Search Results (66)

This study developed a simple genetic algorithm (SGA) enhanced by a random forest (RF) surrogate model, namely ${SGA}_{RF}$, to solve the permutation flow-shop scheduling problem with order acceptance under the conditions of limited capacity, weighted-tardiness, and past-sequence-dependent (PSD) setup times (PFSS-OAWT with PSD). To the best of our knowledge, this is the first study to investigate this problem. Our proposed algorithm increases the setup time for each successive job by a constant proportion of the cumulative processing time of preceding jobs to capture the progressive slowdown that often occurs on real production lines. In the developed algorithm with maximum ${10}^5$ fitness evaluations, the RF surrogate model predicts objective function values and guides crossover and mutation. On the PFSS-OAWT with PSD benchmark (up to 500 orders and 20 machines, 160 instances), ${SGA}_{RF}$ represents improvements of 0.9% over SGA, 0.8% over ${SGA}_{LS}$, and 5.6% over SABPO. Although the surrogate incurs additional runtime, the gains in both profit and order-acceptance rates justify its use for high-margin, offline planning. Overall, the results of this study suggest that integrating evolutionary search into data-driven prediction is an effective strategy for solving complex capacity-constrained scheduling problems. Full article

The ever-expanding market has made organic food a popular research topic, with the primary question being what factors facilitate or hinder consumers in making an organic purchase. The most relevant studies have been conducted in an offline context. As selling organic food online has become a common practice and is underresearched, this study aims to (1) explore the drivers and barriers of online organic food shopping and (2) investigate the shopping behavior of organic food from an omnichannel perspective. The results of partial least square structural equation modeling (PLS-SEM), with 278 valid samples, indicate that trust in organic labels and positive review sentiment significantly contribute to the intention to purchase organic food online, which in turn influences online purchase behaviors. For online shopping behavior, the investigation shows that Taiwanese consumers, on a monthly basis, make an average of 3.22 organic food purchases and spend US$156.44 through offline channels, whereas they make 2.34 purchases of organic food and spend US$114.71 via online channels. Organic vegetables and fruits are the most frequently purchased organic foods. Among online channels, consumers prefer visiting the websites of general grocery stores and specialty stores over social media platforms. Our findings suggest that the determinants of organic food shopping differ between offline and online contexts and reveal interesting behavioral patterns of online organic shopping. Full article

In response to increasing numbers of people switching from offline to online shopping after the COVID-19 pandemic, this study sought to obtain an in-depth understanding of apparel website design and digital accessibility for all people, including people with visual impairments (PVI). Given the Convention on the Rights of Persons with Disabilities Article 9, which mandates that all 191 international parties take measures to ensure people with disabilities have equal access to information and communication technology, this study explored the challenges PVI face while accessing informational content about apparel products online. To achieve this goal, Culnan’s dimensions of perceived accessibility to information, a lens for understanding how consumers experience and evaluate the accessibility of information systems, were used as the theoretical framework. We applied phenomenological methods to explore the daily “lived experience” in depth through observations and semi-structured interviews with eight female participants in their 20 s to 60 s, each lasting more than 45 min. Based on thematic analysis, the findings highlighted the unmet website meta descriptions for product information and navigation functionality for assistive technology, which, as a result, negatively impacts digital accessibility for PVI to shop online for apparel. The study concludes with contributions that extend the theoretical framework to the digital landscape, addresses the gap of inclusive digital apparel retailing practices, and emphasizes the opportunities for apparel educators to incorporate an inclusive design curriculum. Full article

This paper proposes an innovative, intelligent shopping cart system with an interdisciplinary approach using Bluetooth low energy (BLE) beacons. The research integrates online and offline retail strategies by presenting campaigns and ads to the customers during in-store navigation. In a testing environment, BLE beacons are strategically positioned to monitor the purchasing process and deliver relevant insights to retailers. The technology anonymously logs customers’ locations and the duration of their browsing at each sales shelf. Through the analysis of client movement heatmaps, retailers may discern high-traffic zones and modify product placement to enhance visibility and sales. Additionally, the system provides an additional revenue model for store owners through location specific targeted ads displayed on a tablet mounted on the cart. Unlike previous BLE-based tracking solutions, this research bridges the gap between customer movement analytics and real-time targeted advertising in retail settings. The system achieved an accuracy of 82.4% when the aisle partition length was 3.00 m and 91.7% when the aisle partition length was 6.00 m. This system, which can generate additional income for store owners by generating 0.171 USD in a single test simulation as a result of displaying ads to three test customers in a two-partitioned aisle layout, offers a new and scalable business model for modern retailers. Full article

We study an online–merge–offline (OMO) system that integrates a retailer’s online and offline channels through an e-commerce platform and a wireless applet. The customers in the online channel are generated through paid advertising in an e-commerce platform, while the offline channel is a regional retail chain. The OMO system omnichannelizes sole-channel customers from either channel by converting them into omnichannel ones with the wireless applet and then providing them online and offline options at each touch point along the shopping journey. The prices in the OMO system across both channels are uniform. To validate the effectiveness of this new omnichannel system, we construct a legacy system that maintains separate online and offline channels with independent customer populations. Using the legacy system as a benchmark, we assume the OMO system has arbitrary omnichannelization rates of the customers flowing into the two channels. We analyse the perfect OMO system which has all the customers omnichannelized, and show its advantage over the legacy system. We then numerically find that if the omnichannelization rates in the OMO system are general then it is most efficient when products are either highly digital or highly nondigital. Full article

In the context of new retail, the development of omni-channels is flourishing. The entry threshold for the clothing industry is low, and the popularity of online shopping has, to some extent, reduced consumers’ perception of the authenticity of clothing. As a result, returns are a serious issue in the clothing industry. This article focuses on a clothing retailer while addressing retail and return issues in the clothing industry. It develops and analyzes models for an online single-channel strategy and two omni-channel showroom strategies: “Experience in Store and Buy Online (ESBO)” with an experience store and “Buy Online and Return in Store (BORS)” with a physical store. These models are used to examine the pricing and return decisions of the retailer in the three strategic scenarios. Additionally, this study considers the impact of fashion trends on demand. It explores pricing and return strategies in two showroom models under the influence of the fashion trend decay factor. Moreover, sensitivity analyses and numerical analyses of the important parameters are performed. This research demonstrates the following: (1) In the case of high return transportation costs and online return hassle costs, clothing retailers can attract consumers to increase profits through establishing offline channels; (2) extending the sales time of fashionable clothing has a positive effect on profits, but blindly prolonging the continuation of the sales time will lead to a decrease in profits; (3) the larger the initial fashion level or the smaller the fashion level decay factor, the greater the optimal retailer profits. The impacts of the initial fashion level and fashion level decay factor on profits are more significant in omni-channel operations. This article aims to identify optimal strategies for retailers utilizing omni-channel operations and offer managerial insights for the sale of fashionable apparel. Full article

VR shopping combines the advantages of both online and offline shopping, demonstrating significant potential. In online settings, where consumers cannot directly experience products, providing detailed product information is essential. However, research on the impact of product information provision methods in VR shopping on perceived product emotion and ease of product recognition is limited. Therefore, we compare the effects of the existing e-commerce product information provision method and the VR shopping method on perceived product emotion and product information recognition. We focus on shoes as a product where emotion and detailed information heavily influence the final purchase decision. The results showed that the VR shopping method delivered product emotions more consistently and demonstrated higher product information recognition ease. This study is significant as it provides practical verification of the effectiveness of product information provision methods in VR shopping and suggests directions for future research in this field. Full article

Customer context analysis (CCA) in brick-and-mortar shopping malls can support decision makers’ marketing decisions by providing them with information about customer interest and purchases from merchants. It makes offline CCA an important topic in marketing. In order to analyze customer context, it is necessary to analyze customer behavior, as well as to obtain the customer’s location, and we propose an analysis system for customer context based on these two aspects. For customer behavior, we use a modeling approach based on the time-frequency domain, while separately identifying movement-related behaviors (MB) and semantic-related behaviors (SB), where MB are used to assist in localization and the positioning result are used to assist semantic-related behavior recognition, further realizing CCA generation. For customer locations, we use a deep-learning-based pedestrian dead reckoning (DPDR) method combined with a node map to achieve store-level pedestrian autonomous positioning, where the DPDR is assisted by simple behaviors. Full article

This study investigates what drives consumer engagement in online-only omnichannel commerce, a topic often overlooked in favor of online-offline channel integration. Using both Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA), it examines the role of key factors like information needs, shopping convenience, shopping innovation, channel integration quality, and integrated channel usability. The findings reveal that while each factor contributes to engagement, a combination of these elements is essential for achieving optimal results. This research advances understanding by applying a service-dominant logic framework to purely digital omnichannel contexts, offering practical insights for businesses aiming to enhance consumer engagement in online environments. Full article

The job shop scheduling problem (JSSP) is a popular NP-hard problem in combinatorial optimization, due to its theoretical appeal and its importance in applications. In practical applications, the online version is much closer to the needs of smart manufacturing in Industry 4.0 and 5.0. Here, the online version of the job shop scheduling problem is solved by a heuristics that governs local queues at the machines. This enables a distributed implementation, i.e., a digital twin can be maintained by local processors which can result in high speed real time operation. The heuristics at the level of probabilistic rules for running the local queues is experimentally shown to provide the solutions of quality that is within acceptable approximation ratios to the best known solutions obtained by the best online algorithms. The probabilistic rule defines a model which is not unlike the spin glass models that are closely related to quantum computing. Major advances of the approach are the inherent parallelism and its robustness, promising natural and likely successful application to other variations of JSSP. Experimental results show that the heuristics, although designed for solving the online version, can provide near-optimal and often even optimal solutions for many benchmark instances of the offline version of JSSP. It is also demonstrated that the best solutions of the new heuristics clearly improve over the results obtained by heuristics based on standard dispatching rules. Of course, there is a trade-off between better computational time and the quality of the results in terms of makespan criteria. Full article

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, the impact of dynamic disturbance events on production, and the need for energy-efficient production, the integrated scheduling of production equipment and automated guided vehicles (AGVs) in a flexible job shop environment is investigated in this study. Firstly, a static model for the integrated scheduling of production equipment and AGVs (ISPEA) is developed based on mixed-integer programming, which aims to optimize the maximum completion time and total production energy consumption (EC). In recent years, reinforcement learning, including deep reinforcement learning (DRL), has demonstrated significant advantages in handling workshop scheduling issues with sequential decision-making characteristics, which can fully utilize the vast quantity of historical data accumulated in the workshop and adjust production plans in a timely manner based on changes in production conditions and demand. Accordingly, a DRL-based approach is introduced to address the common production disturbances in emergency order insertions. Combined with the characteristics of the ISPEA problem and an event-driven strategy for handling dynamic events, four types of agents, namely workpiece selection, machine selection, AGV selection, and target selection agents, are set up, which refine workshop production status characteristics as observation inputs and generate rules for selecting workpieces, machines, AGVs, and targets. These agents are trained offline using the QMIX multi-agent reinforcement learning framework, and the trained agents are utilized to solve the dynamic ISPEA problem. Finally, the effectiveness of the proposed model and method is validated through a comparison of the solution performance with other typical optimization algorithms for various cases. Full article

Omni-channel retailing is a novel form that combines online, offline, and mobile channels to provide consumers with a seamless shopping experience. Nevertheless, the implementation of omni-channel retailing necessitates effective logistics support. Hence, the quality of logistics services is critical for omni-channel retailing services. This research aims to investigate logistics service quality (LSQ) and its impact on consumer satisfaction by combining the decomposed SERVQUAL framework, Kano model and hierarchical regression analysis. A total of 460 valid responses were obtained. Building upon the SERVQUAL framework, this study presents a comprehensive framework for evaluating omni-channel retail logistics service quality. Using the Kano model, 11 logistics service quality attributes were categorised into three categories: must-be, one-dimensional, and attractive, based on their respective impact on satisfaction. The results of the hierarchical regression analysis confirm that the attributes belonging to the must-be category exert the most significant influence on satisfaction. The findings add to theoretical studies of omni-channel retailing LSQ and provide insights for omni-channel retailers and logistics service providers. Full article

This study explores the context of buy-online-and-return-in-store (BORS) policy from an environmental perspective and conducts a comprehensive analysis through the theory of planned behavior (TPB). Adding environmental awareness and awareness of consequences provides a new perspective on how sustainable practices can be enhanced through an omni-channel retail strategy. Survey responses from 405 participants were analyzed using structural equation modeling. Results show that attitudes, subjective norms, and perceived behavioral control are key determinants of practical BORS policy. The study found that environmental awareness significantly influenced BORS adoption directly and indirectly by enhancing perceived behavioral control, whereas consequence awareness primarily affected adoption by shaping consumer attitudes. Compared with consumers who choose to return online, consumers who prefer in-store returns show higher environmental awareness, highlighting the environmental advantages of BORS. In addition, the BORS policy improves overall shopping satisfaction by integrating the convenient process of online purchases and offline returns, allowing consumers to switch freely between different channels. These findings provide valuable insights for retailers and policymakers seeking to promote sustainable consumer behavior to effectively promote the importance of sustainable retail practices. Full article

Addressing climate change necessitates reducing carbon emissions, with green behavior adoption being crucial. This study examines how green consumption awareness (GCA) and carbon emission disclosures influence consumer shopping channel choices, offering a practical approach to converting awareness into actionable behavior. Using stated preference (SP) data, we investigate the impact of green awareness and information disclosure on consumers’ choices between online and offline shopping channels. The key findings include the following: (1) GCA affects shopping channel choices in certain scenarios, though not always significantly. (2) Detailed carbon information disclosure steers consumers towards lower-emission channels, especially when specific carbon data are provided. (3) The type of goods significantly influences shopping channel decisions, serving as a variable across scenarios. (4) Effective scenarios, such as a 3 km shopping trip for categories like tissue and trash bags, where the difference in channel choice under varying levels of information disclosure is statistically significant, have been identified. These insights inform recommendations for information disclosure strategies that not only enhance GCA but also drive behavioral change, thereby fostering environmentally friendly consumption behaviors that contribute to a reduction in consumers’ carbon footprint. Full article

Previous studies have acknowledged the impact of weather changes on retail uncertainty. They primarily focus on understanding how weather conditions affect offline consumer behavior and aim to develop effective marketing strategies. However, there is little research on the complex impact of weather on online shopping behavior. To bridge this gap, we conduct a study with a sample of 261 consumers from China with shopping experience in community retail shops (CRSs). We utilize the S-O-R model and theories, including meteorological emotional effect theory, emotional coherence, and meteorological psychology, to model and elucidate the relationship between weather and consumers’ online shopping behavior in CRS. Our findings reveal that weather conditions affect consumers’ spending patterns and purchase diversity, mediated by consumers’ emotions and risk aversion when they comfortably shop online at home. Furthermore, employing the fsQCA model, we identify the critical path through which weather conditions and consumer types influence risk-aversion awareness. The results provide management implications for retailers to develop online marketing strategies for different consumer types. Full article