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Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Demonstrate proof-of-concept for conducting NFL Draft trades on a blockchain network using smart contracts.

Using Ethereum smart contracts, the authors model several types of draft trades between teams. An example scenario is used to demonstrate contract interaction and draft results.

The authors show the feasibility of conducting draft-day trades using smart contracts. The entire negotiation process, including side deals, can be conducted digitally.

Further work is required to incorporate the full-scale depth required to integrate the draft trading process into a decentralized user platform and experience.

Cutting time for the trade negotiation process buys decision time for team decision-makers. Gains are also made with accuracy and cost.

Full-scale adoption may find resistance due to the level of fan involvement; the draft has evolved into an interactive experience for both fans and teams.

This research demonstrates the new application of smart contracts in the inter-section of sports management and blockchain technology.

This study aims to develop the understanding of learning processes related to the new ways of interacting in the enforced digital workplace over time.

A multiple, longitudinal case study of knowledge-based workers in three firms located in Sweden has been conducted from March 2020 to March 2023. In total, 89 interviews with 32 employees in three knowledge-based firms have been collected.

The study shows how the intricate interaction between rules and norms for interaction and work must be renegotiated as well as un- and relearned when the physical work environment no longer frames the work context. Furthermore, technology can be viewed as both an enable and a barrier, that is, technology has enhanced collaboration between organizational members yet also created social difficulties, for example, related to communication and interaction. The study emphasizes that individuals learned through trial and error. That is, they tried behaviors such as translating social interactions" to a digital arena, appraised the outcomes and modified the practices if the outcomes were poor.

The present study does have several limitations. First, it is based on interviews with respondents within three organizations in Sweden. To broaden and deepen the understanding of both organizational and learning, future studies can contribute by studying other contexts as well as using a mixed method approach in other countries.

Results from the study can provide a practical understanding of how the rapid change from working at the office to working from home using digital technologies can be understood and managed.

Contributions include combining interaction order and un- and relearning among organizational employees. This insight is important given that the rapid digital transformation of our society has changed how work is performed and how the future workplace will be both structured and organized.

This study explores the potential of computational design processes in creating contextually responsive envelopes for high-rise residential buildings in the Middle East. This includes considering both physical constraints and social preferences, with a focus on balancing sunlight exposure, privacy and views.

A two-phase simulation study analyzed various exterior envelope systems in Baghdad high-rise buildings. The first phase examined two commonly used exterior envelopes – fully glazed and window-based – to assess sunlight exposure, privacy and views. In the second phase, a multi-objective optimization process was applied to derive contextually optimized design solutions addressing the challenges identified in the first phase.

The study reveals that contextually optimized design solutions significantly improved direct sunlight exposure and privacy while maintaining satisfactory views. Although fully glazed exterior envelopes provided better-uninterrupted views, the optimized solutions offered more balanced performance across all factors, demonstrating the potential of computational design processes in creating contextually responsive building envelopes.

This paper emphasizes the importance of considering both physical and social contexts in the development of algorithms for architecture in the Middle East. This paper supports a progressive interpretation of traditional building references and demonstrates how computational design processes can create contextually responsive building envelopes that satisfy social needs and provide better-performing buildings for inhabitants.

This study aims to explore the empirical correlation between patient flow issues, quality of green health services and patient satisfaction in specialist medical department factors from patients’ perspectives as service consumers.

This research is a type of nonintervention empirical research that uses an open survey to explore the views and experiences of users of specialist medical department services. The targeted population is hospital patients included in the top five national PERSI (Indonesian Hospital Association) Award 2022 Green Hospital Category, with a total number of respondents of 572 people. This study uses the partial least square-structural equation modeling analysis method with the SmartPLS application.

Patient flow problems generally affect the quality of eco-friendly health services, except for the waiting time problem, which affects service quality. It should be understood as a top priority for patients to receive services from medical specialists without risking time as a core service aspect from the patient’s perspective. In addition, all variables in eco-friendly hospital services affect patient satisfaction, except in the case of visits to specialist medical departments, which do not affect medical support services and hospital practices that are responsive to the delivery of care services resulting from medical support services that are inseparable in integrated services as well as health care following medical ethics.

This study has a novelty in understanding the implications of green practice in determining patient satisfaction in medical specialist department as the epicenter of hospital services and the main object of assessment for the quality of hospital services.