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For successful management of construction projects, a precise analysis of the balance between time and cost is imperative to attain the most effective results. The aim of this study is to present an innovative approach tailored to tackle the challenges posed by time-cost trade-off (TCTO) problems. This objective is achieved through the integration of the multi-verse optimizer (MVO) with opposition-based learning (OBL), thereby introducing a groundbreaking methodology in the field.

The paper aims to develop a new hybrid meta-heuristic algorithm. This is achieved by integrating the MVO with OBL, thereby forming the iMVO algorithm. The integration enhances the optimization capabilities of the algorithm, notably in terms of exploration and exploitation. Consequently, this results in expedited convergence and yields more accurate solutions. The efficacy of the iMVO algorithm will be evaluated through its application to four different TCTO problems. These problems vary in scale – small, medium and large – and include real-life case studies that possess complex relationships.

The efficacy of the proposed methodology is evaluated by examining TCTO problems, encompassing 18, 29, 69 and 290 activities, respectively. Results indicate that the iMVO provides competitive solutions for TCTO problems in construction projects. It is observed that the algorithm surpasses previous algorithms in terms of both mean deviation percentage (MD) and average running time (ART).

This research represents a significant advancement in the field of meta-heuristic algorithms, particularly in their application to managing TCTO in construction projects. It is noteworthy for being among the few studies that integrate the MVO with OBL for the management of TCTO in construction projects characterized by complex relationships.

Most UK ambulance services undertake remote assessments of 999 calls with nurses and paramedics to manage demand and reduce inappropriate hospital admissions. However, little is known about the differences in the types of cases managed by the two professions comparatively, their clinical outcomes, and the quality and safety they offer.

The retrospective descriptive study analysed data collected at Welsh Ambulance Services University NHS Trust (WAST) from prioritisation, triage, and audit tools between the 17th May 2022 to 8th November 2022. A total of 21,076 cases and 728 audits were included for review.

There was little difference in the type and frequency of the presenting complaints assessed, and clinical outcomes reached in percentage terms. Whilst paramedics had more highly compliant call audits and fewer non-compliant call audits, there was, again, little difference in percentage terms between the two, indicating positive levels of safety across the two professional groups.

There continues to be a substantial difference between UK paramedics to those in the Middle East, the United States, and some African nations, which may limit the applicability of findings. This study also looked at a six-month window from only one UK service using one type of prioritisation and triage tool. Future research could explore longer periods from multiple services using various tools. It is important to note that this study did not link outcome data with primary, secondary or tertiary care settings. Thus, it is impossible to determine if the level of care aligned closely with the final diagnosis.

The practical implications of this work include better workforce planning for agencies who have perhaps only employed one type of clinician or a reaffirmation to those who have employed both. The authors suggest that the training and education of both sets of clinicians could remain general in nature, with no overt requirement for specificity based on professional registration alone. Commissioners and stakeholders in the wider health economy should consider ensuring equitable access to alternative pathways for patients assessed by both nurses and paramedics.

It has been posited that UK nurses and paramedics are, by virtue of their consistency in education, skill set, licensure, and general experience, both able to achieve safe and effective remote outcomes in 999 settings. This study provides evidence to support that hypothesis. These results say more about the two professions' ability to work together rather than just the professions themselves. The multidisciplinary team approach is well-established in acute care settings, and is broadly considered to improve communication, coordination decision making, adherence to up-to-date treatment recommendations, and be positive for shared learning and development for younger colleagues.

Most UK services use a mix of nurses and paramedics; however, little is known about the differences in the types of cases managed by the two professions comparatively, their clinical outcomes, and the quality and safety they each offer. The most recent studies of this nature were published in 2003 and 2004 and looked only at low-acuity 999 calls when remote assessment was not even an established role for UK paramedics. This study updates the literature, identifies areas for future research, and applies to the international setting for the most part.

In order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.

First, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.

The cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.

This study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

This study aims to perform innovation analysis for a product based on market, design and process dimensions. This integrated approach provides sustainability for product design and development.

A significant aspect of innovation is investigated to provide energy from the wastes collected in the reverse chain. First, the indicators related to the product opportunity gap were collected and ranked by the structural equation modeling (SEM) method. Indicators with a factor loading above 0.6 are selected and inserted into the proposed mathematical model. The proposed mathematical model was implemented in GAMS 28.2.0 to maximize energy production from waste and minimize the cost of product innovation. A case study on pistachio new packaging process innovation is investigated.

The results showed that in today’s competitive world where sustainability and the environment are important, the index of converting waste into energy is one of the main indicators of innovation. Consequently, flammability is extracted from the mathematical model as one of the most significant indicators leading to higher energy production with the lowest innovation cost.

New product development (NPD) is significant to sustain market share and satisfy customer needs. Different approaches are proposed to handle NPD, mostly focusing on the customer and design requirements.

Many practical control problems require achieving multiple objectives, and these objectives often conflict with each other. The existing multi-objective evolutionary reinforcement learning algorithms cannot achieve good search results when solving such problems. It is necessary to design a new multi-objective evolutionary reinforcement learning algorithm with a stronger searchability.

The multi-objective reinforcement learning algorithm proposed in this paper is based on the evolutionary computation framework. In each generation, this study uses the long-short-term selection method to select parent policies. The long-term selection is based on the improvement of policy along the predefined optimization direction in the previous generation. The short-term selection uses a prediction model to predict the optimization direction that may have the greatest improvement on overall population performance. In the evolutionary stage, the penalty-based nonlinear scalarization method is used to scalarize the multi-dimensional advantage functions, and the nonlinear multi-objective policy gradient is designed to optimize the parent policies along the predefined directions.

The penalty-based nonlinear scalarization method can force policies to improve along the predefined optimization directions. The long-short-term optimization method can alleviate the exploration-exploitation problem, enabling the algorithm to explore unknown regions while ensuring that potential policies are fully optimized. The combination of these designs can effectively improve the performance of the final population.

A multi-objective evolutionary reinforcement learning algorithm with stronger searchability has been proposed. This algorithm can find a Pareto policy set with better convergence, diversity and density.

This paper aims to explore how the effect of knowledge sharing through mergers and acquisitions (M&As) on new product development (NPD) performance is contingent upon two different types of control mechanisms: behavior control and outcome control.

Leveraging the theory from transaction cost economics, this study provides answers regarding the roles of behavior and outcome controls. The hypotheses were tested empirically across a sample of 143 UK cross-border M&A firms.

The results provide the increasing call for an integrative perspective and theory in the M&A literature in that knowledge sharing through M&As is deemed decisive for NPD performance, and while both control mechanisms are effective, behavior control is more effective in enhancing NPD performance than outcome control.

The relevant M&A studies lack insights into the use of control mechanisms as a way to monitor the target firm’s behavior and performance and reduce the risk of its opportunistic behavior. Appreciating the need for M&A literature that elaborates control strategy and structure, this study incorporates behavior control and outcome control into M&A mechanisms.

The purpose of this systematic review is to critically analyze pharmaceutical industry case studies on the implementation of Lean 4.0 methodologies to synthesize key lessons, benefits and best practices. The goal is to inform decisions and guide investments in related technologies for enhancing quality, compliance, efficiency and responsiveness across production and supply chain processes.

The article utilized a systematic literature review (SLR) methodology following five phases: formulating research questions, locating relevant articles, selecting and evaluating articles, analyzing and synthesizing findings and reporting results. The SLR aimed to critically analyze pharmaceutical industry case studies on Lean 4.0 implementation to synthesize key lessons, benefits and best practices.

Key findings reveal recurrent efficiency gains, obstacles around legacy system integration and data governance as well as necessary operator training investments alongside technological upgrades. On average, quality assurance reliability improved by over 50%, while inventory waste declined by 57% based on quantified metrics across documented initiatives synthesizing robotics, sensors and analytics.

As a comprehensive literature review, findings depend on available documented implementations within the search period rather than direct case evaluations. Reporting bias may also skew toward more successful accounts.

Synthesized implementation patterns, performance outcomes and concealed pitfalls provide pharmaceutical leaders with an evidence-based reference guide aiding adoption strategy development, resource planning and workforce transitioning crucial for Lean 4.0 assimilation.

This systematic assessment of pharmaceutical Lean 4.0 adoption offers an unprecedented perspective into the real-world issues, dependencies and modifications necessary for successful integration, absent from conceptual projections or isolated case studies alone until now.