Pub Date : 2025-01-04DOI: 10.1016/j.tre.2024.103951
Yingna Cao, Ruolin Ding, Yugang Yu, Zhe Yin
We consider an online retailer that sells one product to consumers with product fit uncertainty. In addition to the return strategy, the retailer can introduce a virtual showroom online. Each consumer in a virtual showroom strategically optimizes the experience process prior to the purchase decision-making, which involves the trade-off between the expended effort cost in the product interaction and the obtained product value information. It is found that the virtual showroom simultaneously generates a demand increase effect and a price improvement effect for the online retailer. Consumers with intermediate levels of willingness to pay who leave the market when the virtual showroom is not introduced may purchase the product due to the interaction with the product in the virtual showroom, which can increase the product selling quantity. Consumers who receive positive match information through their self-optimization experiences in the virtual showroom will purchase the product with enhanced confidence, which drives the retailer to improve the retail price. The value of the virtual showroom is enhanced when the retailer has to bear the return loss costs, whereas may be weakened when the virtual showroom’s information delivery ability is endogenously determined with an investment cost. Furthermore, we reveal the impacts of the consumer self-optimization behavior on the interrelationship between the virtual showroom and the return strategies. In particular, when return is not feasible, it is not recommended for the online retailer to introduce the virtual showroom. In this case, a consumer will directly leave the market if the updated product matching probability through the virtual showroom experience decreases. Then, the retailer has to reduce the price to mitigate the demand decrease problem, which ultimately results in the profit reduction.
{"title":"Consumer self-optimization in a virtual showroom experience and its implications on online product return strategies","authors":"Yingna Cao, Ruolin Ding, Yugang Yu, Zhe Yin","doi":"10.1016/j.tre.2024.103951","DOIUrl":"https://doi.org/10.1016/j.tre.2024.103951","url":null,"abstract":"We consider an online retailer that sells one product to consumers with product fit uncertainty. In addition to the return strategy, the retailer can introduce a virtual showroom online. Each consumer in a virtual showroom strategically optimizes the experience process prior to the purchase decision-making, which involves the trade-off between the expended effort cost in the product interaction and the obtained product value information. It is found that the virtual showroom simultaneously generates a demand increase effect and a price improvement effect for the online retailer. Consumers with intermediate levels of willingness to pay who leave the market when the virtual showroom is not introduced may purchase the product due to the interaction with the product in the virtual showroom, which can increase the product selling quantity. Consumers who receive positive match information through their self-optimization experiences in the virtual showroom will purchase the product with enhanced confidence, which drives the retailer to improve the retail price. The value of the virtual showroom is enhanced when the retailer has to bear the return loss costs, whereas may be weakened when the virtual showroom’s information delivery ability is endogenously determined with an investment cost. Furthermore, we reveal the impacts of the consumer self-optimization behavior on the interrelationship between the virtual showroom and the return strategies. In particular, when return is not feasible, it is not recommended for the online retailer to introduce the virtual showroom. In this case, a consumer will directly leave the market if the updated product matching probability through the virtual showroom experience decreases. Then, the retailer has to reduce the price to mitigate the demand decrease problem, which ultimately results in the profit reduction.","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"75 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-04DOI: 10.1016/j.tre.2024.103948
Liangqi Cheng, Lerong Xu, Xiwen Bai
To mitigate the significant environmental impacts of the shipping industry, the International Maritime Organization (IMO) introduced the Carbon Intensity Indicator (CII), which measures CO2 emissions per unit of cargo-carrying capacity and distance traveled. While the implementation of energy-efficient technologies is crucial for meeting CII regulations, these advancements often entail substantial investment costs. Consequently, optimizing operations has become a more practical short-term approach; however, operational adjustments made solely to comply with CII regulations may also have unintended adverse effects. To address this issue, this research develops a pick up and delivery optimization model for tramp ships, which operate on irregular schedules and routes, to minimize total emissions and costs while complying with CII regulations. The model investigates the combination of cargo selection, route planning, and speed optimization, reflecting the comprehensive and unique characteristics of tramp shipping. The problem is solved using Danzig-Wolfe decomposition and a branch-and-price algorithm, with the CII regulations being met in the pricing problem through a customized heuristic. Numerical results demonstrate that the proposed approach can find optimal or near-optimal solutions within a short time. Various experiments explore the effects of CII regulations on tramp shipping operations, environmental performances, and economic benefits. The results indicate that demand-based CII and stricter CII regulations cause ships to carry fewer cargoes, sail shorter ballast distances, reduce speed, and increase load on board. This ultimately reduces CO2 emissions but also lowers total profits. The findings assist industry stakeholders in complying with stringent environmental regulations and aid policymakers in designing targeted regulatory policies, thereby promoting sustainable maritime transport.
{"title":"Cargo selection, route planning, and speed optimization in tramp shipping under carbon intensity indicator (CII) regulations","authors":"Liangqi Cheng, Lerong Xu, Xiwen Bai","doi":"10.1016/j.tre.2024.103948","DOIUrl":"https://doi.org/10.1016/j.tre.2024.103948","url":null,"abstract":"To mitigate the significant environmental impacts of the shipping industry, the International Maritime Organization (IMO) introduced the Carbon Intensity Indicator (CII), which measures CO2 emissions per unit of cargo-carrying capacity and distance traveled. While the implementation of energy-efficient technologies is crucial for meeting CII regulations, these advancements often entail substantial investment costs. Consequently, optimizing operations has become a more practical short-term approach; however, operational adjustments made solely to comply with CII regulations may also have unintended adverse effects. To address this issue, this research develops a pick up and delivery optimization model for tramp ships, which operate on irregular schedules and routes, to minimize total emissions and costs while complying with CII regulations. The model investigates the combination of cargo selection, route planning, and speed optimization, reflecting the comprehensive and unique characteristics of tramp shipping. The problem is solved using Danzig-Wolfe decomposition and a branch-and-price algorithm, with the CII regulations being met in the pricing problem through a customized heuristic. Numerical results demonstrate that the proposed approach can find optimal or near-optimal solutions within a short time. Various experiments explore the effects of CII regulations on tramp shipping operations, environmental performances, and economic benefits. The results indicate that demand-based CII and stricter CII regulations cause ships to carry fewer cargoes, sail shorter ballast distances, reduce speed, and increase load on board. This ultimately reduces CO2 emissions but also lowers total profits. The findings assist industry stakeholders in complying with stringent environmental regulations and aid policymakers in designing targeted regulatory policies, thereby promoting sustainable maritime transport.","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"16 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.tre.2024.103905
Marie-Sklaerder Vié, Nicolas Zufferey, Leandro C. Coelho
In a supply chain network, satisfying the demand at the shop level while having a smooth production at the manufacturer (or plant) level are usually conflicting objectives. For instance, the production variations will be high if the shops can order exactly what they need and when they need it. On the other hand, producing the same amount each day prevents to adapt to the variations of the demand and may generate shortages or excess inventory. This study, performed in collaboration with a major fast-moving consumer goods company, proposes a lexicographic model for managing the supply chain in an integrated manner. Seven objective functions are considered to represent the goals of various stakeholders along the supply chain (from the shop to the plant) and different priority levels. A matheuristic combining both local-search procedures and exact methods is designed for scheduling the production orders and the shipments along the supply chain to optimize the overall cost structure. The proposed neighborhood structures employed in the local-search heuristics are able to perform dedicated modifications with respect to a single objective function (e.g., shortage, production variability, inventory level) and a single solution characteristic (e.g., production, shipment) without degrading the value of higher-level objectives. As computation time is limited, a time management approach for this method is proposed. Experiments are performed on 120 instances generated with the company to capture the real situations it faces. Using a rolling-window simulation with forecasted demands, we show that our method clearly outperforms a commercial solver and several common policies used in practice. The benefit of the proposed approach is highlighted both in terms of runtime and solution quality.
{"title":"A production and distribution scheduling matheuristic for reducing supply chain variations","authors":"Marie-Sklaerder Vié, Nicolas Zufferey, Leandro C. Coelho","doi":"10.1016/j.tre.2024.103905","DOIUrl":"https://doi.org/10.1016/j.tre.2024.103905","url":null,"abstract":"In a supply chain network, satisfying the demand at the shop level while having a smooth production at the manufacturer (or plant) level are usually conflicting objectives. For instance, the production variations will be high if the shops can order exactly what they need and when they need it. On the other hand, producing the same amount each day prevents to adapt to the variations of the demand and may generate shortages or excess inventory. This study, performed in collaboration with a major fast-moving consumer goods company, proposes a lexicographic model for managing the supply chain in an integrated manner. Seven objective functions are considered to represent the goals of various stakeholders along the supply chain (from the shop to the plant) and different priority levels. A matheuristic combining both local-search procedures and exact methods is designed for scheduling the production orders and the shipments along the supply chain to optimize the overall cost structure. The proposed neighborhood structures employed in the local-search heuristics are able to perform dedicated modifications with respect to a single objective function (e.g., shortage, production variability, inventory level) and a single solution characteristic (e.g., production, shipment) without degrading the value of higher-level objectives. As computation time is limited, a time management approach for this method is proposed. Experiments are performed on 120 instances generated with the company to capture the real situations it faces. Using a rolling-window simulation with forecasted demands, we show that our method clearly outperforms a commercial solver and several common policies used in practice. The benefit of the proposed approach is highlighted both in terms of runtime and solution quality.","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"4 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1016/j.tre.2024.103956
Zheng Zhao, Junkai Cheng, Jianyi Zhao, Lu Zhen
As the e-commerce industry ascends, the strain on warehouse operation to manage order processing has intensified significantly. This paper pioneers the integration of mobile composite robot (MCR) for order assigning and path planning in warehouse. A mathematical model is established with the objective function of minimizing the time to complete orders. An algorithm based on column generation is developed, with dynamic programming and other acceleration strategies applied to improve the efficiency of the pricing problem. Numerical experiments show that the algorithm’s performance matches that of CPLEX in small-scale instances and exhibits the capacity to handle 50 orders in just 5 min. Additional experiments are conducted to substantiate the efficiency of our proposed algorithm and to offer valuable managerial insights to practitioners who are implementing MCR technology in warehouse environments.
{"title":"Column generation for scheduling mobile composite robots in warehouses","authors":"Zheng Zhao, Junkai Cheng, Jianyi Zhao, Lu Zhen","doi":"10.1016/j.tre.2024.103956","DOIUrl":"https://doi.org/10.1016/j.tre.2024.103956","url":null,"abstract":"As the e-commerce industry ascends, the strain on warehouse operation to manage order processing has intensified significantly. This paper pioneers the integration of mobile composite robot (MCR) for order assigning and path planning in warehouse. A mathematical model is established with the objective function of minimizing the time to complete orders. An algorithm based on column generation is developed, with dynamic programming and other acceleration strategies applied to improve the efficiency of the pricing problem. Numerical experiments show that the algorithm’s performance matches that of CPLEX in small-scale instances and exhibits the capacity to handle 50 orders in just 5 min. Additional experiments are conducted to substantiate the efficiency of our proposed algorithm and to offer valuable managerial insights to practitioners who are implementing MCR technology in warehouse environments.","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"4 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1016/j.tre.2024.103943
Ömer Faruk Yılmaz, Yongpei Guan, Beren Gürsoy Yılmaz
Humanitarian supply chains (HSCs) play a crucial role in mitigating the impacts of natural disasters and preventing humanitarian crises. Designing resilient HSCs is critically important to ensure effective recovery and long-term sustainability during and after such events. This study addresses the design of resilient HSCs with viability consideration under known-unknown demand and capacity uncertainties by formulating a two-stage stochastic programming model. To solve this problem and achieve high-quality solutions, three solution approaches are developed and compared. The first approach introduces risk aversion into a genetic algorithm (GA) through chance constraints, termed GA with chance constraints (GAC). The other two approaches integrate the Random Forest (RF) algorithm with GAC, employing incremental learning (GACRFI) and non-incremental learning (GACRFNI). To evaluate the performance of these algorithms and provide insights into designing a resilient HSC, a full factorial design of experiments (DoE) is established using controllable factors. Problems are generated for three cases, each of which corresponds to a distinct disruption and ripple effect severity degree. Computational analysis shows that integrating the machine learning algorithm into the GA yields superior results across all risk level settings, leading to a win–win situation for all stakeholders in HSCs. This study provides valuable insights for designing resilient HSCs that ensure both short-term recovery and long-term sustainability by considering viability under varying risk levels and severity degrees.
{"title":"Designing a resilient humanitarian supply chain by considering viability under uncertainty: A machine learning embedded approach","authors":"Ömer Faruk Yılmaz, Yongpei Guan, Beren Gürsoy Yılmaz","doi":"10.1016/j.tre.2024.103943","DOIUrl":"https://doi.org/10.1016/j.tre.2024.103943","url":null,"abstract":"Humanitarian supply chains (HSCs) play a crucial role in mitigating the impacts of natural disasters and preventing humanitarian crises. Designing resilient HSCs is critically important to ensure effective recovery and long-term sustainability during and after such events. This study addresses the design of resilient HSCs with viability consideration under known-unknown demand and capacity uncertainties by formulating a two-stage stochastic programming model. To solve this problem and achieve high-quality solutions, three solution approaches are developed and compared. The first approach introduces risk aversion into a genetic algorithm (GA) through chance constraints, termed GA with chance constraints (GAC). The other two approaches integrate the Random Forest (RF) algorithm with GAC, employing incremental learning (GACRFI) and non-incremental learning (GACRFNI). To evaluate the performance of these algorithms and provide insights into designing a resilient HSC, a full factorial design of experiments (DoE) is established using controllable factors. Problems are generated for three cases, each of which corresponds to a distinct disruption and ripple effect severity degree. Computational analysis shows that integrating the machine learning algorithm into the GA yields superior results across all risk level settings, leading to a win–win situation for all stakeholders in HSCs. This study provides valuable insights for designing resilient HSCs that ensure both short-term recovery and long-term sustainability by considering viability under varying risk levels and severity degrees.","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"41 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial intelligence is becoming the new foundation of companies’ business operations. The nature of “technical staff” work is changing as a result of artificial intelligence, affecting their mental workload. According to multiple resource theory, both mental underload and overload might result in operational mishaps. We recruited high-speed rail (HSR) drivers from the transportation industry and stock traders from the financial industry to conduct experiments to verify the relationship between mental workload and operational risk under varying levels of intelligentization. The findings indicate that mental workload has a detrimental impact on operational risk. However, beyond a certain threshold, it has the reverse effect on operational risk. That is, there is a U-shaped relationship between mental workload and operational risk. Furthermore, intelligentization makes the U-shaped curve steeper, enhancing the impact of mental workload on operational risk. To investigate the influence of mental workload on operational risk at various levels of intelligentization, we created a simulation program using the simulink tool. The simulation results confirm the empirical study, revealing that the U-shaped operating risk curve is driven by HSR drivers’ distraction and stress, fatigue has little effect on operational risk. We found that under non-emergency conditions, HSR drivers with higher levels of intelligentization experience a lower mental workload compared to those operating less intelligent trains. However, in emergency situations, although the former’s mental workload is greater than the latter’s, the instantaneous change in mental workload is significantly larger. As a result, under emergency conditions, HSR drivers with higher levels of intelligentization face greater operational risk. The conclusions of this paper have multiple managerial implications for transportation companies.
{"title":"What does intelligentization bring? A perspective from the impact of mental workload on operational risk","authors":"Sihua Chen, Xiang Wen, Shengpan Ke, Qingmiao Ni, Ruicheng Xu, Wei He","doi":"10.1016/j.tre.2024.103944","DOIUrl":"https://doi.org/10.1016/j.tre.2024.103944","url":null,"abstract":"Artificial intelligence is becoming the new foundation of companies’ business operations. The nature of “technical staff” work is changing as a result of artificial intelligence, affecting their mental workload. According to multiple resource theory, both mental underload and overload might result in operational mishaps. We recruited high-speed rail (HSR) drivers from the transportation industry and stock traders from the financial industry to conduct experiments to verify the relationship between mental workload and operational risk under varying levels of intelligentization. The findings indicate that mental workload has a detrimental impact on operational risk. However, beyond a certain threshold, it has the reverse effect on operational risk. That is, there is a U-shaped relationship between mental workload and operational risk. Furthermore, intelligentization makes the U-shaped curve steeper, enhancing the impact of mental workload on operational risk. To investigate the influence of mental workload on operational risk at various levels of intelligentization, we created a simulation program using the simulink tool. The simulation results confirm the empirical study, revealing that the U-shaped operating risk curve is driven by HSR drivers’ distraction and stress, fatigue has little effect on operational risk. We found that under non-emergency conditions, HSR drivers with higher levels of intelligentization experience a lower mental workload compared to those operating less intelligent trains. However, in emergency situations, although the former’s mental workload is greater than the latter’s, the instantaneous change in mental workload is significantly larger. As a result, under emergency conditions, HSR drivers with higher levels of intelligentization face greater operational risk. The conclusions of this paper have multiple managerial implications for transportation companies.","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"34 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-28DOI: 10.1016/j.tre.2024.103942
Lu Zhen, Wencheng Wang, Shumin Lin, Linying Yang, Shenyan Jiang
The increasing demand for container shipping has increased the difficulty of yard management and container processing time. High-density container tower ports are next-generation ports. They have advantages, such as stable steel frame structures, high yard land utilization rates, and eliminating the need for container reshuffling operations. This study investigates berth and storage space allocation in container tower ports by considering flexible storage strategies, container allocation, sub-tower size division, and sub-tower allocation. A mixed-integer programming model is proposed to characterize the problem. A column generation-based heuristic algorithm with high performance in terms of solving speed and quality is explored to solve this problem. Numerical experiments verify the performance of the proposed algorithm. Some management insights that can help ports operate efficiently are revealed.
{"title":"Joint berth and flexible storage space allocation in container tower ports","authors":"Lu Zhen, Wencheng Wang, Shumin Lin, Linying Yang, Shenyan Jiang","doi":"10.1016/j.tre.2024.103942","DOIUrl":"https://doi.org/10.1016/j.tre.2024.103942","url":null,"abstract":"The increasing demand for container shipping has increased the difficulty of yard management and container processing time. High-density container tower ports are next-generation ports. They have advantages, such as stable steel frame structures, high yard land utilization rates, and eliminating the need for container reshuffling operations. This study investigates berth and storage space allocation in container tower ports by considering flexible storage strategies, container allocation, sub-tower size division, and sub-tower allocation. A mixed-integer programming model is proposed to characterize the problem. A column generation-based heuristic algorithm with high performance in terms of solving speed and quality is explored to solve this problem. Numerical experiments verify the performance of the proposed algorithm. Some management insights that can help ports operate efficiently are revealed.","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"110 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-28DOI: 10.1016/j.tre.2024.103888
Eric Ka Ho Leung
This study presents a novel framework called Total Fulfillment Management (TFM), which integrates inbound warehousing and outbound transportation operations into a unified system. TFM draws inspiration from the time-tested philosophies of Total Quality Management and Just-In-Time production, adapting these principles to the modern logistics context. This paper introduces the Fulfillment Synchronization Strategy (FSS) as the operational core of TFM, designed to optimize internal and external resources and improve operational efficiency through real-time data-driven, synchronized decision-making. Within the FSS, a total of nine synchronization practices are defined. Through the integration of emerging technologies like AI, machine learning, IoT, and blockchain, TFM offers a holistic system approach for businesses seeking to enhance their fulfillment operations. Key research avenues and use cases highlight the potential impact of TFM on the evolving logistics landscape.
{"title":"Total fulfillment management: principles, practices and use cases","authors":"Eric Ka Ho Leung","doi":"10.1016/j.tre.2024.103888","DOIUrl":"https://doi.org/10.1016/j.tre.2024.103888","url":null,"abstract":"This study presents a novel framework called Total Fulfillment Management (TFM), which integrates inbound warehousing and outbound transportation operations into a unified system. TFM draws inspiration from the time-tested philosophies of Total Quality Management and Just-In-Time production, adapting these principles to the modern logistics context. This paper introduces the Fulfillment Synchronization Strategy (FSS) as the operational core of TFM, designed to optimize internal and external resources and improve operational efficiency through real-time data-driven, synchronized decision-making. Within the FSS, a total of nine synchronization practices are defined. Through the integration of emerging technologies like AI, machine learning, IoT, and blockchain, TFM offers a holistic system approach for businesses seeking to enhance their fulfillment operations. Key research avenues and use cases highlight the potential impact of TFM on the evolving logistics landscape.","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"28 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-25DOI: 10.1016/j.tre.2024.103936
Mingyang Du, Xuefeng Li, Lin Cheng, Weike Lu, Wenxiang Li
Ridesplitting travel can provide numerous social and environmental benefits, including reducing vehicle usage and traffic congestion, and decreasing energy consumption and greenhouse gas emissions. This study examines the practicability of integrating the function of midway stops into ridesplitting services. Considering two coexisting matching forms: pre-pool-matching and en-route matching, the ridesplitting order dispatch problem with midway stops is formulated as an integer programming model with multiple objectives. Two methods are developed to address the proposed problem, i.e., an exact algorithm based on bipartite graph and a two-stage method based on Kuhn-Munkres algorithm. Based on the ride-hailing trip data, numerical experiments are conducted to examine the performance of the proposed methods. We also quantify the benefits of the ridesplitting matching with midway stops compared with the solo matching with midway stops and the traditional ridesplitting matching without midway stops. The impacts of the characteristics of midway stops on the order dispatching results are also discussed. The results indicate that compared with the solo ride-hailing matching with midway stops, the ridesplitting matching with midway stops could greatly improve distance savings of trips and the matching success rate of passengers. The research results can enrich the landing scene of ridesplitting service and promote the innovation and upgrading of this product.
{"title":"Order matching optimization of the ridesplitting service: A scenario with midway stops","authors":"Mingyang Du, Xuefeng Li, Lin Cheng, Weike Lu, Wenxiang Li","doi":"10.1016/j.tre.2024.103936","DOIUrl":"https://doi.org/10.1016/j.tre.2024.103936","url":null,"abstract":"Ridesplitting travel can provide numerous social and environmental benefits, including reducing vehicle usage and traffic congestion, and decreasing energy consumption and greenhouse gas emissions. This study examines the practicability of integrating the function of midway stops into ridesplitting services. Considering two coexisting matching forms: pre-pool-matching and en-route matching, the ridesplitting order dispatch problem with midway stops is formulated as an integer programming model with multiple objectives. Two methods are developed to address the proposed problem, i.e., an exact algorithm based on bipartite graph and a two-stage method based on Kuhn-Munkres algorithm. Based on the ride-hailing trip data, numerical experiments are conducted to examine the performance of the proposed methods. We also quantify the benefits of the ridesplitting matching with midway stops compared with the solo matching with midway stops and the traditional ridesplitting matching without midway stops. The impacts of the characteristics of midway stops on the order dispatching results are also discussed. The results indicate that compared with the solo ride-hailing matching with midway stops, the ridesplitting matching with midway stops could greatly improve distance savings of trips and the matching success rate of passengers. The research results can enrich the landing scene of ridesplitting service and promote the innovation and upgrading of this product.","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"83 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, we consider a truck carrier aiming to set contracts with multiple hub providers to reserve hub capacities in a hyperconnected relay transportation network. This network enables long-haul freight shipments to be transported by multiple short-haul drivers commuting between fixed-base hubs, promoting a driver-friendly approach. We introduce the dynamic stochastic hub capacity-routing problem (DS-HCRP), which is a two-stage stochastic program to determine hub contracted capacities for each planning period that minimizes hub and subsequent transportation costs given demand and travel time uncertainty. To overcome the difficulty in solving this NP-hard problem, we propose a combinatorial Benders decomposition (CBD) algorithm based on a tailored implementation of branch-and-Benders-cut. In addition, we design a heuristic initial cut pool generation method to restrict the search space within the CBD algorithm. Experimental results from a case study in the automotive delivery sector demonstrate that our algorithm outperforms other commonly used approaches in terms of solution quality and convergence speed. Furthermore, the results show that the proposed model offers potential savings of up to 22.96% in hub costs and 8.47% in total costs compared to its static deterministic counterpart by effectively mitigating the impact of demand fluctuations and network disruptions, thus highlighting the advantages of dynamic and stochastic integration in capacity planning.
{"title":"Dynamic hub capacity planning in hyperconnected relay transportation networks under uncertainty","authors":"Xiaoyue Liu, Jingze Li, Mathieu Dahan, Benoit Montreuil","doi":"10.1016/j.tre.2024.103940","DOIUrl":"https://doi.org/10.1016/j.tre.2024.103940","url":null,"abstract":"In this article, we consider a truck carrier aiming to set contracts with multiple hub providers to reserve hub capacities in a hyperconnected relay transportation network. This network enables long-haul freight shipments to be transported by multiple short-haul drivers commuting between fixed-base hubs, promoting a driver-friendly approach. We introduce the dynamic stochastic hub capacity-routing problem (DS-HCRP), which is a two-stage stochastic program to determine hub contracted capacities for each planning period that minimizes hub and subsequent transportation costs given demand and travel time uncertainty. To overcome the difficulty in solving this NP-hard problem, we propose a combinatorial Benders decomposition (CBD) algorithm based on a tailored implementation of branch-and-Benders-cut. In addition, we design a heuristic initial cut pool generation method to restrict the search space within the CBD algorithm. Experimental results from a case study in the automotive delivery sector demonstrate that our algorithm outperforms other commonly used approaches in terms of solution quality and convergence speed. Furthermore, the results show that the proposed model offers potential savings of up to 22.96% in hub costs and 8.47% in total costs compared to its static deterministic counterpart by effectively mitigating the impact of demand fluctuations and network disruptions, thus highlighting the advantages of dynamic and stochastic integration in capacity planning.","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"29 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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