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Choice models today are ubiquitous across a range of applications in operations and marketing. Real-world implementations of many of these models face the formidable stumbling block of simply identifying the \"right\" model of choice to use. Because models of choice are inherently high-dimensional objects, the typical approach to dealing with this problem is positing, a priori, a parametric model that one believes adequately captures choice behavior. This approach can be substantially suboptimal in scenarios where one cares about using the choice model learned to make fine-grained predictions; one must contend with the risks of mis-specification and overfitting/underfitting. Thus motivated, we visit the following problem: For a \"generic\" model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal information about these distributions), how may one predict revenues from offering a particular assortment of choices? An outcome of our investigation is a nonparametric approach in which the data automatically select the right choice model for revenue predictions. The approach is practical. Using a data set consisting of automobile sales transaction data from a major U.S. automaker, our method demonstrates a 20% improvement in prediction accuracy over state-of-the-art benchmark models; this improvement can translate into a 10% increase in revenues from optimizing the offer set. We also address a number of theoretical issues, among them a qualitative examination of the choice models implicitly learned by the approach. We believe that this paper takes a step toward \"automating\" the crucial task of choice model selection. This paper was accepted by Yossi Aviv, operations management.

The primary challenge for electric vehicles in replacing oil-fueled vehicles today is their limited range, despite significant advancements in energy storage technology and alternative fuel vehicles over the past few decades. Direct-drive in-wheel motors (IWMs) can achieve higher efficiency by eliminating components such as gearboxes, differentials, and clutches, allowing for longer mileage with the same battery capacity. This positions them as a promising technology for the future of electric vehicles. This article primarily analyzes the key challenges that limit the widespread application of direct-drive IWMs in electric vehicles, including torque density, cost, reliability, efficiency, and ease of production. The article also investigates and compares the electromagnetic performance of the most representative motor topologies studied in direct-drive IWMs within both industrial and academic settings, and comprehensively evaluates the performance of these motor architectures with respect to the aforementioned performance requirements. Based on these investigations, this article aims to provide guidance and reference for the electromagnetic design and analysis of direct-drive IWMs.

Purpose This study aims to investigate how relational resources, such as the buyer’s trust in its suppliers and the level of supplier involvement, affect the level of tacit knowledge integration capabilities (TKICs) of the firm, which, in turn, is hypothesized to affect business performance. Design/methodology/approach Based on the dynamic capabilities theory and the relational view, this paper examines how TKIC, a special case of dynamic capability, influences business performance. The research context is the Brazilian automobile industry, in which firms are currently experimenting with modular production and increasing their interactions with suppliers. Using a sample of automobile suppliers, this investigates how relational resources, such as the buyer’s trust in its suppliers and the level of supplier involvement, affect the level of TKIC, which, in turn, is hypothesized to affect business performance. In addition, this paper examines the moderating effect of various communication media on the TKIC-business performance relationship. The findings confirm the importance of relational resources and TKIC on business performance. Finally, this paper explores various theoretical and managerial implications to encourage future research. Findings The results suggested that the two relational resources (supplier involvement and buyer’s trust) are important drivers of TKICs and that the level of supplier involvement in the production process mediates the relationship between buyer’s trust and TKIC. Moreover, this study found that TKIC leads to superior firm performance, but the degree of media naturalness does not seem to facilitate knowledge transfer. The results confirm that supplier involvement is a pivotal process in that the buying firm’s internal resources and the major suppliers’ resources and capabilities are combined to achieve a competitive advantage – TKIC. Research limitations/implications This study is subject to the typical limitations inherent in cross-sectional research designs using subjective measures. That said, this still has some important implications indicating that relational resources, such as buyer’s trust and supplier involvement, are critical in developing TKIC that “seize” opportunities from interfirm relationships and integrate knowledge across and within firm boundaries. Moreover, while knowledge management tools can resemble face-to-face interactions to the largest extent, the research suggested that it cannot substitute face-to-face communications in transferring tacit knowledge. Practical implications Managers deal with complex interactions and linkages due to tacit knowledge from components, systems and modules, which are critical in developing organizational capabilities. Relational resources are important strategic assets facilitating resource combination and coordination. Managers must coordinate among multiple sources of learning and partner with their suppliers at an earlier stage to develop the relational capabilities and efficiently steer the process of boundary redefinition. Finally, managers must have the ability to manage tacit knowledge within the interface with suppliers using organizational mechanisms (i.e. TKIC) to help them absorb external knowledge from their supplier network and integrate it with specific internal competences. Social implications Recent disruptive technological developments pressure organizations to become more flexible by requiring firms to adapt quickly to constantly changing markets and to have the ability to apply different resources and capabilities to specific unique situations. All this with a huge impact on the firm’s employees and society in general. Thus, interfirm relationships and the role of knowledge integration is especially crucial, given the current industry trend in favor of experimenting with innovative production methods (e.g. flexible manufacturing and modular production) that can help managers to rethink work conditions in a more meaningful and flexible for society. Originality/value While prior research treats integrative capability mainly as a mechanism that explains superior firms’ performance in an interfirm relationship, few research efforts have explicated what shapes TKICs. By examining the relationship between relational resources, TKIC and performance, this study fills this research gap and develops and tests a theoretical framework.

The employment of vehicles such as rovers equipped with automictic and robotic systems in agriculture is an emerging field. The development of suitable simulation models can aid in the design and testing of agricultural rovers before prototyping. Here, we propose a simulation test rig based on a multibody model to investigate the main issues connected with agricultural rover designs. The results of the simulations show significant differences between the two structures, especially regarding the energy savings, which is a key aspect for the applicability of a rover in field operations. The modular structure of the proposed simulation model can be easily adapted to other vehicle structures.

This paper proposes a novel hybrid less-rare-earth permanent magnet (HLEPM) machine, which is designed to meet the demands of electric vehicle (EV) traction machines for high torque output and wide-speed-range high-efficiency performance. The designed machine features a unique hybrid permanent magnet arrangement, consisting of V-shaped rare-earth PMs and arc-shaped less-rare-earth PMs, respectively. The V-shaped rare-earth magnets can perform the flux-focusing effect well, not only enhancing the torque output capability but also improving the demagnetization with the standability of the arc-shaped less-rare-earth PMs during active short-circuit (ASC) conditions. First, the proposed machine is thoroughly designed and optimized to balance the torque capability and iron loss. Subsequently, the electromagnetic performance of the proposed HLEPM machine is evaluated using finite-element (FE) analysis and compared with that of a conventional double-layer V-shaped PMSM. Finally, the anti-demagnetization characteristics of the two machines under ASC conditions are analyzed in detail. The results validate the rationality and reliability of the proposed design.

The use of mobile phones has become one of the major threats to road safety, especially in young novice drivers. To avoid crashes induced by distraction, adaptive distraction mitigation systems have been developed that can determine how to detect a driver’s distraction state. A driving simulator experiment was conducted in this paper to better explore the relationship between drivers’ cognitive distractions and traffic safety, and to better analyze the mechanism of distracting effects on young drivers during the driving process. A total of 36 participants were recruited and asked to complete an n-back memory task while following the lead vehicle. Drivers’ vehicle control behavior was collected, and an ANOVA was conducted on both lateral driving performance and longitudinal driving performance. Indicators from three aspects, i.e., lateral indicators only, longitudinal indicators only, and combined lateral and longitudinal indicators, were inputted into both SVM and random forest models, respectively. Results demonstrated that the SVM model with parameter optimization outperformed the random forest model in all aspects, among which the genetic algorithm had the best parameter optimization effect. For both lateral and longitudinal indicators, the identification effect of lateral indicators was better than that of longitudinal indicators, probably because drivers are more inclined to control the vehicle in lateral operation when they were cognitively distracted. Overall, the comprehensive model built in this paper can effectively identify the distracted state of drivers and provide theoretical support for control strategies of driving distraction.

In this study, an e-powertrain for 55 kW electric tractors was designed and analyzed using agricultural workload data. The electric tractor power transmission system structure was analyzed, and three types were selected: the single-motor, the dual-motor, and the dual-motor including a planetary gear set (PGS). The single-motor specification for type I was 62.8 kW at 199.5 Nm. In type II, the power take-off (PTO) motor specification was 55.3 kW at 176.0 Nm, and the traction motor specification was 58.4 kW at 185.3 Nm. In type III, the PTO motor specification was 55.3 kW at 176.0 Nm, and the traction motor specification was 11.8 kW at 37.7 Nm. The power and torque of the single motor of type I were the highest. In type II, both the PTO and traction motor specifications were above those of the 55.3-kW engine. In type III, the PTO motor specifications were identical to those of type II. Moreover, the adoption of the traction motor specification could significantly reduce the required output by 80% compared with that of type II. A comparison of the mechanical components by e-powertrain type showed that the number of mechanical components exhibited the descending order of type II, type III, and type I. Depending on the tractor power, the powertrain structure can be appropriately applied. This study is expected to facilitate future development and optimization of the e-powertrain.

A visual workload model was constructed to determine and evaluate drivers’ visual workload characteristics in high-density interchange-merging areas. Five interchanges were selected, and a real-vehicle driving test was conducted with 47 participants. To address the differences in drivers’ visual characteristics in the interchange cluster merging areas, the Criteria Importance Through Intercriteria Correlation (CRITIC) objective weighting method was employed. Six visual parameters were selected to establish a comprehensive evaluation model for the visual workload in high-density interchange-merging areas. The results show that the average scanning frequency and average pupil area change rate are most strongly correlated with the visual workload, whereas the average duration of a single gaze has the lowest weight in the visual workload assessment system. Different driver visual workloads were observed depending on the environment of the interchange-merging areas, and based on these, recommendations are proposed to decrease drivers’ workload, thereby increasing road safety.

Purpose – The purpose of this paper is to extend previous green supply chain management (GSCM) research by developing and empirically testing a conceptual framework that investigates the relationships between three dimensions of integrated green supply chain management (iGSCM) and multiple dimensions of operational performance. Design/methodology/approach – The study is based on survey data collected from 126 automotive manufacturers in China. The relationships between theoretical constructs are analysed using structural equation modelling. Findings – This study generates important findings of the significant and positive relationships between iGSCM (internal GSCM, GSCM with customers and GSCM with suppliers) and operational performance in terms of flexibility, delivery, quality and cost. Practical implications – It is important for managers to simultaneously consider internal GSCM and GSCM with customers and suppliers when implementing environmental sustainability in the supply chains. Overlooking either internal GSCM or external GSCM may hinder their efforts to improve operational performance. Originality/value – This study contributes to the literature by defining iGSCM that combines three main dimensions, namely, internal GSCM, GSCM with customers and GSCM with suppliers, and empirically testing its impact on multiple operational performance dimensions.