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Organizations around the world schedule jobs (programs) regularly to perform various tasks dictated by their end users. With the major movement toward using a cloud computing infrastructure, our organization follows a hybrid approach with both cloud and on-prem servers. The objective of this work is to perform capacity planning, i.e., estimate resource requirements, and job scheduling for on-prem grid computing environments. A key contribution of our approach is handling uncertainty in both resource usage and duration of the jobs, a critical aspect in the finance industry where stochastic market conditions significantly influence job characteristics. For capacity planning and scheduling, we simultaneously balance two conflicting objectives: (a) minimize resource usage and (b) provide high quality of service to the end users by completing jobs by their requested deadlines. We propose approximate approaches using deterministic estimators and pair sampling-based constraint programming. Our best approach (pair sampling-based) achieves up to 41.6% estimated peak reduction in resource usage compared to manual scheduling without compromising on the quality of service. [ABSTRACT FROM AUTHOR]

This study examines whether, and how, top management influences middle management's support of corporate entrepreneurship activities. Specifically, the study explores the influence of top management characteristics, i.e. strategic planning capacity and entrepreneurial orientation, on manager's decision to support innovation adoption. We report on the findings from a vignette study. Research participants included a group of 259 experienced business professionals: 148 from the United States and 111 from China. The results suggest that the alignment of strategic planning capacity and entrepreneurial orientation influences middle management's support for corporate entrepreneurship in both cultural settings. [ABSTRACT FROM AUTHOR]

Novel Optimality Cuts for Two-Stage Stochastic Mixed-Integer Programs The applicability and use of two-stage stochastic mixed-integer programs is well-established, thus calling for efficient decomposition algorithms to solve them. Such algorithms typically rely on optimality cuts to approximate the expected second stage cost function from below. In "A Converging Benders' Decomposition Algorithm for Mixed-Integer Recourse Models," van der Laan and Romeijnders derive a new family of optimality cuts that is sufficiently rich to identify the optimal solution of two-stage stochastic mixed-integer programs in general. That is, general mixed-integer decision variables are allowed in both stages, and all data elements are allowed to be random. Moreover, these new optimality cuts require computations that decompose by scenario, and thus, they can be computed efficiently. Van der Laan and Romeijnders demonstrate the potential of their approach on a range of problem instances, including the DCAP instances from SIPLIB. We propose a new solution method for two-stage mixed-integer recourse models. In contrast to existing approaches, we can handle general mixed-integer variables in both stages. Our solution method is a Benders' decomposition, in which we iteratively construct tighter approximations of the expected second stage cost function using a new family of optimality cuts. We derive these optimality cuts by parametrically solving extended formulations of the second stage problems using deterministic mixed-integer programming techniques. We establish convergence by proving that the optimality cuts recover the convex envelope of the expected second stage cost function. Finally, we demonstrate the potential of our approach by conducting numerical experiments on several investment planning and capacity expansion problems. Funding: The research of W. Romeijnders has been supported by the Netherlands Organisation for Scientific Research [Grant 451-17-034 4043]. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The location and quantity of hydrogen refueling stations (HRSs) play a crucial role in the development and promotion of hydrogen fuel cell vehicles (HFCVs). This study proposes a multi-period urban HRSs site selection and capacity planning with many-objective optimization framework for hydrogen supply chain (HSC). Firstly, the city's multi-period hydrogen requirement is predicted based on a generalized Bass diffusion model. Using publicly available data, including gas station network data, geographic information system (GIS) data, population data, and regional economic data, a spatially aggregated demand model is established to allocate hydrogen requirement at candidate sites in the city of the geographic grid model with a 1 km resolution. On this basis, four interrelated objective functions (total investment cost, hydrogen requirement coverage, risk coefficient, and environmental factors) are developed. The third-generation non-dominated sorting genetic algorithm (NSGA-III) and the technique for order preference by similarity to an ideal solution (TOPSIS) are employed to achieve multi-period urban HRSs site selection and capacity planning with many-objective optimization for HSC. By comparing the results of single-objective optimization focusing on minimum cost and maximum hydrogen requirement coverage, it is observed that many-objective optimization achieves a better balance among the four conflicting objectives. After comprehensive analysis, the distribution of HRSs exhibits a clustered structure, influenced by the population and economic structure of the city. In the initial stages of HFCV development, most stations serve the peripheral areas around the city center; while n the later stages of the optimization period, a few high-capacity HRSs begin to concentrate in the city center. • In this study, two main contributions are achieved: • First, based on the generalized Bass diffusion model and low-resolution GIS data, population data, and regional economic data, the urban hydrogen demand is predicted for multiple periods. • Second, a novel multi-period urban hydrogen fueling station and HSC many-objective optimization framework is proposed. This provides a brand-new solution for the site selection and sizing of urban HRSs and the many-objective optimization of HSCs. [ABSTRACT FROM AUTHOR]

A cloud manufacturing (CMfg) system is presented as a novel service- and customer-oriented manufacturing paradigm that integrates the distributed manufacturing enterprises to share their manufacturing capabilities or resources and collaborate as an interconnected system in a dynamic environment. Since the high performance of this system depends on the formation of a suitable group of manufacturing service providers, this paper develops an integrated c onfiguration design and capacity planning problem for the CMfg system by considering the dynamic environment of this system. In this regard, dynamic service providers and dynamic demand are considered as two aspects of the dynamic nature of this system. A multi-period multi-objective mathematical model is proposed by maximising the utilities of all three stakeholders of the system. Moreover, three extensions of a discrete multi-objective grey wolf optimiser (DMOGWO) algorithm are devised to solve the medium- and large-scale instances. A comprehensive computational experiment is conducted to assess the performance of the developed meta-heuristic algorithms. Furthermore, by carrying out a sensitivity analysis, some managerial insight is suggested for the managers. [ABSTRACT FROM AUTHOR]

Many Tribal Nations are working to build capacity to plan, scope, execute, and operate construction projects internally, yet they face a lack of resources designed to support their efforts to self-perform such projects. In a research study conducted by the authors, 22 professionals in the construction industry were interviewed to identify best practices and strategies for designing and building facilities on tribal lands. This paper presents a construction planning tool borne from these interviews, the Project Definition Rating Index for Tribal Building Projects (PDRI—Tribal Building Projects), which supports planning and executing projects on tribal land considering their unique features. The paper further explores the PDRI—Tribal Building Projects through the lens of the Diné (Navajo) philosophy of Sa'ąh Naagháí Bik'eh Hózhóón (SNBH), and the guiding principles of Nistáhákees (thinking), Nahat'á (planning), Iiná (living), and Sihasin (assurance/reflection). This paper contributes to the architectural engineering and construction planning bodies of knowledge by presenting a new tool to support planning and executing projects on Tribal lands, the PDRI—Tribal Building Projects, and its benefits, including the promotion of culturally responsive communication, ensuring capital projects meet the needs of the tribe, and building capacity for front-end planning in Tribal Nations. The paper also makes explicit connections between the Diné philosophy and the front-end planning process for tribal construction projects. [ABSTRACT FROM AUTHOR]

Hyperledger Fabric is a platform for permissioned blockchain networks that enables secure and auditable distributed data storage for enterprise applications. There is a growing interest in applications based on this platform, but its use requires the configuration of different blockchain parameters. Various configurations impact the system's non-functional qualities, especially performance and cost. In this article, we propose a Stochastic Petri Net to model the performance of the Hyperledger Fabric platform with different blockchain parameters, computer capacity, and transaction rates. We also present a set of case studies to demonstrate the feasibility of the proposed model. This model serves as a practical guide to help administrators of permissioned blockchain networks find the best performance for their applications. The proposed model allowed us to identify the block size that leads to a high mean response time (ranging from 1 to 25 seconds) caused by a change in the arrival rate. [ABSTRACT FROM AUTHOR]

The data movement in large-scale computing facilities (from compute nodes to data nodes) is categorized as one of the major contributors to high cost and energy utilization. To tackle it, in-storage processing (ISP) within storage devices, such as Solid-State Drives (SSDs), has been explored actively. The introduction of computational storage drives (CSDs) enabled ISP within the same form factor as regular SSDs and made it easy to replace SSDs within traditional compute nodes. With CSDs, host systems can offload various operations such as search, filter, and count. However, commercialized CSDs have different hardware resources and performance characteristics. Thus, it requires careful consideration of hardware, performance, and workload characteristics for building a CSD-based storage system within a compute node. Therefore, storage architects are hesitant to build a storage system based on CSDs as there are no tools to determine the benefits of CSD-based compute nodes to meet the performance requirements compared to traditional nodes based on SSDs. In this work, we proposed an analytical model-based storage capacity planner called CsdPlan for system architects to build performance-effective CSD-based compute nodes. Our model takes into account the performance characteristics of the host system, targeted workloads, and hardware and performance characteristics of CSDs to be deployed and provides optimal configuration based on the number of CSDs for a compute node. Furthermore, CsdPlan estimates and reduces the total cost of ownership (TCO) for building a CSD-based compute node. To evaluate the efficacy of CsdPlan, we selected two commercially available CSDs and four representative big data analysis workloads. [ABSTRACT FROM AUTHOR]

The research interest in topics related to production and supply chain planning and control in engineer-to-order (ETO) environments has grown significantly over the last three decades. One of the strategically important and challenging decision areas for planning and control in ETO environments is estimating and quoting feasible delivery dates and assessing the feasibility of customer-imposed delivery dates, collectively referred to as delivery date setting (DDS). While DDS has received substantial attention in literature, research supporting the process in ETO companies is fragmented – lacking clear guidelines for industrial practice and gaps to guide future research on the topic. To address these issues, this study systematically reviews literature supporting DDS in ETO environments, identifying tools and practices proposed in the extant literature, and proposing an agenda for future research. Findings suggest that most of the research has focused on developing planning and decision-support tools for tactical capacity planning to support reliable DDS, however, with a noticeable lack of alignment with industrial decision-support needs of ETO environments. Furthermore, despite previous research emphasising the importance of high levels of coordination and formalisation in the DDS process, there is a lack of research to guide practitioners in achieving high levels of coordination and formalisation. [ABSTRACT FROM AUTHOR]

Sterile services departments are special units designed to perform sterilisation operations in an efficient way within a hospital. The delays in sterilisation services cause significant disruptions on surgery schedules and bed management. To prevent the delays, an upper time limit can be imposed on the time spent in the sterilisation services. In this paper, we propose a mathematical modelling approach for the optimum capacity planning of a sterilisation service unit considering the uncertainties in the sterilisation process. The model aims to find the optimum capacity on four tandem steps of the sterilisation whilst at the same time minimising the total cost and keeping the maximum time in the system below a limit. Assuming general distributions for service and interarrival times, an approximation structure based on robust optimisation is used to formulate the maximum time spent in the system. We analysed the structural property of the resulting model and found that the relaxed version of the model is convex. The real data from a large sterilisation services unit is used for computational experiments. The results indicated that the approximation fits well against the simulated maximum time in the system. Other experiments revealed that an upper limit of 7 hours for the sterilisation services balances the cost vs. robustness trade-off. [ABSTRACT FROM AUTHOR]

As China gradually transitions towards a low-carbon energy structure, the proportion of grid-connected new energy sources like wind and solar power continues to increase. To ensure the safe and reliable operation of the power system while meeting the capacity planning for future new energy installations, there is a need for flexible resources with corresponding adjustment capabilities in the power system. In response to this situation, this paper presents an optimization model for the allocation of multiple types of flexible resources that takes into account spatiotemporal response characteristics. Firstly, a flexibility evaluation model is developed based on spatial and temporal response characteristics. Flexibility evaluation indices, such as flexibility average deficit and flexibility coverage index, are constructed. These indices are used for screening nodes with inadequate flexibility in the power system and analyzing the flexibility adequacy at various nodes. Next, the adjustment characteristics of multiple types of flexible resources are analyzed, and a model for their adjustment capabilities is established. Finally, by considering constraints based on time flexibility evaluation indices, a two-stage optimization model for flexible resource allocation is constructed. This model leverages the multiscale matching characteristics between flexibility resources and the fluctuation patterns of new energy sources to guide the allocation of flexible resources at nodes with insufficient flexibility. The effectiveness and applicability of the proposed flexible resource allocation method are validated using the IEEE 9-node system. [ABSTRACT FROM AUTHOR]

Copyright of GV-executivo is the property of Fundacao Getulio Vargas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation. [ABSTRACT FROM AUTHOR]

The adoption of cloud computing has been accelerating over the last decade, while enterprise cloud users ("firms") are struggling to manage their growing cloud expenditures in the face of intermittent digital demand surges caused by planned or random events. To deal with such challenges, a firm can employ reserved instances with the standard contract length (e.g., one year) to meet the stationary base demand, which we refer to as the base contracts, complemented by additional reserved instances with either standard or shorter contract lengths, which we refer to as the supplementary contracts, to cope with the demand surges. We first analyze a model whereby the surge and inter-surge durations are deterministic, demand magnitude is random, and cancellation of the reservations is allowed. We develop a capacity management plan for the firm including not only the optimal capacity levels, which follow a newsvendor-type solution, but also the optimal policy for managing the purchase, renewal, cancellation, or expiration of the supplementary contracts, which can be characterized as a two-threshold policy. Due to the complexity of the structure of the optimal policy, we also construct an effective heuristic policy by excluding the renewal option from the action space, which can be applied to a more general setting where the surge and inter-surge durations are random. We examine two model extensions: (1) when trades of reserved instances are allowed in a secondary marketplace; (2) when the firm does not have exact information about the distributions of the surge magnitude and duration while it can adjust the capacity levels as data unveils. Our analysis shows that the optimal policy for managing the supplementary contracts depends on the relative magnitude of the surge and inter-surge durations in relationship to the cancellation fee. Moreover, our numerical results show that cloud platforms that offer a secondary marketplace are more attractive to firms from a cost standpoint than those that allow cancellation only. The latter, without the secondary marketplace, however, can achieve parity with the former by offering a deeper discount rate for the reserved instances, thereby bypassing the cost of administering the secondary marketplace. [ABSTRACT FROM AUTHOR]

To sustain the clean energy transition without interruption and to ensure the reliable operation of the transmission system, it is required to have enough additional transmission capacity in the future horizons. The transmission expansion planning (TEP) problem is a core issue in deciding additional transmission capacity in the planning activities. TEP aims to find the best expansion plan while satisfying technical and economic constraints. In this study, a new binary version of the original FBI algorithm called the BFBI (binary forensic-based investigation) algorithm is developed to solve the binary TEP problem. The effectiveness and performance of the developed BFBI are assessed by implementing it in two different test systems: the standard Garver 6-bus test system and the modified 400 kV Turkish grid. Seasonal scenarios are created for 5- and 10-year planning periods to cover all possible generation and load conditions and to assess the impact of the increased share of RES on the grid in the TEP studies conducted for the modified 400 kV Turkish grid created as a bulk realistic grid. The TEP problem is solved by including investment, reliability, and operational costs in two different objective functions for cases while considering the N-1 contingency criterion. The efficacy and robustness of the BFBI algorithm are justified by comparing it with well-known algorithms such as GA and PSO. [ABSTRACT FROM AUTHOR]

In the present paper, we develop a non-homogeneous semi-Markov reward model, deriving expressions for a healthcare system's expected structure along with the expected costs generated by medical services and patients' holding times in the states. We provide a novel definition and investigation for states' availability, which is critical for capacity planning based on service demand in an environment of limited resources. The study is based on patients' mobility through hospital care, where each patient spends an amount of time in every state of the hospital (emergency room, short-term acute care, hospitalization, surgery room, and intensive care unit). Multiple outcomes, such as discharge or death, can also be taken into account. We envisage a situation where any discharges are immediately replaced by a number of new admissions that carry on the pathways of the patients who exit. By assuming an expanding system, the new idea of states' inflows is considered due to new patients who create pathways through hospital care, along with internal entrances. The theoretical results are illustrated numerically with simulated hospital data informed by aggregated public data of the Greek public health sector. The framework can be used for both strategic planning and cost evaluation purposes for hospital resources. [ABSTRACT FROM AUTHOR]

The quest for sustainable urban development requires innovative planning approaches more apt to cater for transformative action. Based on a case study approach, this study aims to explore the potential for local planning to develop practical approaches able to accelerate the transition towards sustainable urban development. To guide spatial development towards sustainability, the analysis of the results shows that local planners need to envision a process design as to advance the understanding of how a site or area can be developed. The act of process design involves pending between processes of contextualization (i.e. exploring five distinguishable domains of planning inquiry) and concretization (i.e. deciding upon a course of action by executing activities that form a process trajectory). Regarding the potential of local planning to develop transformative approaches, it is concluded that: (i) local planners possess capabilities that allow them to probe the future of places and produce insights about prospective change, (ii) the transformative capacity of local planning practices is bound to the development of the identified domains of planning inquiry and (iii) forthcoming evaluations organized around the act of process design can reveal insights regarding the possibilities to put the notion of sustainability into practice at the local level. [ABSTRACT FROM AUTHOR]

Fossil fuel combustion to produce power is responsible for a substantial amount of greenhouse gases and is essential to climate change and global warming. Therefore, the leading industrial countries are urged to invest in renewable energy and reduce reliance on traditional fossil fuel supplies. Photovoltaic (PV) solar systems are a promising renewable energy technology that has developed and expanded rapidly in recent years. However, capacity planning for the PV system is a strategic decision-making process that lasts about 20 years and is affected by climate and economic changes during this period. For this purpose, this research proposes a robust capacity planning approach by formulating a multiobjective mixed-integer linear programming (MO-MILP) optimization model. Climate and economic changes such as global irradiation, ambient temperature, and inflation rate are represented by a set of possible scenarios with a probability of occurrence. MO optimization helps study the trade-offs among economic, technical, and environmental objectives. A real-world case of capacitating a grid-connected PV system to supply power to the residential area at King Fahd University of Petroleum and Minerals is provided to demonstrate the practicability of the optimization model. In addition, a sensitivity analysis is conducted to get more technical and managerial insights into the planning under more scenarios. As a result, it is recommended to use 1252 PV arrays at an annual cost of M$ 1.18 and M$ 0.127 per year to satisfy the shortage and reduce CO2 emissions by 96.31%. [ABSTRACT FROM AUTHOR]

Successful capacity planning and production scheduling is built on the understanding of market opportunities and the costs of capacity, production, sourcing, inventory, and distribution over the planning horizon. Increasingly, companies attempt to integrate capacity planning and production scheduling to improve upon the commonly used sequential decision process, but most related research works fail to capture the granularity of actual operational decisions and therefore may overlook potential cost-saving opportunities. The contributions of this study include: (1) a detailed integrated capacity and production scheduling model with multiple discrete and continuous options for varying short and medium-term capacity, (2) a heuristic algorithm that exploits the problem structure to solve the nonlinear mixed integer problem, (3) an evaluation of the value of the integrated model relative to traditional practice and its sensitivity to parameters, (4) a review of past contributions to integrated planning, particularly focused on IJPR, and (5) a case study originated from a world-class automobile manufacturer illustrating how the model can be applied and confirming its value relative to hierarchical and less detailed modelling approaches. [ABSTRACT FROM AUTHOR]

This paper presents a quantitative assessment of the value of hydroelectric power plants (HPPs) in power systems with a significant penetration of variable renewable energy sources (VRESs). Through a capacity expansion planning (CEP) model that incorporates a detailed representation of HPP operating principles, the study investigates the construction and application of HPP rule curves essential for seasonal operation. A comparative analysis is also conducted between the proposed rule curve formulation and alternative modeling techniques from the literature. The CEP model optimizes installed capacities per technology to achieve predefined VRES penetration targets, considering hourly granularity and separate rule curves for each HPP. A case study involving twelve reservoir hydropower stations and two open-loop pumped hydro stations is examined, accounting for standalone plants and cascaded hydro systems across six river basins. The study evaluates the additional generation and storage required to replace the hydropower fleet under high VRES penetration levels, assessing the resulting increases in total system cost emanating from introducing such new investments. Furthermore, the study approximates the storage capabilities of HPPs and investigates the impact of simplified HPP modeling on system operation and investment decisions. Overall, the findings underscore the importance of reevaluating hydro rule curves for future high VRES penetration conditions and highlight the significance of HPPs in the energy transition towards carbon neutrality. [ABSTRACT FROM AUTHOR]

This work presents an implementation of a Nested Decomposition Algorithm (NDA) applied to co-optimizing generation and transmission capacity expansion planning problems in power systems, including operational flexibility constraints. The proposed methodology has been gaining relevance in recent years, as it can efficiently solve large mixed-integer problems faster than the conventional extensive formulation (mixed-integer linear programming). Three case studies are conducted on two IEEE test power systems to evaluate the algorithm's performance and cut configuration. The first case study compares the performance between the NDA and the extensive formulation. The second case study compares the performance of each cut type, analyzing differences in simulation times and algorithm convergence. The third case study proposes a set of cut patterns based on the prior outcomes, whose performance and convergence are tested. Based on the simulation results, conclusions are drawn about the capability and performance of the NDA applied to the capacity expansion planning problem. The study shows that obtaining results with reasonable convergence in less simulation time is possible using a particular pattern. [ABSTRACT FROM AUTHOR]

This article presents the findings of an interview and digital methods-based study of young Australian hospitality workers who lost work in the wake of the pandemic. In so doing it focuses on the relationship between one's present-day material conditions and their capacity to form plans for the future, and the role of planning in young people's future thinking. The article argues that the impact of the pandemic on the participants' plans can be best understood not through analysis of their orientations to planning, but through consideration of the degree to which their present-day circumstances brought them further away from the desired futures that they had been planning. Indeed, some of the participants who found themselves in states of deprivation identified that their plans remained unchanged, while others who were little affected in an immediate, material sense experienced it as a crisis or as a catalyst for re-evaluation. By identifying the relationship between present-day conditions and future plans for our participants, we ultimately contend that understanding planning as a form of practice through which one's present is connected to their desired future allows for insight into how young people prioritise and address goals in various spheres of life. [ABSTRACT FROM AUTHOR]

This paper proposes a novel route optimization framework to solve the problem of instant pick-up and delivery for e-grocery orders. The proposed framework extends the traditional time-windowed package delivery problem. We demonstrate the effectiveness of our approach for this integrated problem using actual delivery data from HepsiJet, a leading e-commerce logistics provider in Turkey. We first employ several machine learning algorithms and simulations to investigate the capacity of the courier. Subsequently, a dynamic route planning workflow is executed with a highly specialized and novel routing algorithm. Our proposed heuristic approach considers combined fleet operations for delivering regular packages originating from a central depot and dynamic e-grocery orders picked up at local supermarkets and delivered to the customers. The heuristic algorithm constitutes k-opt and node transfer operation variations customized for this integrated problem. We report the performance of our approach in problem instances from the literature and instances from HepsiJet's daily operations, which we also publicly share as new route optimization problem instances. Our results suggest that, despite the more complex nature of the integrated problem, our proposed algorithm and solution framework produce more efficient and cost-effective solutions that offer additional business opportunities for companies such as HepsiJet. The computational analyses reveal that implementing our proposed approach yields significant efficiency gains and cost reductions for the company, with a distance reduction of over 30%, underscoring our approach's effectiveness in achieving substantial cost savings and enhanced efficiency through integrating two distinct delivery operations. [ABSTRACT FROM AUTHOR]

This study aims to analyze indigenous rural governance in community processes of sustainable development from the perspective of strategic planning, in Alausí, Ecuador, in 2023. The research is characterized as descriptive, documentary, and field-based, adopting a mixed methodological approach that encompasses both quantitative and qualitative data collection, allowing for a more comprehensive understanding of the topic. Through a hermeneutic paradigm, efforts have been made to interpret the information to reach consensus knowledge. The research identified a weakened governance process characterized by aspects such as: an overload of managerial responsibilities, limited leadership capacity, reactive management by stakeholders to local needs, a scant culture of planning and coordination, and a patriarchal hierarchical leadership structure. These factors constrain the leadership and management process to a reactive stance of stakeholders regarding the expectations and interests of the community inhabitants, lacking commitment and empowerment. This condition underscores the need to overcome old hierarchical patterns through linear and cooperative rural governance processes adapted to the cultural context, through training and development of innovative governance and strategic planning capacities to guide the territory toward sustainable development processes. [ABSTRACT FROM AUTHOR]

Capacity planning should be performed to balance investment costs and benefits of investing to meet the current and future demand in intensive care units. Having a high capacity to increase patient admission will lead to unutilized capacity in some periods, thereby increasing costs. On the other hand, patient admission requests from inborn and transported patients might be rejected due to lack of equipment. It should be considered in terms of cost-effectiveness and patient health; therefore, optimal equipment capacity must be determined. In this study, the optimal capacity planning problem has been considered for the neonatal intensive care unit of a hospital adopting the simulation-optimization approach. A discrete event simulation model is proposed for a neonatal intensive care unit in Adana, Turkey. Then, the optimization model identified the optimal numbers of incubators, ventilators, and nitric oxide devices to maximize equipment efficiency and minimize total inborn patient rejection and transport ratios. Three different resource allocations are presented, and the best is obtained from these three objectives as 72 incubators, 35 ventilators, and three nitric oxide devices. The application results obtained have revealed that the rejection and transport rate, which is found to be 1.12% in the current situation, can be reduced to 0.2% with different numbers of equipment and that equipment efficiency can be achieved with optimal quantities of each equipment. The results of the study can help the decision-makers when minimum transport and rejection ratios are critical which almost all intensive care units are required. Furthermore, the proposed simulation-optimization model can be adapted to different neonatal intensive care units having the same characteristics. [ABSTRACT FROM AUTHOR]

In recent years, environmental issues have gradually become one of the most concerning issues in the world. New energy vehicles have attracted wide attention by their good environmental and social benefits such as zero exhaust emissions and low noise pollution. This paper proposes a regional charging demand forecasting method for electric vehicles (EVs) based on hierarchical charging decision model to solve the problem of charging pile capacity planning, which affects the charging decision behavior of EVs users. The Monte Carlo sampling model is adopted to obtain the time distribution of electric vehicle arriving at each functional area, the distribution of battery state of charge when arriving, and the distribution of average daily mileage. The negative exponential distribution model is used to obtain the average mileage distribution. The fuzzy system theory is proposed to calculate the charging probability, and the fuzzy rule base is adjusted according to the actual data to ensure that the fuzzy rule base is real and effective. Finally, the charging demand distribution curve of electric vehicles in each functional area is obtained by sampling simulation. Through experimental analysis, optimizing the capacity configuration of charging facilities in the station can well describe how electric vehicle users make charging decisions in the actual situation. The research on electric vehicle charging stations can slow down the waiting time of electric vehicle charging, and then affect the process of replacing fuel vehicles with electric vehicles to achieve the development goal of popularizing electric vehicles. It is of great significance for the promotion of new energy vehicles to rationally plan the location and capacity of electric vehicle charging stations. [ABSTRACT FROM AUTHOR]

Increased productivity and cost reduction alongside customer satisfaction have been identified by firms as sources of competitive advantage. Different methodologies such as the Lean principle, pull scheduling, and Six Sigma are used by firms to achieve higher productivity as a competitive advantage. However, firms cannot produce infinitely due to limitations such as resources that is why it is so important to spot the light on capacity planning. This paper seeks to explore the significance of the application of Six Sigma on Companies' capacity planning to raise production efficiency by reducing defects and maximizing profits. Different companies of water in Egypt are used as study sites. A mix of qualitative and quantitative methods is used for data collection and analysis. A total of 200 participants were sampled for data collection using interviews and questionnaires. It is found that there is a significant correlation between the Utilization of Six Sigma techniques and monitoring of capacity planning in studied companies through improving and controlling quality processes until reaching zero defects products. It is also found that applying Six Sigma techniques could not be achieved appropriately without having a powerful information system and well-trained employees applying Six Sigma techniques efficiently. [ABSTRACT FROM AUTHOR]

This article examines the growing tensions between Russia and Ukraine and their impact on regional and global geopolitical dynamics, focusing on the importance of military organizational management in this complex situation. The confrontation between the two countries, fuelled by Moscow's geopolitical aspirations to consolidate its control over Ukraine, has escalated into a military conflict with far-reaching repercussions. Ukraine's rapprochement with the European Union and NATO has exacerbated tensions, generating indirect arms deliveries and threatening regional stability. In this context, military organisational management becomes crucial to manage and mitigate the escalation of the conflict. The capacity for strategic planning and coordination of military actions is essential to maintain stability and prevent wider conflict. Also, the ability to manage human and material resources effectively and strategically can influence the outcome and duration of conflict. In addition, it is important to stress that tensions between Russia and Ukraine not only have bilateral consequences, but also affect relations between Russia and NATO. Military management must therefore be adaptable and address the complex and interlinked challenges of this rapidly changing geopolitical context. The paper further argues that an appropriate approach to military organisational management can be essential for managing the conflict between Russia and Ukraine, as well as for maintaining regional and global stability in the face of the complex challenges of current geopolitical tensions. [ABSTRACT FROM AUTHOR]

New energy storage methods based on electrochemistry can not only participate in peak shaving of the power grid but also provide inertia and emergency power support. It is necessary to analyze the planning problem of energy storage from multiple application scenarios, such as peak shaving and emergency frequency regulation. This article proposes an energy storage capacity configuration planning method that considers both peak shaving and emergency frequency regulation scenarios. A frequency response model based on emergency frequency regulation combined with low-frequency load shedding is established, taking into account the frequency safety constraints of the system and the principle of idle time reuse, to establish a bi-level programming model. In the upper-level model, the optimization objective is to minimize the annual operating cost of the system during the planning period, combined with the constraints of power grid operation to plan the energy storage capacity. The lower-level model embeds frequency safety constraints with the optimization objective of minimizing the cost of fault losses. To solve the bi-level optimization problem, the Karush–Kuhn–Tucher (KKT) condition and Big-M method were used to transform the bi-level model into a single-layer linear model. Finally, an improved IEEE RTS-24 system was used for numerical verification. The results show that the method proposed in this article can reasonably plan the capacity of energy storage, improve frequency safety during system operation, and reduce the operating cost of the power grid. [ABSTRACT FROM AUTHOR]

The problem of power grid capacity expansion focuses on adding or modernizing generation and transmission resources to respond to the rise in demand over a long-term planning period. Traditionally, the problem has been mainly viewed from technical and financial perspectives. However, with the rise in the frequency and severity of natural disasters and their dire impacts on society, it is paramount to consider the problem from a nexus of resilience, sustainability, and equity. This paper presents a novel multi-objective optimization framework to perform power grid capacity planning, while balancing the cost of operation and expansion with the life cycle impacts of various technologies. Further, to ensure equity in grid resilience, a social vulnerability metric is used to weigh the energy not served based on the capabilities (or lack thereof) of communities affected by long-duration power outages. A case study is developed for part of the bulk power system in the state of Colorado. The findings of the study show that, by considering life cycle impacts alongside cost, grid expansion solutions move towards greener alternatives because the benefits of decommissioning fossil-fuel-based generation outweigh the costs associated with deploying new generation resources. Furthermore, an equity-based approach ensures that socially vulnerable populations are less impacted by disaster-induced, long-duration power outages. [ABSTRACT FROM AUTHOR]

Throughout his career, Michael Bratman has developed a detailed model of individual 'planning agency', and, more recently, models of joint action and aspects of social life that he argues such planning agency helps support. How might we empirically investigate whether these models capture what is going on in actual human lives? In this article, we critically engage with this broad question by focusing on what Bratman calls the 'core capacity thesis', which is at the center of his most recent discussions of how his work in the philosophy of action hangs together. In his own words, the core capacity thesis claims that 'our capacity for planning agency plays basic roles in multiple, interrelated systems of human, mind‐shaped practical organization, individual and social'. We explore how one might go about testing this thesis, focusing on Bratman's particular way of defending this thesis using the idea of 'nested Cummins functions'. In so doing, we hope to contribute to a broader discussion about what the relationship is (or, perhaps more importantly, should be) between the kind of philosophy of action Bratman does and empirical work in fields such as psychology, cognitive science, and biology. [ABSTRACT FROM AUTHOR]

Planning allows us to coordinate our actions over time, and the ability to plan is crucial in many areas of our lives. I argue that while planning is deeply embedded in contemporary societies, not all individuals have equal access to the structures that support such planning. This article explores how external planning‐support structures are essential to our capacity to plan and how inequality in access to these structures can impact an individual's ability to deliberate and pursue long‐term plans. I conclude by suggesting that we need to think more deeply about unequal access to the structures that support planning directly, not just inequality in resources. [ABSTRACT FROM AUTHOR]

Following Bratman, I distinguish between the Cummins or component‐function of the planning capacity (its role as a component of larger forms of practical organizations) and its Wright or existence‐function – the planning capacity's effect that explains its existence. I agree with Bratman that these functions are distinct. The planning capacity's role within larger practical organizations need not explain its origin. But I argue that the distinction is less stark for future‐oriented existence‐functions, which concern the future persistence and stability of the capacity. In mature practical organizations, component‐functions usually give rise to matching future‐oriented existence‐functions, given that pressures for the persistence of practical organizations transfer to their components (such as the planning capacity) because of their component‐functions. But in the special case of individual temporally extended agency, there is an even stronger alignment of functions. The planning capacity and individual temporally extended agency are part of an inextricable package. The capacity is not just a distinct component. This entails that, within individual agency, the two functions merge and the planning capacity is the primary rather than the derivative target of pressures of persistence, care, and value. Or so I speculatively conjecture. [ABSTRACT FROM AUTHOR]

Our capacity for planning agency is a core capacity that underlies interrelated forms of mind‐shaped practical organization: cross‐temporal organization of individual agency, shared agency, social rules, and rule‐guided organized institutions. A function of our capacity for planning agency is the support of these forms of practical organization. I highlight Peter Godfrey‐Smith's contrast between the 'Wright function' of something as 'the effect it has which explains why it is there' and 'Cummins functions' that 'are capacities or effects of components of systems, which are salient in the explanation of capacities of the larger system'. Drawing on Paul Grice's strategy of 'creature construction', I articulate a sequence of nested constructions: from temporally extended planning agency to shared agency to shared policies to social rules to rule‐guided organized institutions. We see our capacity for planning agency as part of an explanation of how we achieve such practical organization, and as having nested Cummins functions of supporting those forms of organization. This sheds light on related ideas in H.L.A. Hart's theory of law, a challenge from J. David Velleman, the centrality of such forms of organization to the philosophy of action, and the moral and political significance of our capacity for planning agency. [ABSTRACT FROM AUTHOR]

COVID-19 has been a severe crisis for global health, which caused significant loss of life and property. One of the most effective ways to prevent the spread of the virus during an epidemic is to provide nucleic-acid tests for the population. Management of testing resources is both critical and challenging because outbreaks are irregular and resources are scarce. In this study, we develop a decision support tool for city governments by districting testing facilities and determining their capacities. Considering the stochastic testing demand during a disease outbreak, a set-partitioning model embedded with a two-stage distributionally robust optimisation is formulated. Tractable reformulations are derived to solve the problems efficiently and a conservative approximation method is introduced to achieve acceptable accuracy while reducing the computational burden. Compared with different benchmark models, the numerical analyses demonstrate the effectiveness of the proposed territory design, which realises a robust testing infrastructure network and saves the cost while pursuing capability. [ABSTRACT FROM AUTHOR]

This paper addresses a multistage capacity planning problem for a hierarchically skilled workforce in a production environment. Recruits are hired with little or no experience and are trained over multiple periods to perform jobs that require increasing levels of skill. Training can take place either off-the-job, on-the-job or a combination thereof. The problem is complicated by random resignations that can lead to labor shortfalls that jeopardise continuous operations. The objective is to balance workforce costs with penalty costs associated with skill shortages. The problem is modelled as a Markov decision process for which several parameterised decision rules are proposed to find solutions. A large-scale neighbourhood search is developed to deal with 'noisy' cost function measurements. Experiments show that good parameter values can be found in less than four hours using real-world data. When training requires extensive supervision, the results indicate that the number of workers concurrently in training should be limited. They also show that a shorter, intense training period during which employees do not perform regular tasks is generally preferable to a longer training period where employees spend time both on and off the job. Finally, we demonstrate the value of worker flexibility when downgrading is applied. [ABSTRACT FROM AUTHOR]

In recent years, global warming and environmental problems have become more serious due to greenhouse gas (GHG) emissions. Harvesting solar energy for production and logistic activities in supply chains, including factories and distribution centers, has been promoted as an effective means to reduce GHG emissions. However, it is difficult to balance the supply and demand of solar energy, owing to its intermittent nature, i.e., the output depends on the daylight and season. Moreover, the use of large-capacity solar power generation systems and batteries incurs higher installation costs. In order to maintain low costs, demand-to-supply management of solar energy, based on appropriate seasonal analysis of power generation and consumption and the capacity planning for power generation and the storage battery, is necessary. In this study, the on-demand cumulative control method is applied to actual power consumption data and solar power generation data estimated at a distribution center. Moreover, the monthly, seasonal, and temporal characteristics of power generation and consumption at the distribution center are analyzed. Additionally, the total amount of power purchased is investigated for solar energy demand-to-supply management. [ABSTRACT FROM AUTHOR]

Planning production capacities in multi-product production networks is challenging due to the multitude of decision factors, inter-organisational interests, and a high degree of uncertainty. Particularly, the organisational separation of different products that share sites induces planning complexity. This paper proposes an interactive-two model concept integrating product-specific network planning and a site capacity planning perspective. Stochastic mixed integer linear programming determines order allocations, line investments, and personnel plans. The potential to swiftly adapt plans while obeying local constraints is demonstrated with a large automotive supplier. The approach should allow quicker and more adaptive planning, leading to more resilient organisations. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic has resulted in an inflow of patients into the hospitals and overcrowding of healthcare resources. Healthcare managers increased the capacities reactively by utilizing expensive but quick methods. Instead of this reactive capacity expansion approach, we propose a proactive approach considering different realizations of demand uncertainties in the future due to COVID-19. For this purpose, a stochastic and dynamic model is developed to find the right amount of capacity increase in the most critical hospital resources. Due to the problem size, the model is solved with Approximate Dynamic Programming. Based on the data collected in a large tertiary hospital in Turkey, the experiments show that ADP performs better than a benchmark myopic heuristic. Finally, sensitivity analysis is performed to explore the impact of different epidemic dynamics and cost parameters on the results. [ABSTRACT FROM AUTHOR]

The budget planning and implementation process in North Central Timor Regency (TTU) is faced with significant challenges due to the high level of inconsistency in the budgeting system at each stage. This research aims to explore the relationship between several variables in the North Central Timor Regency. First, an analysis is carried out on the influence of Human Resource capacity (X1) on Good Governance (Y1). Second, the influence of planning quality (X2) on Good Governance (Y1) was examined. Third, audit the impact of Human Resources capacity (X1) on Budgeting Consistency (Y2). Fourth, the impact of planning quality (X2) on Budgeting Consistency (Y2) is analyzed. Fifth, the influence of Human Resource capacity (X1) on Budgeting Consistency (Y2) which is mediated by the Good Governance variable (Y1) is explored. And sixth, we studied the influence of planning quality (X2) on Budgeting Consistency (Y2) with the mediation variable Good Governance (Y1). The research uses quantitative Path Analysis. Samples were taken using a purposive random sampling method, with the criteria being employees in the financial administration field at each regional organization. The research results show that in the first structural equation, HR capacity and planning quality have a direct effect on Good Governance. In the second structural equation, HR capacity, planning quality, and Good Governance influence Budgeting Consistency. However, HR capacity and planning quality cannot be mediated by Good Governance on Budgeting Consistency. [ABSTRACT FROM AUTHOR]

Considering the controversy around strategic planning effectiveness for nonprofits, we first ask, "Does strategic planning matter?". A survey was administered to 1,256 nonprofit organizations to determine a. their capacity to plan and b. what strategic planning processes they used. Using regression analysis, it was found that the presence of a strategic plan acted as a statistically significant indicator of higher organizational capacity. Next, strategic methods were evaluated to determine which ones were associated with higher capacity scores. This research makes two main contributions. First, it enhances, using a large national sample the academic debate as to the usefulness of strategic planning. Second, for practitioners, this research demonstrates which strategic methods were found to be associated with the highest performing organizations, using our capacity indicator. Based on these findings we believe that nonprofit managers can optimize their resources and focus on those methods that provide them with the highest return on their strategic efforts. Additionally, nonprofit faculty now have data to suggest what strategic planning methods should be taught in our classrooms. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) networks provide network slicing capabilities, enabling the deployment of multiple logically isolated network slices on a single infrastructure platform to meet specific requirements of users. This paper focuses on modeling and analyzing resource capacity planning and reallocation for network slicing, specifically between two providers transmitting elastic traffic, such during as web browsing. A controller determines the need for resource reallocation and plans new resource capacity accordingly. A Markov decision process is employed in a controllable queuing system to find the optimal resource capacity for each provider. The reward function incorporates three network slicing principles: maximum matching for equal resource partitioning, maximum share of signals resulting in resource reallocation, and maximum resource utilization. To efficiently compute the optimal resource capacity planning policy, we developed an iterative algorithm that begins with maximum resource utilization as the starting point. Through numerical demonstrations, we show the optimal policy and metrics of resource reallocation for two services: web browsing and bulk data transfer. The results highlight fast convergence within three iterations and the effectiveness of the balanced three-principle approach in resource capacity planning for 5G network slicing. [ABSTRACT FROM AUTHOR]

Reliable forecasts are key to decisions in areas ranging from supply chain management to capacity planning in service industries. It is encouraging then that recent decades have seen dramatic advances in forecasting methods which have the potential to significantly increase forecast accuracy and improve operational and financial performance. However, despite their benefits, we have evidence that many organizations have failed to take up systematic forecasting methods. In this paper, we provide an overview of recent advances in forecasting and then use a combination of survey data and in-depth semi-structured interviews with forecasters to investigate reasons for the low rate of adoption. Finally, we identify pathways that could lead to the greater and more widespread use of systematic forecasting methods. [ABSTRACT FROM AUTHOR]