordingly, this paper aims to demonstrate competent CM dimensions under post COVID-19 scenario for ease managing construction projects by the stakeholders.

The authors have implicated Grey Sets Theory along with decision-making trial and evaluation laboratory (DEMATEL) technique for understanding significant outcomes. Varieties of diverse decision aspects responsible for strategically influencing supplier sourcing decision is projected under post COVID-19 scenario for handling construction projects by the stakeholders.

This study investigated sustainable construction management dimensions (SCMD) at the stage of resource deliveries and client aspirations under post COVID-19 situation. The study demonstrated “Lead time” as the most crucial, “Product Range” as the second and “Customers dealings and relationship” as the third crucial aspect considering by the stakeholders for selecting supplier sources based on the attainment of performance score of 0.1338, 0.1273 and 0.1268, respectively. It is found that high lead time stimulates the stakeholders to divert their orders to other competent supplier sources holding a low degree of lead time as compared.

The present study rollovers its existence by serving critical thinking, conceptual modelling, criteria identification and evaluation under CM domain for drafting effectual strategies by the suppliers. The study investigated the impact of COVID-19 on stakeholders’ decision-making and enlisted SCMD that strategically stimulated them in choosing supplier sourcing decision.

The present study realizes the insights of stakeholders in the post COVID-19 scenario related to the supplier sources based on performance score. The study quantified sustainable supplier attribute for construction work and practices. The study analysed the expectations of the stakeholders purchasing different varieties of construction materials from supplier sources for civil works in the post COVID-19 scenario.

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.

The purpose of this article is to analyze the existing studies on the application of artificial intelligence (AI) in lean construction management (LCM). Further, this study offers a classification scheme that specifies different categories of AI tools, as applied to the field of LCM to support various principles of LCM.

This research adopts the systematic literature review (SLR) process, which consists of five consecutive steps: planning, searching, screening, extraction and synthesis and reporting. As a supplement to SLR, a bibliometric analysis is performed to examine the quantity and citation impact of the reviewed papers.

In this paper, seven key areas related to the principles of LCM for which AI tools have been used are identified. The findings of this research clarify how AI can assist in bolstering the practice of LCM. Further, this article presents directions for the future evolution of AI tools in LCM based on the current emerging trends.

This paper advances the LCM systems by offering a lens through which construction managers can better understand key concepts in the linkage of AI to LCM.

This research offers a new classification scheme that allows researchers to properly recall, identify and group various applications of AI categories in the construction industry based on various principles of LCM. In addition, this study provides a source of references for researchers in the LCM discipline, which advances knowledge and facilitates theory development in the field.

Cross operation is a common operation method in the building construction process nowadays. Due to the crossover, each other's operations are disturbed, and risks also interact. This superimposed relationship of risks is worthy of attention. The study aims to develop a model for analyzing cross-working risks. This model can quantify the correlation of various risk factors.

The concept of cross operation and the cross types involved are clarified. The risk factors were extracted from cross-operation accidents. The association rule mining (ARM) was used to analyze the results of various cross-types accidents. With the help of visualization tools, the intensity distribution and correlation path of the relationship between each factor were obtained. A complete cross-operation risk analysis model was established.

The application of ARM method proves that there are obvious risk correlation deviations in different types of cross operations. A high-frequency risk common to all cross operations is on-site safety inspection and process supervision, but the subsequent problems are different. Cutting off the high-lift risk chain timely according to the results obtained by ARM can reduce or eliminate the danger of high-frequency risk factors.

This is the first systematic analysis of cross-work risk in the construction. The study determined the priority of risk management. The results contribute to targeted cross-work control to reduce accidents caused by cross-work.