Transportation capacity shortage influence on logistics performance: evidence from the driver shortage

Wang, Michael; Wood, Lincoln C.; Wang, Bill

doi:10.1016/j.heliyon.2022.e09423

Cited by 16 publications

(12 citation statements)

References 67 publications

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“…Data as resources are essential to create resilience in the decision-making process to be deployed in the 4.0 forest. According to Visser [57] and Wang et al [58] they support innovation, increase responsiveness, and assign flexibility to activity. However, they initially had to go through the data wrangling process.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning

Almeida

Munis

Camargo

et al. 2022

Forests

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Among the activities that burden capital in the supply chain of forest-based industries, the activity of road transport of wood deserves to be highlighted. Machine learning techniques are applied the knowledge extracted from real data, and support strategies that aim to maximize the resources destined for it. Based on variables inherent to the wood transport activity, we verified whether machine learning models can act as predictors of the volume of wood to be transported and support strategic decision-making. The database came from companies in the pulp and paper segments, which totaled 26,761 data instances. After the data wrangling process, machine learning algorithms were used to build models, which were optimized from the hyperparameter adjustment and selected to compose the blended learning hierarchy. In addition to belonging to different methodological basis, a CatBoost Regressor, Decision Tree Regressor, and K Neighbors Regressor were selected mainly for providing minimal values to errors metrics and maximal values to determination coefficient. The learning by stack stands out, with a coefficient of determination of 0.70 and an average absolute percentage error of 6% in the estimation of the volume of wood to be transported. Based on variables inherent to the wood transport process, we verified that machine learning models can act in the prediction of the volume of wood to be transported and support strategic decision-making.

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Section: Discussionmentioning

confidence: 99%

Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning

Almeida

Munis

Camargo

et al. 2022

Forests

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show abstract

“…The novelty of this study is represented in the creation of a theoretical framework that directs the logistics capital towards a successful implementation and management of Logistics 4.0 practices. Human is an important element of logistics and supply chain operations (Wang et al, 2022). The purpose of this research is to provide a theoretical framework to assist logistics human capital in effectively administering and managing Logistics 4.0 practices to bridge the gap between present fledgling logistics practices and globally recognized practices, i.e., Logistics 4.0.…”

Section: Discussionmentioning

confidence: 99%

“…Nowadays with the constant development caused by Industry 4.0 the role of the human capital grew more complex, relying less on the operational aspect and more on strategizing and decision making to achieve synchronisation between the information, technology and human factor. Wang et al (2022) found that human factor played a vital role in logistics operations, for example driver shortage could influence both logistics capability and logistics performance. Goffee & Scase (2015) argued that the architecture of an Industry 4.0 workforce comprises of five elements, namely; Strategic apex, Middle line, Operation core, Techno-structure and support staff.…”

Section: Human Capital Development Theoriesmentioning

confidence: 99%

A New Human Capital Development Framework in Logistics and Supply Chain Incorporating Industry 4.0

Rahman

Hamid

Şahin

et al. 2022

International Journal of Applied Logistics

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When Industry 4.0 technologies are utilized in logistics practices, they create a Logistics 4.0 environment, and the key to successfully implementing Logistics 4.0 practices is the human capital of the logistics industry. The human capital of the logistics industry was found to be classified into four major categories, namely operative, supervisor, management, and government. This study aims to develop a new human capital framework that guides the logistics industry towards successfully implementing and managing Logistics 4.0 practices. The significance of this study is to utilize a new framework to shorten the gap between current basic logistics practices into Industry 4.0 practices. An integrative literature review technique was used thoroughly and reviewed and analyzed in order to create the Logistics 4.0 human capital development framework in a manner that satisfies the categories of logistics human capital. A combination of the business logistics management (BLM) framework and Industry 4.0 elements creates a new human capital development (HCD) framework in logistics and supply chains.

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“…At this point, the hub-based multi-fleet platoon coordination problem has been presented completely in (2), where the stage reward function g i,m (t a i,m , t w i,m ) is modeled in (8) while the terminal reward function g i,Ni (t a i,Ni ) is modeled in (9). By solving the problem (2) under the constraint (3), the optimal waiting time decisions of each truck can be obtained.…”

Section: Terminal Reward Function Modelingmentioning

confidence: 99%

Approximate Dynamic Programming for Platoon Coordination under Hours-of-Service Regulations

Bai

Johansson

et al. 2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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Freight drivers of electric trucks need to design charging strategies for where and how long to recharge the truck in order to complete delivery missions on time. Moreover, the charging strategies should be aligned with drivers' driving and rest time regulations, known as hours-of-service (HoS) regulations. This letter studies optimal charging problems of electric trucks with delivery deadlines under HoS constraints. We assume that a collection of charging and rest stations are given along a pre-planned route with known detours and the problem data are deterministic. The goal is to minimize the total cost associated with the charging and rest decisions during the entire trip. This problem is formulated as a mixed integer program with bilinear constraints, resulting in a high computational load when applying exact solution approaches. To obtain real-time solutions, we develop a rollout-based approximate scheme, which scales linearly with the number of stations while offering solid performance guarantees. We perform simulation studies over the Swedish road network based on realistic truck data. The results show that our rollout-based approach provides near-optimal solutions to the problem in various conditions while cutting the computational time drastically.

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Transportation capacity shortage influence on logistics performance: evidence from the driver shortage

Cited by 16 publications

References 67 publications

Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning

Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning

A New Human Capital Development Framework in Logistics and Supply Chain Incorporating Industry 4.0

Approximate Dynamic Programming for Platoon Coordination under Hours-of-Service Regulations

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