Optimization of Resources

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 5806

Special Issue Editor


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Guest Editor
Computing Science Department of Universidad Complutense de Madrid, Madrid, Spain
Interests: functional programming; formal methods; nature-inspired computation; optimization; parallel programming

Special Issue Information

Dear Colleagues,

The optimization of resources is a key matter in computer science due to the necessity to improve the efficiency of mechanisms depending on these resources. Regardless of whether this optimization is the ultimate goal itself or it is instrumental to achieve other objectives, it is a recurrent problem appearing both inside the computer science domain and in other fields aided by computational techniques, such as other sciences, engineering, and economics. Unfortunately, in many cases finding the optimal solution is not feasible in general due to the hardness of the problem. In fact, even finding good approximations can also be a challenge. Despite these well-known theoretical limits, resource optimization problems appear whenever there is a sophisticated system, and we have to face them by using non-exhaustive methods. These methods can be specific to the concrete problem or adaptations of generic-purpose metaheuristics (e.g., evolutionary computation or swarm optimization methods).

The goal of this Special Issue is to collect new proposals in the area of optimization of resources, including both theoretical approaches and practical applications. We solicit contributions related (but not limited) to the following topics:

  • Formal methods applied to the optimization of resources;
  • Definition and application of optimization algorithms and metaheuristics;
  • Comparison of optimization algorithms and metaheuristics in concrete case studies;
  • Optimization of resources on industrial data;
  • Benchmark usage and generation.

Prof. Dr. Fernando Rubio
Guest Editor

Manuscript Submission Information

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Keywords

  • Distribution of computational resources
  • Computer-aided optimization of resources in science, engineering, and economics
  • Formal methods in optimization
  • Metaheuristics
  • Nature-inspired methods for optimization

Published Papers (2 papers)

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Research

15 pages, 319 KiB  
Article
On the Hardness of Lying under Egalitarian Social Welfare
by Jonathan Carrero, Ismael Rodríguez and Fernando Rubio
Mathematics 2021, 9(14), 1599; https://doi.org/10.3390/math9141599 - 07 Jul 2021
Cited by 2 | Viewed by 1298
Abstract
When it comes to distributing resources among different agents, there are different objectives that can be maximized. In the case of egalitarian social welfare, the goal is to maximize the utility of the least satisfied agent. Unfortunately, this goal can lead to strategic [...] Read more.
When it comes to distributing resources among different agents, there are different objectives that can be maximized. In the case of egalitarian social welfare, the goal is to maximize the utility of the least satisfied agent. Unfortunately, this goal can lead to strategic behaviors on the part of the agents: if they lie about their utility functions, then the dealer might grant them more goods than they would be entitled to. In this work, we study the computational complexity of obtaining the optimal lie in this context. We show that although it is extremely easy to obtain the optimal lie when we do not impose any restrictions on the lies used, the problem becomes Σ2P-complete by imposing simple limits on the usable lies. Thus, we prove that we can easily make it hard to lie in the context of egalitarian social welfare. Full article
(This article belongs to the Special Issue Optimization of Resources)
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16 pages, 686 KiB  
Article
Teaching–Learning Based Optimization (TLBO) with Variable Neighborhood Search to Retail Shelf-Space Allocation
by Yan-Kwang Chen, Shi-Xin Weng and Tsai-Pei Liu
Mathematics 2020, 8(8), 1296; https://doi.org/10.3390/math8081296 - 05 Aug 2020
Cited by 8 | Viewed by 3825
Abstract
Shelf space is a scarce and expensive resource in the retail industry because a large number of products compete for limited display space. Thus, shelf-space allocation is frequently implemented in shops to increase product sales and profits. In the past few decades, numerous [...] Read more.
Shelf space is a scarce and expensive resource in the retail industry because a large number of products compete for limited display space. Thus, shelf-space allocation is frequently implemented in shops to increase product sales and profits. In the past few decades, numerous models and solution methods have been developed to deal with the shelf-space allocation problem (SSAP). In this paper, a novel population-oriented metaheuristic algorithm, teaching–learning-based optimization (TLBO) is applied to solve the problem and compared with existing solution methods with respect to their solution performance. Further, a hybrid algorithm that combines TLBO with variable neighborhood search (VNS) is proposed to enhance the performance of the basic TLBO. The research results show that the proposed TLBO-VNS algorithm is superior to other algorithms in terms of solution performance, in addition to using fewer control parameters. Therefore, the proposed TLBO-VNS algorithm has considerable potential in solving SSAP. Full article
(This article belongs to the Special Issue Optimization of Resources)
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