Hierarchical Objective Research Articles

The Ukraine invasion: Hierarchy, discipline and counterbalance

This article examines Russia’s 2022 military intervention in Ukraine through an eclectic theoretical framework that synthesises hierarchy theory, foreign-imposed regime change, and negative balancing strategy. We argue that Russia justified the attack as a means to protect its hierarchical objectives in the post-Soviet region against three perceived imminent threats: Ukraine’s independent trajectory, the growing alignment of certain post-Soviet states with the US-led hierarchy, and the expansion of the US-led Western hierarchy into the post-Soviet space. In response, Russia employed the hard face of its negative balancing strategy by launching an overt foreign-imposed regime change operation to replace Zelensky’s government with a pro-Moscow regime. From the outset, this operation has been tasked with two key aims: discipline and balance. The disciplinary objective has focused on ensuring Ukraine’s loyalty to Moscow and deterring other Western-leaning post-Soviet states from emulating Ukraine by sending a clear disciplinary message. The balance objective has sought to counter the negative and outbidding strategies of the US-led hierarchy, which Russia perceives as a neo-containment agenda aimed at undermining its regional influence.

Can we stop reinventing the wheel in blue-green infrastructure planning? Using value-focused thinking to enable transferability of a multicriteria planning support system

Strategic planning approaches are essential for addressing the complex and multi-dimensional challenges in selecting blue-green infrastructure (BGI) location. The development of planning support systems (PSS) for BGI siting often lacks a value-based approach with early stakeholder engagement. In this study, we used value-focused thinking (VFT) to adapt and develop a BGI planning support framework to the Canadian urban context, moving towards an objective-driven framework. We provide a hierarchical objectives framework for BGI planning in urban areas, aligned with stakeholder values, with three-level information: higher-level objectives, lower-level objectives and measurable spatial attributes. To assess site performance across these attributes, value scales elicitation was conducted with 17 experts. The engagement of 37 stakeholders in co-creating and validating our objectives set helped identify common concerns and refine our framework, focusing on the most relevant objectives for stakeholders. The VFT approach and the resulting framework can be easily transferred to other cities in Quebec (Canada), but also to other urban contexts looking to optimize BGI site selection to maximize benefits across the multiple functions of BGI and opportunities arising from the urban context.

Branch-cut-and-price algorithms for two routing problems with hotel selection

The Traveling Salesperson Problem with Hotel Selection (TSPHS) and the Orienteering Problem with Hotel Selection (OPHS) are combinatorial optimization problems that arise in various real-world applications, from logistics to tourism. Both problems consider a set of customers and hotels and aim to find a sequence of concatenated trips limited in time. A trip is defined as a sequence of visited customers, starting and ending at hotels, such that any other nodes are customers. The main differences between the problems are the following. The TSPHS assumes that all customers are visited and involves two hierarchical objectives: reducing the number of trips and minimizing the total travel time. On the other hand, in the OPHS, not all customers are required to be visited; rather, each customer is associated with a positive profit, and the objective is to maximize the total profit related to the visited ones. In this paper, we propose exact branch-cut-and-price algorithms for these problems that are coded within the VRPSolver framework. For TSPHS and OPHS, we show that the proposed methods outperform the best exact models from the literature and optimally solve most of the open benchmark instances.

Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data.

Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a nonlinear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared. We propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods. Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO) available at https://github.com/ICB-DCM/pyPESTO.

Multi-Trip Vehicle Routing Problem with Time Windows and Resource Synchronization on Heterogeneous Facilities

Inspired by long-distance road transport in industrial logistics in China, this paper studies a simultaneous loading scheduling and vehicle routing problem over a multi-workday planning horizon. Industrial cargo often requires specialized facilities, and these facilities vary in performance and quantity and are subject to available time constraints. Consequently, achieving coordinated optimization of vehicle routing and loading scheduling becomes a significant challenge in practice. We describe the studied problem as a multi-trip vehicle routing problem with time windows and resource synchronization on heterogeneous facilities. First, we develop a mixed integer programming model in a multi-workday setting to minimize the total travel distance and the number of vehicles. Moreover, a three-phase heuristic approach is developed. An initial solution is constructed using a sequential strategy in the first phase, and then an adaptive large neighbourhood search and a post-optimization procedure based on ejection chains are, respectively, designed to optimize the two hierarchical objective functions. Finally, extensive computational experiments are conducted to demonstrate the effectiveness of the proposed method. Specifically, the research results indicate that in long-distance road transport in industrial scenarios, expanding the planning horizon from a single workday to a multi-workday period could significantly reduce logistics operational costs and improve service quality.

Select, route and schedule: optimizing community paramedicine service delivery with mandatory visits and patient prioritization.

Healthcare delivery in the United States has been characterized as overly reactive and dependent on emergency department care for safety net coverage, with opportunity for improvement around discharge planning and high readmissions and emergency department bounce-back rates. Community paramedicine is a recent healthcare innovation that enables proactive visitation of patients at home, often shortly after emergency department and hospital discharge. We establish the first optimization-based framework to study efficiencies in the management and operation of a community paramedicine program. The collective innovations of our modeling include i) a novel hierarchical objective function with the goals of fairly increasing patient welfare, lowering hospital costs, and reducing readmissions and emergency department visits, ii) a new constraint set that ensures priority same-day visits for emergent patients, and iii) a further extension of our model to determine the minimum supplemental resources necessary to ensure feasibility in a single optimization formulation. Our medical-need based objective function prioritizes patients based on their clinical features and seeks to select and schedule patient visits and route healthcare providers to maximize overall patient welfare while favoring shorter tours. We use our methods to develop managerial insights via computational experiments on a variety of test instances based on real data from a hospital system in Upstate New York. We are able to identify optimal and nearly optimal tours that efficiently select, route, and schedule patients in reasonable timeframes. Our results lead to insights that can support managerial decisions about establishing (and improving existing) community paramedicine programs.

Influence of motivation on teachers’ job performance

Motivation is the key to success in educational institutions, and it empowers a teacher to work with an affection that contributes to the accomplishment of hierarchical objectives. Yet, what drives school teachers to be pleased or motivated to achieve exceptional performance? This contemplation must be considered thoroughly in different regions with different predictors. Therefore, this study aims to identify the factors influencing teachers’ motivation and evaluate the influence of motivation on teachers’ job performance in private schools in Mirpurkhas, Pakistan. We use quantitative statistics and a partial least-squares structural equation modeling (PLS-SEM analysis) design; the data was collected through a survey questionnaire. We found that motivation significantly influences teachers’ job performance. The study revealed that self-determined and non-self-determined motivation and factors influencing teachers’ motivation significantly impact teachers’ job performance. The administration must formulate teachers’ motivational policies and practices to meet their needs. Furthermore, school administrations should provide adequate resources like bonuses, rewards, good communication, moral support, emotional support, and an increment in salaries to ensure quality learning and yield high performance from their teaching staff to improve the relevant education system.

An enhanced adaptive large neighborhood search for fatigue-conscious electric vehicle routing and scheduling problem considering driver heterogeneity

Driving fatigue is an important safety hazard for logistics enterprises as it increases accident risks and deteriorates drivers’ well-being. To effectively manage driving fatigue, a novel problem structure named fatigue-conscious electric vehicle routing and scheduling problem considering driver heterogeneity (FEVRSPH) is investigated in this paper. To address both profit and fatigue concerns, hierarchical objectives are proposed in the model of FEVRSPH, with minimizing the number of vehicles as the primary objective and minimizing the maximum driving fatigue as the secondary. In particular, the impact of driver heterogeneity in terms of circadian rhythm and driving time on fatigue accumulation and alleviation is considered in the model. To address the computational challenges of FEVRSPH, an enhanced adaptive large neighborhood search (EALNS) is introduced in this paper. Several enhancement strategies based on the problem knowledge are designed in EALNS, including an independent search mechanism of customer nodes enabled by the feasibility test operator to enlarge the search space, a modified initialization method to improve the quality of initial solutions, and a reverse scheduling approach to rapidly reduce the maximum driving fatigue. The following findings are obtained from the comprehensive experiments: (1) the proposed strategies considerably enhance the performance of EALNS; compared with four state-of-art meta-heuristics, EALNS reduces the average values of the number of vehicles by 3.63%–10.61% and obtains the optimal average fatigue index in 11 out of the 12 sets; (2) considering driver heterogeneity is necessary for practical applications as it improves the quality of the solution and avoids violating time windows and fatigue thresholds; (3) by directly optimizing driving fatigue, the average fatigue index is reduced by 14.97%–20.58% at the cost of a maximum 6.09% increase in driving distance; (4) the proposed model and EALNS are effective in real-world applications by evaluating a case study.

Force evaluation of internal cable of prestressed grids based on field monitoring and hierarchical objective model updating

Force evaluation of internal cable of prestressed grids based on field monitoring and hierarchical objective model updating

New Algorithms for Hierarchical Optimization in Kidney Exchange Programs

New Exact Approaches Tailored for Kidney Exchange Programs with Hierarchical Objectives Kidney exchange programs increase the rate of living donor kidney transplantation. Whereas effective integer programming models aimed at maximizing the total number of transplants have been proposed in the literature, these cannot always be extended to handle a hierarchy of objectives, which is often a requirement in practice. In “New Algorithms for Hierarchical Optimization in Kidney Exchange Programs,” Delorme, García, Gondzio, Kalcsics, Manlove, and Pettersson introduce a new integer programming framework to solve large-size instances of kidney exchange programs. The authors use an ad hoc preprocessing and a reduced-cost variable fixing algorithm to dramatically decrease the size of the models, and they devise a diving algorithm that exploits the hierarchical structure of the problem to significantly reduce the number of integer programs that need to be solved. They also show that it is possible to transition between models as different layers are traversed in the hierarchy, allowing each layer to be solved with the most effective model. Experiments on three different European kidney exchange programs show that running times can be reduced by up to three orders of magnitude.

THE CONTEMPORARY HEALTH MANAGEMENT OF COVID-19 PANDEMIC CRISIS: BRIDGING FROM THE PAST TO THE FUTURE

Contemporary management is a current process and updating science by which the manager plans, organizes, leads, and achieves organizational ultimate goals to accomplish hierarchical objectives and outcomes. Contemporary management skills also include overseeing individuals achieve institutional goals and objectives, maintaining power, ensuring discipline, and making sure that the strategy aligns with the company’s vision, mission, and long-term goals. There are many management theories that could be adopted by the managers of organizations and healthcare institutions and can be used as a guideline for successful and effective management styles at their management level. System Management and contingency theory are examples of the theories of contemporary management. COVID-19, for instance, is one of the global emergencies that required all levels of healthcare sector managers to respond appropriately and promptly and to be dynamic in their management style. In the light of the Covid-19 pandemic, this essay will address the management strategy and the macro factors impacting contemporary management, the contemporary management skills and attributes, the management system and change management theory, and the management style and practice, with some management insights and reflections from readings and experiences, conclusions, and recommendations.
 Keywords: Contemporary management, COVID-19, Crisis management, Health management, Resources management.

Heuristic Approaches for Solving bi-objective Single Machine Scheduling Problems

In this research paper, n jobs have to be scheduled on one-machine to minimize the sum of maximum earliness and maximum tardiness. We solved a series of bi-criteria scheduling problems that related to minimize the sum of the maximum earliness and tardiness. Three new algorithms were presented, two for hierarchical objective and one for the simultaneous objective. Using the results of these algorithms, we minimize the sum of maximum earliness and maximum tardiness. This objective considered as one of the NP-hard problem, and it is also irregular, so this objective missed some helpful properties of regularity. The proposed algorithms had simple structures, and simple to implement. Lastly, they tested for different n.

Using public transport in a 2-echelon last-mile delivery network

In this paper, we investigate the integration of public transport services into last-mile delivery networks. The free capacity of an already established public transport system that operates according to a given timetable on predetermined lines is used to carry goods from outside of the city into the city center. Dedicated bus or tram stations of the public transport network serve as satellites. From these satellites, city freighters pick up the goods and deliver them to the final customers. The main contribution is an extensive computational study, in which we consider various scenarios of the shared part of the public transport network and investigate different hierarchical objectives minimizing two of the three key indicators (number of city freighters, routing costs, and number of trips). We quantify the tradeoff between the key indicators for different objectives and the impact of limiting the capacity of public transport vehicles on two instance sets with different demand distributions. The key instrument to conduct all these experiments are effective exact and heuristic branch-price-and-cut algorithms that we develop and evaluate in detail.

An Empirical Study On Employee's Welfare Facilities In Hospitals In Madurai

Wellbeing, security and welfare are the measures of advancing the productivity of representative. The different welfare measures gave by the business will have prompt effect on the wellbeing, physical and mental proficiency, readiness, spirit and general effectiveness of the laborer and along these lines adding to the higher efficiency. The essential propose of representative welfare is to improve the life of workers and to keep them upbeat and directed. Welfare measures might be both statutory and non-statutory; laws require the business to extend certain advantages to representatives notwithstanding wages or pay rates. In the present study an endeavor has been made to think about the worker welfare offices and its effect on representatives effectiveness at Apollo healing center, Madurai The study demonstrate the Employees welfare offices and its effect on workers productivity at Apollo clinic, Madurai seem great. The normal mean score and rate score of the generally speaking of 22 things has been figured at 3.64(66%).It can be presume that the worker welfare offices gave by the organization to representatives are fulfilled and it is exemplary, yet of degree is there for further change. So that proficiency, viability and profitability can be improved to perform the hierarchical objectives.

A GP-Based Hierarchical Objectives Decision-Making Method for Building Energy Efficiency Optimization

Building energy efficiency, which is critical in reducing environmental impact, has become one of the most important objectives of building designs. In order to precisely express the goals of building designs, and help decision makers estimate the ultimate performance of design schemes in advance when searching for the optimal building design, the Goal Programming Model (GPM) is introduced in this study to provide a solution for explicit design objective delivery and multi-stakeholder involved decision-making support. In this proposed method, EnergyPlusTM works as a simulation engine to search for the relationship between design parameter combinations and building energy consumption. Simultaneously, Genetic Algorithm (GA) is used to improve the efficiency of overall building energy performance optimization by processing multiple iterations. A case study with five possible design scenarios was dedicated in this study to implement the proposed optimization method, and the optimization results verified the capacity of the established GP-based optimization method to satisfy various design requirements for decision makers and/or stakeholders, especially in facing the hierarchical objectives with different priorities. In this case, the envelope-related variables, including the exterior wall and window, serve as optimization objectives. The optimization is carried out under the ideal air conditioning system, considering different energy usage patterns. Meanwhile, comparing with the vague and restricted expression of objectives in multi-objective optimization, the proposed GP-based optimization method provides explicit trade-off relationships among various objectives for designers, which improves the practical value of the optimized designs, so as to ensure the project success and facilitate the development of green buildings.

Fairness in ambulance routing for post disaster management

Disaster management generally includes the post-disaster stage, which consists of the actions taken in response to the disaster damages. These actions include the employment of emergency plans and assigned resources to (i) rescue affected people immediately, (ii) deliver personnel, medical care and equipment to the disaster area, and (iii) aid to prevent the infrastructural and environmental losses. In the response phase, humanitarian logistics directly influence the efficiency of the relief operation. Ambulances routing problem is defined as employing the optimisation tools to manage the flow of ambulances for finding the best ambulance tours to transport the injured to hospitals. Researchers pointed out the importance of equity and fairness in humanitarian relief services: managing the operations of ambulances in the immediate aftermath of a disaster must be done impartially and efficiently to rescue affected people with different priority in accordance with the restrictions. Our research aim is to find the best ambulance tours to transport the patients during a disaster in relief operations while considering fairness and equity to deliver services to patients in balance. The problem is formulated as a new variant of the team orienteering problem with hierarchical objectives to address also the efficiency issue. Due to the limitation of solving the proposed model using a general-purpose solver, we propose a new hybrid algorithm based on a machine learning and neighbourhood search. Based on a new set of realistic benchmark instances, our quantitative analysis proves that our algorithm is capable to largely reduce the solution running time especially when the complexity of the problem increases. Further, a comparison between the fair solution and the system optimum solution is also provided.

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Probability distributions are key components of many learning from demonstration (LfD) approaches, with the spaces chosen to represent tasks playing a central role. Although the robot configuration is defined by its joint angles, end-effector poses are often best explained within several task spaces. In many approaches, distributions within relevant task spaces are learned independently and only combined at the control level. This simplification implies several problems that are addressed in this work. We show that the fusion of models in different task spaces can be expressed as products of experts (PoE), where the probabilities of the models are multiplied and renormalized so that it becomes a proper distribution of joint angles. Multiple experiments are presented to show that learning the different models jointly in the PoE framework significantly improves the quality of the final model. The proposed approach particularly stands out when the robot has to learn hierarchical objectives that arise when a task requires the prioritization of several sub-tasks (e.g. in a humanoid robot, keeping balance has a higher priority than reaching for an object). Since training the model jointly usually relies on contrastive divergence, which requires costly approximations that can affect performance, we propose an alternative strategy using variational inference and mixture model approximations. In particular, we show that the proposed approach can be extended to PoE with a nullspace structure (PoENS), where the model is able to recover secondary tasks that are masked by the resolution of tasks of higher-importance.

Lexicographic multi-objective road pricing optimization considering land use and transportation effects

This paper develops a bi-level multi-objective model for road pricing optimization considering land use and transportation effects. The upper-level problem determines a cordon-based road pricing scheme, while the lower-level problem models the interaction between land use and transportation. To facilitate decision-making in a scenario characterized by a hierarchical ordering of objectives, a novel α-conditional lexicographic optimization method is established, which uses an α value to capture the decision-maker's perceived acceptability of the trade-off between different objectives with respect to the hierarchical objective ordering. The properties associated with this approach are derived, and an algorithm to find the α-conditional lexicographic dominance solutions is developed. To solve the model, a revised genetic algorithm is further developed to illustrate how the proposed α-conditional lexicographic optimization method can be embedded into existing heuristic or metaheuristic methods. A case study using data from Jiangyin, China, demonstrates the significance of considering land use effects when evaluating road pricing scenarios. The results reveal the trade-off between transportation and various land use objectives and the variation of such a trade-off among different types of traffic analysis zones. It is demonstrated that the proposed α-conditional lexicographic approach can improve most of the land use objective values while ensuring that the total travel time is constrained within an acceptable range, enabling a balance between various land use and transportation objectives.

Distributed Secondary Control of Islanded Microgrids for Fast Convergence Considering Arbitrary Sampling

This paper proposes a novel distributed secondary control of MGs for fast convergence considering asynchronous sampling. With the employment of the algorithm, optimal power sharing and voltage restoration are implemented simultaneously. First, the hierarchical control objectives concerned with economic operation and voltage quality are introduced. Then, the execution process of the fast convergence algorithm is described for weighted average state estimation, with the illustration of corresponding features and the application in cooperative control. Further, the relevant stability issue is discussed based on large-signal dynamic modeling and a sufficient stability condition is derived based on the Lyapunov–Krasovskii theory. Our approach offers superior reliability, flexibility and robustness because of the loose implementation in terms of its performance concern, which is essential when the distributed consensus protocol is likely to yield toward deviation or even instability under arbitrary sampling and delays. The effectiveness of the proposed methodology is verified via simulations.

A MCDM-Based Meta-Heuristic Approach for U-shaped Disassembly Line Balancing Problem

Since the rapid development of technology advancement and accelerated upgrading rate of mechanical, electrical, and electronic equipment, dealing with the recovery, recycling, and remanufacturing of end-of-life (EOL) products are of increasing importance. The disassembly line balancing problem (DLBP) aims at addressing such concern in the most efficient and effective manner. In this paper, a meta-heuristic approach involving both multi-criterion decision making (MCDM) and variable neighborhood search (VNS) is proposed to the U-shaped layout of the disassembly line. Following the MCDM method combining fuzzy set theory, grey relational analysis, and Choquet fuzzy integral, an improved VNS approach with a specially designed decoding method is utilized to balance a U-shaped line with three hierarchical objectives. Finally, computational experiments validate the effectiveness of the proposed approach.