A multicriteria-optimization model for cultural heritage renovation projects and public-private partnerships in the hospitality industry

2021 ◽  
pp. 1-26
Author(s):  
Madjid Tavana ◽  
Abdolreza Azadmanesh ◽  
Arash Khalili Nasr ◽  
Hassan Mina
Author(s):  
E. Loukis

Public-private partnerships (PPPs) provide an alternative model for producing and delivering public services, both the traditional public services and the electronic ones (i.e., the ones delivered through electronic channels, such as the Internet or other fixed or mobile network infrastructures; Aichholzer, 2004; Andersen, 2003; Broadbend & Laughlin, 2003; Jamali, 2004; Lutz & Moukabary, 2004; McHenry & Borisov, 2005; Nijkamp, Van der Burch, & Vidigni, 2002; Spackman, 2002; Wettenhall, 2003). The basic concept of the PPP model is that the public and the private sectors have different resources and strengths, so in many cases, by combining them, public services can be produced and delivered more economically and at higher quality. In this direction, a PPP is a medium to a long-term relationship between public organizations and private-sector companies, involving the utilization of resources, skills, expertise, and finance from both the public and the private sectors, and also the sharing of risks and rewards in order to produce some services, infrastructure, or other desired useful outcomes for the citizens and/or the businesses. Information and communication technologies, and in particular the Internet and WWW (World Wide Web) technologies, have opened a new window of opportunity for a new generation of PPPs for offering new electronic public services in various domains, for example, for developing and operating public information portals (Andersen, 2003), electronic transactions services (Lutz & Moukabary, 2004), electronic payment services (McHenry & Borisov, 2005), value-added services based on public-sector information assets (Aichholzer, 2004), and so forth. However, before such a new service is developed, it is of critical importance to design systematically and rationally its business model, which, according to Magretta (2002), incorporates the underlying economic logic that explains how value is delivered to customers at an appropriate cost and how revenues are generated. Vickers (2000) argues that most of the failures of e-ventures (also referred to as dot-coms) are due to the lack of a sound business model or due to a flawed business model. However, most of the research that has been conducted in the area of e-business models is dealing mainly with the description and abstraction of new emerging e-business models, the development of e-business-models classification schemes, and the clarification of the definition and the components of the business model concept, as described in more detail in the next section. On the contrary, quite limited is the research on e-business-models design methods despite its apparent usefulness and significance; moreover, this limited research is focused on private-sector e-business models. No research has been conducted on the design of PPP business models for offering electronic services. In the next section of this article, the background concerning PPPs and e-business-models research is briefly reviewed. Then a new framework for the design of e-business models is presented, which has been customized for the design of PPP business models for offering electronic services. Next, the above framework is applied for the design of a PPP business model for the electronic provision of cultural-heritage education for the project E-Learning Resource Management Service for the Interoperability Network in the European Cultural Heritage Domain (ERMIONE) of the eTEN Programme of the European Union (Grant Agreement C517357/2005). Finally, the future trends and the conclusions are outlined.


1997 ◽  
Vol 119 (4) ◽  
pp. 448-457 ◽  
Author(s):  
R. S. Krishnamachari ◽  
P. Y. Papalambros

Optimal design of large systems is easier if the optimization model can be decomposed and solved as a set of smaller, coordinated subproblems. Casting a given design problem into a particular optimization model by selecting objectives and constraints is generally a subjective task. In system models where hierarchical decomposition is possible, a formal process for selecting objective functions can be made, so that the resulting optimal design model has an appropriate decomposed form and also possesses desirable properties for the scalar substitute functions used in multicriteria optimization. Such a process is often followed intuitively during the development of a system optimization model by summing selected objectives from each subsystem into a single overall system objective. The more formal process presented in this article is simple to implement and amenable to automation.


2021 ◽  
Vol 7 (Extra-C) ◽  
pp. 459-465
Author(s):  
Аlla Leontievna Blagodir ◽  
Larisa Ivanovna Skabeeva ◽  
Yulia Sergeevna Sergeeva ◽  
Pavel Nikolaevich Sharonin ◽  
Alexander Grigorievich Fedorov

The article is devoted to improving the methods of state support for the tourism and hospitality industry in the context of digitalization. It is proved that the policy in the field of tourism and hospitality industry should take into account the goals of socio-economic policy that determines the line of reforms. The authors state that the tourism and hospitality industry has become a special system that combines both the historical and cultural heritage of the state and the most recent information technologies in the field of territorial development and communications, as well as note that tourist and hotel companies are digitalizing their activities rigorously and effectively, receiving significant revenues.


Author(s):  
Sergey V. Chebakov ◽  
Liya V. Serebryanaya

An algorithm is developed for finding the structure of the optimal subset in the knapsack problem based on the proposed multicriteria optimization model. A two-criteria relation of preference between elements of the set of initial data is introduced. This set has been split into separate Pareto layers. The depth concept of the elements dominance of an individual Pareto layer is formulated. Based on it, conditions are determined under which the solution to the knapsack problem includes the first Pareto layers. They are defined on a given set of initial data. The structure of the optimal subset is presented, which includes individual Pareto layers. Pareto layers are built in the introduced preference space. This does not require algorithms for enumerating the elements of the initial set. Such algorithms are used when finding only some part of the optimal subset. This reduces the number of operations required to solve the considered combinatorial problem.  The method for determining the found Pareto layers shows that the number of operations depends on the volume of the knapsack and the structure of the Pareto layers, into which the set of initial data in the entered two-criteria space is divided.


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