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Automatic test paper design is critical in education to reduce workloads for educators and facilitate an efficient teaching process. However, current designs fail to satisfy the realistic teaching requirements of educators, including the consideration of both test quality and efficiency. This is the main reason why teachers still manually construct tests in most teaching environments. In this paper, the quality of tests is quantitatively defined while considering multiple objectives, including a flexible coverage of knowledge points, cognitive levels, and question difficulty. Then, a model based on the technique of linear programming is delicately designed to explore the optimal results for this newly defined problem. However, this technique is not efficient enough, which cannot obtain results in polynomial time. With the consideration of both test quality and generation efficiency, this paper proposes a genetic algorithm (GA) based method, named dynamic programming guided genetic algorithm with adaptive selection (DPGA-AS). In this method, a dynamic programming method is proposed in the population initialization part to improve the efficiency of the genetic algorithm. An adaptive selection method for the GA is designed to avoid prematurely falling into the local optimal for better test quality. The question bank used in our experiments is assembled based on college-level calculus questions from well-known textbooks. The experimental results show that the proposed techniques can construct test papers with both high effectiveness and efficiency. The computation time of the test assembly problem is reduced from 3 hours to 2 seconds for a 5000-size question bank as compared to a linear programming model with similar test quality. The test quality of the proposed method is better than the other baselines.

The object of this study is the process of analyzing the volume of product sales in the retail market, where producers and consumers with different levels of social responsibility operate. Advancements are aimed at solving the problem of insufficient consideration of the social responsibility of economic entities in modern economic and mathematical models. In the work, the improvement of the linear programming model, intended for the evaluation of sales of alternative variants of goods with different social utility to consumers with different social responsibility, by taking into account some real conditions of making purchases, was carried out. As a result of the study, a modification of the model was proposed that makes it possible to take into account the behavior of buyers who choose a product randomly. Also, with the help of additional restrictions, the model takes into account the fact that consumers with different social responsibility make purchases in parallel in time. In addition, the approach to identifying a set of consumer groups in the aspect of social responsibility using the method of questionnaires of the ranking type and building a decision tree of consumers is described. The proposed modification of the model was tested on the example of evaluating the sales volumes of environmentally-friendly and non-environmentally-friendly varieties of goods in the office paper market. It is shown that not taking into account the added restrictions in the model could significantly affect the estimates of sales volumes. The proposed improvements to the model make it possible to obtain more realistic estimates of the volume of sales of goods, taking into account the social responsibility of consumers, as well as quantitative and qualitative characteristics of competitors’ offers. This contributes to better decision-making support for manufacturing socially useful products, in particular, environmentally-friendly ones [ABSTRACT FROM AUTHOR]

The present paper corrects an assertion of the author from [1]. The pivoting algorithms referred to, search for solving the linear programming problem. [ABSTRACT FROM AUTHOR]

Debris flow risk assessment can provide some reference for debris flow prevention and control projects. In risk assessment, researchers often only focus on the impact of objective or subjective indicators. For this purpose, this paper proposed a weight calculation method based on t-distribution and linear programming optimization algorithm (LPOA). Taking 72 mudslides in Beichuan County as an example, this paper used analytic hierarchy process (AHP), entropy weight method (EWM) and variation coefficient method (VCM) to obtain the initial weights. Based on the initial weights, weight intervals with different confidence levels were obtained by t-distribution. Subsequently, the final weights were obtained by LOPA in the 90% confidence interval. Finally, the final weights were used to calculate the risk score for each debris flow, thus delineating the level of risk for each debris flow. The results showed that this paper's method can avoid overemphasizing the importance of a particular indicator compared to EWM and VCM. In contrast, EWM and VCM ignored the effect of debris flow frequency on debris flow risk. The assessment results showed that the 72 debris flows in Beichuan County were mainly dominated by moderate and light risks. Of these, there were 8 high risk debris flows, 24 medium risk debris flows, and 40 light risk debris flows. The excellent triggering conditions provide favorable conditions for the formation of high-risk debris flows. Slightly and moderate risk debris flows are mainly located on both sides of highways and rivers, still posing a minor threat to Beichuan County. The proposed fusion weighting method effectively avoids the limitations of single weight calculating method. Through comparison and data analysis, the rationality of the proposed method is verified, which can provide some reference for combination weighting method and debris flow risk assessment. [ABSTRACT FROM AUTHOR]

This paper provides a recent overview of the exact, approximate, and hybrid optimization methods that handle Multi-Objective Mixed-Integer Non-Linear Programming (MO-MINLP) problems. Both the domains of exact and approximate research have experienced significant growth, driven by their shared goal of addressing a wide range of real-world problems. This work presents a comprehensive literature review that highlights the significant theoretical contributions in the field of hybrid approaches between these research areas. We also point out possible research gaps in the literature. Hence, the main research questions to be answered in this paper involve the following: (1) how to exactly or approximately solve a MO-MINLP problem? (2) What are the drawbacks of exact methods as well as approximate methods? (3) What are the research lines that are currently underway to enhance the performances of these methods? and (4) Where are the research gaps in this field? This work aims to provide enough descriptive information for newcomers in this area about the research that has been carried out and that is currently underway concerning exact, approximate, and hybrid methods used to solve MO-MINLP problems. [ABSTRACT FROM AUTHOR]

Hyperlocal delivery service is a digital platform business that receives ondemand orders from customers to deliver products from a local offline shop in logistics. Deploying drones/Unmanned Aerial Vehicles (UAVs) on the hyperlocal market is still a relatively unexplored phenomenon and that demands attention on sustainable drone delivery strategies. This paper introduces both non-flexible and flexible drone delivery strategies for the hyperlocal market. Further, drone operational complexities like deciding a precise location for landing and take-off in congested cities, limited flight endurance, incorporating docking stations in drone routes and drone safe-return strategy are investigated. This problem is formulated as the Mixed-Integer Linear Programming (MILP) model, which minimises the drone’s total distance by the optimal energy consumption to complete the scheduled services. For practical feasibility, this paper focuses on heuristic solutions that effectively solve the problem efficiently and near optimally. The approach facilitates the operations manager to decide on an appropriate drone travel capacity based on service distance and the size of on-demand customers in an urban hyper-local logistics environment. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive network (TSN), as a new generation of deterministic Ethernet communication technology, is the main development direction of time-critical communication technology applied in industrial environments, and Wi-Fi technology has become the main way of wireless access for users due to its advantages of high portability and mobility. Therefore, accessing WiFi in the TSN is a major development direction of the current industrial internet. In this paper, we model the scheduling problem of TSN and WiFi converged networks and propose a scheme based on a greedy strategy distributed estimation algorithm (GE) to solve the scheduling problem. Compared with the integer linear programming (ILP) algorithm and the Tabu algorithm, the algorithm implemented in this paper outperforms the other algorithms in being able to adapt to a variety of different scenarios and in scheduling optimization efficiency, especially when the amount of traffic to be deployed is large. [ABSTRACT FROM AUTHOR]

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Given the swift expansion of fresh e-commerce, the front warehouse mode can respond quickly and ensure the quality of fresh products. However, the complexity of the supply chain structure under front warehouse mode poses a challenge in reducing logistics costs and improving distribution efficiency while meeting consumers' immediate delivery demands. Therefore, this paper studies the vehicle routing problem of two-echelon fresh e-commerce under front warehouse mode. Considering trans-shipment time constraints between the two echelons and the characteristics of terminal distribution, this paper initially models the vehicle routing problem for front warehouses as a two-echelon multi-trip capacitated vehicle routing problem with time windows. A mixed-integer linear programming model is subsequently established. To solve the model, a hybrid genetic algorithm integrated with neighborhood search is developed. Matrix coding is employed to merge vehicle selection and route assignment decisions. Simultaneously, neighborhood search is applied to enhance the search capability of algorithms, thereby improving the quality of solutions. Furthermore, the effectiveness and efficiency of the model and algorithm are verified through experiments of varying scales. Finally, comparative strategies and sensitivity analysis highlight the advantages of multi-trip strategies and provide insights into the optimal vehicle capacity limit. [ABSTRACT FROM AUTHOR]

The preparation of laser scanning missions is important for efficiency and data quality. Furthermore, it is a prerequisite for automated data acquisition, which has numerous applications in the built environment, including autonomous inspections and monitoring of construction progress and quality criteria. The scene and potential scanning locations can be discretized to facilitate the analysis of visibility and quality aspects. The remaining mathematical problem to generate an economic scan strategy is the Viewpoint Planning Problem (VPP), which asks for a minimum number of scanning locations within the given scene to cover the scene under pre-defined requirements. Solutions for this problem are most commonly found using heuristics. While these efficient methods scale well, they cannot generally return globally optimal solutions. This paper investigates the VPP based on a problem description that considers quality-constrained visibility in 3D scenes and suitable overlaps between individual viewpoints for targetless registration of acquired point clouds. The methodology includes the introduction of a preprocessing method designed to simplify the input data without losing information about the problem. The paper details various solution methods for the VPP, encompassing conventional heuristics and a mixed-integer linear programming formulation, which is solved using Benders decomposition. Experiments are carried out on two case study datasets, varying in specifications and sizes, to evaluate these methods. The results show the actual quality of the obtained solutions and their deviation from optimality (in terms of the estimated optimality gap) for instances where exact solutions can not be achieved. [ABSTRACT FROM AUTHOR]

Recently, evolutionary computation (EC) has experienced significant advancements due to the integration of machine learning, distributed computing, and big data technologies. These developments have led to new research avenues in EC, such as distributed EC and surrogate-assisted EC. While these advancements have greatly enhanced the performance and applicability of EC, they have also raised concerns regarding privacy leakages, specifically the disclosure of optimal results and surrogate models. Consequently, the combination of evolutionary computation and privacy protection becomes an increasing necessity. However, a comprehensive exploration of privacy concerns in evolutionary computation is currently lacking, particularly in terms of identifying the object, motivation, position, and method of privacy protection. To address this gap, this paper aims to discuss three typical optimization paradigms, namely, centralized optimization, distributed optimization, and data-driven optimization, to characterize optimization modes of evolutionary computation and proposes BOOM (i.e., oBject, mOtivation, pOsition, and Method) to sort out privacy concerns related to evolutionary computation. In particular, the centralized optimization paradigm allows clients to outsource optimization problems to a centralized server and obtain optimization solutions from the server. The distributed optimization paradigm exploits the storage and computational power of distributed devices to solve optimization problems. On the other hand, the data-driven optimization paradigm utilizes historical data to address optimization problems without explicit objective functions. Within each of these paradigms, BOOM is used to characterize the object and motivation of privacy protection. Furthermore, this paper discuss the potential privacy-preserving technologies that strike a balance between optimization performance and privacy guarantees. Finally, this paper outlines several new research directions for privacy-preserving evolutionary computation. [ABSTRACT FROM AUTHOR]

Welding is the most critical operation in the shipbuilding process and has a significant influence on the production cost and quality of ships. Therefore, the welding operation must be optimised. This paper presents a real-world welding gantry robot scheduling problem at shipyards, in which three gantry robots function in parallel. Welding gantry robots cannot cross each other and should operate over a certain distance to avoid collisions. To minimise the makespan, the welding tasks given by line segments should be evenly distributed among the three gantry robots. The welding tasks assigned to each robot should be optimally sequenced to minimise the completion time, including the waiting time required to prevent collisions with neighbouring robots. In addition, long welding edges are split, and the split small length edges are assigned to the gantry robots. This paper proposes a mixed-integer linear programming model, three-stage solution approach, and variable neighbourhood search algorithm to solve this problem. Experimental tests conducted on 20 real problem instances revealed that the proposed approach can reduce the makespan by 14% on average when compared with the conventional method. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are fundamental to modern applications like face recognition and autonomous driving. However, their security is a significant concern due to various integrity risks, such as backdoor attacks. In these attacks, compromised training data introduce malicious behaviors into the DNN, which can be exploited during inference or deployment. This paper presents a novel game-theoretic approach to model the interactions between an attacker and a defender in the context of a DNN backdoor attack. The contribution of this approach is multifaceted. First, it models the interaction between the attacker and the defender using a game-theoretic framework. Second, it designs a utility function that captures the objectives of both parties, integrating clean data accuracy and attack success rate. Third, it reduces the game model to a two-player zero-sum game, allowing for the identification of Nash equilibrium points through linear programming and a thorough analysis of equilibrium strategies. Additionally, the framework provides varying levels of flexibility regarding the control afforded to each player, thereby representing a range of real-world scenarios. Through extensive numerical simulations, the paper demonstrates the validity of the proposed framework and identifies insightful equilibrium points that guide both players in following their optimal strategies under different assumptions. The results indicate that fully using attack or defense capabilities is not always the optimal strategy for either party. Instead, attackers must balance inducing errors and minimizing the information conveyed to the defender, while defenders should focus on minimizing attack risks while preserving benign sample performance. These findings underscore the effectiveness and versatility of the proposed approach, showcasing optimal strategies across different game scenarios and highlighting its potential to enhance DNN security against backdoor attacks. [ABSTRACT FROM AUTHOR]

This paper pertains to the finite-element lower bound limit analysis (FELA-LB) of vertical strip anchors embedded in cohesionless and cohesive soils and subjected to static and earthquake forces (using pseudostatic analysis) to estimate the optimal horizontal pullout capacity using nonlinear programming (NLP) technique for isolating the optimal solution. In the developed procedure, a mesh of finite-number triangular elements and assuming a linear stress field that satisfies all the equations of internal equilibrium at all points within the soil medium, elemental interface equilibrium, boundary conditions and no-yield conditions at all the nodal points, has been adopted. In contrast to the use of linear programming (LP), as used in the early phase of the development of FELA of stability problems, in the adopted optimization scheme (NLP), the nonlinear no-yield conditions are incorporated directly, eliminating the necessity of successive linearization of the no-yield constraints. The convergence of the solutions (by varying the number of elements in the soil mesh) and extensibility of the selected stress field (by extending the mesh of elements) has been checked and ensured. The correctness of the estimated lower bound has been checked by comparing the obtained solutions with those reported in the literature. Parametric studies showing the effect of the embedment depth of the vertical anchor, soil properties, and earthquake acceleration on the horizontal pullout capacity of the vertical anchor have also been presented in the paper. Practical Applications: Anchor plates are used in the design and construction of foundation for retaining walls, sheet piles, bulkheads, transmission towers, bridge abutments, and buried pipelines to withstand the horizontal, vertical, or inclined loads. The present study adds to the existing state of art for the design and installation of anchor systems subjected to static and seismic conditions and may improve the performance of such foundation systems. [ABSTRACT FROM AUTHOR]

This paper explores a new way of designing water supply networks for fire safety using ideas from graph theory, focusing on a method called certified domination. Ensuring a good water supply is crucial for fire safety in communities, this study looks at the rules and problems in Poland for how much water is needed to fight fires in different areas and how this can be achieved at a lowest possible cost. We present a way to plan water supply networks for fire protection as a graph, where each point (node) is a place that needs water, and the lines (links) show where water can go between these points. The main idea is to find the best places to put pumping stations and wells in the network to save money and still meet all the fire safety requirements. Our approach assumes that it costs more to build a pumping station than a well. We use some examples to show how this method can find cost-effective solutions for water supply networks, while ensuring that they meet fire safety requirements and are not too expensive to build. This approach is a new and efficient way to improve the design of water supply networks for fire safety. Key challenges that are solved in this paper are a linear time algorithm finding an optimal solution for networks without cycles and a BLP (Binary Linear Programming) algorithm solving the problem in arbitrary networks. [ABSTRACT FROM AUTHOR]

Affected by climatic factors (e.g., low temperature and intense ultraviolet radiation), high-altitude regions experience numerous pavement diseases, which compromise driving safety and negatively impact user travel experience. Timely planning and execution of pavement maintenance are particularly critical. In this paper, considering the characteristics of pavement maintenance in high-altitude regions (e.g., volatility of traffic volume, seasonality of maintenance timing, and fragility of the ecological environment), we aim to derive optimal monthly maintenance plans. We develop a multi-objective nonlinear optimization model that comprehensively accounts for minimizing maintenance costs, affected traffic volume and carbon emissions, and maximizing pavement maintenance effectiveness. Utilizing linearization methods, the model is reconstructed into a typical mixed-integer linear programming (MILP) model, enabling it to be solved directly using conventional solvers. We consider five types of decision strategies to reflect the preferences of different decision-makers. Given the uncertainty of maintenance costs, we also utilize the robust optimization method based on the acceptable objective variation range (AOVR) to construct a robust optimization model and discuss the characteristics of optimistic, robust, and pessimistic solutions. The results suggest that different decision strategies show differences in the indicators of maintenance costs, affected traffic volume, carbon emissions, and pavement performance. When multiple decision objectives are comprehensively considered, the indicators are between the maximum and minimum values, which can effectively balance the decision needs of maintenance effectiveness, maintenance timing, and environmental protection. The number of maintenance workers, the requirement of the minimum pavement condition index (PCI), and the annual budget influence the maintenance planning. The obtained robust solution can somewhat overcome the conservative nature of the pessimistic solution. The method proposed in this paper helps address the complexities of pavement maintenance decisions in high-altitude regions and provides guidance for pavement maintenance decisions in such areas. [ABSTRACT FROM AUTHOR]

In recent years, due to the rapid growth of the world's population, the demand for agricultural products and food is growing increasingly. Therefore, the agricultural supply chain optimization has been grabbed by researchers to reduce food security concerns. On the other hand, the production amount of farmers is affected by various factors, including environmental conditions. In this paper, a supply chain network is investigated by developing a Mixed-Integer Linear Programming (MILP) model to effectively improve economic objectives under uncertainty. Then, a scenario-based robust optimization approach is employed to deal with the uncertainty. One of the novelities of our paper is considering weather conditions and economic fluctuations in different scenarios. The effectiveness of the proposed mathematical model has been confirmed by a real case study of dates farms. Dates and its by-products have a significant role in GDP, job creation, export, and the creation of various packaging and processing. Moreover, three meta-heuristic algorithms including Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and a hybrid algorithm based on them (WOA–PSO) are adapted to deal with the NP-hardness of the problems. Moreover, the parameters of the proposed algorithms are improved by the Taguchi method, and to achieve more exact measurements, sensitivity analysis is performed. Finally, the numerical results confirmed that the accuracy of the hybrid algorithm was between 1.9 and 2.8%. Therefore, this approach could be practical and efficient for solving large-sized problems. The obtained outcomes demonstrated that the planned model provides tactical considerations for the related managers. [ABSTRACT FROM AUTHOR]

This paper presents new mixed-integer linear programming formulations for multi-activity shift scheduling problems (MASSP). In these formulations, the rules governing shift feasibility are encoded in block-based state-expanded networks in which nodes are associated with states and arcs represent assignments of blocks of work or break periods inducing state transitions. A key advantage of these formulations is that for the anonymous MASSP in which all employees are considered as equal only a single network with integer flow variables is needed as long as the network encodes all shift composition rules. A challenging aspect is that the networks can become very large, yielding huge models that are hard to solve for large problem instances. To address this challenge, this paper proposes two exact modeling techniques that substantially reduce the size of the model instances: First, it introduces a set of aggregate side constraints enforcing that an integer flow solution can be decomposed into paths representing feasible shifts. Second, it proposes to decouple the shift composition from the assignment of concrete activities to blocks of work periods, thereby removing a large amount of symmetry from the original model. In a computational study with two MASSP instance sets from the literature dealing with shift scheduling problems, we demonstrate the effectiveness of these techniques for reducing the both size of the model instances and the solution time: We are able to solve all instances, including more than 70 previously open instances, to optimality–the vast majority of them in less than 30 min on a notebook computer. [ABSTRACT FROM AUTHOR]

The rapid development of renewable energy presents challenges to the security and stability of power systems. Aiming at addressing the power system scheduling problem with load demand and wind power uncertainty, this paper proposes the establishment of different error fuzzy sets based on the Wasserstein probability distance to describe the uncertainties of load and wind power separately. Based on these Wasserstein fuzzy sets, a distributed robust chance-constrained scheduling model was established. In addition, the scheduling model was transformed into a linear programming problem through affine transformation and CVaR approximation. The simplex method and an improved differential evolution algorithm were used to solve the model. Finally, the model and algorithm proposed in this paper were applied to model and solve the economic scheduling problem for the IEEE 6-node system with a wind farm. The results show that the proposed method has better optimization performance than the traditional method. [ABSTRACT FROM AUTHOR]

This paper proposes a discrete-time model for dynamic trading, interconnecting cash and asset stocks. The trading action or control is based on the evolution of the asset prices, and any suitable asset price predictor can be used. Based on the model introduced, a one step ahead optimal control strategy, based on linear programming, is proposed. This leads to a trading algorithm which specfies a rule to buy or sell assets in a given portfolio. The addition of trade trigger logic to the basic scheme is also proposed, in order to allow return and risk to be traded off in the dynamic one step ahead trading scheme. The proposed one step ahead optimal policy is independent of the predictor of prices and their variances, chosen in this paper as the moving average, but replaceable by any desired estimator. Numerical examples are given to show that the proposed strategy performs reasonably well, with and without risk reduction, over datasets relating to different portfolios (banks, computers, ETFs and stocks). [ABSTRACT FROM AUTHOR]

Pick-and-pass systems are a part of picker-to-parts order-picking systems and constitute a very common storage solution in cases where customer orders are usually small and need to be completed very quickly. As workers pick items in the zones connected by conveyors, their work needs to be coordinated. The paper presents MILP models that optimize the order-picking process. The first model uses information about the expected demand for items to solve the storage location problem and balance the workload across zones. The task of the next model is order-batching and sequencing - two concepts are presented that meet different assumptions. The results of the exemplary tasks solved with the use of the proposed MILP models show that the total picking time of a set of orders can be reduced by about 35-45% in comparison with random policies. The paper presents an equation for the lower bound of a makespan. Recommendations about the number of zones that guarantee the required system efficiency are also introduced. [ABSTRACT FROM AUTHOR]

This paper introduces a novel distribution network planning method that addresses the limitations of conventional approaches. The existing methods primarily focus on optimizing component objectives using reliability analysis, which results in inadequate operational power control performance due to neglecting the coupling degree analysis of distribution network subprojects. To overcome this limitation, the proposed method incorporates the coupling of the traffic and distribution networks into the planning process. The method involves modeling the transportation network and analyzing the coupling characteristics of the planning items. Specifically, the energy efficiency coupling degree is calculated to assess the degree of coupling. Based on this analysis, the planning nodes are strategically deployed, and a comprehensive planning model is constructed. The model is then subjected to constraints and solved to obtain an optimal distribution network planning scheme. To evaluate the effectiveness of the proposed method, experiments are conducted to assess its operational power control capability. The experimental results demonstrate that when the proposed method is employed for distribution network planning, it reduces operating power and achieves a more desirable planning outcome. The novelty of this work lies in integrating the coupling analysis of the traffic network and the distribution network into the planning process. Considering the interdependencies between these networks, the proposed method enables a more comprehensive and efficient distribution network planning scheme. This approach enhances operational power control performance and improves the overall effectiveness of distribution network planning. [ABSTRACT FROM AUTHOR]

The optimized layout of electric vehicle (EV) chargers is not only crucial for users' convenience but also a key element in urban sustainable development, energy transition, and the promotion of new energy vehicles. In order to provide a basis for the problem of localization and capacity determination of chargers and compare the merits of several mainstream algorithms, this paper first establishes an optimization model with the objective of minimizing the total investment cost of all the chargers and the constraint of meeting the charging demands of all electric vehicles. Optimizations were performed using genetic algorithm (GA), surrogate optimization algorithm (SOA), and mixed integer linear programming (MILP) algorithm, respectively. In the case of using MILP, the original nonlinear optimization problem was transformed into a linear problem. In the planning of city-level EV chargers, MILP took 14182.57 s to calculate the minimum cost of 34.62 million yuan. After retaining only 10% of the original data amount, SOA took 87651.34 s to calculate the minimum cost of 3.01 million yuan. The results indicate that GA is prone to falling into local optima and is not suitable for large-scale optimization problems. SOA, on the other hand, requires significant memory consumption, so the issue of memory usage needs to be carefully considered when using it directly. Although MILP is only applicable to linear programming problems, it has the advantages of lower memory usage and higher reliability if the problem can be transformed into a linear one. [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]