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Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

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 primary objective of this article is to examine current procurement, inventory control and management practices in modern healthcare, with a particular focus on the procurement and management of surgical supplies in a prominent public, highly specialized healthcare sector.

This study was conducted in three phases. In Phase 1, the study team interacted with various hospital management stakeholders, including the surgical hospital store, examined the current procurement process and identified challenges. Phase 2 focused on selecting items for a detailed study and collected the qualitative and quantitative details of the store department of the healthcare sector chosen. A detailed study analyzed revenue, output/demand, inventory levels, etc. In Phase 3, a decision-making framework is proposed, and inventory control systems are redesigned and demonstrated for the selected items.

It was observed that the demand for many surgical items had increased significantly over the years due to an increase in disposable/disposable items, while inventories fluctuated widely. Maximum inventory levels varied between 50 and 75%. Storage and availability were important issues for the hospital. It is assumed the hospital adopts the proposed inventory control system. In this case, the benefits can be a saving of 62% of the maximum inventory, 20% of the average stock in the system and optimal use of storage space, improving the performance and productivity of the hospital.

This study can help the healthcare sector administration to develop better systems for the procurement and delivery of common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels, and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.

This study can help the healthcare sector administration develop better systems for procuring and delivering common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.

This study is an early attempt to develop a decision framework and inventory control system from the perspective of healthcare inventory management. The gaps identified in real hospital scenarios are investigated, and theoretically based-inventory management strategies are applied and proposed.

Conflict and violence are the main drivers of globally escalating humanitarian needs. Local grassroots initiatives are pivotal in distributing humanitarian supplies in the acute response phase until more established humanitarian aid organizations can enter. Nevertheless, scant research exists regarding the role of grassroots associations in providing humanitarian assistance during a military conflict. The purpose of this paper is to understand the role of grassroots associations and identify important themes for effective operations.

This paper adopts a case-study approach of three Ukrainian grassroots associations that began operating in the immediate days of the full-scale invasion of Ukraine. The findings are based on analyzing primary sources, including interviews with Ukrainian volunteers, and are supported by secondary sources.

Grassroots associations have local contacts and a contextual understanding of population needs and can respond more rapidly and effectively than large intergovernmental agencies. Four critical themes regarding the operations of grassroots associations emerged: information management, inventory management, coordination and performance measurement. Grassroots humanitarian response operations during conflict are challenged by personal security risks, the unpredictability of unsolicited supplies, emerging volunteer roles, dynamic transportation routes and shifting demands.

Grassroots responses are central to humanitarian responses during the acute phase of a military conflict. By examining the operations of grassroots associations in the early months of the 2022 war in Ukraine, the authors provide a unique perspective on humanitarian logistics. Nonetheless, more inclusive models of humanitarian responses are needed to harness the capacities and resilience of grassroots operations in practice.

The purpose of this paper is twofold: first, to present a rigorous bibliometric analysis and a systematic literature review of the critical success factors (CSFs) for Building information modelling (BIM)-based digital transformation; second, to identify the relationship between the dimensions in favour of BIM implementation.

This study adopts a two-step approach to combine bibliometric and systematic literature review to explore the research topic of BIM and CSFs. Bibliometric tools such as Biblioshiny in R language and Ucinet software were applied to this study.

Besides identifying the two most influential authors (e.g. Bryde and Antwi-Afari), the key journal for disseminating articles, and the most influential countries in this discourse (e.g. Hong Kong and Australia), the study also identifies four pivotal research themes derived from the co-occurrence analysis of keywords: the fusion of sustainability and technology with BIM; practical application and its integration within construction management; innovation and engineering paradigms; and the advent of emerging technologies (e.g. Blockchain) within developing nations. Additionally, the paper introduces a comprehensive framework for selecting CSFs pertinent to BIM-centred digital transformation as viewed through the lens of dynamic capabilities.

This paper establishes a link between dynamic capabilities theory, CSFs, and BIM dimensions, presenting a multifaceted framework guiding future paths and offering practical insights for managerial and political decision-makers engaged in digital transformation endeavours. The study positions dynamic capabilities as pivotal, aligning digital technologies with continuous business performance, and advocates for a strategic focus on digital transformation.

This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.

The paper aims at addressing the following research questions: does institutional quality improve countries' environmental efficiency? And which pillars of institutional quality improve countries' environmental efficiency?

By specifying a directional distance function in the context of stochastic frontier method where GHG emissions are considered as the bad output and the GDP is referred as the desirable one, the work computes the environmental efficiency into the appraisal of a production function for the European countries over three decades.

According to the countries' performance, the findings confirm that high and upper middle-income countries have higher environmental efficiency compared to low middle-income countries. In this environmental context, the role of institutional quality turns out to be really important in improving the environmental efficiency for high income countries.

This article attempts to analyze the role of different dimensions of institutional quality in different European countries' performance – in terms of mitigating GHGs (undesirable output) – while trying to raise their economic performance through their GDP (desirable output).

1. The paper aims at addressing the following research question: does institutional quality improve countries' environmental efficiency?

2. We adopt a directional distance function in the context of stochastic frontier method, considering 40 European economies over a 30-year time interval.

3. The findings confirm that high and upper middle-income countries have higher environmental efficiency compared to low middle-income countries.

4. The role of institutional quality turns out to be really important in improving the environmental efficiency for high income countries, while the performance decreases for the low middle-income countries.

The paper aims at addressing the following research question: does institutional quality improve countries' environmental efficiency?

We adopt a directional distance function in the context of stochastic frontier method, considering 40 European economies over a 30-year time interval.

The findings confirm that high and upper middle-income countries have higher environmental efficiency compared to low middle-income countries.

The role of institutional quality turns out to be really important in improving the environmental efficiency for high income countries, while the performance decreases for the low middle-income countries.

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.

The purpose of this paper is to ascertain if practising healthy lifestyles improves the technical efficiency of farms in Kwara state, Nigeria. In theory, all deviations from the optimum level of output are due to random effects and inefficiency of producers in which their health plays a key part and is dependent on the kind of lifestyle practiced whether healthy or unhealthy.

Cross-sectional data were employed through a three-staged sampling technique to pick 320 arable crop farmers across the state using a well-defined questionnaire. Data analysis was carried out using descriptive statistics, healthy lifestyles index (HLI), stochastic production frontier (SPF) and propensity score matching (PSM).

First, the analysis showed that about one-third of the sampled arable crop farmers practised healthy lifestyles. Second, the average technical efficiency of arable crop production for farmers who practised a healthy lifestyle was 0.893, and the level of technical inefficiency of the farms was determined by health-related lifestyle status, number of day's illness and educational level. Third, technical efficiency was improved by 0.00431067 for farms whose farmers practised a healthy lifestyle.

Rather than seeing that technical efficiencies of farms are attributed to farm characteristics, inputs used and socioeconomic characteristics alone, the findings suggest that technical inefficiencies of arable crop farmers were also due to the kind of lifestyle practised, which was evidenced in the increased efficiency for farmers who practised healthy lifestyle.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0353