Supply Chain Analytics最新文献_第4页

A deep reinforcement learning and fractional packing framework for routing and scheduling in healthcare waste supply chains 用于医疗废物供应链中路由和调度的深度强化学习和分级包装框架

Supply Chain Analytics

Pub Date : 2025-09-16 DOI: 10.1016/j.sca.2025.100164

Norhan Khallaf , Osama Abdel‑Raouf , Mohiy Hadhoud , Mohamed Dawam , Ahmed Kafafy

Artificial intelligence (AI) is increasingly utilized in healthcare logistics, including automated systems for collecting hazardous medical waste from hospitals under strict time and capacity constraints. This study compares three routing algorithms: (1) Google Maps Destination using an application programming interface (API), (2) hybrid clustering with Deep Q-Network (DQN), and (3) a hybrid method combining clustering, the fractional knapsack strategy, and DQN. These algorithms aim to optimize route planning and scheduling for medical waste collection vehicles operating under real-world constraints such as limited vehicle capacity and fixed service windows. The routing problem is modeled as both a capacitated vehicle routing problem (CVRP) and a CVRP with time windows (CVRPTW), capturing complexities. A multi-trip routing strategy is integrated into the promising algorithms to assess its impact on performance metrics, including capacity utilization, travel distance, total operational time, and number of trips. Experimental results indicate hybrid approach with clustering, fractional knapsack, and DQN outperforms others. It achieved capacity utilization rates of 96.47 percent for CVRP and 76.01 % for CVRPTW, requiring six vehicles, a 25 % reduction compared to the Google Maps API method, while matching the performance of clustering with DQN under time constraints. The CVRP model improved capacity utilization by 28.9 % over Google Maps API and 85.1 % over clustering with DQN. Although travel distance increased slightly (0.61 % in CVRP and 7.2 % in CVRPTW), total operational time was reduced by 7.6 and 4.6 %. The proposed model also minimized extra trips, requiring none for CVRP and only one for CVRPTW, compared to two additional trips needed by clustering with DQN in both scenarios. These findings highlight the hybrid approach as a robust, efficient solution for medical waste transportation under complex conditions.

人工智能（AI）越来越多地用于医疗保健物流，包括在严格的时间和能力限制下从医院收集危险医疗废物的自动化系统。本研究比较了三种路由算法：(1)谷歌使用应用程序编程接口（API）映射目的地，(2)与Deep Q-Network （DQN）混合聚类，(3)结合聚类，分数背包策略和DQN的混合方法。这些算法旨在优化在车辆容量有限和固定服务窗口等现实约束下运行的医疗废物收集车辆的路线规划和调度。将路径问题建模为有能力车辆路径问题（CVRP）和带时间窗口的车辆路径问题（CVRPTW），以捕获复杂性。将多行程路由策略集成到有前途的算法中，以评估其对性能指标的影响，包括容量利用率、行程距离、总操作时间和行程数。实验结果表明，基于聚类、分数背包和DQN的混合方法优于其他方法。CVRP的容量利用率为96.47%，CVRPTW的容量利用率为76.01 %，需要6辆车，与谷歌Maps API方法相比降低了25 %，同时在时间限制下与DQN的聚类性能相当。CVRP模型比谷歌Maps API提高了28.9 %的容量利用率，比DQN集群提高了85.1% %的容量利用率。虽然旅行距离略有增加（CVRP为0.61 %，CVRPTW为7.2 %），但总操作时间分别减少了7.6和4.6 %。所提出的模型还最小化了额外的行程，CVRP不需要额外的行程，CVRPTW只需要一次行程，相比之下，在两种情况下，使用DQN聚类都需要额外的两次行程。这些发现突出表明，混合方法是复杂条件下医疗废物运输的一种稳健、有效的解决方案。

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引用次数: 0

A scoping review and bibliometric analysis of sustainable and resilient supply chain network design 可持续和弹性供应链网络设计的范围回顾和文献计量学分析

Supply Chain Analytics

Pub Date : 2025-09-08 DOI: 10.1016/j.sca.2025.100162

Rahmi Yuniarti , Suparno , Niniet Indah Arvitrida

Designing sustainable and resilient supply chain networks (SRSCND) has become a strategic priority amid intensifying environmental pressures, market volatility, pandemic disruptions, and geopolitical uncertainties such as trade wars, resource nationalism, and regional conflicts. This study employs a hybrid bibliometric–scoping review (ScoRBA) combined with the PAGER framework to systematically map and synthesize 528 peer-reviewed articles published between 2015 and 2025. The analysis identifies five thematic clusters: (1) digitalization for sustainable decision-making, (2) energy and environmental priorities in low-carbon supply chains, (3) resilience and strategic planning under uncertainty, (4) value-oriented and data-driven reverse supply chains, and (5) heuristic optimization in green and closed-loop systems. Cross-cluster insights highlight that the most innovative solutions emerge at the intersections of these themes—for example, integrating digital decision-support systems with adaptive heuristic optimization for real-time network reconfiguration; coupling circular economy strategies with resilience planning to create low-carbon yet disruption-ready systems; and combining traceability infrastructures with value-recovery optimization in closed-loop networks. Although conceptual maturity is well established, operational maturity remains limited: most studies rely on theoretical modeling, simulation, or isolated case studies, with few sector-specific real-world applications. Social and behavioral dimensions, governance integration, and multi-sector disruption modeling remain underexplored. Future research should prioritize scaling pilot projects into multi-sector industrial implementations, embedding social, cultural, and behavioral factors into quantitative models, and developing adaptive real-time decision systems that integrate environmental, economic, and social objectives. Strengthening industry–academia collaboration, improving open-data access, and leveraging digital twin technologies will be critical to accelerate the transition from theoretical advances to scalable, practice-oriented solutions for building sustainable and resilient supply chains in an era of complex global risks.

在不断加剧的环境压力、市场波动、流行病破坏以及地缘政治不确定性（如贸易战、资源民族主义和地区冲突）的背景下，设计可持续和有弹性的供应链网络（SRSCND）已成为战略重点。本研究采用混合文献计量学-范围审查（ScoRBA）与PAGER框架相结合，系统地绘制和综合了2015年至2025年间发表的528篇同行评议文章。该分析确定了五个主题集群：(1)可持续决策的数字化；(2)低碳供应链的能源和环境优先事项；(3)不确定性下的弹性和战略规划；(4)价值导向和数据驱动的逆向供应链；(5)绿色闭环系统的启发式优化。跨集群洞察强调，最具创新性的解决方案出现在这些主题的交叉点上，例如，将数字决策支持系统与实时网络重构的自适应启发式优化相结合；将循环经济战略与弹性规划相结合，创建低碳但可应对破坏的系统；将可追溯性基础设施与闭环网络中的价值恢复优化相结合。虽然概念成熟度已经建立，但操作成熟度仍然有限：大多数研究依赖于理论建模、模拟或孤立的案例研究，很少有特定部门的实际应用。社会和行为维度、治理集成和多部门中断建模仍未得到充分探索。未来的研究应优先考虑将试点项目扩展到多部门的工业实施中，将社会、文化和行为因素嵌入定量模型中，并开发整合环境、经济和社会目标的自适应实时决策系统。在复杂的全球风险时代，加强产学研合作、改善开放数据获取和利用数字孪生技术，对于加速从理论进步向可扩展、以实践为导向的解决方案的转变，对于构建可持续和有弹性的供应链至关重要。

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引用次数: 0

An integrated analytical framework for inventory and pricing of perishable products in multi-echelon supply chains 多级供应链中易腐产品库存与定价的综合分析框架

Supply Chain Analytics

Pub Date : 2025-08-24 DOI: 10.1016/j.sca.2025.100157

Jesús Isaac Vázquez-Serrano , Leopoldo Eduardo Cárdenas-Barrón , Julio C. Vicencio-Ortiz , Neale R. Smith , Rafael Ernesto Bourguet-Díaz , Armando Céspedes-Mota , Rodrigo E. Peimbert-García

Inventory management and pricing strategies are fundamental to supply chain operations, particularly for wholesalers who serve as intermediaries between manufacturers and retailers at specific times. A wholesaler's profitability depends critically on two key operational decisions: effective inventory control to minimize costs and strategic price-setting for retail customers. This paper introduces an innovative hybrid model that combines optimization and discrete-event simulation to address these challenges, with a specific focus on perishable goods management and determining break-even pricing points. The proposed hybrid model is comprehensive in scope, accommodating multiple perishable products across various time periods and suppliers while accounting for the inherent uncertainties in wholesale operations. Its dual-component structure leverages optimization techniques for inventory cost minimization while employing simulation to address operational variability. The model provides detailed mathematical frameworks for calculating unit-level critical selling prices, both inclusive and exclusive of operational costs. To validate the model's effectiveness, the research presents a case study of a pharmaceutical wholesaler, drawing on data from the United Nations Office for Project Services. The hybrid model's performance was evaluated against two established empirical methodologies in the supply chain: the Lowest Acquisition Cost Approach and the Earliest Product Acquisition Approach. The results demonstrate significant improvements, with the hybrid model achieving a 20 % reduction in average total costs and an 18 % decrease in average critical selling price compared to traditional approaches.

库存管理和定价策略是供应链运作的基础，特别是对于在特定时间充当制造商和零售商之间中介的批发商。批发商的盈利能力主要取决于两个关键的经营决策：有效的库存控制以最大限度地降低成本和为零售客户制定战略价格。本文介绍了一种创新的混合模型，该模型结合了优化和离散事件模拟来解决这些挑战，特别关注易腐货物管理和确定盈亏平衡定价点。所提出的混合模型在范围上是全面的，在考虑批发业务中固有的不确定性的同时，可以适应不同时间段和供应商的多种易腐产品。它的双组件结构利用优化技术来最小化库存成本，同时采用模拟来解决操作的可变性。该模型提供了详细的数学框架，用于计算单位水平的关键销售价格，包括和不包括运营成本。为了验证该模型的有效性，本研究利用联合国项目事务厅的数据，对一家药品批发商进行了个案研究。混合模型的性能对比了两种已建立的供应链经验方法：最低获取成本法和最早产品获取法。结果显示了显著的改进，与传统方法相比，混合模型的平均总成本降低了20% %，平均关键销售价格降低了18% %。

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引用次数: 0

A comparative study of multi-algorithm optimization for inventory analytics in supply chains 供应链库存分析的多算法优化比较研究

Supply Chain Analytics

Pub Date : 2025-08-21 DOI: 10.1016/j.sca.2025.100154

Oussama Zabraoui, Yahya Hmamou , Anas Chafi , Salaheddine Kammouri Alami

Effective management of inventory is essential for achieving high service levels, minimizing costs, and maintaining the overall resilience of retail supply chains—particularly in complex, real-world environments. Conventional strategies often prove inadequate because they rely on rigid assumptions or single-technique models that fail to accommodate practical challenges such as fluctuating demand, unpredictable lead times, and disruptions in supply.

To bridge this gap, our research undertakes a comprehensive comparison of multiple approaches — including Reinforcement Learning (RL), Genetic Algorithms (GA), Deep Learning (DL), Machine Learning (ML), and heuristic techniques — evaluated within a consistent and realistic testing framework based on the Walmart M5 dataset. This dataset offers a robust benchmark, containing multi-store, multi-item sales data that captures seasonal trends, event-driven demand variations, and price sensitivity. We introduce and evaluate an innovative hybrid methodology that combines a Genetic Algorithm with a Deep Q-Network (GA–DQN). The GA component conducts a broad, global search to optimize static inventory parameters such as reorder points and safety stock, while the DQN module learns adaptive, state-aware ordering strategies that can respond to dynamic, uncertain conditions. Our results show that this hybrid GA–DQN model achieves a significant improvement over a standalone DQN baseline—raising the service level from 61% to 94% and simultaneously lowering overall inventory costs. The framework we propose is modular and includes three key components: demand forecasting using Long Short-Term Memory (LSTM) networks to capture temporal sales patterns; GA-based optimization to fine-tune static policy parameters; and RL-driven adaptive control to support responsive, real-time ordering decisions. This integrated approach delivers a scalable, data-driven solution well-suited to the demands of modern retail supply chains, effectively addressing issues such as supplier unreliability, demand uncertainty, and the management of perishable goods.

有效的库存管理对于实现高服务水平、最小化成本和保持零售供应链的整体弹性至关重要，特别是在复杂的现实环境中。传统战略往往被证明是不够的，因为它们依赖于僵化的假设或单一技术模型，无法适应需求波动、不可预测的交货时间和供应中断等实际挑战。为了弥补这一差距，我们的研究对多种方法进行了全面比较-包括强化学习（RL），遗传算法（GA），深度学习（DL），机器学习（ML）和启发式技术-在基于沃尔玛M5数据集的一致和现实的测试框架内进行评估。该数据集提供了一个强大的基准，包含多商店、多项目销售数据，这些数据捕获了季节性趋势、事件驱动的需求变化和价格敏感性。我们介绍并评估了一种结合遗传算法和深度q -网络（GA-DQN）的创新混合方法。GA组件进行广泛的全局搜索，以优化静态库存参数，如再订货点和安全库存，而DQN模块学习自适应、状态感知的订购策略，可以响应动态、不确定的条件。我们的研究结果表明，与单独的DQN基线相比，这种混合GA-DQN模型实现了显著的改进——将服务水平从61%提高到94%，同时降低了总体库存成本。我们提出的框架是模块化的，包括三个关键组成部分：使用长短期记忆（LSTM）网络进行需求预测，以捕捉时间销售模式；基于遗传算法的静态策略参数优化和rl驱动的自适应控制，以支持响应，实时订购决策。这种集成方法提供了一种可扩展的、数据驱动的解决方案，非常适合现代零售供应链的需求，有效地解决了供应商不可靠性、需求不确定性和易腐货物管理等问题。

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引用次数: 0

An evaluation of traceability dynamics in dairy supply chains through causal modeling in emerging economies 通过新兴经济体因果模型对乳制品供应链可追溯性动态的评估

Supply Chain Analytics

Pub Date : 2025-08-15 DOI: 10.1016/j.sca.2025.100156

Shahab Bayatzadeh , Hamidreza Talaie

Traceability capability to track the history, location, and application of dairy products is crucial for ensuring food safety, quality, and transparency across supply chains. However, its development in emerging economies, particularly in Iran, remains limited due to infrastructural and technological challenges. This study addresses this gap by identifying and analyzing the key factors that influence traceability in Iran’s dairy sector, which plays a critical role in national nutrition and public health. Using a hybrid approach, the fuzzy Delphi method was first applied to refine a set of 19 factors extracted from the literature, validating 14 context-relevant elements based on expert consensus. Subsequently, the fuzzy DEMATEL method, designed to model causal relationships under uncertainty, was used to determine interdependencies among these factors. The results highlight food safety and quality, supply chain process management, data analysis and forecasting, and data integration as the most influential drivers of traceability. Meanwhile, competitive advantage, sourcing transparency, and environmental sustainability were found to be dependent outcomes. This research contributes a contextualized, expert-based framework tailored to the Iranian dairy industry and offers practical implications for improving transparency, reducing waste, and building consumer trust. The methodology and findings are transferable to other developing country contexts facing similar challenges.

跟踪乳制品历史、位置和应用的可追溯性能力对于确保整个供应链的食品安全、质量和透明度至关重要。然而，由于基础设施和技术方面的挑战，其在新兴经济体，特别是在伊朗的发展仍然有限。本研究通过确定和分析影响伊朗乳制品行业可追溯性的关键因素来解决这一差距，而伊朗乳制品行业在国家营养和公共卫生中发挥着关键作用。采用混合方法，首先应用模糊德尔菲法对从文献中提取的19个因素进行细化，基于专家共识验证了14个与上下文相关的元素。随后，采用模糊DEMATEL方法对不确定条件下的因果关系进行建模，确定这些因素之间的相互依赖关系。结果强调食品安全和质量、供应链流程管理、数据分析和预测以及数据集成是可追溯性最具影响力的驱动因素。同时，竞争优势、采购透明度和环境可持续性被发现是依赖结果。这项研究为伊朗乳制品行业量身定制了一个基于专家的背景框架，并为提高透明度、减少浪费和建立消费者信任提供了实际意义。方法和研究结果可适用于面临类似挑战的其他发展中国家。

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引用次数: 0

A metaheuristic approach for optimizing drone routing in healthcare supply chains 优化医疗供应链中无人机路线的元启发式方法

Supply Chain Analytics

Pub Date : 2025-08-14 DOI: 10.1016/j.sca.2025.100153

Tejinder Singh Lakhwani, Yerasani Sinjana

Healthcare logistics continue to encounter significant challenges in the timely and reliable delivery of blood bags, mainly due to urban traffic congestion, rugged terrain, and the perishability of medical supplies. Conventional transportation systems frequently fall short of meeting the stringent temporal and thermal requirements inherent to healthcare supply chains. Unmanned Aerial Vehicles (UAVs), or drones, offer a compelling alternative; however, their effective deployment is hindered by constraints such as limited payload capacity, restricted flight range, narrow delivery time windows, and evolving regulatory frameworks. This study proposes the HybridNGS algorithm, a hybrid metaheuristic framework that integrates Nearest Neighbour (NN) for solution initialization, Genetic Algorithm (GA) for global search, and Simulated Annealing (SA) for local refinement, to address the Drone Routing Problem (DRP) in blood logistics. The model incorporates domain-specific constraints, including blood-type compatibility, energy-aware routing, and cold-chain preservation. Empirical evaluations using synthetic and real-world datasets comprising 20–100 hospitals reveal that HybridNGS consistently outperforms benchmark approaches such as GRASP and TSP-D, achieving up to 20 % cost savings, 15 % reduction in drone usage, and notable energy efficiency. The algorithm demonstrates strong scalability and robustness under variable demand and environmental conditions. It is a viable solution for enhancing accessibility, reliability, and sustainability in routine and emergency healthcare delivery systems.

医疗物流在及时、可靠地运送血袋方面继续面临重大挑战，主要原因是城市交通拥堵、地形崎岖以及医疗用品易腐烂。传统的运输系统经常不能满足医疗保健供应链固有的严格的时间和热量要求。无人驾驶飞行器（uav）或无人机提供了一个令人信服的替代方案；然而，它们的有效部署受到诸如有限的有效载荷能力、有限的飞行距离、狭窄的交付时间窗口和不断发展的监管框架等制约因素的阻碍。本研究提出了HybridNGS算法，这是一种混合元启发式框架，集成了用于解决初始化的最近邻（NN），用于全局搜索的遗传算法（GA）和用于局部优化的模拟退火（SA），以解决血液物流中的无人机路由问题（DRP）。该模型结合了特定领域的约束，包括血型兼容性、能量感知路由和冷链保存。利用20 - 100家 医院的合成和真实数据集进行的实证评估表明，HybridNGS始终优于GRASP和TSP-D等基准方法，可节省高达20% %的成本，减少15% %的无人机使用，并显着提高能源效率。该算法在可变需求和环境条件下具有较强的可扩展性和鲁棒性。这是提高常规和紧急医疗保健提供系统的可及性、可靠性和可持续性的可行解决方案。

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引用次数: 0

A two-stage approach to enhancing biofuel supply chains through predictive and optimization analytics 通过预测和优化分析加强生物燃料供应链的两阶段方法

Supply Chain Analytics

Pub Date : 2025-08-14 DOI: 10.1016/j.sca.2025.100155

Mehdi Soltani Tehrani, Siamak Noori, Ehsan Dehghani

The escalating pressures of population growth, surging global energy needs, water shortages, reliance on fossil fuels, and urban air pollution underscore the critical demand for sustainable energy alternatives. Biofuels present a viable solution, yet their successful adoption hinges on an efficient supply chain. This study introduces a comprehensive two-stage optimization framework to advance the design and operation of biofuel supply chains. In the initial stage, a novel hybrid methodology integrates data envelopment analysis with artificial neural networks to identify optimal sites for agricultural waste collection facilities. This approach combines the performance assessment strengths of data envelopment analysis with the predictive capabilities of neural networks, enabling a data-informed site selection process. The second stage employs a mixed-integer linear programming model to optimize a closed-loop biofuel supply chain under uncertain conditions, targeting both cost reduction and minimized carbon emissions. A probabilistic scenario-based approach is utilized to address uncertainties, enhancing the model’s real-world applicability. Additionally, the Lagrangian relaxation technique is implemented to achieve precise solutions while preserving computational efficiency. For large-scale scenarios, the study leverages the non-dominated sorting genetic algorithm and multi-objective simulated annealing to generate near-optimal solutions. A practical case study validates the proposed framework and provides decision-makers with clear and actionable strategies to optimize site planning, reduce operational costs, and enhance environmental sustainability in biofuel supply chain management.

不断升级的人口增长压力、激增的全球能源需求、水资源短缺、对化石燃料的依赖以及城市空气污染都凸显了对可持续能源替代品的迫切需求。生物燃料是一种可行的解决方案，但其成功采用取决于高效的供应链。本研究引入了一个全面的两阶段优化框架，以推进生物燃料供应链的设计和运作。在初始阶段，一种新的混合方法将数据包络分析与人工神经网络相结合，以确定农业废物收集设施的最佳地点。该方法将数据包络分析的性能评估优势与神经网络的预测能力相结合，实现了基于数据的选址过程。第二阶段采用混合整数线性规划模型优化不确定条件下的闭环生物燃料供应链，以降低成本和最小化碳排放为目标。利用基于概率场景的方法来解决不确定性，增强模型在现实世界中的适用性。此外，采用拉格朗日松弛技术，在保证计算效率的同时获得精确解。对于大规模场景，研究利用非支配排序遗传算法和多目标模拟退火来生成近最优解。一个实际的案例研究验证了所提出的框架，并为决策者提供了明确可行的策略，以优化现场规划，降低运营成本，并提高生物燃料供应链管理的环境可持续性。

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引用次数: 0

An artificial intelligence framework for recycling dormant and obsolete inventory in supply Chains 一个用于回收供应链中休眠和过时库存的人工智能框架

Supply Chain Analytics

Pub Date : 2025-08-05 DOI: 10.1016/j.sca.2025.100152

Youssef Raouf , Zoubida Benmamoun , Hanaa Hachimi

In the automotive sector, excess inventory increases costs and prevents progress toward Sustainable Development Goals, particularly those related to Industry, Innovation, and Infrastructure (SDG 9) and Responsible Consumption and Production (SDG 12). This study introduces an innovative approach to converting obsolete or recycled dormant inventory into a stock that meets customer demand and is marketable by examining the real case of an automotive manufacturer. The optimization, driven by an artificial intelligence tool, transforms at-risk inventory into demand-responsive stock. Results indicate that the tool can modernize up to 84,31 % of the affected inventory, offering substantial benefits, including reduced storage costs and the freeing up of strategic space for new opportunities. This method enhances supply chain resilience and sustainability by reducing waste, improving resource efficiency, and boosting adaptability to disruptions. This paper explores how supply chain innovations in this field address economic, environmental, and social imperatives. It draws on quantitative research into the role of advanced analytics and artificial intelligence technologies in inventory innovation to advance global goals for more resilient and sustainable supply chains.

在汽车行业，过剩的库存增加了成本，阻碍了可持续发展目标的实现，特别是与工业、创新和基础设施（可持续发展目标9）以及负责任的消费和生产（可持续发展目标12）相关的目标。本研究通过研究汽车制造商的真实案例，介绍了一种创新的方法，将过时或回收的休眠库存转化为满足客户需求并可销售的库存。由人工智能工具驱动的优化，将有风险的库存转化为需求响应型库存。结果表明，该工具可以使受影响的库存现代化高达84,31 %，提供了实质性的好处，包括降低存储成本和释放战略空间以获得新的机会。该方法通过减少浪费、提高资源效率和增强对中断的适应性，增强了供应链的弹性和可持续性。本文探讨了该领域的供应链创新如何解决经济、环境和社会问题。它通过定量研究先进分析和人工智能技术在库存创新中的作用，以推进更具弹性和可持续供应链的全球目标。

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引用次数: 0

A machine learning approach to dynamic pricing in multi-channel transportation supply chains 多渠道运输供应链动态定价的机器学习方法

Supply Chain Analytics

Pub Date : 2025-07-30 DOI: 10.1016/j.sca.2025.100151

Amirsalar Ghaffari , Mohsen Afsharian , Ata Allah Taleizadeh

Effective pricing strategies are critical for optimizing revenue and maintaining competitiveness in transportation supply chains, particularly in multi-channel environments. This paper presents a machine learning-driven dynamic pricing approach designed to optimize ticket prices across multiple transportation modes, service classes, and sales channels. In particular, the proposed approach integrates predictive analytics and optimization techniques to estimate customer demand, price sensitivity, and revenue potential while accounting for operational constraints such as capacity limits and market share requirements. Machine learning enables the optimization model to dynamically adjust pricing strategies based on historical demand patterns and real-time market fluctuations. To demonstrate its applicability, the approach is applied in a case study in the transportation sector, illustrating its role in optimizing pricing decisions. Additionally, sensitivity analysis highlights the model’s robustness against capacity changes, demand fluctuations, and pricing constraints. The findings emphasize the role of supply chain analytics in enhancing pricing strategies, making them more adaptive, data-driven, and resilient to market dynamics.

有效的定价策略对于优化收入和保持运输供应链的竞争力至关重要，特别是在多渠道环境中。本文提出了一种机器学习驱动的动态定价方法，旨在优化多种运输方式、服务类别和销售渠道的票价。特别是，建议的方法集成了预测分析和优化技术，以估计客户需求、价格敏感性和收入潜力，同时考虑诸如容量限制和市场份额要求等运营约束。机器学习使优化模型能够根据历史需求模式和实时市场波动动态调整定价策略。为了证明其适用性，将该方法应用于运输部门的案例研究中，说明其在优化定价决策中的作用。此外，敏感性分析强调了模型对容量变化、需求波动和定价约束的鲁棒性。研究结果强调了供应链分析在提高定价策略方面的作用，使其更具适应性、数据驱动性和对市场动态的弹性。

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引用次数: 0

A simulation-based optimization approach for sustainable energy supply chain transitions 基于仿真的可持续能源供应链转型优化方法

Supply Chain Analytics

Pub Date : 2025-07-27 DOI: 10.1016/j.sca.2025.100150

Zakka Ugih Rizqi

Emissions are emitted throughout the energy supply chain. In demand sector, transitioning from fossil fuel-based vehicles to Electric Vehicles (EVs) is a key step. However, the growth of EVs will lead to higher emissions if, in supply sector, the energy sources rely mainly on Non-Renewable Energy Sources (NRES), forcing to transition to Renewable Energy Sources (RES). While managing these transitions, it is also important to make sure that energy requirements can still be fulfilled which is driven by population dynamics. Thus, this complex interdependence indicates the importance of using systemic framework, although most previous studies focusing on one of transitions which can lead to policy misalignment and unintended trade-offs of sustainability performance. This research proposes a System Dynamics model for assessing the impact of those three interplaying factors on sustainability performance including macroeconomic, job availability, and total emissions, followed by Response Surface Methodology (RSM) for performing efficient optimization. A case study from the United States is used. Through simulation and statistical analysis, 8 insightful propositions are generated. Subsequently, the metamodels based on second-order regression are developed, forming multi-objective non-linear programming revealing the optimal growth rates for sustainable transition which are very useful for helping the policy makers to make more informed decisions toward a sustainable energy system.

排放贯穿整个能源供应链。在需求领域，从化石燃料汽车向电动汽车过渡是关键的一步。然而，如果在供应部门，能源主要依赖于不可再生能源（NRES），迫使向可再生能源（RES）过渡，电动汽车的增长将导致更高的排放。在管理这些转变的同时，确保能源需求仍然能够得到满足也很重要，这是由人口动态驱动的。因此，这种复杂的相互依存关系表明了使用系统框架的重要性，尽管大多数先前的研究侧重于可能导致政策失调和可持续性绩效意外权衡的过渡之一。本研究提出了一个系统动力学模型来评估这三个相互作用的因素对可持续发展绩效的影响，包括宏观经济、就业机会和总排放量，然后采用响应面法（RSM）进行有效优化。本文采用了美国的一个案例研究。通过模拟和统计分析，得出了8个有见地的命题。在此基础上，建立了基于二阶回归的元模型，形成了多目标非线性规划，揭示了可持续转型的最优增长率，有助于决策者对可持续能源系统做出更明智的决策。

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