Road pavement costs expend a significant share of financial resources in road construction, and finding the optimal pavement thickness design with minimal cost remains a concern that can be determined using intelligen...
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Road pavement costs expend a significant share of financial resources in road construction, and finding the optimal pavement thickness design with minimal cost remains a concern that can be determined using intelligent search algorithms. This study aims to evaluate the performance of the particle swarm optimization (PSO) algorithm based on Iranian Highway Asphalt Paving (IHAP) Code 234. Thus, using the PSO algorithm, the problem of pavement thickness design was solved numerically, and a simulation-optimization technique was devised. Comparing PSO and the linear algorithms (LP) indicates that PSO is accounted as an optimal pavement thickness design regardless of different ranges of the equivalent single-axle loads (ESALs) and resilient moduli (Mr). Considering the four-layer and three-layer pavement designs revealed that the different bitumen-stabilized bases in the four-layer design are not costeffective. Moreover, the PSO design resulted in 22-29 percent cost savings at various ESALs and Mr;however, the three-layer design was 23-31 percent less expensive. (c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/ by-nc-nd/4.0/).
linear programming has had a tremendous impact in the modeling and solution of a great diversity of applied problems, especially in the efficient allocation of resources. As a result, this methodology forms the backbo...
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linear programming has had a tremendous impact in the modeling and solution of a great diversity of applied problems, especially in the efficient allocation of resources. As a result, this methodology forms the backbone of introductory courses in operations research. What students, and others, may not appreciate is that linear programming transcends its linear nomenclature and can be applied to an even wider range of important practical problems. The objective of this article is to present a selection, and just a selection, from this range of problems that at first blush do not seem amenable to linear programming formulation. The exposition focuses on the most basic models in these selected applications, with pointers to more elaborate formulations and extensions. Thus, our intent is to expand the modeling awareness of those first encountering linear programming. In addition, we hope this article will be of interest to those who teach linear programming and to seasoned academics and practitioners, alike.
The aim was to design culturally acceptable and healthy diets with reduced energetic share of ultra-processed foods (UPF%) at no cost increment and to evaluate the impact of the change in the UPF% on diet quality. Foo...
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The aim was to design culturally acceptable and healthy diets with reduced energetic share of ultra-processed foods (UPF%) at no cost increment and to evaluate the impact of the change in the UPF% on diet quality. Food consumption and price data were obtained from the Household Budget Survey (n 55 970 households) and National Dietary Survey (n 32 749 individuals). linear programming models were performed to design diets in which the mean population UPF% was reduced up to 5 % with no cost increment relative to the observed costs. The models were isoenergetic or allowed the energy content to vary according to the UPF%, and they were not constrained to nutritional goals (nutrient-free models) or maximised the compliance with dietary recommendations (nutrient-constrained models). Constraints regarding food preference were introduced in the models to obtain culturally acceptable diets. The mean population UPF% was 23 center dot 8 %. The lowest UPF% attained was approximately 10 %. The optimised diet cost was up to 20 % cheaper than the observed cost, depending on the model and the income level. In the optimised diets, the reduction in the UPF% was followed by an increase in fruits, vegetables, beans, tubers, dairy products, nuts, fibre, K, Mg, vitamin A and vitamin C in the nutrient-constrained models, compared with the observed consumption in the population. There was little variation in most nutrients across the UPF% reduction. The UPF% reduction in the nutrient-free models impacted only trans-fat and added sugar content. UPF% reduction and increase in diet quality are possible at no cost increment.
Homomorphic Encryption (HE) is a set of powerful properties of certain cryptosystems that allow privacy-preserving operation over the encrypted text. Still, HE is not widespread due to limitations in terms of efficien...
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Homomorphic Encryption (HE) is a set of powerful properties of certain cryptosystems that allow privacy-preserving operation over the encrypted text. Still, HE is not widespread due to limitations in terms of efficiency and usability. Among the challenges of HE, scheme parametrization (i.e., the selection of appropriate parameters within the algorithms) is a relevant multi-faced problem. First, the parametrization needs to comply with a set of properties to guarantee the security of the underlying scheme. Second, parametrization requires a deep understanding of the low-level primitives since the parameters have a confronting impact on the scheme's precision, performance, and security. Finally, the circuit to be executed influences, and it is influenced by, the parametrization. Thus, there is no general optimal selection of parameters, and this selection depends on the circuit and the scenario of the application. Currently, most existing HE frameworks require cryptographers to address these considerations manually. It requires a minimum of expertise acquired through a steep learning curve. In this paper, we propose a unified solution for the aforementioned challenges. Concretely, we present an expert system combining Fuzzy Logic and linear programming. The Fuzzy Logic Modules receive a user selection of high-level priorities for the security, efficiency, and performance of the cryptosystem. Based on these preferences, the expert system generates a linear programming Model that obtains optimal combinations of parameters by considering those priorities while preserving a minimum level of security for the cryptosystem. We conduct an extended evaluation showing that an expert system generates optimal parameter selections that maintain user preferences without undergoing the inherent complexity of analyzing the circuit.
Since its inception, fuzzy linear programming (FLP) has proved to be a more powerful tool than classical linear programming to optimize real-life problems dealing with uncertainty. However, the proposed models are par...
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Conflict-driven pseudo-Boolean solvers optimize 0-1 integer linear programs by extending the conflict-driven clause learning (CDCL) paradigm from SAT solving. Though pseudo-Boolean solvers have the potential to be exp...
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Conflict-driven pseudo-Boolean solvers optimize 0-1 integer linear programs by extending the conflict-driven clause learning (CDCL) paradigm from SAT solving. Though pseudo-Boolean solvers have the potential to be exponentially more efficient than CDCL solvers in theory, in practice they can sometimes get hopelessly stuck even when the linear programming (LP) relaxation is infeasible over the reals. Inspired by mixed integer programming (MIP), we address this problem by interleaving incremental LP solving with cut generation within the conflict-driven pseudo-Boolean search. This hybrid approach, which for the first time combines MIP techniques with full-blown conflict analysis operating directly on linear inequalities using the cutting planes method, significantly improves performance on a wide range of benchmarks, approaching a "best-of-both-worlds" scenario between SAT-style conflict-driven search and MIP-style branch-and-cut.
When operating hybrid desalination plants combining multistage flash (MSF) and reverse osmosis (RO) processes, it is crucial to determine the blend ratios of water produced by these two processes. These blend ratios m...
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When operating hybrid desalination plants combining multistage flash (MSF) and reverse osmosis (RO) processes, it is crucial to determine the blend ratios of water produced by these two processes. These blend ratios must take economic and environmental aspects of the hybrid plant's sustainability into consideration, and must enable the plant to meet constraint conditions with respect to water demand, energy consumption savings, and saline content. We mathematically resolve this issue by formulating it as a linear programing problem and by computing that problem's solutions. Permissible solutions occur as an area in a triangle in the MSF-RO blend ratio diagram, and the most desirable solutions occur at (1) the intersection point between the water demand limit line and salinity limit line and at (2) the intersection point between the water demand limit line and the energy savings limit line.
This report addresses the problem of daily net revenue maximization in a fruit wholesale company. For this purpose, a linear programming model has been formulated whose objective function is the maximization of the ne...
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We consider the problem of designing output feedback controllers that use measurements from a set of landmarks to navigate through a cell-decomposable environment using duality, control Lyapunov function and control b...
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We consider the problem of designing output feedback controllers that use measurements from a set of landmarks to navigate through a cell-decomposable environment using duality, control Lyapunov function and control barrier function, and linear programming. We propose two objectives for navigating in an environment, one to traverse the environment by making loops and one by converging to a stabilization point while smoothing the transition between consecutive cells. We test our algorithms in a simulation environment, evaluating the robustness of the approach to practical conditions, such as bearing-only measurements, and measurements acquired with a camera with a limited field of view.
According to Dantzig (Econometrica, 17, p.200, 1949), von Neumann was the first to observe that for any finite two-person zero-sum game, there is a feasible linear programming (LP) problem whose saddle points yield eq...
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According to Dantzig (Econometrica, 17, p.200, 1949), von Neumann was the first to observe that for any finite two-person zero-sum game, there is a feasible linear programming (LP) problem whose saddle points yield equilibria of the game, thus providing an immediate proof of the minimax theorem from the strong duality theorem. We provide an analogous construction going in the other direction. For any LP problem, we define a game and, with a brief and elementary proof, show that every equilibrium either yields a saddle point of the LP problem or certifies that one of the primal or dual programs is infeasible and the other is infeasible or unbounded. We thus obtain an immediate proof of the strong duality theorem from the minimax theorem. Taken together, von Neumann's and our results provide a succinct and elementary demonstration that matrix games and linear programming are "equivalent" in a classical sense.
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