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.
In the current competitive market, service level, product price, and item quality are crucial components of supply chain management. This study integrates these concepts by formulating a two-level supply chain system. In this model, the retailer's demand is dependent on price, quality, and time, while the end-customer demand is influenced by the price, time, and service level provided by the retailer. The manufacturer's per-unit production cost is treated as a nonlinear, increasing function of the item's quality index. Furthermore, the manufacturing firm's carbon emission rate is linearly dependent on the production time. Centralized and decentralized scenarios are formulated and solved using Pontryagin's maximum principle. The resulting optimal control problems exhibit significant nonlinearity. Due to these nonlinear assumptions, finding an analytical solution is challenging. To justify the model, a numerical example is considered. We employed the recently developed Crayfish Optimization Algorithm (COA) to find the best-found solution for the corresponding optimization problems (both centralized and decentralized systems). To validate the COA's results, six additional metaheuristic algorithms (APO, AEFA, GBO, JSO, DA, and ZOA) were also utilized for comparison. Finally, a sensitivity analysis was conducted to examine how various system factors affect the optimal solution.
In a multi-project environment, collaboration in material provisioning while maintaining project managers' autonomy in decision-making regarding scheduling and resource management leads to cost reduction and enhances project performance. This paper studies the resource investment and materials ordering problem for multi-projects with the possibility of collaboration in shared materials ordering in a decentralized approach. Due to the interactions between the project managers, a Stackelberg game is utilized for problem formulation, and a bi-level mixed integer programming model is proposed. To solve this bi-level model, two nested bi-level memetic metaheuristic algorithms, namely a nested bi-level memetic genetic algorithm (MGA) and a nested bi-level memetic particle swarm optimization (MPSO) algorithm have been developed. To evaluate the performance of the proposed solution methods, a set of sample problems was solved. The numerical results indicate that collaborative material procurement yields substantial cost savings, with reductions of up to 16% for the leader and 15% for the follower when using the MGA algorithm, and approximately 12% for both parties when using the MPSO algorithm. These findings confirm the effectiveness of the proposed methods in achieving cost-efficient project planning.
Annually, large amounts of citrus waste are generated across supply chains. This waste yields minimal profit and improper disposal can cause environmental pollution, highlighting the need for improved citrus supply chain design. In recent years, the circular economy has emerged as a preferable alternative to linear models, while green productivity aims to enhance both economic and environmental performance. Accordingly, this study develops a novel bi-objective mixed-integer linear programming (MILP) model for a citrus supply chain. The first objective maximizes total profit, and the second maximizes green productivity. Citrus waste is converted into bioethanol, biogas, bio-oil, and biochar in biorefineries using biochemical and thermochemical technologies. The epsilon-constraint method generates Pareto-optimal solutions, which are ranked using the EDAS method. The model is validated through a real case study in Mazandaran province, Iran, and further assessed via sensitivity analyses. Results show that establishing biorefineries under economic and environmental considerations is feasible, achieving zero citrus waste by converting all waste into valuable bio-products. Additionally, green productivity increases by 36% with only a 1.7% reduction in supply chain profit.
Industry 5.0 marks a transformative era in manufacturing, with Collaborative robots (Cobots) positioned at the core of this paradigm shift. Cobots are designed to work alongside human operators to enhance productivity and efficiency in manufacturing environments, yet they face several deployment barriers, including technological limitations, integration complexities, financial constraints, and workforce adaptation issues. This study identifies the barriers to Cobot deployment in developing countries, with a focus on Pakistan's manufacturing sector, using a sequential exploratory design that integrates qualitative analysis, ISM - MICMAC modelling, and SEM validation. Interviews with industry stakeholders identify five thematic categories of barriers: social, organizational, ethical, economic, and regulatory. The findings reveal both commonly reported barriers and three novel, context-specific barriers: lack of business case (B7), lack of vendor support (B11), and lack of infrastructure and energy reliability (B8). The integrated ISM - MICMAC - SEM framework identifies the hierarchical relationships among barriers and validates their overall influence. This novel framework serves as a decision-support tool enabling engineering managers and policymakers to prioritize strategies and strengthen organizational readiness for Cobot deployment. The empirically grounded framework addresses a key literature gap and provides a theoretical foundation for future research.
In today's competitive business, accurate evaluation of suppliers and relevant optimal order allocation to them is quite important and challenging. To overcome its underlying difficulties, this research proposes a hybrid approach for evaluating suppliers, forecasting demands, and allocating orders. In its implementation process, it initially applies Data Envelopment Analysis method based on Z-numbers to obtain more accurate, transparent and reliable data on suppliers. It then uses numerous machine learning algorithms to forecast future demands for various relevant items. Finally, it employs a multi-objective optimization model to minimize total supply costs, while maximizing order allocation to efficient suppliers and minimizing the number of suppliers to whom orders are placed subject to constraints of authorized delay in order delivery, capacity and demand satisfaction. The good accuracy of its generated results is related to its innovative part of using different machine-learning algorithms and integrating supply chain operation for more accurate evaluation of suppliers and relevant optimal order allocation to them. Its multi-purpose approach integrates supply chain decision-making regarding procurement costs while focusing on efficient and collaborative suppliers with strong, sustainable network connections. Its fuzzy programming approach has also played an effective role in the optimization process of its nonlinear multi-objective model.
Various factors, such as demographic characteristics, medical history, and claims history influence health insurance pricing. Predicting pricing strategies and maintaining data security are important aspects of health insurance. This study proposes the factors influencing health insurance data and develops predictive models using Artificial Intelligence (AI) techniques to understand pricing strategies in the health insurance sector. This predicted AI model is encrypted and stored in a cloud database to ensure data security and management. Initially, the US Health Insurance Dataset is utilized, Hot Deck Imputation is applied for missing values, and Cook's distance for outlier detection to increase the reliability of the data. The discrete cosine transform key features are extracted for prediction using the transform. Attention in an Echo State Network (ESN) increases the accuracy of pricing. Brakerski-Fan-Vercauteren encryption ensures sensitive insurance data is protected, and private cloud deployment is made possible by encryption, which secures the AI model. The experimental results proved the efficiency of the method for price predictions with a 0.07235 Mean Absolute Error and R-2 score of 99.57%, while imposing security with an encryption time of 26.65 seconds for 1000MB of model data, thus providing a robust and scalable solution for health insurance pricing.
Blockchain is a promising solution to combat counterfeits. We employ a game-theoretic approach to explore the blockchain adoption game between two competitive platform retailers to combat counterfeits. We develop a duopoly model with one high-end and one low-end platform, both vulnerable to counterfeits. The high-end platform provides a superior quality of genuine products over the low-end platform. Blockchain allows consumers to discern product authenticity. We demonstrate that in a scenario of low blockchain cost, either both platforms will utilize blockchain technology or only the high-end platform will adopt it. Both platforms adopt blockchain if the product authenticity of the low-end platform is low, or the quality of authentic products on the high-end platform is high; otherwise, only the high-end platform adopts blockchain. Surprisingly, we also show that when counterfeit quality is high, adopting blockchain to combat counterfeiting is more likely to reduce the profit of the low-end platform.
In the face of increasing uncertainty and sustainability challenges, inventory management must develop to balance financial, environmental, and social objectives. This study presents a multi-objective sustainable inventory model that integrates machine learning for demand prediction, addressing uncertainty through robust programming. By using machine learning techniques, demand forecasting is made more accurate, even under uncertain conditions, improving decision-making in inventory control. Preservation and green technologies are employed to reduce product deterioration and decrease carbon emissions. Additionally, the model accounts for the influence of inflation on costs, ensuring financial sustainability over time. Weighted goal programming approach is adopted to address three key objectives: maximum profit, increasing labour payment and decreasing carbon emissions. This method allows for balancing the conflicting goals by optimizing inventory levels, ultimately promoting sustainability while ensuring labour payments and emissions. The results demonstrate that the proposed model can successfully achieve sustainable and social responsible inventory management, and the proposed model is the superior model of traditional model. Sensitivity analyses are conducted to assess the robustness of the proposed model under varying parameters, providing valuable insights for decision-makers. The study concludes with a discussion of the findings and suggestions for future research directions.
Assembly planning is a critical stage in manufacturing, accounting for up to 40-60% of production time and over 20% of costs. Although most studies on matrix-structure assembly systems focus on minimizing automated guided vehicle (AGV) trajectories, far less attention has been devoted to workstation utilization and task sequencing, both of which are essential for system profitability. This paper addresses the scheduling problem in robotic matrix-structure assembly systems (RMSAS) for multi-model production, where sequencing, task assignment, and workstation efficiency are strongly interdependent. Three approaches are evaluated: a Mixed-Integer Linear Programming (MILP) model, a math-heuristic (M-H) formulation, and a dispatching rule based on longest remaining processing time (LRPT). Results indicate that MILP guarantees optimality but suffers from scalability issues due to the NP-hard nature of the problem, while remaining a valuable benchmark. The M-H formulation achieves near-optimal solutions in a practical amount of time, significantly reducing computational effort. LRPT offers rapid scheduling but results in lower utilization and longer makespan. Overall, the M-H formulation stands out as a practical and efficient approach, advancing RMSAS scheduling beyond AGV path minimization toward enhanced workstation performance.
Online reviews are one of the main channels for consumers to obtain product information when they make online purchases. To well describe and analyze the influence of online reviews and different power structures on the online dual channel supply chain consisting of a platform and a manufacturer, we construct some basic models considering online reviews and different power structures, and exploit them to explore the optimal decisions under different power structures and analyze the influence of online reviews on the supply chain. The results indicate that (1) the manufacturer is always constrained by decisions, and the constraints on the manufacturer are minimized only when the manufacturer is dominant; (2) online reviews play an important role in increasing the price of the product and expanding the demand of the market; and (3) the supply chain members can gain the highest profit under a single dominant position, but the lowest profit under the same level of power under the influence of the online reviews.
The improvement of the electric vehicle (EV) industry is a core strategic initiative for China to advance the green economy and enhance domestic automotive competitiveness. However, China's EV sector still faces two critical challenges: a gap in core technologies compared to developed countries and the risk of subsidy fraud amid equal foreign market access, both hindering its high-quality development. To address these issues, this study contributes a novel quality-oriented subsidy policy (QOSP) for domestic EV manufacturers (DEVMs), designed to fundamentally drive technological innovation while mitigating moral hazard. Methodologically, we constructed sequential game models to investigate the dynamic optimal pricing strategies of DEVMs and imported EV manufacturers (IEVMs), and analyzed the policy's impact on domestic EV (DEV) quality improvement. The results reveal three key findings: (1) The QOSP effectively incentivizes DEVMs to enhance product quality, directly addressing the core technology gap; (2) Sufficient market demand potential and technical innovation capacity are prerequisites for the QOSP's effective implementation; (3) An optimal subsidy intensity balances national financial burden and urgent DEV quality improvement, ensuring the steady development of the DEV market. This study provides a practical policy framework for governments to optimize EV industry support, balancing innovation, market fairness and financial sustainability.
Environmental sustainability is a major concern for businesses, prompting a shift away from profit goals toward environmental responsibility. Closed-loop supply chains address waste and landfill issues by enabling the recovery and remanufacturing of used products, reducing environmental impact while creating economic and social benefits. In today's dynamic business environment, manufacturers are increasingly adopting multi-retailer distribution strategies to expand their market reach, mitigate risks, foster competition, and enhance supply chain efficiency. This paper presents a closed-loop supply chain model that integrates multi-retailer distribution with a multi-shipment policy, considering price-dependent demand. This paper examines the determination of optimal policies for manufacturers and retailers, employing both cooperative and non-cooperative approaches. The cooperative approach employs Nash Bargaining and Integrated approaches, whereas the noncooperative framework utilizes the Manufacturer-led Stackelberg approach. Additionally, a Pareto-efficient approach is explored based on the results from the other approaches. The findings indicate that the Pareto Efficient approach ensures a fair allocation of profits between manufacturers and retailers, while the Integrated approach maximizes total expected profit. Sensitivity analysis reveals that product return rates, price elasticity, and shipment frequency significantly affect profit distribution and total supply chain performance. These insights provide practical guidance for designing sustainable and economically efficient closed-loop supply chains.
This study develops a dual-channel coordination model that integrates online platforms with retail channels within a Closed Loop Supply Chain (CLSC) system to optimize profitability. The model outlines strategies for setting selling prices across both channels and determining optimal investment levels to reduce emissions and maximize returns on used products. Demand across both channels is analyzed by factoring in critical elements such as selling price, green technology, and retailers' advertising efforts. The proposed model contributes to the existing literature by integrating pricing and investment decisions within CLSC management. The model is presented through two scenarios: centralized and decentralized. A Green Technology Revenue Investment Profit Sharing Contract (GRIS Contract) is proposed to involve the sharing of investment costs and profits to improve the overall efficiency and sustainability of the supply chain. The findings indicate that the centralized scenario generates higher total profits, greater levels of green technology, and superior online selling prices, whereas the decentralized scenario results in elevated selling prices for retailers. The GRIS contract fosters collaboration between both parties to encourage environmentally friendly products, boosting profits while maintaining system efficiency. Additionally, the results reveal that self-price sensitivity and carbon tax are significant parameters influencing pricing and investment decisions.
This paper presents an Adaptive Elite Hybrid Binary Particle Swarm-Grey Wolf Optimizer (AE-BPSO-BGWO) for the simultaneous optimization of distribution system reconfiguration (DSR), distributed generator (DG) sizing and placement, and electric vehicle (EV) charger allocation. The algorithm combines the exploration capability of Binary Particle Swarm Optimization with the exploitation strength of Binary Grey Wolf Optimizer and introduces an elite-adaptive mechanism that dynamically adjusts the search process in binary solution spaces. The performance of the proposed method is evaluated in two stages. First, benchmark tests are conducted on Sphere, Ackley, Griewank, Rosenbrock, Rastrigin, Schwefel, Zakharov, Levy, Michalewicz, and Bent Cigar functions, showing superior convergence speed and robustness compared with conventional and hybrid metaheuristic algorithms. Second, the algorithm is applied to IEEE 33- and 69-bus distribution systems under six scenarios: base case, reconfiguration only, DG allocation only, DG allocation after reconfiguration, reconfiguration after DG allocation, and simultaneous reconfiguration with DG allocation. In the 33-bus system, the reconfiguration-after-DG scenario achieves the lowest power loss of 38.28 kW and a minimum voltage of 0.9861 p.u. In the 69-bus system, the simultaneous optimization scenario reduces power loss to 27.16 kW with a minimum voltage of 0.9796 p.u., confirming the effectiveness of the proposed method.
Airfreight plays an increasingly vital role in global trade, particularly within complex supply chains and multimodal transport systems. Given the numerous uncertainties inherent in Airfreight Forwarder Shipment Planning (AFSP), a novel robust optimization-based planning approach is developed to construct shipment plans through integration and consolidation strategies, to reduce costs and improve overall transport efficiency. In particular, two robust optimization models are introduced: one based on an ellipsoidal uncertainty set and the other on a polyhedral uncertainty set, to handle uncertainties in AFSP. To solve medium- and large-scale instances efficiently, a Hybrid Genetic Tabu Search with Probabilistic Perturbation Algorithm (HGTP) is designed, incorporating a probability learning-based perturbation mechanism. The effectiveness of the two models under complex management environments and uncertain conditions is validated through real-world case analyses and simulation experiments. Their performance and applicability in AFSP are compared, and corresponding managerial insights are derived.
