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The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

Exact solution of time–cost trade-off problem (TCTP) by the state-of-the-art meta-heuristic algorithms can be obtained for small- and medium-scale problems, while satisfactory results cannot be obtained for large construction projects. In this study, a hybrid heuristic meta-heuristic algorithm that adapts the search domain is developed to solve the large-scale discrete TCTP more efficiently.

Minimum cost slope–based heuristic network analysis algorithm (NAA), which eliminates the unfeasible search domain, is embedded into differential evolution meta-heuristic algorithm. Heuristic NAA narrows the search domain at the initial phase of the optimization. Moreover, activities with float durations higher than the predetermined threshold value are eliminated and then the meta-heuristic algorithm starts and searches the global optimum through the narrowed search space. However, narrowing the search space may increase the probability of obtaining a local optimum. Therefore, adaptive search domain approach is employed to make reintroduction of the eliminated activities to the design variable set possible, which reduces the possibility of converging into local minima.

The developed algorithm is compared with plain meta-heuristic algorithm with two separate analyses. In the first analysis, both algorithms have the same computational demand, and in the latter analysis, the meta-heuristic algorithm has fivefold computational demand. The tests on case study problems reveal that the developed algorithm presents lower total project costs according to the dependent t-test for paired samples with α = 0.0005.

In this study, TCTP is solved without considering quality or restrictions on the resources.

The proposed method enables to adapt the number of parameters, that is, the search domain and provides the opportunity of obtaining significant improvements on the meta-heuristic algorithms for other engineering optimization problems, which is the theoretical contribution of this study. The proposed approach reduces the total construction cost of the large-scale projects, which can be the practical benefit of this study.

Construction workers’ safety compliance is attracting considerable critical attention as it plays a decisive role in improving safety on construction sites. This study applied the concept of differentiating safety compliance into deep compliance (DC) and surface compliance (SC) and relied on trait activation theory to investigate the effects of situational awareness (SA) and emotional intelligence (EI) on safety compliance.

Cross-sectional survey data were collected from 239 construction workers in Australia, and these responses were statistically analyzed using the partial least squares structural equation modeling (PLS-SEM) to validate the proposed model.

Results revealed that both EI and SA positively impacted DC and negatively impacted SC. Moreover, SA partially mediated the link between EI and two types of safety compliance (DC and SC). The outcomes showed that construction workers’ ability in regulating their emotions could influence their perception of environmental cues and the effectiveness of safety compliance behavior.

This study sheds light on investigating the antecedents of DC and SC from the perspective of trait activation theory. The findings also have practical implications, stating that construction site managers or safety professionals should consider providing training on construction workers’ EI and SA to enhance their willingness to expend conscious efforts in complying with safety rules and procedures, which can lead to improved safety outcomes.

High-efficiency video coding (HEVC) is the latest video coding standard that has better coding efficiency than the H.264/advanced video coding (AVC) standard. The purpose of this paper is to design and develop an effective block search mechanism for the video compression-HEVC standard such that the developed compression standard is applied for the communication applications.

In the proposed method, an rate-distortion (RD) trade-off, named regressive RD trade-off is used based on the conditional autoregressive value at risk (CaViar) model. The motion estimation (ME) is based on the new block search mechanism, which is developed with the modification in the Ordered Tree-based Hex-Octagon (OrTHO)-search algorithm along with the chronological Salp swarm algorithm (SSA) based on deep recurrent neural network (deepRNN) for optimally deciding the shape of search, search length of the tree and dimension. The chronological SSA is developed by integrating the chronological concept in SSA, which is used for training the deep RNN for ME.

The competing methods used for the comparative analysis of the proposed OrTHO-search based RD + chronological-salp swarm algorithm (RD + C-SSA) based deep RNN are support vector machine (SVM), fast encoding framework, wavefront-based high parallel (WHP) and OrTHO-search based RD method. The proposed video compression method obtained a maximum peak signal-to-noise ratio (PSNR) of 42.9180 dB and a maximum structural similarity index measure (SSIM) of 0.9827.

In this research, an effective block search mechanism was developed with the modification in the OrTHO-search algorithm along with the chronological SSA based on deepRNN for the video compression-HEVC standard.

The purpose of this paper is to scrutinise the effectiveness of four derivative exchanges’ enforcement efforts since 2007. These exchanges include the Commodity Exchange Inc. and ICE Futures US from the United States and ICE Futures Europe and the London Metal Exchange from the UK.

The paper examines 799 enforcement notices published by four exchanges through a behavioural science lens: HUMANS conceived by Hunt (2023) in Humanizing Rules: Bringing Behavioural Science to Ethics and Compliance.

The paper finds the effectiveness of the exchanges’ enforcement efforts to be a mixed picture as financial markets transition from the digital to artificial intelligence era. Humans remain a key cog in the wheel of market participants’ trading operations, albeit their roles have changed. Despite this, some elements of exchanges’ enforcement regimes have not kept pace with the move from floor to remote trading. However, in other respects, their efforts are or should be, effective, at least in behavioural terms.

The paper’s findings are arguably limited to exchanges based in Anglophone jurisdictions. The information published by the exchanges is variable, making “like-for-like” comparisons difficult in some areas.

The paper makes several recommendations that, if adopted, could help exchanges to increase the potency of their enforcement programmes.

A key aim of the paper is to shift the lens through which the debate concerning the efficacy of exchange-level oversight is conducted. Hitherto, a legal lens has been used, whereas this paper uses a behavioural lens.

This paper aims to explore the implications of algorithmic management on careers and employment relationships in the Nigerian gig economy. Specifically, drawing on labour process theory (LPT), this study provides an understanding of the production relations beyond the “traditional standard” to “nonstandard” forms of employment in a gig economy mediated by digital platforms or digital forms of work, especially on ride-hailing platforms (Uber and Bolt).

This study adopted the interpretive qualitative approach and a semi-structured interview of 49 participants, including 46 platform drivers and 3 platform managers from Uber and Bolt.

This study addresses the theoretical underpinnings of the LPT as it relates to algorithmic management and control in the digital platform economy. The study revealed that, despite the ultra-precarious working conditions and persistent uncertainty in employment relations under algorithmic management, the underlying key factors that motivate workers to engage in digital platform work include higher job flexibility and autonomy, as well as having a source of income. This study captured the human-digital interface and labour processes related to digital platform work in Nigeria. Findings of this study also revealed that algorithmic management enables a transactional exchange between platform providers and drivers, while relational exchanges occur between drivers and customers/passengers. Finally, this study highlighted the perceived impact of algorithmic management on the attitude and performance of workers.

The research presents an interesting case study to investigate the influence of algorithmic management and labour processes on employment relationships in the largest emerging economy in Africa.

At the forefront of current research is the investigation of how big data analytics capability (BDAC) and artificial intelligence capability (AIC) can enhance performance in concert. Therefore, current study intended to conduct more deep research into emerging phenomena and attempts to cover the gap by exploring how entrepreneurial orientations (EO) emphasize the use of two emerging capabilities under the moderating role of environmental dynamism which in turn augment co-innovation and hotel performance.

Data were collected from four-star and five-star hotels located in Kula Lumpur and Langkawi in Malaysia. A total of 260 responses were obtained from IT staff and senior managers with the assistance of a Manpower agency for data analysis. The hypotheses were examined by analyzing the data using PLS-SEM technique through Smart PLS 3 software.

The result revealed that EO has a positive and significant effect on co-innovation (CIN). Additionally, the BDAC and AIC have been tested and proven to be potential mediators between EO and CIN. Also, environmental dynamism as moderator has positive and significant effect on BDAC and co-innovation performance, however, not significant impact on AIC and co-innovation performance. Lastly, findings displayed positive and significant moderated mediation impact of environmental dynamics on BDAC and CIN with hotel performance, but not significant influence on AIC and co-innovation with hotel performance. For theoretical corroboration of the research findings, the current study integrated EO, resource-based view theory and contingent dynamic capabilities (CDC), because neither single stance can explicate an extant research framework.

This study anticipated the several implications for the entrepreneurs of hospitality industry. Managers are recommended to invest in the entrepreneurial traits of the employees/organizations and make strategic readjustment of their capabilities for sustained business performance.

The study goes beyond the normal inquiry by investigating moderated mediation impact of environmental dynamism between two emerging capabilities, co-innovation and hotel performance relationships. Another novelty of this study is to culminate the exploitation and adoption of emerging IT-based capabilities in cross domains of management, entrepreneurship, information systems management within the hotel industry.

Considering the relevance of understanding what influences environmentally sustainable consumer choices, the present study aims to examine and synthesize the key determinants factors from literature and outline a new conceptual framework for explaining green purchasing behaviors (GPBs).

A bibliometric analysis was conducted on 161 articles extracted from Web of Science and Scopus databases, which were systematically evaluated and reviewed, and represent the current GPB knowledge base. Content analysis, science mapping and bibliometric analysis techniques were applied to uncover the major theories and constructs from the state-of-the-art.

The evolving debate between altruistic and self-interest consumer motivations reveals challenges for rational-based theories, as most empirical applications are not focused on buying behaviors, but instead either on pro-environmental (non-buying) activities or on buying intentions. From the subset of leading contributions and emerging topics, nine thematic clusters are unveiled in this investigation, which were combined to create the new PSICHE framework with the purpose of predicting GPB: (P)roduct-related factors, (S)ocial influences, (I)ndividual factors, (C)oncerns about the environment, (H)abits and (E)motions.

By uncovering the multiple intervening factors in GPB decision processes, this study will assist practitioners and academics to move forward on how to foster more sustainable consumer behaviors.

The present study provides readers a summary of an unprecedentedly broad collection of papers, from which the key themes are categorized, the domain's intellectual structure is captured and an actionable framework for enhancing the understanding GPB is proposed. Four new thrust areas and a set of future research questions are included.

The choice of crops to produce at a location depends to a large degree on the climate. As the climate changes and food demand evolves, farmers may need to produce a different mix of crops. This study assesses how much cropland may be subject to such upheavals at the global scale, and then focuses on China as a case study to examine how spatial heterogeneity informs different contexts for adaptation within a country.

A global agricultural economic model is linked to a cropland allocation algorithm to generate maps of cropland distribution under historical and future conditions. The mix of crops at each location is examined to determine whether it is likely to experience a major shift.

Two-thirds of rainfed cropland and half of irrigated cropland are likely to experience substantial upheaval of some kind.

This analysis helps establish a global context for the local changes that producers might face under future climate and socioeconomic changes. The scale of the challenge means that the agricultural sector needs to prepare for these widespread and diverse upheavals.

The digital warehouse management system is an emergence that forms a critical part of the transformation of economic structure in Industry 4.0. In the present business scenario, the warehouse management system encounters a messy layout, poor damage control, unsatisfactory order management, lack of visibility and lack of technological interventions. Digital twin (DT) based warehouse system shows the ontology and knowledge graphs for competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. There remains a lack of clarity on how the DT can be implemented successfully in warehouse management.

The current literature remains largely unstructured and scattered due to a lack of a systematic approach to integrating the research implications and analysis. This paper probes the conceptualization of the DT with the help of theoretical analysis using the systematic literature analysis method.

The study explores essential concepts such as interoperability and integrability in implementing DT. Further, it analyzes the role of a supply chain control tower (SCCT) in modern supply chain management. A research framework is proposed for practitioners and academicians by incorporating the opportunities and challenges associated with DT implementation. The research findings are mainly threefold: Conceptualization of DT, Featuring SCCT and Exploration of cross-computer platform interfaces, scalability and maintenance strategies.

This study is among the first to analyze and review DT applications in warehouse management. Moreover, the study proposes a theoretical toolbox for the practitioners to successfully implement the DT in warehouse DT-based warehouse management system: A theoretical toolbox for future research and applications.