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Sustainable e-grocery home delivery: an optimization model considering on-demand vehicles
Get PDFThe e-grocery sector has experienced a significant boost since the COVID-19 pandemic, dramatically changing consumer buying behaviours. As demand for faster and more efficient delivery options grows, e-grocery retailers face increasing pressure to optimize home delivery operations. Collaborations with third-party logistics providers (3PLs), although still overlooked, have emerged as promising, offering operational flexibility and environmental benefits. This work introduces an optimization model that supports the design of an on-demand delivery fleet conjunctly with delivery routings and schedules, while considering both cost and environmental impact. To this aim, a vehicle routing problem with time windows (VRPTW) is extended to incorporate on-demand fleet design and three different objective functions embodying a cost-efficient, an environmentally-effective and a cost-environmental balanced perspective respectively. Numerical experiments based on an Italian case study show that prioritizing environmental objectives reduces emissions by over 90%, with marginal increases in annual costs. Besides, on-demand vehicles enable flexibility that facilitates the adoption of sustainable delivery options without requiring challenging investments such as delivery fleet. Several contributions are provided: insights into using on-demand vehicles are proposed; a mathematical model jointly optimizing fleet design and delivery routing and scheduling, while considering both costs and environmental objectives, is developed and its practical application is demonstrated using real-world data. The findings highlight the significant impact of environmental considerations on fleet composition and operational efficiency, offering actionable strategies for e-retailers to reduce emissions while maintaining service quality.Computers & Industrial Engineerin
Multilateral development banks: contributions and challenges
No full textThis chapter examines the contributions of Multilateral Development Banks (MDBs) as well as discusses the challenges confronting them. MDBs obtain their funding from a variety of sources, including the global capital market, contributions and special donations from members, proceeds from the issue of bonds and partnerships, etc., which aid in their ability to fund their activities. Their main operations include the provision of financing, the creation of knowledge products, and serving as a significant source of data collection and analysis, which results in the improvement of policy and the generation of ideas for efficient policy planning and execution. Their contributions include providing a steady source of funding, aiding middle-income nations, meeting a range of development needs, and providing a variety of complementary comparative advantages. However, the severity and regularity of numerous crises have also created major difficulties for MDBs, including resource competition, increase in ad hoc funding decisions, funding vulnerabilities, proliferation of MDB alternatives, and geographical distribution of power. In order to align national and global requirements, it is crucial to revamp the financing patterns of multilateral activities. This can be achieved through the implementation of innovative financial and policy incentives, meticulous evaluation of multilateral funding decisions, enhanced data collection mechanisms, and a more effective prioritisation of multilateral activities.Perspectives on development banks in Africa: case studies and emerging practices at the national and regional leve
Experimental-based Cramér-Rao Lower Bound Estimation of Triaxial Accelerometer Calibration Methods using Monte Carlo Simulation
No full textThe data deposited contains:
1) Code for running the Monte Carlo MATLAB simulations for the six-position and multi-position methods.
2) Calibration algorithms replicated from the literature to run in MATLAB.
3) Experimental data from sampling three LSM6DS3 IMUs for one hour via the multi-position method under constant temperature and humidity.
4) The derivatives required for deriving the CRLB for the six-position method.Low-cost accelerometers can be found in many systems requiring accurate attitude estimation. Their unique thermomechanical responses necessitate frequent recalibrations to maintain a certain level of performance. Established accelerometer calibrations are the six position and multi-position methods requiring static conditions. This paper presents a Monte Carlo simulation comparing these for a sensor with a comprehensive set of calibration errors in the model. Precision of the simulations are compared with the Cramér-Rao lower bound (CRLB) and, for the first time, the CRLB for the six-position method is defined. For the multi-position method, calibration accuracy of six algorithms is assessed by Monte Carlo simulation. The precision of the best performing algorithm is compared with the CRLB using experimental data. The effect of parameter initialisation on the algorithms is observed via simulation and experimentally with two algorithms shown to have sensitivity to initialisation. The types and order of positions sampled in the multi-position method are analysed for the case when only the minimum number are available. One sequence is shown to be best in terms of calibration accuracy and precision.BAE Systems
Experimental-based Cramér-Rao lower bound estimation of triaxial accelerometer calibration methods using Monte Carlo simulation
No full textLow-cost accelerometers can be found in many systems requiring accurate attitude estimation. Their unique thermomechanical responses necessitate frequent recalibrations to maintain a certain level of performance. Established accelerometer calibrations are the six position and multi-position methods requiring static conditions. This paper presents a Monte Carlo simulation comparing these for a sensor with a comprehensive set of calibration errors in the model. Precision of the simulations are compared with the Cramér-Rao lower bound (CRLB) and, for the first time, the CRLB for the six-position method is defined. For the multi-position method, calibration accuracy of six algorithms is assessed by Monte Carlo simulation. The precision of the best performing algorithm is compared with the CRLB using experimental data. The effect of parameter initialisation on the algorithms is observed via simulation and experimentally with two algorithms shown to have sensitivity to initialisation. The types and order of positions sampled in the multi-position method are analysed for the case when only the minimum number are available. Cardinal positions are shown to be best in terms of calibration accuracy and precision.This work was supported by the EPSRC iCASE grant reference EP/S513623/1, BAE Systems and Cranfield University.Sensors and Actuators A: Physica
Provisioning the requirement: machine gun needs of the British expeditionary force in France, 1914-1916
No full textAt the outbreak of the First World War, the British Army was in the process of replacing its Maxim machine guns with the Vickers machine gun it had adopted in 1912. Although both weapons were of the same calibre and shared some common accessories, they were not wholly compatible. Furthermore, with the expansion of the British Expeditionary Force and the concomitant need to increase its automatic firepower, the introduction of more machine guns had to be managed with increased demand and complexity, with additional new models introduced as the war progressed. By analysing the war diaries of the lines of communication, this article evaluates the rollout of the Vickers and later models, and the implications their introduction had on logistics, personnel, and organisation across the Force. It is hoped this approach will offer an insight into the prioritisation and decision-making of this critical period, as well as an opportunity to understand the growing importance of the machine gun ahead of the formation of the Machine Gun Corps in October 1915.Armax: The Journal of Contemporary Arm
Forming rate dependence of novel austenitising bending process for a high-strength quenched micro-alloyed steel: experiments and simulation
Get PDFThis article belongs to the Special Issue Processing, Manufacturing and Properties of Metal and AlloysThis austenitising bending investigation was carried out in a vacuum environment with the forming rates of 1, 10, and 100 mm/min under a certain bending temperature of 900 °C by a thermomechanical simulator. The enhanced strength at the accelerated forming rate and on the compression/tension zones throughout the thickness of the bent plates was discussed in detail in terms of dislocation pile-up, smaller prior austenite grain size, dynamic recrystallisation, smaller martensite packet, and stress-neutral layer. Since the simulation results were validated to match the experimental trend, this investigation could be applied as a valuable reference to simulate the practical manufacturing process of railway fasteners.Processe
Public debt and income inequality in times of austerity: dynamic panel evidence
Get PDFThis paper examines the relationship between public debt levels and income inequality during periods of fiscal consolidation (austerity). Specifically, it investigates two key questions: (a) whether high public debt during fiscal adjustments exacerbates income inequality, and (b) whether the composition of these adjustments influences the debt–inequality link. To address these issues, we apply a panel threshold methodology using annual data from 16 OECD countries over the period 1980–2019. Our findings reveal that public debt significantly affects income inequality, with the impact intensifying during fiscal adjustments, particularly at moderate debt thresholds (30–60%). Furthermore, when comparing the effects of tax-based versus spending-based adjustments, the evidence shows that tax-based consolidations tend to produce more persistent negative effects on income inequality.Forum for Social Economic
V2V UAS communications and use cases for advanced air mobility
Get PDFAdvanced Air Mobility (AAM) is emerging as a transformative application in smart mobility with the latest advances in hardware, software, policy development, and regulations. Unmanned Aircraft Systems (UAS) are supposed to become the backbone of emerging AAM services and applications as connected and software-intensive platforms. However, Vehicle-to-Vehicle (V2V) communications in the AAM still deserve further effort in terms of safety, communication protocols, data exchange requirements, concrete use cases and their subsequent standardization. To this end, this paper presents several use cases for UAS communications and relevant message exchange protocols currently investigated in IEEE P1920.2 Standardization Work Group. The use case constellation entails five fundamental use cases for V2V UAS communications in the AAM domain. This paper begins with an overview of these use cases for potential scenarios. Then, it further delves into two critical ones and describes the relevant data exchange and protocol flows for UAS communications. We believe that this contribution will facilitate the discussion of AAM use cases and crucial aspects of V2V communications employed in these scenarios.2024 IEEE Conference on Standards for Communications and Networking (CSCN
Psychological modeling for community energy systems
Get PDFThis paper introduces a novel framework for community energy system (CES) optimization that integrates Social Cognitive Theory (SCT) with Distributionally Robust Optimization (DRO) to address both behavioral and technical challenges. The rapid integration of renewable energy resources and the proliferation of peer-to-peer (P2P) trading platforms necessitate solutions that balance economic efficiency, environmental sustainability, and user engagement under uncertainty. While existing studies focus predominantly on technical optimization, they often neglect the significant influence of behavioral dynamics on community energy systems. This research bridges the gap by explicitly incorporating peer influence, observational learning, and engagement incentives into a robust optimization framework. The proposed methodology models user behavior through SCT, enabling dynamic adjustments to trading patterns and energy-sharing decisions. A DRO model is employed to handle uncertainties in renewable energy generation and demand, ensuring system reliability and resilience. To enhance computational efficiency, a primal–dual algorithm is developed, offering faster convergence compared to traditional DRO methods. The framework is validated through a comprehensive case study on a 50-household microgrid equipped with solar, wind, and storage systems, alongside a P2P trading platform. Results demonstrate the framework's ability to reduce carbon emissions by up to 30%, improve renewable energy utilization to over 90%, and increase trading participation by 25%, compared to baseline scenarios. This study makes four key contributions: (1) introducing SCT-based behavioral modeling to enhance user engagement in CES operations, (2) leveraging DRO to address uncertainties in renewable energy and demand profiles, (3) proposing a primal–dual algorithm for scalable and efficient optimization, and (4) presenting a unified framework that balances economic, environmental, and social objectives. The findings highlight the transformative potential of integrating behavioral and technical approaches for sustainable and resilient community energy management.The authors would like to acknowledge the support provided by Researchers Supporting Project (Project number: RSPD2025R635), King Saud University, Riyadh, Saudi Arabia.Energy Report
Selective exploration and information gathering in search and rescue using hierarchical learning guided by natural language input
Get PDFIn recent years, robots and autonomous systems have become increasingly integral to our daily lives, offering solutions to complex problems across various domains. Their application in search and rescue (SAR) operations, however, presents unique challenges. Comprehensively exploring the disaster-stricken area is often infeasible due to the vastness of the terrain, transformed environment, and the time constraints involved. Traditional robotic systems typically operate on predefined search patterns and lack the ability to incorporate and exploit ground truths provided by human stakeholders, which can be the key to speeding up the learning process and enhancing triage. Addressing this gap, we introduce a system that integrates social interaction via large language models (LLMs) with a hierarchical reinforcement learning (HRL) framework. The proposed system is designed to translate verbal inputs from human stakeholders into actionable RL insights and adjust its search strategy. By leveraging human-provided information through LLMs and structuring task execution through HRL, our approach not only bridges the gap between autonomous capabilities and human intelligence but also significantly improves the agent's learning efficiency and decision-making process in environments characterised by long horizons and sparse rewards.2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC