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The paper presents the problem of optimal management of the resources in multiunit construction project. During the creation of the models of this kind of the project the flow-shop system is rarely used. Flow-shop system now is widely applied in modelling of industrial processes, computer systems. In the model presented in the paper flow shop syste...
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... schedule for the reference solution is shown in Figure 4. Calculations with TS algorithm were performed three times. The obtained approximate minimum dura- tion time of the entire project as shown in the example (the best value C max ) was 264 working days, which was achieved in the 16250 iterations of the algorithm opera- tion (Fig. 5). The value of the objective function for the reference decisive variable was 534 working days. This important confirmation not only of the effectiveness of the algorithm but also of the need to search for optimal solutions in the planning of construction projects (Podolski 2008). On one hand such realization of works (various units ...
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... duration of the project was obtained for the de- cisive variable π = (π 1 , π 2 , ..., π 15 ), where: π 1 = ( (11,10,9,7,1), (12,8,5,4), (2,6,3)), π 2 = ((12,10,4,8),(11,6,1,7,9),(2,5,3)), π 3 = ((12,6,3),(2,4,8),(10,1,7), (11,5,9)), π 4 = ((12,11,9,2,4,5), (10,7,6,3,1,8)), π 5 = ((6,4,10),(3,12),(1),(11,5,2,9,8,7)), π 6 = ( (3,12,10),(2,6,4), (5,11,1,8,7,9)), π 7 = ((2,4,9),(12,6,3), (11,5,8), (10,1,7)), π 8 = ((12,2,5,7,9),(6,1,3), (11,10,4,8)), π 9 = ((6),(11,1), (12,2,10,4,5,3,7,8), (9)), π 10 = ((2,4,8),(6),(1,3,7),(11), (12,10,5,9)), π 11 = ((12,6,1,3,8),(11,10),(4,9),(2,5,7)), π 12 = ( (6,4,10,1,3,8),(11,2,9),(12,5,7)), π 13 = ((2,4,7,8),(12,6,5,9), (11,10,1,3)), π 14 = ((10,1,4,8),(6,12),(11,2,5,7,3,9)), π 15 = ( (11,12,10,7,9),(2,5,3), (6,1,4,8)). ...
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... duration of the project was obtained for the de- cisive variable π = (π 1 , π 2 , ..., π 15 ), where: π 1 = ( (11,10,9,7,1), (12,8,5,4), (2,6,3)), π 2 = ((12,10,4,8),(11,6,1,7,9),(2,5,3)), π 3 = ((12,6,3),(2,4,8),(10,1,7), (11,5,9)), π 4 = ((12,11,9,2,4,5), (10,7,6,3,1,8)), π 5 = ((6,4,10),(3,12),(1),(11,5,2,9,8,7)), π 6 = ( (3,12,10),(2,6,4), (5,11,1,8,7,9)), π 7 = ((2,4,9),(12,6,3), (11,5,8), (10,1,7)), π 8 = ((12,2,5,7,9),(6,1,3), (11,10,4,8)), π 9 = ((6),(11,1), (12,2,10,4,5,3,7,8), (9)), π 10 = ((2,4,8),(6),(1,3,7),(11), (12,10,5,9)), π 11 = ((12,6,1,3,8),(11,10),(4,9),(2,5,7)), π 12 = ( (6,4,10,1,3,8),(11,2,9),(12,5,7)), π 13 = ((2,4,7,8),(12,6,5,9), (11,10,1,3)), π 14 = ((10,1,4,8),(6,12),(11,2,5,7,3,9)), π 15 = ( (11,12,10,7,9),(2,5,3), (6,1,4,8)). ...
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... The assignment of human resources to project tasks that employ AI heuristics is another appearing topic in the literature [48][49][50][51]. The works [42] and [48] applied feasible ACO algorithms (improved Max-Min ACO and Hyper-Cube ACO, respectively) for workertask assignment in software projects to minimize the project duration. ...
... Findings revealed that the new adaptative ACO outperforms common ACO and GAs. A tabu search algorithm was employed in [50] so as to solve the resource management problem for multi-unit construction projects. The case study analyzed manifested a reduction of 50% in the project execution time when using that algorithm. ...
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.
... The parameters of this project were represented by fuzzy numbers or random variables with a normal distribution or the Erlang distribution. In [43], a scheduling model was presented with the possibility of performing one type of work by more than one working group and with sequence relationships given by any graph. The optimization task in this model was solved using the tabu search algorithm. ...
Construction projects are often delayed. Their budgets are being exceeded. This is partly because construction project models rarely take human factors into account. The article focuses on the repetitive multi-unit projects that are often found in construction practices. They are an excellent example of construction works for which the optimization of certain processes can bring measurable financial benefits. In particular, the focus was on improving the key investment success indicator, the total cost of a project. This article presents the use of learning and forgetting phenomenon to improve construction schedules. The authors proposed an innovative model of the construction phase and optimized the schedule using a proprietary calculation algorithm using the simulated annealing phenomenon. The method was verified by a series of experiments and showcased by a case study. The results show that the presented methods can improve the construction planning process and better reflect the current construction conditions. The findings may impact residential, infrastructure, industrial, and commercial construction branches.
... The tabu search (TS) algorithm [7] is a metaheuristic algorithm proven to have high efficiency in both VRP problems and multiproject scheduling problems. Additionally, this algorithm has frequently been used to solve similar problems, such as MDOVRP [30], MDVRP [4,22,24], and multi-project scheduling [19,20,40], which has led to desirable results. Hence, we suggested a metaheuristic algorithm mod(a, b) the remainder on dividing a by b S1, S2, … Supplier 1, Supplier 2, … P1, P2, … Project 1, Project 2, … RR12 ...
In a construction supply chain, supply-related decisions can affect project scheduling because the project activities require essential resources to start. This study wants to show how the integration of project scheduling with supplier selection and transportation routing creates value in the entire project supply chain. Therefore, an integrated problem including the three mentioned concepts is considered in an organization managing multiple construction projects. Moreover, the following conditions are assumed in the problem to make it closer to the real situations of construction projects: (1) the quality of all projects is inspected by a single committee; (2) suppliers sell the resources with incremental discounts; (3) generalized precedence relations are considered between the project activities. In order to address the research problem, a mathematical model is proposed based on mixed-integer linear programming. The model is hard to solve because it simultaneously involves multi-project scheduling and vehicle routing. Therefore, a solution method based on the tabu search (TS) algorithm is suggested, validated with small-scale and big-scale examples. This method is used to compare the suggested model with two related models. The comparisons show that the proposed model creates a better balance between costs of different sectors in the project supply chain. Achieving this balance decreases the total cost associated with the problem and increases customer satisfaction. As a result of these findings, project managers and related researchers should pay more attention to supply chain integration in their problems.
... The analysis made by Podolski [25] indicates that the issues of scheduling construction works are significantly related to the theory of task scheduling. It models the functioning of real manufacturing and industrial production systems. ...
Erection of a built facility means conducting a set of construction processes by crews of various trades. To reduce the construction time, these crews may be scheduled to work concurrently. This requires dividing the whole object (or objects of the investment plan) into work zones. The work zones may be of various shapes and sizes, with the quantities of work related with particular processes not following a clear pattern. Therefore, the duration of work differs process to process and zone to zone, what hinders synchronizing the work of crews and causes idle time of at least some of them.
Scheduling often aims at eliminating resource idle time reduce project duration. However, the goals of minimizing the overall construction duration and making a rational use of resources may prove contradictory. It is thus necessary to consider the problem of scheduling repetitive processes in non-uniform work zones a two-criteria optimization problem and search for a trade-off solution.
The authors provide a mixed integer programming model of the repetitive processes scheduling problem. In contrast to models presented in the literature, it enables the user to find optimal crew formation modes and, at the same time, reduce the crews' idle time under the constraint of a fixed project completion date, or to generate non-dominated solutions. The idea is illustrated by a simple case.
In the analyzed example, ensuring continuous work of the teams requires the extension of the project duration by 16% in relation to the minimum duration. The possibility of changing crew formation modes helps the planner reduce both the project completion time and the crews' idle time.
... Other studies discuss the topic of clash relevance prediction (Hu and Castro-Lacouture, 2019), resolving design clashes (Hsu et al., 2020), and validation of change requests (Dawood et al., 2019). Publications focused on logistics include the utilisation of AI in resource management (Xing et al., 2016;Podolski, 2016;Camacho et al., 2018), resourceconstrained scheduling (Li and Womer, 2015;Zheng and Wang, 2015), and the resource-levelling optimisation (Iyer et al., 2015). For the physical construction site, material layout planning (Cheng and Chang, 2019), and site layout design (Amiri et al., 2017) are explored. ...
The use of artificial intelligence (AI) in construction projects has surged in recent years and is believed to represent a significant potential for increasing productivity and efficiency in the industry. The purpose of this paper is to present a state-of-the-art view of the field by conducting a review of publications concerning the topic of AI in construction and comparing the findings to previously conducted reviews. This paper provides an overview of the recent and current uses of AI in construction projects, through a descriptive analysis of the characteristics and contents of 86 peer-reviewed articles from 2015 to 2020. Although the application of AI in the industry is not entirely new, construction appears to currently be behind other industries in terms of adopting and adapting to AI. The results show that a wide range of research is conducted on AI in construction projects. A limited number of publication channels and authors stand behind a significant part of the reviewed publications. Most studies are conceptual or use a mixed-methods research design. The research addresses several areas of application, but there is a predominance of quantitatively based subfields of construction, such as estimation and cost control, logistics, planning, and scheduling. Future research should focus on developing holistic and process-oriented frameworks for projects to move from ambition to practice. Findings can inform the future development and implementation of AI in the construction industry context. For researchers, this study identifies areas in need of further attention and examines possibilities for future exploration of multidisciplinary approaches that combine construction engineering, project management, AI, and social science. For practitioners, the study highlights current trends and work within the field, providing an overview of the potential for pilot studies, tests, and innovations. Copyright © Journal of Engineering, Project, and Production Management (EPPM-Journal).
... Minimizing repetitive project makespan was also the key objective of the following publications out of the set selected for the analysis: Hassanein and Moselhi (2005), , Podolski (2017), Radziszewska-Zielina and Sroka (2018), Suresh et al. (2011), Zhang and Qi (2012). ...
Construction projects that involve repetitive operations are often referred to as repetitive construction projects. Scheduling them proves a task more demanding than in the case of projects in other industries. Typical objectives of optimization, a characteristic set of constraints, as well as the schedule’s susceptibility to the propagation of disruptions caused by materializing risks, call for specific scheduling methods. The authors review the literature to summarize the existing repetitive scheduling methods and put forward their classification to identify the method’s aspects needing refinement. This is done to point to directions of further research. The authors hope that this study will contribute to better identification of existing problems in planning repetitive construction projects and faster development of decision support systems, eagerly anticipated by the construction practitioners. Though the focus is on applications to construction projects, the repetitive scheduling methods that account for volatile operating conditions may be of interest to researchers who develop planning techniques for other industries.
... Research papers [5], [20], [25], [54], [58] deal with flow shop problems. Papers [10], [11], [18], [61], [62], [45] concern problems related to human resource management and scheduling of work teams. On the other hand, papers [12], [16], [19], [21], [22], [26], [29], [30], [31], [33], [34], [51], [57] concern task scheduling problems in cloud computing. ...
... An interesting adaptation of the task scheduling problem is Crew Scheduling in the building industry. The problem of multi-object undertakings was presented in [61]. The author presents more than 50% reduction of the project execution time due to the use of the TS algorithm. ...
The idea of task scheduling is to increase the efficiency of a system by minimising wasted time, evenly loading machines, or maximising the throughput of machines. Moreover, the use of appropriate scheduling algorithms often leads to a reduction in the energy costs of the process. Task scheduling problems are found in a variety of industrial areas, and their scale changes significantly depending on the problem. This review shows the extent to which task scheduling methods are applied in industry. This paper presents methods and algorithms for solving task scheduling problems. In addition, an analysis of the possibility of using task scheduling methods to improve the efficiency of biological wastewater treatment plants was also conducted. This approach is based on the assumption of a balanced workload for multiple reactors. Analysing the case study of a wastewater treatment plant in Swarzewo in Northern Poland is applied.
... In the DPE module developed in this study, the user's requirements can be restored and modified. Podolski [33] proposed a solution using artificial intelligence for project scheduling in construction projects. In this study, a way to shorten the schedule through project resource management was developed. ...
Engineering, Procurement, and Construction (EPC) projects span the entire cycle of industrial plants, from bidding to engineering, construction, and start-up operation and maintenance. Most EPC contractors do not have systematic decision-making tools when bidding for the project; therefore, they rely on manual analysis and experience in evaluating the bidding contract documents, including technical specifications. Oftentimes, they miss or underestimate the presence of technical risk clauses or risk severity, potentially create with a low bid price and tight construction schedule, and eventually experience severe cost overrun or/and completion delays. Through this study, two digital modules, Technical Risk Extraction and Design Parameter Extraction, were developed to extract and analyze risks in the project’s technical specifications based on machine learning and AI algorithms. In the Technical Risk Extraction module, technical risk keywords in the bidding technical specifications are collected, lexiconized, and then extracted through phrase matcher technology, a machine learning natural language processing technique. The Design Parameter Extraction module compares the collected engineering standards’ so-called standard design parameters and the plant owner’s technical requirements on the bid so that a contractor’s engineers can detect the difference between them and negotiate them. As described above, through the two modules, the risk clauses of the technical specifications of the project are extracted, and the risks are detected and reconsidered in the bidding or execution of the project, thereby minimizing project risk and providing a theoretical foundation and system for contractors. As a result of the pilot test performed to verify the performance and validity of the two modules, the design risk extraction accuracy of the system module has a relative advantage of 50 percent or more, compared to the risk extraction accuracy of manual evaluation by engineers. In addition, the speed of the automatic extraction and analysis of the system modules are 80 times faster than the engineer’s manual analysis time, thereby minimizing project loss due to errors or omissions due to design risk analysis during the project bidding period with a set deadline.
... To describe and solve such problems, we can use the flow shop theory of scheduling [32]. Research on the problem of multi-unit projects scheduling is focusing mainly on the improvement of current optimization models and improvement of optimization methods [33][34][35][36][37]. In [33], the problem of scheduling with minimization of penalties for exceeding the project's objects completion deadlines is considered and solved by the metaheuristic scatter search algorithm. ...
... The parameters of this project were represented by fuzzy numbers or random variables with a normal distribution or the Erlang distribution. In [36], a scheduling model was presented with the possibility of performing one type of work by more than one working group and with sequence relationships given by any graph. The optimization task in this model was solved using the tabu search algorithm. ...
The article presents the profit optimization model for multi-unit construction projects. Such projects constitute a special case of repetitive projects and are common in residential, commercial, and industrial construction projects. Due to the specific character of construction works, schedules of such projects should take into account many different aspects, including durations and costs of construction works, the possibility of selecting alternative execution modes, and specific restrictions (e.g., deadlines for the completion of units imposed by the investor). To solve the NP-hard problem of choosing the order of units' construction and the best variants of works, the authors used metaheuristic algorithms (simulated annealing and genetic search). The objective function in the presented optimization model was the total profit of the contractor determined on the basis of the mathematical programming model. This model takes into account monthly cash flows subject to direct and indirect costs, penalties for missing deadlines, costs of work group discontinuities, and borrowing losses. The presented problem is very important for maintaining a good financial condition of the enterprise carrying out construction projects. In the article, an experimental analysis of the proposed method of solving the optimization task was carried out in a model that showed high efficiency in obtaining suboptimal solutions. In addition, the operation of the proposed model has been presented on a calculation example. The results obtained in it are fully satisfying.
... Minimalizacja czasu powtarzalnego przedsięwzięcia budowlanego była też głównym celem publikacji następujących autorów: Hassanein i Moselhi [166], Lucko, Said i Bouferguene [167], Podolski [168], Radziszewska-Zielina i Sroka [169], Suresh, Dutta i Jain [170], Zhang i Qi [171]. ...
W pracy przedstawiono metodę harmonizacji wykonania procesów wieloobiektowego przedsięwzięcia budowlanego. Celem pracy było opracowanie metody harmonizacji wykonania niejednorodnych procesów wieloobiektowego przedsięwzięcia budowlanego w warunkach deterministycznych umożliwiającej minimalizację czasu realizacji przedsięwzięcia i obiektów budowlanych oraz przerw w pracy z uwzględnieniem preferencji decydenta co do istotności tych celów optymalizacji harmonogramu budowy.
Pierwszym krokiem zrealizowanych badań było studium literatury, które wykonano w celu analizy istniejących metod harmonizacji wieloobiektowych przedsięwzięć budowlanych. Z analizy literatury wynika, że tak postawiony problem harmonizacji przedsięwzięć budowlanych, jak w niniejszej pracy, nie był dotychczas sformułowany i nie doczekał się metod rozwiązań.
Kolejnym krokiem realizowanej metodyki badań było przeprowadzenie badań ankietowych wśród kadry kierowniczej przedsięwzięć budowlanych. Uzyskane w niej informacje pozwoliły rozpoznać stosowane w praktyce kryteria oceny harmonogramów budowlanych: minimalizacji czasu realizacji całego przedsięwzięcia i poszczególnych obiektów budowlanych oraz minimalizacji przerw w pracy brygad. Istotność zdefiniowanych kryteriów oceny harmonogramów można wprowadzić za pomocą jednego z trzech sposobów:
• dyrektywnego wskazania wag przez decydenta,
• autorskiej grupowej metody wspomagania podejmowania decyzji będącej rozwinięciem rozmytej metody AHP w ujęciu Mikhailova,
• interaktywnej metody wspomagania podejmowania decyzji wzorowanej na metodzie przeglądu wiązką światła.
Następnie, dokonano identyfikacji modelu problemu harmonizacji wykonania procesów przedsięwzięcia budowlanego oraz jego formalizacji matematycznej. Zaproponowane ujęcie zagadnienia uniemożliwia generowanie rozwiązań niedopuszczalnych, a jednocześnie nie pomija rozwiązań, które mogą być użyteczne. Zaproponowany w pracy sposób matematycznego ujęcia problemu znacząco skraca czas obliczeń oraz ułatwia jego rozwiązanie. Kolejny etap badań polegał na opracowaniu oraz oprogramowaniu algorytmu metaheurystycznego (algorytmu optymalizacji rojem cząstek) w celu poszukiwania rozwiązań niezdominowanych zagadnienia (dokładnych lub przybliżonych).
Model zagadnienia oraz uzyskiwane za pomocą systemu komputerowego rozwiązania poddano badaniom weryfikacyjnym. Polegały one na wykonaniu testów: konfrontacji danych, warunków brzegowych, stabilności otrzymywanego rozwiązania, analizy wrażliwości i efektywności czasowej uzyskanego rozwiązania. Opracowany system komputerowy pozytywnie przeszedł wszystkie przeprowadzone testy kontrolne.
Stworzoną metodę zastosowano do harmonizacji wykonania procesów drugiego etapu Elizówka Park. Redukcja czasu wykonania przedsięwzięcia, dla preferowanego rozwiązania przez kierownika budowy, otrzymana za pomocą opracowanego systemu, względem harmonogramu skierowanego w rzeczywistości do realizacji wyniosła od 25 % do 35 % (w zależności od sposobu wyboru wag kryteriów oceny).
Na końcu pracy zestawiono uzyskane wyniki i wnioski. Opracowana metoda harmonizacji wykonania procesów budowlanego przedsięwzięcia wieloobiektowego może wspomagać decyzje zarządzających budową przy projektowaniu organizacji robót budowlanych i poprawić efektywność realizacji przedsięwzięcia (zgodnie z preferencjami decydenta).
