Planning model

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Planning model for Peruvian university system

Planning model for Peruvian university system

The proposed planning model should have the broadest consensus possible with the following stakeholders: (1) The Peruvian Government or State, (2) Outside agency for quality accreditation, (3) The rectors or rectors’ associations, (4) Public and private universities. These four stakeholders constitute specific matters that characterize the policy analysis planning model (Cazorla et al., 2007) and that contribute to promoting quality, relevance and transparency of Peruvian universities and to achieving the sustained success of the projects of public scope, which in this case is university higher education. These four key stakeholders of the policy analysis model are considered for the Peruvian university model because they have been chosen based on the analyzed conceptual matters and according to the Peruvian reality which are considered essential to achieve university higher education that responds to the demands of higher education projects in the rest of the world (Fig. 3).

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A mixed integer distribution network planning model using a tight power flow relaxation

A mixed integer distribution network planning model using a tight power flow relaxation

This thesis presents a mixed-integer optimization model for the distribution network planning problem. The model considers important aspects including investments in feed- ers, substations, and distributed generation (DG), as well as power imports, over a given planning horizon. The main feature of this model is that it accurately represents the physics of power flows through the use of a tight polyhedral relaxation, based on a known outer approximation of the second-order cone. An extensive set of computational experiments show the value of the proposed approach for understanding the effects of DG integra- tion, the effectiveness of the relaxation employed, and the computational efficiency of the overall planning model.

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Design of Self-regulating Planning Model

Design of Self-regulating Planning Model

The supply chain in the industry 4.0 uses a branch of the AI called machine learning, which allows the discovery of patterns in the data through algo- rithms; these identify which elements that have greatest influence in the supply network by means of continuous learning. The algorithms find new patterns every day without human intervention or some previously estab- lished classification. This allows orienting the search, making use of models based on restrictions they find. A set of x elements with high predictive ac- curacy identify patterns that influence supplier quality, inventory levels, demand forecasts, purchasing processes, production planning, and trans- portation management. Through the entire value chain, companies have the freedom to evolve considerably, resulting in them being more compet- itive thanks to automatic learning skills (Columbus, 2018).

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Long term power systems planning with operational flexibility

Long term power systems planning with operational flexibility

In recent years, there have been unprecedented levels of renewable energy penetration in power systems. As several countries have set ambitious future goals for renewable energy generation, the inclusion of these sources is expected to increase. This have drawn great attention to the operational challenges associated with their volatile nature. In this regard, the concept of flexibility –a power systems’s ability to react to sudden changes in demand and supply– becomes key. While significant advances have been made to improve the modelling of flexibility of power systems in operational stages, this issue is generally neglected in expansion planning models, due to computational capabilities. To address this issues, this work presents a tractable power system generation and transmission expansion planning model that enables to obtain a near-optimal capacity mix, considering renewable penetration with detailed operation. This is achieved by considering a relaxation of unit commitment (UC) constraints to account for operational flexibility requirements. The proposed formulation is compared in terms of optimality and computation time with two benchmark models: a planning model with exact UC representation, and another in which UC constraints are not considered. The results show that the proposed formulation is able to represent the operational flexibility in planning decisions, with reduced computation times.

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MEDIA CONSUMpTION AND CONSUMER pURCHASING

MEDIA CONSUMpTION AND CONSUMER pURCHASING

at the 2004 esoMaR Worldwide audience Measure- ment (WaM) conference, schultz and Pilotta presented a new type of media planning model based on measures of audience media consumption rather than media and marketer message distribution (schultz and Pilotta, 2004). Using the proprietary Bigresearch siMM (simultaneous Media Usage) database, they argued that in a fragmented media marketplace, in which consumers are increasingly multi-tasking with multiple media forms, how consumers come to or access media is more relevant for media planning than the current media distribution methodology. their model was based on the amount of time individual consumers spend with each media form and the combinations of those media accessed by day-part. four key elements comprised the media consumption model, i.e., (a) the amount of time each person spent with each media form by day-part, (b) the amount of simultaneous media usage (multi-tasking) that occurred, (c) the various media combinations attended to, which allowed the identification of “foreground” and “background” media development, and (d) the impact of the synergy or media interactions at the consumer level, to enhance or decrease consumer response to the media exposures. the media consumption model is shown as figure .

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Multistage distribution system expansion planning considering distributed generation and reliability

Multistage distribution system expansion planning considering distributed generation and reliability

Reference [39] used an immune-based evolutionary optimization algorithm to solve the problem under nodal demand uncertainty. The fault cost was used to estimate the reliability of the network. References [40] and [41] formulated the problem as a horizon planning model which was solved using continuous constrained nonlinear programming methods. Reliability was assessed through an analytical method, namely simplified radial network modeling, by computing standard re- liability indices. In [42], several heuristic optimization techniques were applied to solve the distribution network expansion planning problem in the presence of DG. Uncertain demand and generation were considered by using suitable normally- distributed probability density functions. As done in [34] and [35], the reliability cost was computed as a function of the average number and duration of service interruptions. Reference [43] modeled the problem as a multiobjective mixed- integer nonlinear program simultaneously minimizing investment, operating, and reliability costs, which was solved using Benders decomposition. The uncertainty of load demand and wind-based DG was integrated using a fuzzy power flow whereas reliability was assessed by the cost of the expected energy not supplied. Lotero et al. [44] used an iterative algorithm based on standard mathematical programming to generate a pool of candidate topologies with no regard to reliability. Subsequently, for each candidate topology, reliability was assessed by calculating several standard reliability indices and costs using simulation.

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An adaptive robust optimization model for power systems planning with operational uncertainty

An adaptive robust optimization model for power systems planning with operational uncertainty

The need for sustainable power systems is driving the adoption of large shares of vari- able renewable energy. Due to this, there is an increasing necessity for new long-term planning models that can correctly assess the reserve capacity and flexibility requirements to manage significant levels of short-term operational uncertainty. Motivated by this key challenge, this work proposes an adaptive robust optimization model for the Generation and Transmission Expansion Planning Problem. The proposed model has a two-stage structure that separates investment and operational decisions, over a planning horizon with multiple periods. The key attribute of this model is the representation of daily operational uncertainty through the concept of representative days and an uncertainty set for demand and the availability of wind and solar power built over such days. Also, the model em- ploys a DC-power flow representation for the transmission network. This modelling setup allows an effective representation of the reserve capacity and flexibility requirements of a system with large shares of renewable energy. To efficiently solve the problem, the col- umn and constraint generation method is employed. Extensive computational experiments on a 20-bus representation of the Chilean power system over a 20-year horizon show the advantages of the proposed robust expansion planning model, compared to an approach based on deterministic representative days, due to an effective spatial placement of both variable resources and flexible resources.

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Revista Ingeniería Investigación y Tecnología | Facultad de Ingeniería UNAM

Revista Ingeniería Investigación y Tecnología | Facultad de Ingeniería UNAM

The lot size is a significant issue related to the efficiency and the cost of the production program. In a multi-pro- duct environment, a big lot size for a given product can delay the production of the other products. This situa- tion is consequence of the shared resources (machines and hard-work force). In the previously traditional planning method, the considered lots had an average of two thousand kilograms. The proposed ILP model is able to find a more equitable replenishment of the diffe- rent products. Smaller lots are found as described Table 3. In this table, the following notation is used: Average is the average size of the lots, STD is the standard devia- tion for scheduled lots, Q1 is the first quartile (lowest 25% of lots), Q2 is the second quartile (data set in half),and Q3 is the third quartile, lowest 75%.

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Electric vehicles charging stations planning in transportation networks and their impact on power distribution systems

Electric vehicles charging stations planning in transportation networks and their impact on power distribution systems

• Chapter 4 presented and discussed a novel mathematical optimization model for the optimal location of EVCSs in transportation networks, considering the impact on the power distribution system (PDS). The developed mathematical model (MINLP formulation) incorporates in the objective function the cost of EVCSs installation and cost of routing for EVs utilized in merchandise delivery, following the Capacitated Vehicle Routing Problem (CVRP) approach. Other terms correspond to the increment of energy losses cost in PDS, respect to the benchmark case (No EVCSs installed), and a penalization term used to make feasible the solution. This latter was activated in case that the EVCSs installed were not sufficient for EVs to complete the routes and meeting the customers demand merchandise. Otherwise, a change of the current batteries used in the EVs would have been necessary, for others with a larger driving range. The weight factors presented in the objective function, were chosen consistently with the relative importance of each term. This is, the need to increase the battery autonomy was largely penalized in comparison with the other terms (change of battery is not attractive); and routing cost is of greater importance than EVCSs installation and energy losses cost. By the other side, the parameter M permitted to control the revisit to an EVCS. Accordingly, the amount of EVCSs installed, routing and energy losses cost were affected, since parameter M represented a sensitive factor in the problem, besides the battery autonomy.

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From Urbanism to Planning to Urban Project. The pursuit of ‘urbanity’ in Spanish plans and projects

From Urbanism to Planning to Urban Project. The pursuit of ‘urbanity’ in Spanish plans and projects

ence Convenor (Barcelona 2004). He is interested in urban projects, landscape urbanism, planning history and theories of urbanism. Carmen Díez medina. Architect (eTSA, madrid, 1988). Ph.D. (Tu Wien, 1996). Associate Professor of Theory and Architectural History at the Architecture Department of the eINA (School of engineering and Architecture, university of Zaragoza, Spain), where she is in charge of the architectural history and theory disciplines and coordinator of the Ph.D. Program “New Territories in Architecture”. Collaborating architect at Nigst, Hubmann&Vass (Viena, 1998-94) and at Rafael moneo (madrid,1996-2001). Research Projects: “espacios para la enseñanza”, Ceu (2012-14); “Paisajes residenciales urbanos”, eINA (2010-11); “la construcción de la ciudad liberal”, uPm (2008-09); “españa en los CIAm”, Ceu (2007-08). Both authors are responsible of the research Project “urban Regeneration of Housing estates in Spain” (http://pupc.unizar.es/urhesp/).

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An integrated pedagogical approach for distance learning courses: curriculum, ICT and management

An integrated pedagogical approach for distance learning courses: curriculum, ICT and management

It is argued in this paper that for the definition of a flexible pedagogical model adapted to distance learning it is essential to consider the requirements of the distance learning methods with respect to face-to-face ones. Also, the pedagogical model and the technological options adopted have to be adjusted, eventually case by case, to the specificities of the target audience and specific course characteristics. With respect to course implementation and operation, the approach adopted should additionally be integrated in terms of curriculum, ICTs and management.

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La dimensión arquitectónica del 'planning' británico: 'amenity'

La dimensión arquitectónica del 'planning' británico: 'amenity'

6. En 1964 el Gobierno inglés, ante el descontento existen- te en distintos ámbito sociales y profesionales por los resul- tados prácticos del planning, constituyó un grupo de traba- jo, el Planning Advisory Group (P.A.G), el informe elaborado fue hecho público en 1965, y proporcionó la base para una profunda revisión de la vigente ley británica (P.A.G 1965). Un examen de la crítica al denominado Physical Planning a partir de los años 60 puede verse en Cherry 1981, 172-175. 7. En LYNCH, Kevin, City sense and city design: writings and projects of Kevin Lynch, MIT Press, Cambridge, Mass., 1991, el propio autor realiza un exhaustivo análisis de cómo se percibe el espacio urbano a través de los sentidos. 8. “Place making, where place is not just a specific space, but all the activities and events that it makes posible”. BUCHANAN, P., “What City? A plea for place in the public realm”, en Architectural Review, n. 184, p. 33.

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Planning en agentes inteligentes

Planning en agentes inteligentes

El esquema de algoritmo anterior expone los principales análisis realizados sobre los datos enviados para la generación de un plan de acción. En éste se remarca una invocación a una función choose, ya que es un punto importante que hace que el plan resultante, si buscamos sólo uno cualquiera, sea más eficiente. Es el único punto que se sugiere especializar en los diferentes dominios en los cuales se puede aplicar un algoritmo de planning.

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Argumentation driven planning

Argumentation driven planning

The research line reported here involves developing an argumentation-based formalism that an agent could use in constructing plans. The argumentation formalism will be combined with well known planning tecniques producing a novel way of constructing a plan. Below, we wil sketch the formalism and introduce some clarifying examples.

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Foresight in designing sun-beach destinations

Foresight in designing sun-beach destinations

Foresight contemplates the future shaped by complex, uncer- tain and multiple visions. The future is open and not pre- determined. Foresight mostly employs qualitative tools. The use of foresight studies is now spreading, and they are becoming a decisive part of many planning exercises, especially in technology, business, environment and energy related areas (European Commission, 2009). This trend is due to fast, unpre- dictable changes in society, markets, technology and science. This dynamic and sometimes turbulent environment puts enormous pressure on rational planning systems, many of which have been designed to simulate highly stable and predictable functional sys- tems, while foresight methods have fewer technical constraints and * Corresponding author. Departamento de Urbanística y Ordenación del Terri-

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A Mine Planning Tool to Facilitate Planning Under Uncertainty.

A Mine Planning Tool to Facilitate Planning Under Uncertainty.

There are many sources of internal uncertainty that attempt towards the underlying concept of push planning approach. Some of these uncertainties are classified as the variability of the material handling systems, grades distributions, ore body continuity, geomechanical variability, operational events in the mining system, and the lack of integration: Mining Design ↔ Material Handling Systems ↔ Operational Philosophy ↔ Production Schedule. All these lead to having a mining system that is unreliable in terms of production outcomes.

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An MILP model for planning of batch plants operating in a campaign- mode

An MILP model for planning of batch plants operating in a campaign- mode

In this work, the detailed planning problem of multistage batch plants with an operation based on MPCs is addressed using a MILP model. It is assumed that the plant manager must produce known demands using a cyclic campaign during a time horizon. Nonidentical parallel units, ZW transfer policy and sequence-dependent changeover times are considered. Given the plant configuration and unit sizes, the total amount of each product to be produced in the campaign and the product recipes, the approach determines the number of batches that compose the production campaign and their sizes, the batches assignment to units, the sequencing of batches in each unit for each stage, and the initial and final times of the batches processed in each unit in order to minimize the campaign cycle time. With the aim of

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A Reference Model for Virtual Organizations Planning and Launching Edición Única

A Reference Model for Virtual Organizations Planning and Launching Edición Única

In this study case, the VO planning and launching stages are triggered by a business opportunity related to Aerospace Maintenance Tooling fabrication, this product is required by a foreign customer who requires low cost production, high quality and accurate delivery time. In this study case, VO frame phases are used as a General Use (Step-by-Step), other VO Frame elements also are included during the implementation. The following section describes the implementation of VO Frame modules according to figure 13. For this study case the phases of VO frame were developed according to figure 28. Each phase was composed of specific activities developed with particular methods and tools.

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Strategic planning for community sustainability in model forests: Case study of the Yoro model forest, Honduras

Strategic planning for community sustainability in model forests: Case study of the Yoro model forest, Honduras

Finally, the questions on the generation and sustainability of the timber resource and knowledge of the procedures for production licenses revealed that most were aware of the existence of forestry plantations created in recent years in the model forest and in the surrounding area. They admit that most of the timber they process is illegal, although they do not relate this directly to deforestation –which tends to be more closely associated with extending the areas suitable for farming– but attribute it to the legal impediments for its use. Only 23% were acquainted with forestry certification but none of them acknowledged the need for it.

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DETAILED PLANNING AND INVENTORY OPTIMIZATION MODEL IN THE MATTRESS INDUSTRY

DETAILED PLANNING AND INVENTORY OPTIMIZATION MODEL IN THE MATTRESS INDUSTRY

Mattress companies have to deal with a very competitive environment. In order to capture consumers’ preference they constantly introduce new products in the market and handle a huge variety of them. Nowadays, the demand of simple foam mattresses is lower than the spring mattresses. This situation leads to more complex production planning process. In addition, given the large size and diversity of the foams required in the final products, keeping a balanced and reasonable inventory is also a challenging issue.

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