Search results

Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

Commercial entities have recently expressed growing interest in commercialising indigenous knowledge (IK) due to its enormous economic and intrinsic value. As this happens, custodial communities must not be disadvantaged in the process. This paper aims to understand the legal framework of the commercialisation of IK to identify the opportunities and factors impeding or affecting the commercialisation of indigenous knowledge in Kenya.

The study used a qualitative research approach. An extensive exploratory literature review of existing legal instruments was done to establish the progress and gaps for commercialising indigenous knowledge in Kenya.

The study shows that the legal framework of IK in Kenya is inadequate. There are no well-established frameworks and policies to protect IK in Kenya, and thus, host communities are subjected to exploitation. The diversity of tribes and communities makes it challenging to have a clear framework, mainly because IK is a devolved function. The study identifies the Protection of Traditional Knowledge and Cultural Expressions Act 2016, The National Museums and Heritage Act 2006 and the Natural Products Industry as the key milestones towards commercialisation of IK, while inadequate documentation of IK, communal ownership and inadequate legislation were identified as the main impediments to commercialisation of IK in Kenya.

Owing to the diverse cultures and tribal communities in Kenya, the research could not access all the literature on all traditional IK in Kenya, and very few case studies have been conducted in Kenya.

The gaps identified in the legal framework can form a basis for legislation, policy change, actions and research needed to improve the commercialisation of IK.

The paper underscores the importance of balancing economic empowerment with preserving cultural integrity and protecting indigenous rights in commercialisation.

This paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects.

This paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed.

The results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted.

The paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers.

This study develops a model and algorithm to solve the decentralized resource-constrained multi-project scheduling problem (DRCMPSP) and provides a suitable priority rule (PR) for coordinating global resource conflicts among multiple projects.

This study addresses the DRCMPSP, which respects the information privacy requirements of project agents; that is, there is no single manager centrally in charge of generating multi-project scheduling. Accordingly, a three-stage model was proposed for the decentralized management of multiple projects. To solve this model, a three-stage solution approach with a repeated negotiation mechanism was proposed.

The experimental results obtained using the Multi-Project Scheduling Problem LIBrary confirm that our approach outperforms existing methods, regardless of the average utilization factor (AUF). Comparative analysis revealed that delaying activities in the lower project makespan produces a lower average project delay. Furthermore, the new PR LMS performed better in problem subsets with AUF < 1 and large-scale subsets with AUF > 1.

A solution approach with a repeated-negotiation mechanism suitable for the DRCMPSP and a new PR for coordinating global resource allocation are proposed.

This paper aims to analyse how local agri-food systems (LAFS), compared to other production models, can offer innovative responses to the important environmental challenges facing food production under the twin transition. These responses are more conducive to community inclusion and local development.

The paper combines territorial development, clusters and industrial districts literature with studies on agri-food industry environmental problems and twin transition technologies to develop an agri-food systems typology. This typology is based on a territorial approach to environmental challenges of food production and serves to illustrate the ways in which LAFS can provide innovative responses to these challenges.

The study allows to visualise the differences between LAFS and other agri-food production models, showing how the operationalisation and implementation of digitisation occur at territorial level and how rural communities are involved in the process. The theoretical proposal emphasises not assuming that technology is inherently beneficial but ensuring that its implementation is inclusive and generates social value for the communities.

The paper aims to enrich future research by adopting a territorial perspective to study the twin transition challenges associated with food production systems.

This study aims to explore the grain chain in Kosovo. This study also aims to analyse the role of actors involved in the supply, production, processing, marketing and distribution of the grain value chain.

The study uses qualitative methods. A total of 60 semi-structured interviews are conducted with actors involved in the entire grain value chain.

Findings reveal that the country depends on grain imports and lacks an organised grain market, which is often distorted by the present political situation. Stakeholders are partly integrated in the grain value chain, and they are not very efficient in production. The existence of an informal market influences the decision-making of actors involved in the grain chain. The grain value chain displays mixed governance types, and the relationships among actors are based on the trust mechanism.

The research draws the importance of agriculture’s public policies to sustain domestic grain production. Public–private partnerships should be created to restore the grain market. Trading policies should be revised because they play a crucial role in enhancing fair competition between domestic and foreign traders.

As climate change increasingly affects rural food production, there is an urgent need to adopt agricultural adaptation strategies. Because the agricultural sector in Nepal is one of the most vulnerable to the effects of climate change, the adaptation strategies of household farmers in rural areas are crucial. This study aims to address the impacts of agricultural climate change adaptation strategies in Nepal. The research empirically analyzed climate hazards, adaptation strategies and local adaptation plans in Mangalsen Municipality, Achham District, Sudurpashchim Province, Nepal.

This study used a purposive sampling of household lists, categorized as resource-rich, resource-poor and intermediate households. The analysis used primary data from 110 household surveys conducted among six focus groups and 30 informants were selected for interviews through purposive random sampling.

Climate change significantly impacts rainfall patterns and temperature, decreasing agriculture productivity and increasing household vulnerability. To overcome these negative impacts, it is crucial to implement measures such as efficient management of farms and livestock. A comprehensive analysis of Nepalese farmers' adaptation strategies to climate change has been conducted, revealing important insights into their coping mechanisms. By examining the correlation between farmers' strategies and the role of the local government, practical policies can be developed for farmers at the local level.

This study represents a significant breakthrough in the authors' understanding of this issue within the context of Nepal. It has been conclusively demonstrated that securing land tenure or land security and adopting appropriate agricultural methods, such as agroforestry, can be instrumental in enabling Nepalese households to cope with the effects of climate change effectively.

The paper aims to investigate key aspects of climate change adaptation strategies of Multinational Corporations (MNCs) across two different climate-vulnerable country contexts, developed, i.e. Australia and developing, i.e. Bangladesh, while identifying the key factors affecting the formulation and implementation of such strategies.

The research uses a qualitative research method using interviews and document analysis while considering distinctive factors manifest in Australia and Bangladesh and focussing on the agricultural seed business sector.

The research reveals that no specific pattern of adaptation strategies exists across MNCs. They either follow a proactive “deliberate” strategy or a reactive “emergent” strategy. MNCs also follow a distinct strategy, “subliminal”, i.e. unintended or inadvertent strategy, by following the “business as usual” approach.

In recent years, many MNCs have started embracing strategies to reduce their negative environmental footprint but barely adopted any formal strategies to adapt to climate change impacts on their business operations. This study provides insights into the existing climate change adaptation strategies of MNCs, which could be beneficial for companies in better planning and implementing their existing as well as future climate change adaptation strategies.

Based on a developed-developing country comparison and together with a novel focus on the agricultural seed business sector, the paper has used a variety of business strategies in providing insights and understanding of the status of MNC climate change adaptation strategies. The research has identified and coined the term, “subliminal” or unintended strategy as a new addition to the MNC adaptation strategy literature.

The blue and green firms are notable contributors to sustainable development. Similar to other businesses in circular economies, blue and green firms also face financing constraints. This paper aims to assess whether blue and green lending help in optimizing the interest rate spreads and the likelihood of default.

This analysis is based on an unbalanced panel of banks from 20 eurozone countries for eleven years between 2012 and 2022. The key indicators of banking include interest rate spread and a market-based probability of default. The paper assesses how these indicators are influenced by exposure to green and blue firms after controlling for several exogenous factors.

The results show a positive relationship between green and blue lending and spread, while there is a negative link with the probability of default. This confirms that the blue and green exposure positively supports the credit portfolio both in terms of profitability and risk management.

The banking system is among the key contributors to corporate finance and to enable continuous access to sustainable finance, the banking firms must be incentivized. While many studies analyze the impact of green lending, to the best of the authors’ knowledge, this study is among the very few that extend this analysis to blue economy firms.

This study assesses postreplanting oil palm farming risks, analyzes seed procurement parameters, investigates seed institutions' performance factors and develops a framework for improved sustainability.

Incorporating data from 219 smallholder farmers in designated replanting areas, our study comprehensively evaluates seed supply performance, examining the roles of stakeholders and identifying potential risks in seed management. We assess these risks using the Risk Priority Number (RPN) methodology and Multidimensional Scaling (MDS) techniques.

The results show that the timing and quantity of oil palm seed supply have a relatively small impact on postreplanting failure risk. To mitigate this risk, focus on monitoring seed purity using high-quality Tenera oil palm-type seeds and early detection technology. Encourage seed-producing cooperatives to become legal seed producers for an inclusive system and consider smallholders' variety preferences.

This study’s significance lies in its comprehensive assessment of the risks associated with oil palm replanting on smallholder plantations, detailed analysis of critical parameters in seed procurement, investigation into the performance of palm oil seed institutions across various dimensions and development of a strategic framework to strengthen inclusive seed institutions for sustainable oil palm farming. This strategy holds valuable potential for the development of oil palm in Indonesia, particularly in expediting the smallholders' replanting program.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2023-0811