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In order to investigate the influence of gradual stress relieving treatment on the microstructure, texture evolution, grain structure, and misorientation distribution of the hot-rolled Ti6Al4V–5Cu (α+β) alloy deformed in various thickness reduction ratios (TRR's) of 15%, 58%, and 73% to maintain the burger orientation relationship (BOR), different stress reliving treatment was specially designed for relieving the residual stresses occurred due to the hot deformation process. Using IPF maps, the majority of the α grains, specifically the elongated ones were found nearly at 45 ° with respect to the rolling and transverse direction. The study also show that the heat treatment schedule on the 58% deformed alloy significantly promote the transformation of β to α phase and a special microstructure with an equiaxed grains is produced. The local misorientation angle distribution due to stress reliving treatment has also discussed. Using EBSD analysis, the alloy processed under highest TRR's (i.e., 73%) and subsequent stress relieving treatment could result a serious internal crystalline instability with various degree of element diffusion rate, which promote a severe and ease of alloy coarsening. Thus, coarsening encourage the formation of grains with the highest softening potential, comparatively. It is obvious that the stress-relived treatment does not result a change in microstructure. However the internal residual stresses on the hot deformation were relieved, the texture intensity had increased quietly. A significant textural behavior could be achieved on the 73% hot-rolled alloys by the stress-relieving process to align the α/β fiber texture interface in parallel coincidence to maintain the BOR.

Heavy-duty diesel truck (HDDT) is one of the major sources of air pollution and energy consumption. To reduce the estimation bias and improve the interpretability, the random parameters logit (RPL) model was employed to examine the effects of influencing factors on fuel consumption of HDDTs in the real world. The unobserved heterogeneity effects varying across the samples on fuel efficiency were extracted from the long-term daily trip-based data. In order to further illustrate the advantages of the RPL model in explaining the impacts of factors, a fixed parameters logit model with twenty parameters was constructed and compared. The Akaike information criterion and the Bayesian information criterion were used to select a more reasonable model structure. The findings show that the RPL model performs better and the unobserved heterogeneity would affect the effects of factors of rolling without engine load proportion and temperature and, consequently, map the level of fuel consumption. This reveals the variability of the fuel consumption among the samples. Driving compensation effects were also identified in this study (i.e., the drivers tend to perform the fuel-saving operations in adverse driving circumstances and vice versa). The methodology proposed in this paper can provide a new insight for researchers to identify the instability of energy-related factors under real road conditions. Future research could be implemented to assess the similar effects of alternative fuel vehicles.

This work investigates the viability of fabricating Ti-3Al-8V-6Cr-4Mo-4Zr (Beta-CTM) using laser-based and powder-fed directed energy deposition. To determine an appropriate ageing strategy, heat treatments were conducted at 440 °C and 480 °C over different periods of time and their effectiveness were determined through hardness testing. After identifying an optimal ageing treatment, tensile properties were investigated on the samples in the as-built and aged conditions. The microstructure, hardness and tensile properties of the as-built sample was found to resemble that of a conventionally-manufactured and solution-treated alloy. However, as compared to conventionally-manufactured Ti-3Al-8V-6Cr-4Mo-4Zr subjected to the same ageing temperature, the ageing process was much quicker with the DED-processed alloy because of its high dislocation density. Furthermore, the latter can also achieve much higher strength while also having comparable ductility. To achieve a similar ageing response and tensile behavior, conventionally-manufactured Ti-3Al-8V-6Cr-4Mo-4Zr would require cold-working prior to ageing. Carbon additions were also investigated to further accelerate the ageing response and to refine the β grain size. While the ageing response was much quicker and a much harder alloy can be produced, the carbon amount added was insufficient for β grain refinement, which resulted in reduced ductility without a change in strength.

The concept of circular economy (CE) significantly lessen the waste and environment pollution. A growing number of articles support the need to consider implementing CE within supply chains. Unfortunately, most corporations have not been successful in pursuing this goal, greatly due to existence of several challenges. Up to now, limited articles have analyzed the challenges to CE practices in the leather sector context. To address this issue, this paper introduces a decision support framework for investigating the interdependencies among challenges to CE practices in the leather industry context using Rough-based Decision-Making Trail and Evaluation Laboratory (Rough DEMATEL) technique. According to the results of the study “lack of financial support from authorities” is the most pressing challenge that impede CE implementation. Findings can assist industrial decision-makers to focus on the challenges to CE practices and employ effective strategies and solutions for moving the leather industry towards sustainable development.

Investigating the relationship between the months and traffic crashes is a foremost task for the safety improvement of mountainous freeways. Taking a mountainous freeway located in China as an example, this paper proposed a combined modeling framework to identify the relationships between months and different crash types. K-means and Apriori were initially used to extract the monthly distribution patterns of different types of crashes. A graphical approach and a risk calculation equation were developed to assess the output of K-means and Apriori. Then, using the assessment results as the input, a logistic regression model was constructed to quantify the effects of each month on crashes. The results indicate that the monthly distribution patterns of different crash types are inconsistent, i.e., for a specific month, the high risk of a certain crash type may be covered up if experts only focus on the total number of crashes. Moreover, when identified as high-risk months by K-means and Apriori, the crash-proneness will significantly increase several times than months identified as high-risk by only one of K-means and Apriori, thereby illustrating the superior performance of the mix-method. The conclusions can assist local relevant organizations in formulating strategies for preventing different types of traffic crashes in different months (e.g., the risk of rear-end crashes in August, the risk of fixed-object hitting crashes in February, and the risk of overturning crashes in October) and provide a methodological reference for relevant studies in other regions.

Vessel big data play a significant role in understanding vessel behaviors and thus facilitating the prosperity of waterway transportation. However, relevant research regarding vessel trajectory recognition in a broad range of narrow channels still lacks, especially using VITS data. The major objective of this paper is to conduct vessel trajectory analysis based on the novel VITS data and examine its availability in inland waterway vessel transportation. An alternate aim is to develop a more comprehensive framework to extract the vessel trajectory of multiple narrow waterways. This paper utilized vessel trajectory information of multiple narrow channels belonging to Yangtze River captured by VITS. Four compression algorithms were conducted. Additionally, the performances of three clustering approaches were evaluated. Speed distribution analysis was also implemented. The results indicated that slide window (SW) algorithm outperforms its other counterparts. Relative to DBSCAN, K-means and hierarchical clustering analysis (HCA) tend to be more capable of balanced classification. This paper is the first to utilize VITS data in vessel trajectory feature extraction and can potentially provide useful insight for vessel trajectory extraction in multiple narrow channels.

Purpose – There is great uncertainty and volatility in the evaluation and measurement of green supplier satisfaction. The purpose of this paper is to fill this gap based on the information entropy theory (IET) to describe the probability of green supplier satisfaction degree. Design/methodology/approach – The authors introduce a formal model using analytic hierarchy process (AHP), IET and entropy technique for order preference by similarity to an ideal solution (TOPSIS) method to evaluate green supplier satisfaction and promote them for the better implementation of green supply chain management practices. Findings – The first finding is developing an effective framework for green supplier satisfaction, incorporating various measures of environmental dimension. Second, a hybrid uncertainty decision method is introduced, by integrating AHP and IET and entropy-TOPSIS. Research limitations/implications – One of the main limitations of the research is that the authors introduced a conceptual example. Real-world applications need to investigate the accuracy and effectiveness of these measures, and the operational feasibility of this method. Originality/value – This is one of the first works to provide a comprehensive appraisal model for evaluation of green supplier satisfaction. This study and research method can form general guidelines, and organizations can increasingly benefit from using green supplier satisfaction evaluation as a management tool. Green supplier satisfaction evaluation is just the beginning.

The hot workability behavior of antibacterial Ti6Al4V-5Cu alloy was investigated using a hot compression experiment in the temperature range of 790–1040 °C and strain rate of 10−3–10 s−1 with a strain of 0.4. The deformation behavior of the alloy was characterized by Gleeble 3800 compression experiment, and the relationship among deformed microstructures and deformation parameters was established. The deformations of Ti6Al4V-5Cu alloy were temperature and strain rate-dependent. Higher temperature and lower strain rate made power dissipation efficiency (η) increase and reach 89%. The activation energies (Q) in the dual-phase (α + β) and single β phase regions were calculated as 175.43 and 159.03 kJ mol−1, respectively. In the dual (α + β) phase region, with an increase in strain rate, flow-softening behavior was dominated, however in the single β phase region such as processing at 940 °C. Flow stress increased slightly in which work-hardening behavior was dominated (especially between strain rates of 10−3–1 s−1). The deformation at various conditions exhibited different stress-strain profiles, providing an insight that work hardening and flow softening coexisted in Ti6Al4V-5Cu alloy. The relative intensity of oscillatory change in flow stress profile decreased as the strain rate decreased. The hot workability of Ti6Al4V-5Cu alloy was also accessed from the viewpoint of the sub-grain structure.

Despite the broad application of advanced fleet management systems (FMSs) in third-party logistics (3PL) companies, there is a marginally limited understanding of how to employ them to enhance transport energy efficiency. In a case study of a Chinese 3PL company, this paper analyzed data obtained from the online FMS to assess drivers’ awareness of fuel-saving factors. A questionnaire was primarily designed to investigate the drivers’ awareness of fuel-saving factors based on the reliability and validity test. Then, Extreme Gradient Boosting (XGBoost), a machine learning algorithm, was utilized to explore the intrinsic impacts of various factors on fuel consumption with the outputs providing the evaluation basis for individual awareness of the drivers. The results show a significant deviation in the driver’s awareness of fuel-saving factors, among which the three indicators of engine speed, idling condition, and rolling without engine load are seriously underestimated, while the indicators related to the environment are seriously overestimated due to social expectations. In addition, the average speed was found to be the most important fuel-saving indicator besides the load. Based on these findings, this paper recommends that the 3PL companies choose a route with more freeways when planning, and the mileage should be controlled within 800 km as far as possible.

In the real business environment, due to unpredictable market demand or high prediction difficulty and low prediction accuracy, there will be inevitably operational loss in the field of e-commerce logistics caused by undersupply or oversupply of express service capabilities. At present, China is deepening the supply-side structural reforms. Confronted with the growing demand for e-commerce logistics express delivery, especially the urgent demand for tackling orders piling up to 1 billion pieces during the recent “Double 11” shopping carnival, it is very important and practical for us to study how to make scientific decisions on the supply side in the field of e-commerce express delivery. Therefore, in this paper, we design a sharing logistics cooperation mechanism considering both the undersupply and oversupply of express delivery service capabilities under stochastic demand. By comparing the earnings data of several listed express companies, we analyze four types of optimization strategies: the order flow proportion revenue sharing strategy (RE-OFP), the combined factors revenue sharing strategy (RE-RSF), the order flow proportion risk sharing strategy (RS-OFP), and the combined factors risk sharing strategy (RS-RSF). The research results demonstrate that the four optimization strategies of RE-OFP, RE-RSF, RS-OFP, and RS-RSF could achieve Pareto improvements in the operational performance of e-commerce express service. The performance of four revenue sharing and risk sharing strategies varies with different revenue sharing or risk sharing factors. Under some certain combined factors, the revenue sharing contract could help realize the supply chain coordination of the sharing logistics service. The sharing logistics cooperation mechanism based on equity investment proposed hereafter provides a feasible solution to solve the problems of high empty driving rate and operational cost of e-commerce express delivery service in urban areas.

Collusion can increase the transaction value among supply chain members to obtain higher loans from supply chain finance (SCF) service provider, which will bring some serious risks for SCF. However, it is difficult to be identified and restrain the SCF service provider due to its stability and hiddenness. Different SCF transaction structures will affect the profits of supply chain members from collusion. This paper develops various game models for collusion and not collusion for different SCF transaction structures and investigates the impact of SCF transaction structures on the boundary conditions of collusion. Through comparative analysis, the findings of models are as follows: (1) in a two-echelon supply chain, the supplier and retailer are more likely to conduct collusion under the sequential game than under the simultaneous game; (2) collusion in the two-echelon supply chain can obtain higher loans than that in the three-echelon supply chain, so it has more serious hidden danger; (3) in the two-echelon supply chain, collusion is easier to form than in the three-echelon SCF supply chain that has spontaneous endogenous constraints. We also develop two types of mechanisms to restrain collusion behavior from profit sharing and incomplete information perspectives. Finally, we summarize the theoretical implications and analyze the management implications through a case study.

Sustainable supplier selection plays an important role in sustainable supply chain management operations and implementation. In this paper a novel formal modeling approach is conceptually developed and presented to address sustainable supplier selection. Grey theory and TOPSIS, a distance based multiple criteria method, are used for the integration and evaluation of sustainable supplier performance for sustainable supplier selection. From a research perspective, TOPSIS is improved to more effectively deal with grey numbers by integrating a degree of likelihood rather than converting grey numbers into crisp numbers functions, and it provides more flexible supplier rankings. This methodology strengthens the sustainable supplier selection process, and can be applied to other multiple criteria decision making problems. Illustrative calculations are made using data on sustainable supplier selection and evaluation published by Bai and Sarkis (2010). The technique is relatively accurate, matching well with results from a published grey rough set approach. The methodology easily implementable with minimal complex calculations required. It can also provide support for sustainable supplier selection, benchmarking, and improvement decisions. This is one of the first papers to integrate a broad set of sustainability factors for grey-based TOPSIS and supplier selection.

Supply chain flexibility is an important operations strategy dimension for organizations to achieve and maintain competitive advantage. With rising greener customer expectations and increasingly stringent environmental regulations, green supply chains are now viewed as another competitive weapon. Green supply chains are characterized by higher complexity and turbulence. Green supply chain flexibility can aid organizations function in this complex and uncertain environment, yet investigation into this area is very limited. This paper aims contribute to this field by investigating green supply chain flexibility achievement through information systems. This paper introduces a green supply chain flexibility matrix framework. Given the large data needs, as described in the matrix, a novel probability evaluation methodology that can help predict rankings of projects and programs is introduced. The methodology extends a TOPSIS based three-parameter interval grey number (TpGN) approach by incorporating neighborhood rough set theory (RST) to evaluate IS programs’ green flexibility support capability. The results of this methodology are more objective and effective for two reasons. (1) The results are predictive rankings based on probability degree instead of the fixed deterministic ranks. (2) Neighborhood rough set theory used in this study can limit loss of information when compared to rough set theory, yet still simplify extensive data sets. This paper also identifies study limitations and future research directions for green supply chain flexibility.