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This paper emphasises the pivotal role of enhancing research and development (R&D) staff competence and boosting team innovation efficiency in configuring R&D project teams for artificial intelligence (AI) product development. Diverging from traditional studies primarily focused on time, quality, and cost objectives, this study proposes prioritising the skill increments of staff and team diversity, aligning with the overarching goals of the R&D cycle. Targeting multi-skilled R&D personnel for scheduling and configuration, a three-objective tradeoff optimisation model is established. The nonlinear mixed-integer constrained programming model incorporates a learning effect for calculating employee skill value and employs a heterogeneity efficiency formula for assessing team diversity, particularly emphasising the diversity of R&D personnel research backgrounds. An algorithm based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to obtain the approximate Pareto optimal solution. Furthermore, it compares the algorithm with the multi-objective particle swarm optimisation (MOPSO), explores practical decision-making methods for Pareto solutions, and conducts sensitivity analyses on the learning rate and diversity level. Our research is relevant to enterprises seeking to enhance R&D capabilities with a certain degree of homogeneity among R&D employees. This paper exemplifies and validates the proposed model and solution approach using a new AI product from a healthcare company. [ABSTRACT FROM AUTHOR]

This paper investigates recruitment decisions of temporary work with several levels of experience, from beginners to experts (heterogeneous efficiency) to accelerate a manufacturing project with the objective of labour cost minimisation. A Mixed Integer Linear Program formulation of the problem is developed to find the optimal pool of temporary work of each experience level so as to minimise the cost subject to a desired deadline. The deadline is decreased decrementally in order to identify any potential patterns of recruitment along the reduction in the project duration. A variety of projects, efficiency levels with respect to years of experience and cost scenarios are considered in an experimental framework. Simulation results show a high sensitivity of the optimal pool of temporary work to the project type, efficiency levels and costs. A worst-case analysis suggests that using solely expert temporary workers might be the best recruiting policy to accelerate a project, whatever the desired deadline. [ABSTRACT FROM AUTHOR]

Most resource-constrained project scheduling problems consider resources that are independent of the job processing sequence. However, solving project scheduling problems in some industrial manufacturing is challenging because the required resources depend on the job processing sequence in existing scheduling approaches. To address this issue, this paper proposes a dependent resource-constrained project scheduling problem formulated with two types of models: a conceptual model and a binary integer programming model. The performance of four proposed algorithms – binary integer programming, reduced subproblem of the binary integer programming, job sequence assignment, and job as early as possible – is tested on 68 combinations of numerical simulation experiments. The computational results show that the job as early as possible algorithm can find a better approximate solution for solving real-world problems. This achievement aids project managers in generating activity schedules and finding the approximate minimum makespan for a project with dependent resource constraints. [ABSTRACT FROM AUTHOR]

This paper addresses developing and assessing a project schedule quality checklist and related contractual requirements for a refinery expansion project managed by Indonesia's national oil company. Faced with typical challenges in large-scale construction projects, including delays and cost overruns, the project requires an effective scheduling and cost management approach to ensure its successful execution. This paper applies the ten best practices from the U.S. Government Accountability Office (GAO) Schedule Assessment Guide to enhance schedule reliability. The project's schedule is benchmarked against these best practices, and a scoring model is developed using a Multi-Attribute Decision-Making (MADM) methodology. The evaluation identifies gaps in the project's current scheduling practices and suggests areas for improvement. The findings highlight the importance of adhering to GAO standards to strengthen schedule quality. The study concludes with actionable recommendations for integrating these best practices into contractual frameworks to enhance project performance and mitigate risks. [ABSTRACT FROM AUTHOR]

In this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns industrial test laboratories in which a large number of tests are performed by qualified personnel using specialised equipment, while respecting deadlines and other constraints. We present different constraint programming models and search strategies for this problem. Furthermore, we propose a Very Large Neighborhood Search approach based on our CP methods. Our models are evaluated using CP solvers and a MIP solver both on real-world test laboratory data and on a set of generated instances of different sizes based on the real-world data. Further, we compare the exact approaches with VLNS and a Simulated Annealing heuristic. We could find feasible solutions for all instances and several optimal solutions and we show that using VLNS we can improve upon the results of the other approaches. [ABSTRACT FROM AUTHOR]

Construction project-oriented companies face numerous challenges, including resource constraints, efficient network design for material delivery, an uncertain nature of decision making, and the increasing importance of environmental issues. This paper proposes a multi-objective mixed-integer mathematical model for the optimal management of a construction project supply chain for multi-project scenarios, with the goal of minimizing total costs, tardiness, and environmental impact. The model considers various environmental factors, such as greenhouse gas concentrations, noise, dust emissions, and visual pollution, as well as inventory management to handle resource constraints in a three-echelon supply chain network. To deal with the inherent uncertainty in project duration times, the model uses a robust optimization approach, and a branch-and-cut algorithm is proposed as a solution methodology. The effectiveness of the proposed model is demonstrated through a case study in petroleum construction supply chain. This paper contributes to the field of construction project management by offering an integrated approach that addresses the complex challenges faced by construction project-oriented companies, resulting in improved efficiency, sustainability, and overall performance. [ABSTRACT FROM AUTHOR]

The widely recognized measure for resources called resource strength (RS) does not fully capture the resources complexity of a project. Therefore, it cannot be used as a standalone measure to distinguish the complexity of various instances of project scheduling problems. Consequently, additional resource measures such as total amount of overflow (TAO) have been introduced, which should be used in conjunction with the RS. Extensive experimental studies have shown that as the value of TAO increases in a project, scheduling schemes with higher dimensional scheduling schemes such as bi-directional and tri-directional result in schedules with shorter makespans. In this study, an effective approach is proposed for integrating projects in multi-project environments, called the integrated project approach (IPA), taking into account the influence of TAO and building upon the relation between the TAO and the scheduling generation schemes. To assess the performance of IPA, we develop a new random multi-project generator based on the well-known benchmark sets, which utilizes TAO as a control tool to generate instances. The findings indicate that prioritizing the projects and frequency of the projects integration, facilitated by the proposed IPA, have a positive impact on the quality of multi-project schedules. [ABSTRACT FROM AUTHOR]

The Multi-Mode Resource-Constrained Multiple Projects Scheduling Problem (MMRCMPSP) is an important combinatorial optimization problem for both real-world situations in industry and academic research. Its objective is to find the best schedule for activities across multiple projects that can be executed in different modes. The schedule must consider shared resource availability and satisfy precedence and time constraints. To tackle this problem, we propose a hybrid approach that combines constraint programming (CP) with meta-heuristic algorithms. We introduce and assess a CP model that incorporates all MMRCMPSP constraints. By leveraging the strengths of CP and meta-heuristics, our approach yields new upper bounds for various MMRCMPSP benchmark instances. Additionally, we evaluate our method using existing benchmark instances for single-project scheduling problems with multiple modes and provide improved solutions for many of them. [ABSTRACT FROM AUTHOR]

Existence of delays and cost overruns frequently puts the project viability in jeopardy. The integrated nature of these threats brings forward project scheduling as the primary determinant of project management success. The quality of project scheduling depends highly on the way resources are assigned to activities. In the project management literature, the efficiency of resource allocation is examined closely by the phenomenon called project crashing. This study introduces traditional and genetic algorithm approaches for the project crashing events and explains their steps in achieving the most efficient resource allocation. Within this context, the project crashing event is visualized, the insights of alternative approaches are described, and their implementations are illustrated with a case study. Besides, the procedures required for adopting the genetic algorithm approach to a typical problem are expressed. The case study illustration reveals the advantages and disadvantages of the genetic algorithm approach over the traditional approach. It is observed that the genetic algorithm approach can reach the solution in a single phase while the traditional approach requires multiple phases. On the other hand, the genetic algorithm approach may not reach the optimum solution unless the toolbox options are appropriately selected. This study presents the contribution of operational research to the project management body of knowledge by demonstrating the applicability and efficiency of genetic algorithm in the project crashing events. Researchers and industry practitioners may benefit from the proposed approach by following the indicated procedures to incorporate genetic algorithm into optimization issues in different fields.

Elaborating and applying a new model for estimating the time buffer size of a project programme, which shall guarantee a 90 % probability of timely project execution. The research included source text analysis to provide information on a research gap and the identification of the research problem. The research problem was identified: the time buffer size in a critical path programme does not guarantee a 90 % probability of timely project execution. A new model was then elaborated to estimate the buffer size; it was applied in a technical production preparation project. An additional comparative analysis was performed using the following methods to verify the model more accurately: half of the time total of a path, the sum of squares (SSQ), and the root square error method (RSEM). The application of the fuzzy model to estimate the buffer size in a critical chain programme offers can shorten the total planned project duration. It has a higher probability of timely project execution than other methods for estimating the buffer size. It guarantees a 90 % probability of timely project execution, keeping aggressive task times, which eliminates unwanted situations such as student syndrome, Parkinson’s law, overestimating task duration, and multitasking. Project programming is an inherent part of the project planning stage in project management. Recently, project management has been increasingly developing, which has been confirmed by the article’s source literature analysis. The analysis revealed a research gap in models estimating project buffer size, which might guarantee a 90 % probability of timely project execution. Thus, a fuzzy model for estimating time buffer size in a critical chain was developed, constituting added value to the science of management and quality of production engineering (currently, mechanical engineering). The fuzzy model for estimating time buffer size was applied in one Polish enterprise in a project for a new product’s technical production preparation. The fuzzy model for estimating time buffer size permits the shortening of the duration of tasks to aggressive times, guaranteeing a 90 % probability of project timely execution. The elaborated model for estimating time buffer size may be applied further in practice in projects programmed using the critical chain method.

Real-world projects encounter numerous issues, challenges, and assumptions that lead to changes in scheduling. This exposure has prompted researchers to develop new scheduling models, such as those addressing constrained resources, multi-skill resources, and activity pre-emption. Constrained resources arise from competition among projects for limited access to renewable resources. This research presents a scheduling model with constrained multi-skill and multi-mode resources, where activity durations vary under different scenarios and allow for partial pre-emption due to resource shortages. The main innovation is the pre-emption of activities when resources are unavailable, with defined minimum and maximum delivery time windows. For this purpose, a multi-objective mathematical programming model is developed that considers Bertsimas and Sim's robust model in uncertain conditions. The model aims to minimize resource consumption, idleness, and project duration. The proposed model was solved using a multi-objective genetic algorithm and finally, its validation was completed and confirmed. Analysis shows that limited renewable resources can lead to increased activity pre-emption and extended project timelines. Additionally, higher demand raises resource consumption, reducing availability and prolonging project duration. Increasing the upper time window extends project time while decreasing the lower bound pressures resources, leading to higher consumption and resource scarcity. [ABSTRACT FROM AUTHOR]

Resilience is a topic that has recently emerged concerning the basics of the construction project supply chain and we can consider it as a response to disruption in the supply chain of the project. Disruption also is an unavoidable reality in today's complex and dynamic construction supply chain, the occurrence of which can cause irretrievable damages to the system, such as financial losses. Successful companies seek to minimize disruption and maintain adequate supply chain performance before disruption occurs, rather than looking for costly and challenging post-disruption solutions. This paper covers this gap by proposing a scenario-based mixed integer-programming model aiming to minimize logistics costs and delays, while scheduling projects to address selecting the appropriate supplier at risk of disruption. So far, this quantitative view was not presented in discussions about disruptions in the project supply chain, therefore different scenarios are applied in the process to validate the model. To improve its resilience level, this model benefits from back-up suppliers' strategy. This study focuses on providing the required materials for the project site in an emergency without incurring additional costs using a back-up supplier. Results reveal the model's suitability in confronting the unavailability of a supplier due to disruption. [ABSTRACT FROM AUTHOR]

Existence of delays and cost overruns frequently puts the project viability in jeopardy. The integrated nature of these threats brings forward project scheduling as the primary determinant of project management success. The quality of project scheduling depends highly on the way resources are assigned to activities. In the project management literature, the efficiency of resource allocation is examined closely by the phenomenon called project crashing. This study introduces traditional and genetic algorithm approaches for the project crashing events and explains their steps in achieving the most efficient resource allocation. Within this context, the project crashing event is visualized, the insights of alternative approaches are described, and their implementations are illustrated with a case study. Besides, the procedures required for adopting the genetic algorithm approach to a typical problem are expressed. The case study illustration reveals the advantages and disadvantages of the genetic algorithm approach over the traditional approach. It is observed that the genetic algorithm approach can reach the solution in a single phase while the traditional approach requires multiple phases. On the other hand, the genetic algorithm approach may not reach the optimum solution unless the toolbox options are appropriately selected. This study presents the contribution of operational research to the project management body of knowledge by demonstrating the applicability and efficiency of genetic algorithm in the project crashing events. Researchers and industry practitioners may benefit from the proposed approach by following the indicated procedures to incorporate genetic algorithm into optimization issues in different fields. [ABSTRACT FROM AUTHOR]

The valuation and planning of complex projects are becoming increasingly challenging with rising market uncertainty and the deregulation of many industries, which have also raised the need for efficient risk management. We take the perspective of a private firm interested in sequential capacity expansion of a project and develop a framework for measuring the downside risk of the serial project and optimising the sequence of the stages. Under general distributional assumptions for the duration of each stage, we present an accurate representation of the project's net present value (NPV) distribution based on a Pearson curve fit, leading to closed-form expressions for the associated risk measures. We then assess the impact of duration variability on the value at risk and demonstrate its role in stochastic project scheduling. We also account for the trade-off between maximising the expected NPV and minimising the risk exposure, and obtain the optimal schedule for risk-averse decision-makers. It becomes obvious that both the duration variability of each stage and the decision-makers' risk preferences can significantly affect the optimal sequence of the stages and that high duration variability is not always undesirable, even for risk-averse decision-makers. [ABSTRACT FROM AUTHOR]

Purpose: Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling. Design/methodology/approach: The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems. Findings: In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation. Originality/value: This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects. [ABSTRACT FROM AUTHOR]

Purpose: The cost-time tradeoff in project scheduling is a significant challenge that has garnered substantial attention. This research aims to establish a balance between time compression and delays in activity execution to optimize resource utilization and facilitate activity scheduling based on existing constraints. To achieve this, a bi-objective mathematical model is developed to support decision-makers in selecting the optimal schedule for project execution. Methodology: In this study, a bi-objective mathematical model is proposed to balance cost and time, incorporating a nonlinear cost function and the time value of money. The model is then solved using the multi-objective particle swarm optimization (MOPSO) algorithm to analyze the effects of time compression and activity delays on the outcomes. Findings: The proposed model's results demonstrate its ability to optimize project resource usage by considering current constraints and capacities. This enables decision-makers to adjust activity scheduling to achieve the best balance between cost and time. Furthermore, the model assumes costs are nonlinear and calculated based on the time value of money. This approach allows for scheduling decisions that account for the time value, reducing delay-related costs and enhancing overall project efficiency. Originality/Value: This research presents a novel approach by introducing a bi-objective decision-making model that integrates the time value of money in project scheduling, marking a new step in optimizing the cost-time tradeoff. Unlike previous studies that focus solely on reducing either cost or time, the proposed model provides a comprehensive solution by accounting for nonlinear complexities and the time value of money. This model assists project managers in gaining better insight into cost-time impacts, optimizing resource allocation, and ultimately improving project performance by reducing delays. [ABSTRACT FROM AUTHOR]

IntroductionAgricultural production involves a series of tasks including tillage, planting, and harvesting, which must be done at the right time for each region and type of product. Failing to complete these tasks on time can lead to a decrease in yield. Farmers may wrongly attribute this to factors such as infertile land, pests, diseases, and uneven rainfall distribution. However, this decrease in yield may not always be evident or tangible. To avoid such losses and unforeseen expenses, it is crucial to plan agricultural mechanization projects using the principles of project control. Agricultural projects, like industrial projects, must be carried out in the correct order and at the right time to achieve optimal results. Given the limited availability of resources for mechanization projects, it is imperative to meticulously plan activities to ensure that they are carried out on time and with maximum utilization of resources. To address these challenges, researchers have used meta-heuristic methods in project control, such as the colonial competition algorithm, which has been proven effective in solving the issue of scheduling projects with limited resources. The algorithm has been tested across various industrial activities and projects, and its performance in scheduling the Resource-Constrained Project Scheduling Problem (RCPSP) has been validated by researchers globally.Materials and MethodsThere is a scheduling issue regarding limited resources in agriculture, and this study presents a novel approach using the imperialist competitive algorithm (ICA). The algorithm not only explores a wider solution space but also strives to minimize deviation from the optimal solution, thereby improving the success rate of the proposed method. This research focuses on two dominant products, wheat and rapeseed, produced in Moghan Agriculture and Industry located in Northwest Iran. To evaluate the effectiveness of ICA, we compared it with other well-known meta-heuristic algorithms. We successfully resolved the problem of project scheduling problem with limited resources by implementing the imperialist competitive algorithm. Our findings have shown that this approach not only significantly increased efficiency but also outperformed other algorithms.Results and DiscussionIn this study, we assessed the efficiency of meta-heuristic methods in solving the RCPSP, which can be useful in optimizing the timeliness of project execution, especially for large-scale projects. Some meta-heuristic methods are only useful for smaller problems, while others can provide near-optimal solutions for larger problems, making them suitable for RCPSP. The algorithm explores a wide range of solutions and avoids premature convergence and getting stuck in local optima, unlike other algorithms such as the genetic algorithm. Optimization reduced the required budget and shortened the duration by 42 days for wheat and 25 days for rapeseed.ConclusionWe utilized the colonial competition algorithm to address the RCPSP problem in agricultural mechanization projects for two agricultural products in Moghan. Our results show that the proposed algorithm converged and reached the optimal solution. The proposed algorithm was compared with other algorithms and it outperformed them.

Abstract. One of the important problems in managing construction projects is selecting the best alternative for activities' execution to minimize the project's total cost and time. However, uncertain factors often have negative effects on activity duration and cost. Therefore, it is crucial to develop robust approaches for construction project scheduling to minimize sensitivity to disruptive noise factors. Additionally, existing methods in the literature rarely focus on environmentally conscious construction management. Achieving these goals requires incorporating the project scheduling problem with multiple objectives. This study proposes a robust optimization approach to determine the optimal construction operations in a project scheduling problem, considering time, cost, and environmental impacts (TCE) as objectives. An analytical algorithm based on Benders decomposition is suggested to address the robust problem, taking into account the inherent uncertainty in activity time and cost. To evaluate the performance of the proposed solution approach, a computational study is conducted using real construction project data. The case study is based on the wall of the east coast of Amirabad port in Iran. The results obtained using the suggested solution approach are compared to those of the CPLEX solver, demonstrating the appropriate performance of the proposed approach in optimizing the time, cost, and environment trade-off problem.

Jalan Pemuda 1 is one of the city roads located in the Samarinda region that often experiences flooding. Therefore, the construction of adequate drainage is necessary to address this flood issue. The construction of this drainage system is expected to effectively solve the flooding problems in the area. The project utilizes precast U-ditches as part of the drainage, replacing the less effective manual methods. The project site frequently experiences tidal fluctuations from the Karangmumus River, which triggers the floods. The U-ditch Precast has dimensions of 240 cm width, 180 cm height, and 10 cm length. Additionally, this research aims to analyze the time optimization in drainage channel construction projects using the Precedence Diagram Method (PDM). The critical tasks in the construction of the drainage channel project are also analyzed using the PDM method. In this research phase, the author chose the Precedence Diagram Method (PDM) not only to clarify the tasks but also to improve project management efficiency and effectiveness to achieve optimal results. The advantage of the Precedence Diagram Method (PDM) is that it does not require dummy or additional activities, simplifies the project network creation, and the interdependence between activities can be arranged without adding new tasks. The accelerated tasks include mobility work, reduced from 7 days to 2 days, and demobilization, reduced from 7 days to 1 day. The occupational safety and health management system (K3) is reduced from 7 days to 2 days, and utility tasks (PDAM, PLN, Telkom) are reduced from 7 days to 2 days. The initial project scheduling indicates a duration of 210 days, but with optimization, the project is completed in 196 days, resulting in a time savings of 13 days. This study provides insights into the effectiveness of the PDM method in addressing critical challenges in construction projects, with implementation leading to more efficient planning and timely project completion.

Project fast-tracking involves executing project activities sequentially and in parallel, accelerating completion time. In this research, the Critical Path Method (CPM) was used to analyze and schedule a residential complex construction project through systematic analysis of project tasks and identification of critical paths, which enables project managers to optimize project schedules and compress the schedule effectively. Fast-tracking also enables simultaneous tasks and overlapping sequential activities, thus helping compress project timelines without compromising on quality, ultimately accelerating project timelines. [ABSTRACT FROM AUTHOR]

This article summarises the research studies published in the special issue on Project Management and Scheduling devoted to the 18th International Conference on Project Management and Scheduling (PMS). The special issue contains state-of-the art research in the field of (non-)robust project and machine scheduling and the contribution of each individual study to the academic literature are discussed. We notice that there is a growing interest in the research community to investigate robust scheduling approaches and optimisation problems observed in real-life business settings. This allows us to derive some interesting future research directions for the project and machine scheduling community. [ABSTRACT FROM AUTHOR]

Real-life applications in project planning often involve grappling with inaccurate data or unexpected events, which can impact the project duration and cost. The delay in the project execution can be overcome by investing in additional resources to avoid compromising the project duration. The goal of the resource leveling problems (RLP) is to determine the optimal amount of resources to invest in, aiming to minimize the associated complementary costs and adhere to the fixed deadline. To tackle data uncertainty in the RLP, the literature has predominantly focused on developing robust and stochastic approaches. In contrast, sensitivity analysis and reactive approaches have received comparatively less attention, especially concerning the generalized RLP with flexible job durations. In this problem, the duration of each job depends on the amount of resources available for its execution. Therefore, utilizing more resources may help reduce the project duration but at an additional cost. This paper introduces a novel approach that addresses the generalized RLP with uncertain job and resource parameters, incorporating reactive and sensitivity-based methodologies. The proposed approach extends the concept of evaluation metrics from machine scheduling to the domain of the RLP with flexible job durations. It is based on a metric-based function that estimates the impact of changes in input data on the solution quality, considering both optimality and feasibility for the new problem instance. The approach is tested through numerical experiments conducted on benchmark instance sets to investigate the impact of variations in different problem parameters. The obtained results demonstrated a meaningful accuracy in estimating the impact on the value of the objective function. Additionally, they underscored the importance of utilizing optimality/feasibility preservation conditions, as for a significant portion of the tested instances, the use of these conditions gave a satisfactory outcome. [ABSTRACT FROM AUTHOR]

In this paper, we consider a resource overload problem and add a tardiness penalty to the objective function when a prescribed project makespan is exceeded, which enables a trade-off between a balanced resource utilization and a project delay. For the tardiness penalty, we distinguish between a constant and variable delay cost variant. Based on the structural properties of the resource overload problem, we show that the search space of the resource overload problem with tardiness penalty can also be reduced utilizing quasistable schedules. In addition, we discuss the application of these findings to further problems, which include objectives composed of a locally concave and a concave function or a reward structure for an early project completion instead of a tardiness penalty. As solution approaches, we present mixed-integer linear model formulations as well as a novel genetic algorithm with a decoding procedure, which exploits the devised structural properties. The performance of the genetic algorithm is improved by implementing learning methods and utilizing lower bounds. Finally, we present results from experiments on small to medium sized problem instances. [ABSTRACT FROM AUTHOR]

In recent years, new resource types have been established in project scheduling. These include so-called partially renewable resources, whose total capacity applies only to a subset of periods in the planning horizon. In this paper, we consider the extension of the resource-constrained project scheduling problem with those partially renewable resources as well as generalized precedence constraints with the objective of minimizing the project duration (RCPSP/max- π ). For this problem it is known that already the determination of a feasible solution is NP-hard in the strong sense. Hence, we present two different generation schemes that are able to find good feasible solutions in short time for most tested instances. The first one is a construction-based heuristic wherein the activities of the project are scheduled iteratively time- and resource-feasibly. The second one is a relaxation-based generation scheme, in which—starting from the schedule consisting of the earliest start times—resource conflicts are identified and resolved by inserting additional resource constraints. [ABSTRACT FROM AUTHOR]

A novel model called Multi-Skilled, Multi-Mode Resource-Constrained Project Scheduling Problem with Discrete Time-Cost-Quality-Risk Trade-Off (MS-MRCPSP-DTCQRTP) was formulated, focusing primarily on minimizing project risks. This integer programming-based model generates various schedules that aim to optimize time, cost, quality, and risk, either individually or by achieving a balanced combination of these factors. By allocating accessible resources to the tasks of the project and adhering to precedence and resource constraints dictated by availability and skills, a collection of viable schedules was generated. Using a uniform distribution for time, cost, quality, and risk, three project case studies were derived from existing literature and expanded upon. These case studies were then employed to showcase the model. To generate optimal solutions, an open-source Python-MIP package employing the branch-and-cut methodology was employed. It was found that time is not a factor in minimizing the risk of a project, except for large-scale projects. Meanwhile, cost is a factor in all sizes of the project. Quality has no significant impact on risk. Project managers must strike a balance between resources that contain skills set with short to medium duration, moderate to high cost, and moderate to high-quality level to minimize project risk. [ABSTRACT FROM AUTHOR]

Critical chain buffer management (CCBM) has been extensively studied in recent years. This paper investigates a new formulation of CCBM, the multimode chance-constrained CCBM problem. A flow-based mixed-integer linear programming model is described and the chance constraints are tackled using a scenario approach. A reinforcement learning (RL)-based algorithm is proposed to solve the problem. A factorial experiment is conducted and the results of this study indicate that solving the chance-constrained problem produces shorter project durations than the traditional approach that inserts time buffers into a baseline schedule generated by solving the deterministic problem. This paper also demonstrates that our RL method produces competitive schedules compared to established benchmarks. The importance of solving the chance-constrained problem and obtaining a project buffer tailored to the desired probability of completing the project on schedule directly from the solution is highlighted. Because of its potential for generating shorter schedules with the same on-time probabilities as the traditional approach, this research can be a useful aid for decision makers. [ABSTRACT FROM AUTHOR]

The resource-constrained project scheduling problem is a widely studied problem in the literature. The goal is to construct a schedule for a set of activities, such that precedence and resource constraints are respected and that an objective function is optimized. In project scheduling literature, summary measures are often used as a tool to evaluate the performance of algorithms and to analyze instances and datasets. They can be classified in two groups, network measures describe the precedence constraints of a project, while resource measures focus on the resource constraints of the instance. In this manuscript we make an exhaustive evaluation of the summary measures for project scheduling. We provide an overview of the most prevalent measures and also introduce some new ones. For our tests we combine different datasets from the literature and generate a new set with diverse characteristics. We evaluate the performance of the summary measures on three dimensions: consistency, instance complexity and algorithm selection. We conclude by providing an overview of which measures are best suited for each of the three investigated dimensions. [ABSTRACT FROM AUTHOR]

The concept of anchored solutions is proposed as a new robust optimization approach to the Resource-Constrained Project Scheduling Problem (RCPSP) under processing times uncertainty. The Anchor-Robust RCPSP is defined, to compute a baseline schedule with bounded makespan, sequencing decisions, and a max-size subset of jobs with guaranteed starting times, called anchored set. It is shown that the Adjustable-Robust RCPSP from the literature fits within the framework of anchored solutions. The Anchor-Robust RCPSP and the Adjustable-Robust RCPSP can benefit from each other to find both a worst-case makespan, and a baseline schedule with an anchored set. A dedicated graph model for anchored solutions is reviewed for budgeted uncertainty. Compact MIP reformulations are derived for both the Adjustable-Robust RCPSP and the Anchor-Robust RCPSP. Dedicated heuristics are designed based on the graph model. For both problems, the efficiency of the proposed MIP reformulations and heuristic approaches is assessed through numerical experiments on benchmark instances. [ABSTRACT FROM AUTHOR]

Over the years, the finance-based and resource-constrained project scheduling problem (FBRCPSP) has been aimed at scheduling activities and offsetting the capital gap without exceeding credit and renewable resource limits in capital-driven construction projects. However, the heuristics proposed for the FBRCPSP in previous studies neglected to focus on identifying efficient priority rules, which are understandable and intuitive for construction practitioners with different roles. Accordingly, this study explores efficient priority rules and evaluates their performance according to modified serial and parallel schedule generation scheme heuristics for the FBRCPSP. First, 11 priority rules that cover the project network, schedule, activity, and resource information are introduced, and three priority rules related to activity information are designed. Second, the two priority rule-based heuristics are applied in an example to generate multiple project schedules according to the different combinations of priority rules and heuristics. Furthermore, the performance of priority rules is tested on the metrics of project duration and profit through numerical experiments. The results show that the priority rules of the latest start time, latest finish time, and old great rank positional weight are the three best priority rules in general. Additionally, the priority rules evenly present more stable and superior results than those obtained by other priority rules for the different levels of weekly fixed overhead cost and the scenarios of contract terms. Based on the intuitive heuristics, the selected efficient priority rules assist contractors in deciding which priority rule should be applied in practice, and project managers can employ an effective priority rule to establish a baseline schedule in the initial project phase and quickly adjust the plan when it becomes infeasible for project execution. This study evaluates the performance of frequently used priority rules on finance-based and resource-constrained project scheduling heuristics in construction project management. The model and priority rule-based heuristics can be embedded into project management software or digitalized scheduling platforms in practice. Moreover, project managers can employ efficient priority rule-based heuristics to generate a desirable project schedule for mastering cash flow and resource-demand plans in the project planning stage. Intuitive priority rules are incorporated into the heuristics to quickly adjust or update schedules and financing alternatives for project management when a baseline schedule is disrupted during the project execution. In addition, practitioners can obtain more adaptive or efficient priority rules following the testing approach proposed in this work if they have rich similar project experience or big data in construction projects. In summary, this study offers construction project schedulers or managers practical scheduling strategies and approaches for addressing finance-based and resource-constrained construction projects. [ABSTRACT FROM AUTHOR]

Resource-constrained project scheduling problem (RCPSP) is a broadly researched issue in the literature. The purpose of the classic form of the problem is scheduling a set of activities considering resource and precedence constraints for minimizing the project completion time. Companies mostly deal with the issue of properly assigning multi-skilled workforces and maintaining the needed skill levels while implementing projects. In this study, a novel MILP model with three objectives is presented to tackle multi-skill RCPSP (MS-RCPSP). This study concentrates on minimizing project makespan, minimizing resource costs as well as tardiness costs, and maximizing quality under uncertainty. However, the standard MS-RCPSP is not able to consider several practical engineering requirements owing to its narrow assumptions. Therefore, key assumptions including overlap between activities, tardiness penalties of activities and the rework duration concept for activities in this model are considered. Due to the complexity of the real world, interval valued fuzzy numbers are taken into account for some of the problem's parameters. The efficiency of the proposed mathematical framework is represented using both a real case study to construct a railway bridge with 34 activities and large-size problem instances from MMLIB (MM50 and MM100). Since this model is multi-objective, a new extended IVF-ABS approach is presented in this study. Finally, the proposed approach is compared with two methods, namely SO and ABS, from the literature. [ABSTRACT FROM AUTHOR]

Introduction Agricultural production involves a series of tasks including tillage, planting, and harvesting, which must be done at the right time for each region and type of product. Failing to complete these tasks on time can lead to a decrease in yield. Farmers may wrongly attribute this to factors such as infertile land, pests, diseases, and uneven rainfall distribution. However, this decrease in yield may not always be evident or tangible. To avoid such losses and unforeseen expenses, it is crucial to plan agricultural mechanization projects using the principles of project control. Agricultural projects, like industrial projects, must be carried out in the correct order and at the right time to achieve optimal results. Given the limited availability of resources for mechanization projects, it is imperative to meticulously plan activities to ensure that they are carried out on time and with maximum utilization of resources. To address these challenges, researchers have used meta-heuristic methods in project control, such as the colonial competition algorithm, which has been proven effective in solving the issue of scheduling projects with limited resources. The algorithm has been tested across various industrial activities and projects, and its performance in scheduling the Resource-Constrained Project Scheduling Problem (RCPSP) has been validated by researchers globally. Materials and Methods There is a scheduling issue regarding limited resources in agriculture, and this study presents a novel approach using the imperialist competitive algorithm (ICA). The algorithm not only explores a wider solution space but also strives to minimize deviation from the optimal solution, thereby improving the success rate of the proposed method. This research focuses on two dominant products, wheat and rapeseed, produced in Moghan Agriculture and Industry located in Northwest Iran. To evaluate the effectiveness of ICA, we compared it with other well-known meta-heuristic algorithms. We successfully resolved the problem of project scheduling problem with limited resources by implementing the imperialist competitive algorithm. Our findings have shown that this approach not only significantly increased efficiency but also outperformed other algorithms. Results and Discussion In this study, we assessed the efficiency of meta-heuristic methods in solving the RCPSP, which can be useful in optimizing the timeliness of project execution, especially for large-scale projects. Some meta-heuristic methods are only useful for smaller problems, while others can provide near-optimal solutions for larger problems, making them suitable for RCPSP. The algorithm explores a wide range of solutions and avoids premature convergence and getting stuck in local optima, unlike other algorithms such as the genetic algorithm. Optimization reduced the required budget and shortened the duration by 42 days for wheat and 25 days for rapeseed. Conclusion We utilized the colonial competition algorithm to address the RCPSP problem in agricultural mechanization projects for two agricultural products in Moghan. Our results show that the proposed algorithm converged and reached the optimal solution. The proposed algorithm was compared with other algorithms and it outperformed them. [ABSTRACT FROM AUTHOR]

Numerous scheduling generation and revision methods have been developed for project scheduling under uncertain environments in previous research. However, most of these methods have yet to address a practical project with more than a hundred activities involving multiple decisions in scheduling generation and execution phases. This study proposes a local search-based scheduling method that comprehensively evaluates a baseline schedule considering decision-making in both the planning and execution phases. The proposed method performs simulations to evaluate schedule robustness accurately using GPU to find a locally optimal solution for large problem instances in a reasonable time. A series of numerical experiments demonstrate that the proposed method can generate a robust schedule for large-scale project instances. These results conclude that the proposed method utilizing the simulation-based evaluation and the GPU acceleration is effective and provide insight into developing a scheduling method for practical projects. [ABSTRACT FROM AUTHOR]

Project schedule management, which involves generating and revising a schedule, is a critical decision-making process in uncertain environments. However, methods that can be used in project schedule management, particularly for large and complex projects, have so far been insufficiently studied. We believe that addressing this issue is essential to enable this process to be successfully adapted to practical projects. This study proposes a scheduling method that can generate a robust schedule insensitive to delays. The proposed method accurately evaluates schedule robustness using simulations, even for complex projects involving multiple decisions. As the simulation element of this process can be time-consuming, particularly for large-scale project instances, it is necessary to accelerate the evaluation process and ensure the search is performed efficiently. The proposed method reduces the computational time required for the evaluation process by employing parallel processing on a graphics processing unit (GPU) and utilizing simple simulations. To search for various solutions efficiently, the kick operation was developed that is specifically tailored to address the target problem. Numerical experiments confirmed the fundamental properties of the proposed method and demonstrated its effectiveness in comparison with previously employed methods in this area.

The Multi-skilled Resource Constrained Project Scheduling Problem (MS-RCPSP) is a complex and multi-faceted problem that involves scheduling activities whilst considering various resource constraints. These constraints include limited availability of workers, equipment, and materials, with each activity requiring a minimal set of skills to be executed. Furthermore, for a better resemblance to reality, workers/machines are assumed to be multi-skilled/multi-purpose posing another dimension of complexity to the problem. The objective is to minimize project duration, cost, or other relevant criteria while accounting for the inherent resources flexibility. This paper provides a systematic review of the literature pertaining to MS-RCPSP, and an in-depth analysis of 171 papers published between 2000 and 2021 inclusive. The conducted bibliometric analysis identifies the top contributing authors, most influential papers, existing research tendencies, and thematic research topics within the field. In addition, this review highlights different aspects of the MS-RCPSP, spanning the significance of performance measures, solution approaches, application areas, and the incorporation of time constraints. While project completion time, cost, and tardiness are common performance indicators, other measures such as multi-skilled staff assignment and schedule robustness are also deemed important. Although various methods have been employed to solve the MS-RCPSP including exact and approximate approaches, the selection of the most-suited approach depends on the problem’s scale, complexity, and constraints, necessitating careful consideration of each method’s strengths and weaknesses. Interestingly, several studies have jointly addressed resource and time constraints in the context of MS-RCPSP, often considering tardiness, and have proposed different algorithms, models, and metaheuristics to tackle these challenges. This paper clearly highlights research gaps and promising avenues for future research. This work provides valuable insights for project managers to effectively schedule tasks in the presence of multiple flexible resources.

Parametric and structural uncertainties complicate the project management processes. The critical path is one of the pivotal parameters, which helps to control the project schedule and is used to determine the criticality of the tasks and activities that are the most decisive and should be treated during a project expediting or controlling. There may be a set of the critical paths in uncertain environment. Therefore, the main question is which of the critical paths to select. The aim of this paper is to answer to this question. We used Monte Carlo simulation to investigate the multiple critical paths. We revealed and explained several paradoxes that emerged as results of the multiple critical paths occurrence. They are inevitable late bias of the project duration under uncertainty, the tasks probability and their correlation effects, the impact of concurrent chains of the tasks on their criticality, multiplicity of the critical paths and especially multi-chain critical paths. We demonstrated that multiple critical paths are not negative effect. On the contrary, they play extraordinary useful role and are the reliable criterion of the project robustness and stability.

The construction supply chain is presently the focus of considerable interest among numerous project-related businesses. Strong project management is essential for the effective completion of a project, since restricted budgets and time constraints are considered for each project. The research uses multi-objective linear programming to create a mathematical model of the building supply chain. The primary aims of the present investigation are to limit the expenses associated with logistics and to diminish the release of greenhouse gases caused by transportation. Given the reality of managing several projects concurrently, the model provided comprises a network of projects. Following the completion of each project, an inspection is arranged to assess its level of success. Estimating the costs of a project relies on several variables. In reality, there are always uncertainties highlighted in several studies about the uncertainty of cost and time parameters. This research incorporates many characteristics concurrently to simulate real-world settings and address the issue of uncertainty. The expression of uncertainty for all costs, activity length, inspection, supplier capacity, and resource demand are represented by triangular fuzzy numbers. Ultimately, the precision of the model's performance has been verified using a numerical illustration.

Construction contractors often rely on external funding to manage financial deficits caused by irregular cash inflows and outflows. To address these cash flow challenges, contractors typically adjust the start times of project activities to prevent shortages while minimizing the overall project duration. However, in severe cases, operations may need to halt if cash flow issues cannot be adequately resolved. This study introduces an alternative strategy to prevent cash shortages by allowing for the temporary suspension of activities, known as activity splitting. In this approach, operations are paused to conserve cash and then resumed when sufficient funds become available. The potential benefit of this method is illustrated through a simple, four-activity project. Extending the application to more complex projects with a set of splittable activities—each having different cash requirements, durations, and associated splitting costs—the challenge lies in identifying the optimal activities to split and determining the precise suspension and resumption times, all while minimizing or avoiding project delays and additional costs. To address this, we present a novel mixed-integer linear programming (MILP) model that optimizes the scheduling and splitting of activities to minimize project duration without breaching financial constraints. The MILP model effectively identifies the best trade-off between activity splitting and project extension, taking into account the financial and cost implications of the contractor’s constraints. This is demonstrated through a case study that considers different scenarios related to splitting costs and financial limitations.

The widely recognized measure for resources called resource strength (RS) does not fully capture the resources complexity of a project. Therefore, it cannot be used as a standalone measure to distinguish the complexity of various instances of project scheduling problems. Consequently, additional resource measures such as total amount of overflow (TAO) have been introduced, which should be used in conjunction with the RS. Extensive experimental studies have shown that as the value of TAO increases in a project, scheduling schemes with higher dimensional scheduling schemes such as bi-directional and tri-directional result in schedules with shorter makespans. In this study, an effective approach is proposed for integrating projects in multi-project environments, called the integrated project approach (IPA), taking into account the influence of TAO and building upon the relation between the TAO and the scheduling generation schemes. To assess the performance of IPA, we develop a new random multi-project generator based on the well-known benchmark sets, which utilizes TAO as a control tool to generate instances. The findings indicate that prioritizing the projects and frequency of the projects integration, facilitated by the proposed IPA, have a positive impact on the quality of multi-project schedules.