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This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

The essence of finance has become essential in the sustainability discussion in recent times as a result of the capital intensive nature of sustainable projects. This has motivated financial experts and institutions to develop various financial instruments and mechanisms to further advance the course of protecting the environment, and decreasing the release of excess carbon and GreenHouse Gases. This is to also provide the opportunity for funding Green or sustainable infrastructure development. This chapter advances a discourse on matters relating to sustainable financing of infrastructure projects. The fundamentals of sustainable or green funding of infrastructure projects, and sustainable schemes of financing green infrastructure projects are discussed.

Although trust is generally taken as a fundamental factor in influencing relational behavior in contractor–subcontractor collaboration, the determination of an optimal level of trust is still lacking. Trust with an optimal tipping point that matches dependence best is considered the optimal trust to improve relational behavior between general contractors and subcontractors. To fill the knowledge gap, this study explores how combinations of trust and dependence trigger relational behavior between general contractors and subcontractors through a configurational approach.

Questionnaires were administered to 228 middle management and technical staff members of the general contractor. The data were analyzed using fuzzy-set qualitative comparative analysis (fsQCA), and the inductive analytic method allowed researchers to explore configurations of different dimensions and levels of dependence and trust.

Necessity analysis results indicated that neither dependence nor trust was a necessary condition for facilitating relational behavior. Through sufficiency analysis, four configurations of optimal trust matched with dependence were identified in contractor–subcontractor collaboration. Even if contractors rely only on subcontractors for resources, the optimal trust between contractors and subcontractors should include both institution- and cognition-based trust. In the event that contractor–subcontractor collaboration involves relational dependence, both affect- and cognition-based trust are necessary for the optimal trust.

This study enhances existing research by delving deeper into a nuanced understanding of optimal trust in dependence scenarios, and enriches project governance theory by uncovering the internal transmission of relational governance. Practically, this study offers general contractors guidance on how to establish optimal trust strategies based on the dual dependence level with subcontractors, which can facilitate subcontractors' relational behavior, and ultimately improve contractor–subcontractor collaboration performance.

The unitized curtain wall system (UCWS), one of the prefabricated technologies, is increasingly attracting attention in the Hong Kong construction industry. However, this innovative technology still lacks on-site implementation in high-rise residential buildings. To promote its development, this study aims at identifying the influential factors of UCWS adoption in Hong Kong's high-rise residential buildings from a multi-stakeholder perspective.

Factors were first selected through an in-depth literature review and a semi-structured interview. Then the factors were validated through a questionnaire survey using Cronbach's Alpha Reliability Test. Next, the factors were ranked regarding their importance using mean-score ranking and standard deviation. Meanwhile, different stakeholders were clustered using an experimental factor analysis (EFA) model to find the shared preferences (namely common factors).

The result shows that reduction of construction time (B1) and insufficient site storage area (C1) are the most important factors. The six stakeholder groups were clustered into two segments. B1 and improved quality control are the shared interests. While C1 and the need of specification change are the common concerns.

There are two major breakthroughs in this study. First is the novelty of research objects. UCWS, particularly its application preference in high-rise residential buildings, has rarely been studied, yet it is urgently required. Second is the novel research perspective. The influential factors were studied from a multi-stakeholder perspective. Not only the significant factors for six specific stakeholders but also the shared preference for stakeholder groups was identified. The findings contribute to promoting UCWS more targeted, efficient and comprehensive, as well as demonstrating the collaborative possibilities of multi-stakeholders.

The understanding of how to mitigate opportunism in construction projects is still limited and conflicting. The complexity of causalities and interdependence among antecedents of opportunism (transaction characteristics and governance mechanisms) is the major obstacle to current research. This study takes a holistic perspective to explore the different combinations of conditions that lead to high opportunism and low opportunism in project management.

Through 2 phases of the interview and questionnaire survey, the 91 valid survey data were collected from the buyer–seller relationships in construction projects and analyzed by adopting fuzzy-set qualitative comparative analysis.

A single transaction characteristic is rarely sufficient to explain opportunism, and combinations of different transaction characteristics and governance mechanisms (performance ambiguity, asset specificity, buyer's requirement certainty, informal control, and formal control) have different effects on opportunism. In the case of extremely unsatisfactory transaction characteristics, even the combination of formal and informal control cannot prevent high opportunism. The combination including low-formal control and high-asset specificity easily leads to high opportunism. Besides, performance ambiguity is a vital factor in mitigating high opportunism or achieving low opportunism.

Previous studies have always addressed the role of one or some factors independently and separately. This study is one of the first to explore the different combinations of conditions that result in high opportunism and low opportunism in project management based on transaction costs economics and agency theory.

This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.

The preferences and adoption of recycled materials by consumers are subject to a variety of factors, such as enablers and barriers. Despite this, there exists a paucity of research concerning stakeholders' perceived value and real purchase decision towards recycled products. Consequently, this research study aims to fill this gap by investigating stakeholders' perceived value of recycled products derived from construction and demolition (C&D) waste and its effect on purchase decisions.

Research data were collected from 219 valid questionnaires completed by Chinese stakeholders. Structural equation modeling (SEM) was then employed to test eight hypotheses.

The results show intrinsic cue (materials) and extrinsic cue (brand) influence the stakeholders’ judgment on C&D waste recycled products’ value and then their purchase intention. However, cues such as quality, word-of-mouth, price, policy and advertised have not play a significant role in practice.

This research study verified the significance of brand and material cues on decision making for purchasing C&D waste recycled products, providing new insights to policy making to enhance the uptake of C&D waste recycled products in construction industry.

In view of the increasing trend in food policies targeting the promotion of consumer interest in locally produced foods and growing developments in willingness-to-pay (WTP) methodologies, the authors investigate consumer preference for packaged traditional drink asaana.

The study used a simple random sample of 336 consumers to draw on perception index and contingent valuation methods to evaluate consumers' perceptions of the attributes of packaged asaana – a traditional maize-based beverage produced in Ghana (also known as Ghana Coca-Cola). A tobit regression model was employed to analyze consumers’ WTP for the product.

Analyzing the factors that influence consumers' WTP for packaged asaana using the tobit regression model, the study established the existence of positive health and nutrition, economic benefits and purchasing decision-making perceptions for asaana. While the results further showed that consumers are willing to pay a premium for well-packaged asaana, demographics such as age, income level, labeling, price of the product and savings were found to exert significant influence on consumers’ WTP for packaged asaana. Salient recommendations for food processors and relevant government agencies and food policy implications are identified.

Comprehending WTP provides valuable understanding regarding consumer qualms, actions and WTP for more secure traditional drinks and an examination of how the different factors that influence WTP for local beverages help boost local beverage production and guarantee employment.

Analyzing WTP data for traditional drinks reveals important implications for production, marketing and public health policies. Certification systems for traditional beverages may be beneficial, and the findings can be used to create public awareness campaigns about the safety of local drinks.

Assessing the WTP among Ghanaian consumers for traditional drinks, specifically asaana, is a ground-breaking study. The contingent evaluation (CE) and tobit regression approaches utilized in this research are strong, and the results obtained can guide decisions related to traditional drink production, marketing and the development of public health policies.

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.

This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.

This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.

The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.

The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.

This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.

The purpose of this paper is to verify how distributed cognition enhances collaborative problem-solving in the context of projects.

Using qualitative research and in-depth interviews, a sample of 32 project managers with experience in traditional and agile methods acting in Brazil and internationally participated in the research process. The analysis process, utilising coding techniques, involved stages: open, axial, coding and selective coding. These stages encompassed the evaluation of categories based on a hierarchy, in order to determine an appropriate level of abstraction that properly explains theoretical findings.

The results indicate that distributed team cognition is significant for collaborative problem-solving. The data from the interviews allowed the proposal of a model of cognition, and the identification of the elements that support it.

Understand how aspects of distributed team cognition can impact the behaviours of the project professional and contribute to problem-solving in the project environment.

The elements observed affects the collaborative problem-solving by presenting a model of distributed cognition, which is composed by directed communication, collective interaction, trust building and collaborative behaviour.