Under increasingly stringent climate policies, it is essential to account for carbon emissions in project scheduling. In this study, we examine a multimode resource-constrained project scheduling problem in which alternative activity modes differ in resource consumption and carbon intensity under renewable-resource constraints, a project-level carbon quota, and a carbon trading price. The objective is to minimize the total cost, which is defined as the sum of the activity execution cost and the net cost of buying and selling emission allowances. We formulate a cost-minimization model that incorporates a cap-and-trade mechanism and derive several analytical properties to identify infeasible activity-mode selections and reveal how changes in activity modes affect the total project cost. Guided by these properties, we develop a hybrid genetic algorithm with feasibility filtering and cost-oriented mode adjustments during population initialization and evolution. Tests on benchmark instances show that, compared with the best competing method, the proposed algorithm reduces the average relative deviation by 89% with only a moderate increase in computation time. Sensitivity analysis further indicates that relaxing the project-deadline coefficient and renewable-resource strength can jointly reduce the total cost by approximately 4%; within the tested range of carbon-quota factors, increasing the quota can reduce the total cost by up to approximately 5%, and increasing the carbon-price factor from 1.0 to 1.4 increases the total cost by approximately 24%, highlighting the strong impact of carbon pricing on project economics.
This work investigates a complex scheduling problem arising in vehicle manufacturing, where assembly operations require the simultaneous allocation of a machine and a worker, and the operations of each vehicle are constrained by a special rooted in-tree precedence structure. The problem combines elements of Dual-Resource Constrained scheduling and Resource-Constrained Project Scheduling Problems. Building on previous contributions, we propose enhancements to a recently presented Constraint Programming model and, leveraging the connection with project scheduling, we introduce eight new discrete-time Mixed Integer Linear Programming models that incorporate disaggregated precedence and tightened resource constraints. The core contribution of this work is a binary search procedure that exploits the linear programming relaxation of these models to compute superior lower bounds for the problem. Experimental results demonstrate that combining these superior lower bounds with the upper bounds of the refined Constraint Programming model significantly improves the quality of solutions, reducing the average optimality gap to just 1% across a diverse set of benchmark instances.
Onshore Power Supply (OPS) allows ships at berth to use electricity from the onshore grid instead of relying on auxiliary diesel engines. This reduces emissions, noise, and local air pollution in port areas. Despite its growing importance for maritime decarbonisation, OPS deployment creates a coordination problem involving welfare, investment efficiency, and grid stability. We address this problem by developing a multi-objective optimisation model that combines demand-elasticity functions with swing-equation dynamics to capture the interaction between market design and electricity network conditions. Our analysis shows that existing market designs face important limitations. Specifically, the two "Intermediary" market designs pose risks of monopoly pricing and inefficient investment, while "Facilitator" market designs improve welfare but compromise grid stability. To address these problems, we propose a new "Extension-to-Grid" market design, in which the electricity network operator assumes a coordinating role. This achieves a welfare-superior balance between efficiency, stability, and consumer protection. Through illustrative scenarios, real-world pricing data and a case study with two ports in Greece, we demonstrate how the proposed model can help avoid distorted price signals or blackout risks under poorly designed OPS market structures.
Nonconvex dynamic optimization problems often admit multiple locally optimal outcomes, requiring decision makers to choose not only how to adjust but which equilibrium to pursue. The Sethi-Skiba point identifies the threshold state where a decision maker is indifferent between trajectories leading to radically different regimes. While well-known in control theory, its implications for management science remain fragmented. This paper provides a unified review of the theory, computation, and applications of Sethi-Skiba thresholds. We present geometric foundations in capital accumulation models, explaining connections to nonconcave value functions, hysteresis, and global optimality. We then survey numerical methods and their high-dimensional limitations. Building on this, we organize the literature into a taxonomy of managerial archetypes: revitalization traps, preservation thresholds, social contagion, and strategic entry barriers. Identical mathematical structures govern problems in advertising, environmental management, epidemics, and industrial competition. By reframing Sethi-Skiba points as a general theory of strategic thresholds, this review highlights a shift from marginal adjustment to basin selection. Managerial success in nonconvex environments depends less on fine-tuning controls than on correctly identifying regime boundaries. We conclude by outlining directions in behavioral modeling, empirical detection of tipping points, and networked systems.
AI-enabled platforms increasingly rely on user information to improve personalisation and service quality, but information disclosure simultaneously exposes users to privacy risks. This paper develops a Stackelberg game framework to investigate users' equilibrium information disclosure under heterogeneous risk preferences. The model endogenizes the interaction among AI learning capability, user information disclosure, and platform security investment decisions. We further characterise optimal platform security strategies and examine the welfare implications of government security regulation. Our analysis generates several insights. First, stronger AI learning capability increases users' equilibrium willingness to disclose information. Second, higher disclosure costs reduce disclosure incentives and weaken AI system performance. Third, platforms are more likely to adopt stronger security investment when security implementation costs are low or when the proportion of information-sensitive users is high. Finally, the optimal degree of security regulation may vary non-monotonically with security implementation costs. At both low and high levels of security implementation cost, relatively lenient regulation may improve overall social welfare.
As emission trading systems (ETS) are increasingly adopted worldwide, understanding strategic interactions between governments and firms is crucial for effective policy design. This paper develops a bi-level Stackelberg game model to optimise ETS performance. At the upper level, the government sets the emission reduction target to maximise social welfare; at the lower level, firms with heterogeneous emission intensity optimise operational decisions under price competition, with an endogenous carbon price mechanism capturing market equilibrium. We transform this bi-level model into a single-level Mixed-Integer Quadratic Programming (MIQP) problem and prove the existence and uniqueness of the Stackelberg equilibrium. Using this model, we analyse how industrial competition and consumer environmental awareness (CEA) affect ETS outcomes. Results reveal that ETS creates differentiated impacts: carbon-intensive firms face greater operational adjustments, while carbon-efficient firms gain competitive advantages and higher profits. Competitive industries exhibit lower carbon prices, greater emission reductions, and higher trading volumes than monopoly industries, and appropriate targets can improve both social welfare and firm profitability. Rising CEA leads to stricter targets and enhances social welfare, but excessive CEA undermines profitability, particularly for carbon-intensive firms in highly competitive industries. These findings guide policymakers in designing ETS that balance emission reduction, economic development, and consumer welfare.
The online portfolio selection (OPS) problem is different from the classical portfolio model problems, as it dynamically adjusts asset positions in response to historical price sequences. Existing OPS strategies based on the reversal effect achieve greater cumulative return than those based on momentum. However, they face theoretical challenges in achieving competitive performance and rely primarily on explicit data variables, such as historical relative prices and trading volumes. Besides, existing research indicates that investor behaviour, an implicit data variable, may influence stock prices. Therefore, we take into account the reversal effect and investor attention to formulate expert strategies, subsequently integrating these strategies through the online gradient update. Theoretically, the regret of our integrated strategy is proven to have a sub-linear upper bound. Empirical results demonstrate that our integrated strategy outperforms existing OPS strategies in most cases. Specifically, across all datasets, annualised returns range from 8.12% to 206.35%. Also, the average Sharpe Ratio, Calmar Ratio, and Information Ratio are 1.2580, 2.8975, and 0.0342, respectively. Moreover, our integrated strategy exhibits robustness under varying parameter settings and remains effective under reasonable transaction costs.
This study introduces a global risk factor as a predictive variable for crude oil returns, assessing its effectiveness relative to a benchmark historical average return model. Three types of forecasting models are employed: autoregressive models, financial variable models, and multivariate forecasting models. These models utilise techniques such as Lasso regression, Complete Subset Regression (CSR), and the Three-Pass Regression Filter (3PRF). Incorporating the global risk factor consistently improves prediction accuracy across all model specifications. The CSR specification achieves the highest directional accuracy and delivers a substantial reduction in mean squared prediction error. The global risk factor captures significant events in international financial markets and enhances forecast stability during periods of high volatility, thereby addressing a key limitation of earlier research that did not adequately account for unexpected shocks. The analysis underscores the importance of U.S.-specific attributes in evaluating crude oil returns within a globally integrated framework.
