Search results

Family planning is a vital component of reproductive health that enables individuals to make informed decisions about their fertility and plan for the timing and spacing of their pregnancies. However, marginalized communities, often face significant barriers to accessing family planning information and services, resulting in higher rates of unintended pregnancies and poor reproductive health outcomes. This chapter summarizes the current state of knowledge about knowledge related to family planning among marginalized communities, including the factors that shape knowledge, the implications for reproductive health outcomes, and the strategies for improving knowledge and access to family planning information and services. The review highlights the need for continued research, advocacy, and policy development to ensure equitable access to family planning information and services for all individuals, regardless of their demographic characteristics.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

The speed of integration has been a salient and longstanding topic in the literature on managing mergers and acquisitions. Yet over the decades, speed has also been the subject of extensive debate. While many have advocated for fast integration, others have recommended a more measured pace. In this chapter, the authors reflect on the discussion by canvasing the variety of views on the speed of integration. The work is positioned at the nexus of the literature on mergers with that on stakeholders, in particular its attention to urgency in stakeholder management. It approaches urgency in mergers and acquisitions as a “dilemma of stake,” a new lens on a well-established but challenging topic. The study draws on ethnographic research to examine accounts of speed of integration in a New Zealand public sector merger. The chapter juxtaposes varied views on the topic against the respective arguments within the merger literature. It examines the overarching themes of “go slow” and the “need for speed” by attending to the tensions between a prosocial service ethos on the one hand and a managerialist ethos on the other. The explication of the respective dilemmas of stake shows how participants articulate their views on urgency both in terms of its effects on their individual professional role, their own stake, as well as in terms of the effects on employees as internal stakeholders. The analysis also explores the role of internal and external context in shaping the views on urgency in merger integration. The work concludes by outlining an agenda for future research.

Previous research regarding shaping factors and major causes behind accidents in the construction field is reviewed. In particular, a hypothetical model is established to correlate activity time, cost and safety in the context of construction activity acceleration planning. Two demonstration cases are presented to illustrate the proposed theoretical model in the context of critical activity expedition planning. Further, a third case uses a 100-activity project to perform the global level total project time and cost analysis, identifying specific activity acceleration plans that would materialize the shortened total project time at the lowest total project cost.

This research proposes a safety-centric application framework to guide construction acceleration planning at both activity and project levels while taking sufficient preventive measures against safety hazards and accidents. As planning construction acceleration by factoring in safety constraints inevitably drives up cost, it is imperative to control increases in activity costs at the local level in connection with schedule acceleration planning while at the same time not compromising on safety. This research also addresses this critical question through performing global level total project time and cost analysis.

An application framework is proposed for guiding a planner through identifying accident shaping factors, obeying schedule acceleration rules and accounting for safety-related costs in attempts to mitigate hazardous situations on-site at both activity level (local) and project level (global), resulting in (1) minimizing the increase of total project cost in schedule acceleration while at the same time not compromising on safety at individual activities; (2) producing specific execution plans on each individual activity in terms of the amount of time to crash and the associated activity cost.

This study is original in developing theories and methods for evaluating the impact of safety constraints upon construction time and cost in activity acceleration planning and project time-cost analysis. The research fills a gap in knowledge in terms of how to factor in sufficient safety constraints while achieving project time and cost objectives on construction acceleration planning at both activity and project levels.

Temporal leadership is a new construct that predicts team outcomes. This study examines the mediating role of shared temporal cognitions and the moderating role of time pressure in the relationship between temporal leadership and project success within sustainable construction projects.

The multi-source and multi-wave data were collected via self-administered questionnaires from teams working on sustainable construction projects. The direct and mediating hypotheses were tested using multi-level structural equation modelling, while moderated mediation hypotheses were examined by applying the bootstrap method using SPSS Process Macro.

The results showed that temporal leadership enables project success via shared temporal cognitions. Temporal leadership is most beneficial for facilitating project success via shared temporal cognitions when teams experience high time pressure.

This is the first study examining shared temporal cognitions as a mediator of the relationship between temporal leadership and project success. Also, this is the first study that considered time pressure as a boundary condition that influences the relationships between temporal leadership, shared temporal cognitions and project success within sustainable construction projects. The study provides valuable advice to project managers and project-based construction organizations about using and managing time within projects.

Being the final end of the logistic distribution, attended home delivery (AHD) plays an important role in the distribution network. AHD typically refers to the service provided by the distribution service provider to the recipient's doorstep. Researchers have always identified AHD as a bottleneck for last-mile delivery. This paper addresses a real-life stochastic multi-objective AHD problem in the context of the Indian public distribution system (PDS).

Two multi-objective models are proposed. Initially, the problem is formulated in a deterministic environment, and later on, it is extended to a multi-objective AHD model with stochastic travel and response time. This stochastic AHD model is used to extensively analyze the impact of stochastic travel time and customer response time on the total expected cost and time-window violation. Due to the NP-hard nature of the problem, an ant colony optimization (ACO) algorithm, tuned via response surface methodology (RSM), is proposed to solve the problem.

Experimental results show that a change in travel time and response time does not significantly alter the service level of an AHD problem. However, it is strongly correlated with the planning horizon and an increase in the planning horizon reduces the time-window violation drastically. It is also observed that a relatively longer planning horizon has a lower expected cost per delivery associated.

The paper does not consider the uncertainty of supply from the warehouse. Also, stochastic delivery failure probabilities and randomness in customer behavior have not been taken into consideration in this study.

In this paper, the role of uncertainty in an AHD problem is extensively studied through a case of the Indian PDS. The paper analyzes the role of uncertain travel time and response time over different planning horizons in an AHD system. Further, the impact of the delivery planning horizon, travel time and response time on the overall cost and service level of an AHD system is also investigated.

This paper investigates a unique and practical AHD problem in the context of Indian PDS. In the present context of AHD, this study is highly relevant for real-world applications and can help build a more efficient delivery system. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.

The most challenging part of an AHD problem is the requirement of the presence of customers during the time of delivery, due to which the probability of failed delivery drastically increases if the delivery deviates from the customer's preferred time slot. The paper modelled an AHD system to incorporate uncertainties to attain higher overall performance and explore the role of uncertainty in travel and response time with respect to the planning horizon in an AHD, which has not been considered by any other literature.

The software development team reviews the testing phase to assess if the reliability growth of software is as per plan and requirement and gives suggestions for improvement. The objective of this study is to determine the optimal review time such that there is enough time to make judgments about changes required before the scheduled release.

Testing utilizes majority of time and resources, assures reliability and plays a critical role in release and warranty decision-making reviews necessary. A very early review during testing may not give useful information for analyzing or improving project performance, and a very late review may delay product delivery and lead to opportunity loss for developers. Therefore, it is assumed that the optimal time for review is in the later stage of testing when the fault removal rate starts to decline. The expression for this time point is determined using the S-curve 2-D software reliability growth model (SRGM).

The methodology has been illustrated using the real-life fault datasets of Tandem computers and radar systems resulting in optimal review time of 14 weeks and 26 months, respectively, which is neither very early in testing nor very near to the scheduled release. The developer can make changes (more resources or postpone release) to expedite the process.

Most of the literature studies focus on determination of optimal testing or release time to achieve considerable reliability within the budget, but in this study, the authors determine the optimal review time during testing using SRGM to ensure the considerable reliability at release.

The literature on the past triggering learning in strategy practice is scant. To fill this gap, this study aims to examine the meaning of the past to learning in strategy practice and expands on the strategy-as-practice (SAP) literature. Understanding the relationship between the past and learning in strategy practice is important because learning is what keeps strategy practice in motion and remains in place, even if organizations and strategy practitioners change.

The authors used a longitudinal case study design combined with historical methods to examine how the past is embedded in present strategy practice. To capture learning in strategy practice over time, the authors applied a four-stage methodology in our analysis of document and interview data.

The authors identified four dimensions of the past embedded in the present strategy practice. These dimensions emerged from the analysis of the interviews and document data. The study’s results showed that the past appears in structures and routines, materiality, positioning and reflecting over repeated rounds of strategic planning. According to the study’s results, reflecting on strategy practice draws on past structures and routines, positioning and materiality. The past facilitates reflecting and reflecting on the past enables learning in strategy practice.

The authors constructed a conceptual model and showed that in strategy practice, reflection triggers learning. The authors contributed to theory development by demonstrating how the past is embedded in present strategy practice and is available for use by strategy practitioners. The authors showed that strategy practice is a continuous learning process.

The goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.

The path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.

The algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.

An improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.