Search Results (1,408)

This research presents a continuous-time, deterministic model to address the problem of determining the order quantity and the dynamic pricing policy in scenarios where the quality of the product gradually decays over time. The decision variables include the order quantity as well as the pricing policy, which in turn consists of the initial price and the price reduction rate. Two conflicting objective functions are considered: maximizing profit and maximizing the average quality of the units sold. The paper examines the trade-offs between these two objectives across scenarios with different parameters, such as the price elasticity of the demand, the quality deterioration rate and the elasticity of the demand to quality. The results indicate that, in almost all scenarios, the optimal ordering policy is to order small quantities. Interestingly, in each scenario, there exists a maximum average quality compatible with positive profits. A higher average quality is only possible by incurring losses. Similarly, there is also a limit to the maximum rate of price discounting above which positive profits are not feasible. Full article

Approximation jobs are widely deployed on Amazon EC2, which compute partial task segments to obtain useful results. For such jobs, maximizing total profit is the primary goal, where profit equals the sum of job utilities minus the total machine costs. Unfortunately, maximizing the total profit of approximation jobs is an NP-hard problem. This problem is further complicated by online job arrivals and heterogeneous resource demands across different tasks. This work builds an optimization framework that clearly characterizes job utility and machine costs to resolve this problem. Within this framework, we propose an efficient dual algorithm for job scheduling. The proposed method leverages the dual-fitting approach to measure algorithm performance by analyzing the primal and dual objective growth at each step. This work proves that our algorithm achieves a constant competitive ratio. The results from the trace-driven simulations demonstrate that our algorithms consistently outperform these baselines across various metrics. Full article

This study explores the selection of a heat recovery system for cogeneration units based on gas engines supplying the district heating system in Opole in order to enhance the efficiency and sustainability of the system. The proposed modifications focus on utilizing low-temperature (LT) waste heat from engine cooling circuits and improving exhaust heat recovery. The research examines retrofitting three cogeneration engines (total thermal capacity of 7.6 MW) by integrating water-to-water heat pumps to upgrade low-temperature waste heat (55–45 °C up to 700 kW), enhancing heat supply to the district heating network. Additionally, a second stage of economizers is evaluated to maximize condensation-based exhaust heat recovery from the existing 95–135 °C system. These system modifications increase the overall thermal capacity up to 9–9.1 MW. To maintain heat supply during cogeneration unit shutdowns (due to failures or electricity price fluctuations), an auxiliary air-to-water cascade heat pump provides an additional 0.8–1 MW. With increasing electricity price volatility, these system modifications provide crucial operational flexibility. Computational simulations confirm that the hybrid configuration successfully upgrades waste heat while strictly maintaining the existing engine return water safety limit. The evaluation demonstrates high economic profitability alongside stable emission reductions. This research presents a case study in optimizing heat recovery in cogeneration-based district heating networks, demonstrating practical and scalable applications for sustainable energy systems. Full article

With the advancement of energy market reform, integrated energy systems (IESs) have achieved rapid development. Considering insufficient research on an electricity–heat coupled master–slave game and the local optimum defect of traditional algorithms, this paper proposes a Stackelberg game optimization strategy for IES considering integrated demand response (IDR), with microgrid operator (MGO) as the leader and load aggregator (LA) as the follower. Firstly, an IDR model containing rigid, shiftable electric loads and reducible thermal loads is established, and a bi-level game model is built: the upper MGO optimizes electricity and heat pricing to maximize profit, while the lower LA adjusts flexible loads for maximum consumer surplus. Secondly, an improved snake optimizer (ISO) is constructed via Hammersley sequence initialization, Lévy flight and random perturbation and combined with quadratic programming (QP) to form the ISO-QP hybrid solving method. Benchmark function and CEC2017 tests verify the superior convergence and stability of ISO against multiple classical intelligent algorithms. Case simulation obtains the Stackelberg equilibrium result, and repeated experiments and parameter sensitivity analysis verify model robustness. Results show that the proposed method smooths load fluctuations via price guidance and synchronously improves MGO revenue and LA consumer surplus on the premise of guaranteed user satisfaction. Full article

The transition to electric bus (EB) fleets is a critical step towards sustainable urban transportation, offering substantial reductions in greenhouse gas and pollutant emissions relative to diesel buses. However, transit authorities face multifaceted challenges in this transition, including limited driving ranges of EBs, the need for widespread charging infrastructure, and potential strain on the electric grid, alongside opportunities such as governmental subsidies and increased fare revenues. This paper proposes a comprehensive multi-period mixed-integer programming model seeking to optimize long-term EB fleet transition plans in urban contexts while jointly accounting for all inherent financial, technical, and operational factors impacting such a transition. The model is operationalized using real data acquired from Dubai’s Roads & Transport Authority (RTA), encompassing 71 bus routes and a 25-year planning horizon to meet a 100% electrification target by 2050. A scenario-based analysis evaluates the robustness of the transition plans under variations in key operational parameters. The results illustrate that optimized long-term planning yields substantial cost savings and emissions reductions, where the incorporation of environmental and social externalities and revenue shifts causes profit maximization to emerge as a more appropriate objective. In addition, it turns out that adequate dwell time is crucial for cost containment and full fleet electrification feasibility. While RTA targets 100% electrification by 2050, the base case is deliberately relaxed to 90% as certain routes, notably double-decker lines, are incompatible with currently available EB configurations. Nevertheless, full electrification is restored under the minimum dwell scenario. Also, a policy of purchasing only EBs accelerates full fleet electrification by roughly a decade with only a marginal increase in total cost, unlike imposing strict interim electrification targets. The optimized transition plans provide actionable insights for transit authorities balancing economic efficiency with sustainability goals. Full article

Early-stage residential building design in dense urban environments involves complex interactions among zoning regulations, geometric configuration, environmental performance, and economic feasibility. Conventional CAD–spreadsheet workflows and parametric BIM-based approaches remain limited in systematically resolving these interdependent trade-offs and typically rely on heuristic iteration and post hoc regulatory verification. To address this limitation, this study proposes REGEN, a regulation-aware BIM-enabled multi-objective optimization framework for sustainable residential building design. The framework formalizes planning and building-control regulations as explicit algebraic constraints embedded within a parametric BIM environment and integrates them with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate regulation-compliant design alternatives with respect to the encoded planning and building-control regulations. REGEN simultaneously optimizes five competing objectives: maximizing project profit, green-area provision, and building efficiency while minimizing geometric shape factor and building footprint area. A real condominium feasibility case in Bangkok, Thailand, is used to benchmark the proposed framework against conventional practice and parametric BIM-based design under identical site and regulatory conditions. The results reveal a non-convex Pareto front that exposes complex trade-offs among environmental, geometric, and economic objectives. The selected closest-to-utopia solution achieves 65.50% building efficiency, 606 m² of green area, a shape factor of 0.399, and a building footprint area of 1078 m² while maintaining a competitive project profit of 104.55 million THB without maximizing FAR utilization. The findings suggest that regulation-aware generative optimization has the potential to serve as an explainable and decision-oriented approach for sustainable construction and early-stage residential development planning. Full article

As the main drivers of the market economy, enterprises must fully grasp the importance and urgency of building an ecological civilization and hasten the transition to green practices. Due to the fundamental goal of enterprises being to maximize profits, the cost-effectiveness of enterprises is directly related to their initiative and implementation effectiveness in carrying out green transformation. This article uses panel data from heavily polluting companies listed on the Shanghai and Shenzhen stock exchanges in China from 2011 to 2020 to empirically test the cost-economic effects of corporate green transformation (CGT). Results reveal: (1) CGT has a positive effect on firm performance, and managerial incentives and capital intensity can strengthen the positive relationship between CGT and firm performance. In addition, in economically developed regions with high levels of environmental regulation, the green transformation of heavily polluting enterprises with lower management agency costs has a more significant positive impact on corporate performance. (2) Environmental costs mediate the link between CGT and firm performance, with the mediating effect of corporate environmental costs playing a role only in the non-three major economic circles. Full article

Background: Avidin (AV) represents the third most important protein of major commercial interest derived from egg white, alongside ovotransferrin and lysozyme. It constitutes only 0.05% of the total protein content. Despite the widespread natural availability of AV, its purification remains a significant challenge due to its low abundance within a highly concentrated and complex protein matrix. Methods: Developing efficient downstream processing for AV has the potential to significantly enhance profitability within the egg protein industry. This work presents a novel integrated process for AV recovery. It comprises ovomucin removal, AV preconcentration, and final purification using pseudo-affinity chromatography. The latter utilizes a novel resin with 4′-hydroxyazobenzene-2-carboxylic acid (HABA) as the ligand. The HABA–agarose matrix was characterized by an adsorption isotherm and breakthrough curves, indicating an AV adsorption performance higher than that of other pseudo-affinity matrices. Results and Conclusions: The HABA pseudo-affinity chromatographic process was a crucial step to purify AV more than 300-fold with high yield (86%). Despite the low AV recovery, the proposed integrated process aligns with the biorefinery concept, which maximizes the economic value of raw materials by utilizing all components. Full article

To promote the sustainable development of agriculture and reduce resource waste, this paper investigates sustainable financing strategies for a green fresh agri-food supply chain. We employ a purely theoretical Stackelberg game model and numerical simulations based on hypothetical parameters to develop three financing models for a supply chain consisting of one capital-constrained farmer and one retailer, considering consumers’ dual sensitivity to product freshness and greenness. Analytical and numerical results reveal that: (1) with low financing rates, internal financing effectively alleviates under investment in preservation, leading to higher wholesale/retail prices. In a green-sensitive market, the resulting price premium compensates for cost increases, avoiding the “low quality–low price” trap under external financing. (2) The retailer’s total profit decreases as the internal financing rate rises; higher interest income cannot offset demand loss caused by reduced preservation effort. Thus, a low- or zero-interest strategy maximizes the retailer’s operational profit. (3) As consumer sensitivity to freshness and greenness increases, profit growth under internal financing displays convexity. However, under extremely high freshness sensitivity, external financing yields stronger marginal incentives, suggesting that retailers should adjust profit allocation in the high-end market. The findings provide theoretical guidance for financing mode selection and practical insights for promoting green agricultural sustainable development. Full article

Perishable products are usually priced in real-time to volatile market environments, thereby optimizing inventory control, minimizing resource wastage, and maximizing corporate profitability. Based on the public dataset from the 2023 Higher Education Press Cup National College Students Mathematical Modeling Competition, this paper addresses the challenge of multi-product joint pricing for perishable fresh produce and proposes an attention-augmented proximal policy optimization algorithm (termed ATT-PPO), which embeds an attention mechanism into the proximal policy optimization (PPO) framework. The integrated attention mechanism confers three core advantages to the model: first, it dynamically captures inter-product interdependencies, enabling an accurate reflection of cross-price elasticity and demand correlations; second, it reduces feature redundancy and computational overhead in multi-product collaborative pricing strategies; third, it enhances both the interpretability and computational efficiency of the model. Experimental results demonstrate that in the scenario of multi-product pricing, the ATT-PPO algorithm achieves competitive performance compared to PPO, DDPG (Deep Deterministic Policy Gradient), SAC (Soft Actor-Critic), and TD3 (Twin Delayed Deep Deterministic Policy Gradient), with the key advantage lying in its ability to provide interpretable attention weights that reveal dynamic cross-product dependencies in pricing decisions. This study not only expands the applicability of DRL (Deep Reinforcement Learning) to practical economic problems in the fresh produce sector but also provides valuable theoretical insights that can be generalized to other short-lifecycle product domains, including fashion apparel and consumer electronics. Full article

Smart grid private communication networks need to support heterogeneous services with varying requirements for reliability, security, bandwidth, and controllability. In such networks, service function chains (SFCs) can provide customized network services by deploying virtual network functions (VNFs) over a shared substrate infrastructure. However, sharing physical servers among different service categories may conflict with the physical isolation requirement between critical grid services and common grid services. To address this problem, this paper investigates physical-isolation-aware SFC deployment for smart grid private communication networks. We first formulate an integer nonlinear programming (INLP) model that maximizes the network resource usage revenue while considering server resource constraints, link bandwidth constraints, flow conservation constraints, virtual link mapping constraints, server energy consumption, and physical isolation constraints. The nonlinear constraints are then linearized into an integer linear programming (ILP) model, which can be solved by an optimizer and used as a benchmark. To reduce the computational cost, we propose a private-network-oriented service function chain isolation deployment (PNO-SSID) algorithm. The proposed algorithm selects a revenue-aware subset of SFC requests, determines the service category to be preferentially processed, selects server nodes based on VNF-layer traffic cost, deploys VNFs using a matching-game-based method, and maps virtual links based on shortest paths. Simulation results show that PNO-SSID requires much less execution time than CPLEX while achieving close revenue in small-scale cases. Compared with online profit maximization (OLPM) variants using different request preprocessing strategies, PNO-SSID achieves higher network resource usage revenue and request acceptance ratio under physical isolation constraints. A prototype platform based on a fifth-generation non-standalone private network and the OAI platform further validates the feasibility of server-level isolated core network service chain deployment under the considered service-category separation requirement. Full article

Industry 5.0, deploying artificial intelligence (AI) at its core, reframes industrial evolution from a predominantly technology- and efficiency-driven innovation model toward a virtuously human-centric, sustainable, and resilient model of value creation by organizations. This review paper, based on an interdisciplinary literature review, explores how AI, within the Industry 5.0 paradigm, reshapes economic logics, the understanding of information asymmetry, and sustainability trajectories, and the implications for entrepreneurial strategy and business model innovation, which demand the development of a new form of organizational intelligence. While the literature suggests that AI, when deployed within a mature Industry 5.0 framework, could generate synergistic economic and sustainability values through circular, human-centered, and digitally augmented systems, human–AI co-intelligence gains are contingent on insights that address systems quality, reskilling, ethics, and reorienting resources from overly short-term profit maximization toward wisdom for long-term socio-ecological, climate resilience, and ESG performance. This study introduces a framework for tackling organizational sustainability dynamics, anticipating the emergence of new industries and the retransformation of enduring ones amid creative destruction in the AI era. Future studies to fill knowledge gaps and implications for human competencies that will enhance organizational intelligence are articulated. Full article

Conventional sterilization methods limit smallholder mushroom cultivation in PNG. This study evaluated alternative disinfection approaches for Pleurotus ostreatus (P. ostreatus) using coffee parchment and Cenchrus fungiraminus (C. fungigraminus) as substrates. After screening 20 strains, the superior strain PXF9 was selected. Three methods were compared: (1) Complete Sterilization with Aseptic Inoculation (CSAI) applied to T1 (experimental) and T2 (sawdust control); (2) Short Sterilization with Open Inoculation (SSOI) applied to T3 (experimental) and T5 (control); and (3) Non sterilization with Open Inoculation (NSOI) applied to T4 (experimental). CSAI (T1) achieved the highest yield (3985.26 ± 2.00 ^d g/24 bags), biological efficiency (83.03%), protein (28.44 g/100 g), and profit (14.76 USD), with the fastest colonization (21 days). SSOI (T3) produced the largest fruiting bodies; NSOI (T4) had the lowest heavy metal levels. SSOI and NSOI were economically beneficial (9.88 and 5.96 UDS per 24 bags). Bioactive compounds (e.g., naringenin, ergosterol peroxide), were detected across treatments. While CSAI maximizes productivity, SSOI and NSOI offer low-cost alternatives for resource-limited farmers. Full article

The volatility of renewable energy generation and nodal electricity prices provides an arbitrage opportunity for Mobile Battery Energy Storage Systems (MBESS) leveraging both temporal and spatial advantages. However, the inherent high complexity and strong randomness of both power and transportation systems lead to complex risks for MBESS control. Existing works mainly consider the market price risk and ignore the transportation system risk caused by traffic congestion. Specifically, they are constrained by two critical limitations: (1) decisions can only be made upon arrival at a destination, making the agent unresponsive on the road, and (2) traffic congestion risk is neither quantified nor controlled, leading to suboptimal routing strategies. To address these limitations, the MBESS needs more flexible “on the road” decision making and multiple risk management capabilities. Guided by this objective, a flexible deep reinforcement learning-based MBESS control framework is proposed, considering both market and traffic congestion risk. First, dynamic routing ability is integrated with the MBESS agent to provide more flexibility in making decisions, regardless of whether the agent has reached the designated location or not. Second, two risk metrics are proposed to quantitatively assess the traffic congestion risk based on moving time, and then the agent can make decisions considering both market and traffic congestion risk. Finally, considering the inefficiency of learning caused by introducing multiple risks, a risk curriculum learning method is proposed to improve the training efficiency and reduce learning costs. These components are unified in the Multiple Risk Estimation SDDPG (MRE-SDDPG) algorithm, which jointly maximizes profitability while controlling electricity price and traffic congestion risk. Simulations in the IEEE 30 bus environment show that the proposed framework can increase profit by 8.6% while reducing the traffic time by 15.8% on average, demonstrating the superiority of our design in considering traffic congestion risk. Full article

Against the backdrop of natural gas energy measurements and pipeline interconnectivity, the supply–demand imbalance has become increasingly prominent in multi-source gas pipeline networks. Existing pipeline scheduling studies mostly focus solely on economic optimization or simple gas quality constraints, while rarely quantifying user satisfaction and integrating it with operational profit within a systematic multi-objective framework, leaving a critical research gap for refined scheduling under energy metering modes. This paper first develops a quantitative user satisfaction function incorporating calorific value and methane content indicators and further establishes a novel multi-objective operational scheduling model coupled with gas quality limitations, which simultaneously maximizes network operating profit and user gas supply satisfaction. The ε-constraint method combined with the GAMS/ANTIGONE solver is adopted to address the constructed Mixed-Integer Nonlinear Programming (MINLP) model. Taking a typical long-distance pipeline in China as the engineering case, a series of Pareto-optimal solutions is obtained. The results show that user satisfaction ranges from 99.70% to 99.77% and operating profit varies from 1403.07 × 10⁴ to 1752.44 × 10⁴ CNY. The derived Pareto frontier quantitatively reveals the inherent trade-off mechanism between user satisfaction and operating profit. The case results demonstrate the applicability of the proposed framework in this specific pipeline scenario, rather than claiming universal validity. It is acknowledged that model validation is currently limited to only one single long-distance pipeline case, with no additional case studies and no comparison with historical operation data conducted in this work. Different from conventional single-objective or simplified gas quality-optimization methods, this study enriches scheduling scheme alternatives and provides theoretical support and a practical decision-making reference for multi-source pipeline operational optimization under energy metering. Full article