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BigBasket.com, an online supermarket established in December 2011 in Bangalore, India, had become one of the major players in the Indian online grocery market by the end of March 2016.₁ Run by Innovative Retail Concepts Private Limited, BigBasket.com was operating in more than 23 cities across the country in 2016. The online grocery market in India was in a stage of growth and transformation, fuelled by India's large urban population who sought a lifestyle of convenience and ease. It had also attracted many entrepreneurs who competed fiercely with each other in a market characterised by thin margins. Intense competition ensured that only a few companies were able to survive and sustain themselves. One of these companies was Big Basket, which succeeded in spite of the competition, attracting Series D_a funding worth USD 150_b million from the United Arab Emirates-based Abraaj Group in March 2016.₂

Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability.

To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems.

First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated.

An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

The present work attempts to determine an appropriate number of different categories of Delivery Persons for a Hyper-local Food Delivery Organization for different intervals within a day and across days within a week which would provide a satisfactory level of service to the target customers and at the same time would become cost-efficient.

Currently the firm estimates the required number of Delivery Persons for “lunch peak” and “dinner peak” of the next week's weekdays and weekend based on the maximum number of orders occurring during the same period of both weekdays and weekend in the current week. The proposed approach involves determining the projected demand in every four-hourly interval of both week-days and weekend in the next week. Subsequently, the study has developed a simple integer programming model for determining the optimum number of Delivery Persons based on the projected demand data.

The existing approach followed by the firm indicates that the Delivery Persons remain unutilized during periods of low demand. The proposed model demonstrated savings to the tune of 21.4% in manpower cost without any erosion in the service level.

The study has made three tangible contributions. First, the development of a simple methodology for estimating the demand of next period allows the Managers to utilize dynamic demand data. Second, the development of a simple integer programming model helps managers determine an appropriate number of Delivery Persons in different intervals in both weekdays and weekend. Third, the development of a framework of hiring strategy aids managers in adopting a particular hiring strategy under a particular context keeping in mind the magnitude of demand for food, demand for delivery service and the cost of providing the service.

Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.

This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.

The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.

This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.

The purpose of this paper is to interrogate the potential for hyper‐local news websites to support and sustain peripheral rural communities by extending and developing the public sphere(s) in which they engage locally and globally.

Theoretical understandings of communicative spaces, monitorial citizenship and “liquid life” and journalism developed by Jurgen Habermas, Michael Schudson, Zygmunt Bauman and Mark Deuze inform this pilot study of a hyper‐local project undertaken by a UK media corporation. Data sets comprising documentation; news‐website content; interviews with journalists; “knowledge café” exploration of audience interactions and questionnaires are analysed to identify themes and sub‐themes in the production and use of media content.

The hyper‐local project was found to have been put in place without engaging effective involvement of the community concerned and the initial conceptualisation, predicated on assumptions of an inward focus for the community, did not recognise the importance of communicative networks which both supported sustainability within the group and situated that community in wider social, cultural, economic and media dimensions. As such the project tended to reinforce, or at least, not mitigate, the community's geographical isolation.

This is a small‐scale pilot project exploring new forms of media/community engagement and, while the results can be regarded as indicative, further research is needed to investigate hyper‐local developments in a wider contextual field.

The paper addresses an important but little‐researched emergent issue: “hyper‐local”. It explores in detail some of the complexities that are beginning to be theorised in broad terms and extends understandings of local‐level practices and processes.

This study aims to find the model fit to understand the consumer behavior in context to the hyper-personalization through digital clienteling by using structural equation modeling. The traditional method of customer passive observance has been transformed to dominance, where, the fundamental challenge for companies is to understand consumer behavior, work on cost-efficiency and implement sustainable innovation.

To investigate this emerging issue, this study aims to find the model fit via applying “Technology Acceptance Model” (TAM) and “Theory of Reasoned Action” (TRA) in context to the hyper-personalization through digital clienteling with special reference to women ethnic fashion wear.

The study findings depict the perceived ease of use (PEOU) and perceived usefulness (PU) of technology, attitude toward clienteling and subjective norm toward customization impact on customer intensions. The findings posited that perceived usefulness is having the strong relationship with purchase intention as compare to other variables. So, the analysis postulated that customer considered hyper-personalization is having perceived usefulness for customer and it also helps customer in getting the information about the product on the Web page.

Because of lack of availability of resources, a specified sampling method has been used for this study. A new research, which will cover the fashion apparel from all the categories with a detailed study from the branded and non-branded point of view, will provide better description on this topic.

By having personalized Web page through big data analytics, customer will have positive experience and positive association with the company. The other parameters also play an important role toward the customer behavioral intention. The current study approaches new way of understanding the participative management of the personalization and tool to guide the work of strategy professionals and management of fashion e-commerce sector internationally and even in the other sectors also.

Because of advancement of technology, the usage of online media is increasing day by day and this change is having high impact on the society, though we can innovate in any field or industry. Hyper-personalization has an impact on the online consumer buying behavior, which will affect the methods of searching information for consumers.

This new area of research is having large scope of future research from the fashion industry point of view. This paper is working as one of the element in the area of hyper-personalization through digital clienteling to gain sustainable results in the fashion industry.

This study aims to focus on a key unanswered question about how digitalization and the knowledge processes it enables affect firms’ strategies in the international arena.

The authors conduct a systematic literature review of relevant theoretical and empirical studies covering over 20 years of research (from 2000 to 2023) and including 73 journal papers.

This review allows us to highlight a relationship between firms’ international strategies and the knowledge processes enabled by applying digital technologies. Specifically, the authors discuss the characteristics of patterns of knowledge flows and knowledge processes (their origin, the type of knowledge they carry on and their directionality) as determinants for the emergence of diverse international strategies embraced by single firms or by populations of firms within ecosystems, networks, global value chains or alliances.

Despite digital technologies constituting important antecedents and critical factors for the internationalization process, and international businesses in general, and operating cross borders implies the enactment of highly knowledge-intensive processes, current literature still fails to provide a holistic picture of how firms strategically use what they know and seek out what they do not know in the international environment, using the affordances of digital technologies.

The purpose of this paper is to probe the potentials of computational intelligence (CI) and bio-inspired computational tools for designing a hybrid framework which can simultaneously design an identifier to capture the underlying knowledge regarding a given plug-in hybrid electric vehicle’s (PHEVs) fuel cost and optimize its fuel consumption rate. Besides, the current investigation aims at elaborating the effectiveness of Pareto-based multiobjective programming for coping with the difficulties associated with such a tedious automotive engineering problem.

The hybrid intelligent tool is implemented in two different levels. The hyper-level algorithm is a Pareto-based memetic algorithm, known as the chaos-enhanced Lamarckian immune algorithm (CLIA), with three different objective functions. As a hyper-level supervisor, CLIA tries to design a fast and accurate identifier which, at the same time, can handle the effects of uncertainty as well as use this identifier to find the optimum design parameters of PHEV for improving the fuel economy.

Based on the conducted numerical simulations, a set of interesting points are inferred. First, it is observed that CI techniques provide us with a comprehensive tool capable of simultaneous identification/optimization of the PHEV operating features. It is concluded that considering fuzzy polynomial programming enables us to not only design a proper identifier but also helps us capturing the undesired effects of uncertainty and measurement noises associated with the collected database.

To the best knowledge of the authors, this is the first attempt at implementing a comprehensive hybrid intelligent tool which can use a set of experimental data representing the behavior of PHEVs as the input and yields the optimized values of PHEV design parameters as the output.

In order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.

Based on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R² = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.

The research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.

The paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.