Search results

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

In this paper, a systematic review of 284 articles published between 2015 and 2022 and a full-text thematic analysis of 70 selected articles was conducted to catalog and synthesize factors in a framework. Thematic analysis subsequently revealed 18 selective codes under three groups of drivers, barriers, and outcomes. These three groups were explained by four key aspects including organization, stakeholders, infrastructure, and business environment that set a framework for the digitalization of construction. The study finally concluded digitalization strategies with a focus on support mechanisms, government incentives, regulations, the transition from manual labor to technicians, organizational technology culture, methodology development, and innovation processes. Such strategies provide insight into prioritizing resources towards smooth digital transformation in construction businesses.

A two-stage methodology is adopted by undertaking a systematic literature review followed by thematic content analysis. This work concludes with an analysis of remaining research gaps and suggestions for potential future research.

In this paper, a systematic review of 284 articles published between 2015 and 2022 and a full-text thematic analysis of 70 selected articles was conducted to catalog and synthesize variables in a framework. Thematic analysis subsequently revealed a set of variables and factors describing construction digitalization under three groups of success factors, barriers, and outcomes. A critical content analysis of the representative studies was conducted to identify five future research trends as well as associated research gaps and directions on the topic.

This study contributes to practice by providing directions concerning the key strategies and priorities associated with the digitalization of construction businesses.

This ground-breaking research brings to light a classified set of factors that are important for the digitalization of construction businesses. The elicited framework contributes to the current body of knowledge by offering a unique conceptualization of both driving and adverse aspects for the seamless digital transformation of construction.

In the reduction of food waste and the provision of food to the hungry, food banks play critical roles. However, as they are generally run by charitable organisations that are chronically short of human and other resources, their inbound logistics efforts commonly experience difficulties in two key areas: 1) how to organise stocks of donated food, and 2) how to assess the donated items quality and fitness for purpose. To address both these problems, the authors aimed to develop a novel artificial intelligence (AI)-based approach to food quality and warehousing management in food banks.

For diagnosing the quality of donated food items, the authors designed a convolutional neural network (CNN); and to ascertain how best to arrange such items within food banks' available space, reinforcement learning was used.

Testing of the proposed innovative CNN demonstrated its ability to provide consistent, accurate assessments of the quality of five species of donated fruit. The reinforcement-learning approach, as well as being capable of devising effective storage schemes for donated food, required fewer computational resources that some other approaches that have been proposed.

Viewed through the lens of expectation-confirmation theory, which the authors found useful as a framework for research of this kind, the proposed AI-based inbound-logistics techniques exceeded normal expectations and achieved positive disconfirmation.

As well as enabling machines to learn how inbound logistics are handed by human operators, this pioneering study showed that such machines could achieve excellent performance: i.e., that the consistency provided by AI operations could in future dramatically enhance such logistics' quality, in the specific case of food banks.

This paper’s AI-based inbound-logistics approach differs considerably from others, and was found able to effectively manage both food-quality assessments and food-storage decisions more rapidly than its counterparts.

This study aimed to investigate the sensemaking strategies employed by early-career employees working within organizationally constrained environments.

Grounded in the sensemaking-as-accomplishment framework, a longitudinal multi-case study was conducted, involving three early-career employees. These participants were interviewed multiple times concerning tasks they themselves identified as anomalous and ambiguous.

The study's findings illuminate how early-career employees utilize sensemaking strategies to accomplish anomalous-ambiguous tasks. These strategies are interwoven with deliberate efforts to mitigate organizational constraints that exist in the organization or arise during the execution of complex tasks.

Notable limitation pertains to the time gap between task completion and the interviews. Conducting real-time interviews concurrently with task execution or immediately afterward was not feasible due to constraints in participant availability. This research has implications for organizational learning initiatives, particularly those encompassing employee-driven self-learning components. Insights derived from studies like this can inform the development of effective self-learning schemes within organizations.

Previous sensemaking research focused on what takes place in high-reliability organizations. This study explored sensemaking strategies in workplaces that are organizationally constrained.

The main issue in the mass customization of apparel products is how to efficiently produce products of various sizes. A parametric pattern-making system is one of the notable ways to rectify this issue, but there is a lack of information on the parametric design itself and its application to the apparel industry. This study compares and analyzes three types of parametric clothing pattern CAD (P-CAD) software currently in use to identify the characteristics of each, and suggest a basic guideline for efficient and adaptable P-CAD software in the apparel industry.

This study compared three different types of P-CAD software with different characteristics: SuperALPHA: PLUS(as known as YUKA), GRAFIS and Seamly2D. The authors analyzed the types and management methodologies of each software, according to the three essential components that refer to previous studies about parametric design systems: entities, constraints and parameters.

The results demonstrated the advantages and disadvantages of methodology in terms of three essential components of each software. Based on the results, the authors proposed five strategies for P-CAD development that can be applied to the mass customization of clothing.

This study is meaningful in that it consolidates and organizes information about P-CAD software that has previously been scattered. The framework used in this study has an academic value suggesting guidelines to analyze P-CAD systems.

This paper aims to develop a multi-objective problem for scheduling the operations of trucks entering and exiting cross-docks where the number of unloaded or loaded products by trucks is fuzzy logistic. The first objective function minimizes the maximum time to receive the products. The second objective function minimizes the emission cost of trucks. Finally, the third objective function minimizes the number of trucks assigned to the entrance and exit doors.

Two steps are implemented to validate and modify the proposed model. In the first step, two random numerical examples in small dimensions were solved by GAMS software with min-max objective function as well as genetic algorithms (GA) and particle swarm optimization. In the second step, due to the increasing dimensions of the problem and computational complexity, the problem in question is part of the NP-Hard problem, and therefore multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment.

Therefore, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are used to solve 30 random problems in high dimensions. Then, the algorithms were ranked using the TOPSIS method for each problem according to the results obtained from the evaluation criteria. The analysis of the results confirms the applicability of the proposed model and solution methods.

This paper proposes mathematical model of truck scheduling for a real problem, including cross-docks that play an essential role in supply chains, as they could reduce order delivery time, inventory holding costs and shipping costs. To solve the proposed multi-objective mathematical model, as the problem is NP-hard, multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Therefore, NSGA-II and NRGA are used to solve 30 random problems in high dimensions.

The purpose of the article is to outline the insights provided by Alan Fox in Man Mismanagement in relation to the rise of the New Right political economy and the spread of unitarist managerialism. The article assesses the contemporary work and employment relations implications of mismanagement arising from a “second wave” of the New Right ideology from 2010 in the UK.

Responding to the Special Issue on Alan Fox, the article focuses on Alan Fox's book Man Mismanagement, considering industrial relations developments arising between the 1st (1974b) and 2nd (1985) editions relating to the political rise of the New Right. It reviews various literature that illustrates the contemporary IR relevance of the book and Fox's insights.

The New Right’s ideology has further fragmented work, disjointed labour rights and undermined collective industrial relations institutions, and macho mismanagement praxis is even more commonplace, compared to when Fox wrote Man Mismanagement. The stripping away of the institutional architecture of IR renders the renewal of pluralist praxis, like collective bargaining and other forms of joint regulation of work, a formidable task.

The value of the article relates to the identification of dramatic historical industrial relations events and change in the UK in Alan Fox's book Man Mismanagement, most notably relating to the rise to power of the Thatcherite New Right in 1979. Originality is evidenced by the authors’ drawing on Fox's ideas and assessing the implications of the “second wave” of the New Right in the contemporary industrial relations (IR) context of the 2020s under the conceptual themes of fragmented work, disjointed labour rights and undermined collectivism.

Organizational technologies can be classified according to the roles they play as either commodity or strategic. Commodity technologies support common operations, while strategic technologies address perceived threats to competitiveness, often identified by strategic foresight. These must go through an adoption process before playing an effective role in strategy execution. The adoption process includes known activities, ranging from sourcing (itself from in-house development to turn-key acquisition) to operational integration. This paper aims to reveal strategic technology adoption risks that arise during strategy execution.

A gradually developed causal loop diagram model, supported by general literature, introduces three general classes of technology adoption risks: mismatched requirements, supplier dependence and unmanaged life cycles.

Rather than managed, these risks are incurred or avoided depending on decisions made during the adoption process.

Despite the scarce literature coverage for the approach, examples revealing the presence of adoption risks are nevertheless available in the well-documented history of enterprise resource planning (ERP).

Although ERP is presented as a general-purpose strategic technology, the unique business features of maritime container terminals pose serious challenges to its adoption, which provides additional support to the discussion and reinforces the conclusions.

The approach to identifying risks in strategic technology adoption departs from the current risk paradigm in two significant ways. First, it emphasizes policy decision-making rather than external events. Second, it views risks as systemic rather than occurring independently.

After completion of the case study, students are expected to demonstrate understanding of the process of strategy formulation (which include conducting situational analysis) and strategy implementation.

Perusahaan Azan, which trades under the brand name Roti Azan for its fresh bread and Azan for its dry bread or rusks, was established as a family business in 1968 by Haji Abu Bakar bin Ali in his hometown in Kuala Pilah, in the state of Negeri Sembilan in Malaysia. In the mid-1980s, the management of the business was passed on by Haji Abu Bakar to one of his sons, Haji Mohd Ghazali bin Haji Abu Bakar. Haji Ghazali was named managing director in 1985 and officially inherited his father’s company in 1987. By 2004, Perusahaan Azan breads had started to penetrate major grocery stores nationwide, and later the business began to expand internationally in 2010, with Oman and Iraq among the first countries it ventured into. The company sold both its fresh and dry bread in local stores; however, in the international market, only dry bread types were sold, specifically wholemeal rusks and long rusks, which had longer shelf lives. Post-pandemic, by 2022, the company had exited the retail fresh bread market and had focused only on its contractual fresh bread and retail dry bread markets. He thought about the main strategic choices he had of going forward, either to revive its retail fresh bread segment or venture into a coffee shop business. The former was the bread and butter of the company in the last 50 years. However, he knew that re-entering this market was getting more difficult, as it requires competing head-to-head with the giant breadmakers. There were also issues of rising costs and high wastage. For the latter coffee shop project, the company did not have experience in directly “serving” the customers, with its businesses so far had been mainly in production. He pondered on the best decision to undertake to sustain the company’s profitability into the next generation. Few family businesses can pass this crucial stage. He knew he had to act fast to ensure that the company’s plans for the future could be successfully implemented. The case study is suitable for use in teaching courses in strategic management, organisational management and integrated case study for advanced undergraduates and postgraduates in the programmes of business administration, Muamalat administration and accounting.

The case study is suitable for use in advanced undergraduate students in management, business administration, Muamalat administration and postgraduate students in MBA, Master in Muamalat Administration or other related master’s programmes with a course in strategic management, organisational management and integrated case study.

Teaching notes are available for educators only.

CSS 11: Strategy.