Efficient resource allocation and leveling are critical to successful construction project scheduling, yet conventional methods often fail to balance project duration with stable resource utilization. This study introduces a modified mountain gazelle optimizer (mMGO), which extends the original single-objective MGO into a multi-objective optimization approach to address the complex trade-offs inherent in construction scheduling. In addition, the algorithm integrates opposition-based learning and dynamically controlled chaotic mapping to enhance global search capability and avoid premature convergence. The mMGO is embedded within a two-phase scheduling framework, in which the first phase generates resource-feasible baseline schedules, while the second phase redistributes activities to minimize fluctuations in multi-resource demand. To validate its performance, mMGO was verified through two case studies. Case study 1 consisted of five projects, each containing six activities, whereas case study 2 included three projects, each involving 60 activities. The results show that mMGO consistently achieved the highest hypervolume values, indicating closer convergence to the true Pareto front and greater diversity of non-dominated solutions. Moreover, mMGO produced schedules that simultaneously reduced project makespan, resource intensity, and resource utilization instability metrics. These findings indicate that mMGO has potential as a computational decision-support approach for multi-objective resource scheduling in benchmark multi-project environments.
This paper tries to investigate remarkable dynamics of retail and distribution centers, examining the impact of demand uncertainties, offered prices, supply disruption, and customer behavior. The main focus is on discriminative pricing within a fiercely competitive distribution system, considering customer behavior during supply disruption. By employing Stackelberg competition, a comprehensive model is constructed that unravels the competition between wholesalers and retailers in the distribution center, providing a profound understanding of the intricate dynamics at play. The model aims to maximize profit and utility for wholesalers and retailers, placing particular emphasis on behavior-based price discrimination at the retail level. To address supply disruption challenges, two resilience strategies are explored: wholesalers holding inventory and establishing supportive contracts with reliable suppliers. These strategies mitigate disruptions and ensure distribution system continuity. To demonstrate practicality, a real-world case problem is implemented and showcases the model's efficacy. Then, sensitivity analyses provide valuable managerial insights. This study contributes to understanding how retailers and distribution centers optimize operations and decision-making in the face of price discrimination and risk of disruption.
Solar energy is a clean, inexhaustible resource that plays a key role in reducing dependence on fossil fuels amid increasing climate change pressures. Building-integrated photovoltaic (BIPV) systems offer significant potential to improve building energy performance; however, existing studies lack efficient approaches for jointly optimizing energy efficiency and investment costs at the early design stage. A machine learning-assisted multi-objective optimization framework is proposed for the BIPV glass fa & ccedil;ade design. A comprehensive simulation dataset is generated using DesignBuilder, and multiple machine learning models are developed as surrogate models, with CatBoost achieving the best performance (R2 = 0.978 for net energy consumption (NetE) and 0.973 for investment cost (IC)). A multi-objective cheetah optimizer is employed to minimize both objectives NetE and IC simultaneously. The Pareto front reveals clear cost-energy trade-offs, with energy savings of up to 24.9%. The proposed framework integrates simulation, machine learning, and optimization, advancing current BIPV design methodologies by enabling efficient multi-objective decision-making. The approach provides practical design solutions and serves as an effective decision-support tool for improving both energy efficiency and economic feasibility.
This paper is about N-person cooperative games embedded in a set of N linear programs. In the 1975 seminal paper about linear production, Owen showed that an optimal dual vector of the Grand Coalition (of the Producers) provided the key for determining a solution in the Core. This Core solution depends entirely on the resources (right-hand sides) of the N linear programs; the technology matrices and objective coefficients have no role. We provide a Shapley inspired alternative Core solution which includes the impact of technology and prices missing in the Owen Core solution.
