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Motivated by the practical supply chain management of the automobile industry, we study the car sequencing problem (CSP) that minimizes the conflicts occur when sequentially manufacturing cars on an assembly line. The CSP is a well-established problem, subject to the paint batching constraints to decrease the energy consumption for color changeovers and production rate constraints in the assembly shop to ensure a smooth usage of car options. However, the existing solution algorithms to this problem do not take into account the block batches, which desires a consecutive production batch of cars requiring a certain option. This requirement often occurs when specialized labor time window is short in the customized car production scenario, and renders additional complexities to the traditional car sequencing problem. In this paper, we present a novel model to deal with these constraints and simultaneously generate the sequencing and replenishment decisions. Besides, we develop two math-heuristic algorithms to solve the proposed large-scale CSP. The presented heuristics are on the basis of relax-and-fix procedures, fix-and-optimize procedures and adaptive variable neighborhood search. To solve the large-sized instances (commercial solvers, i.e., Cplex, cannot obtain a feasible result within 1 h), we design and implement a reinforced parameter tuning mechanism to dynamically select the parameter values, so as to speed up the search process. The proposed models and heuristics are tested on representative instances generated from the benchmark in the literature (CSPLib), as well as large-sized instances generated from real-world cases. We report on extensive computational experiments and provide basic managerial insights into the planning process. [ABSTRACT FROM AUTHOR]

The Free-Floating Bike-Sharing system (FFBSS) connects users to public transit networks and is an important component of the "last-mile" transport network. However, the rapid development of the FFBSS in China has significantly increased the local municipal workload and deteriorated the public transport. To mitigate these negative impacts, the Chinese government has launched a pilot project of electronic fence spots, by selecting several bike-sharing parking spots from the existing ones to set up the virtual stations. Compared to the traditional public shared bicycles with fixed stations, the flexibility of choosing parking spots could dynamically cater the fast-changing traffic environment and facilitate the renting and returning for users and, therefore, render a more sparse and complicate parking spots network. In this paper, we study the location selection of bike-sharing parking points as a multidimensional problem, which considers not only the interests of users and stakeholders but also the environment and safety issues. We propose a multicriteria decision-making (MCDM) model including the analytic hierarchy process (AHP) and the weight-restricted data envelopment analysis (DEA) method to evaluate and determine the optimal bike-sharing parking points. Since the additional weight restrictions in the DEA method might lead to infeasible solutions, we introduce the weight restrictions feasibility theorem to avoid such infeasibility in the proposed weight-restricted DEA model, which has not been thoroughly studied in the literature. Specifically, a hyperplane adjusting model is developed to adjust the infeasible results. In the computational study, we evaluate 36 current parking spots in three regions in Beijing, China, to verify the rationality of the combined approach and put forward some managerial suggestions for this pilot project in Beijing, China. [ABSTRACT FROM AUTHOR]

B Correction to: J Wireless Com Network (2022) 2022:26 b https://doi.org/10.1186/s13638-022-02113-7 Following publication of the original article [[1]], the authors flagged that Wenchao Wei had been erroneously affiliated with affiliation 3 instead of affiliation 2. Reference 1 Yu Y, Lu X, Zhao T, Cheng M, Liu L, Wei W. Heuristic approaches for the car sequencing problems with block batches. [Extracted from the article]

Electric vehicles have many advantages compared with traditional fuel vehicles, whereas the immaturity of technologies and high manufacturing cost make it difficult for EV brands to be promoted in traditional business models. Thus, auto-mobile companies started to establish high-end sales business model to promote the brand. This paper studies the influential mechanism of high-end EV sales business model on brand competitiveness and the mediation effect between high-end EV sales business model and brand competitiveness. A total of 624 consumers' survey data were collected and the structural equation model (SEM) was analyzed to test the hypotheses by using a scale made up of indicators referring to high-end EV sales business model, brand competitiveness and customer perceived value. This paper contributes to the sustainable innovation literature by exploring the psychological perception of customer perceived value in strengthening brand competitiveness. The results show that both the value proposition and value creation of high-end EV sales business model have significant positive impacts on brand competitiveness. Moreover, customer perceived value fully mediates the relationships between the value proposition of high-end EV sales business model and brand competitiveness and between the value creation and brand competitiveness. Customer perceived value cannot mediate the relationship between the value capture of high-end EV sales business model and brand competitiveness. Our insights contribute to the business model research area from a customer-centric perspective. [ABSTRACT FROM AUTHOR]