Advancements in mathematical programming have made it possible to efficiently tackle large-scale real-world problems that were deemed intractable just a few decades ago. However, provably optimal solutions may not be ...
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(纸本)1577358872
Advancements in mathematical programming have made it possible to efficiently tackle large-scale real-world problems that were deemed intractable just a few decades ago. However, provably optimal solutions may not be accepted due to the perception of optimization software as a black box. Although well understood by scientists, this lacks easy accessibility for practitioners. Hence, we advocate for introducing the explainability of a solution as another evaluation criterion, next to its objective value, which enables us to find tradeoff solutions between these two criteria. Explainability is attained by comparing against (not necessarily optimal) solutions that were implemented in similar situations in the past. Thus, solutions are preferred that exhibit similar features. Although we prove that already in simple cases the explainable model is NP-hard, we characterize relevant polynomially solvable cases such as the explainable shortest path problem. Our numerical experiments on both artificial as well as real-world road networks show the resulting Pareto front. It turns out that the cost of enforcing explainability can be very small.
Hydrogen and light hydrocarbon components are essential resources of the *** optimization of the refinery hydrogen system and recovery of the light hydrocarbon components contained in the gas streams are key strategie...
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Hydrogen and light hydrocarbon components are essential resources of the *** optimization of the refinery hydrogen system and recovery of the light hydrocarbon components contained in the gas streams are key strategies to reduce the operating costs for sustainable *** research efforts have been focused on the optimization of single impurity hydrogen network,and the flowrates of the hydrogen sources and sinks are assumed to be ***,their flowrates vary along with the quality of crude oil and refinery processing plans.A general superstructure of multicomponent refinery hydrogen network is proposed,which considers four components,namely H_(2),H_(2)S,CH_(4) and C_(2+),as well as the flowrate variations of hydrogen source and hydrogen *** mathematical model based on the superstructure is developed with objective functions,including the minimization of total annualized cost and the maximization of overall satisfaction of the hydrogen ***,the model considers the removal of hydrogen sulfide and the recovery of light hydrocarbon components(i.e.,C_(2+))in the *** verify the applicability of the proposed mathematical model,a simplified industrial case study with four scenarios is *** optimization results show that the economic benefit can be maximized by considering both the direct reuse of gas streams from high-pressure separator(HP gas stream)and from low-pressure separator(LP gas stream)and the recovery of the light hydrocarbon *** fuzzy optimization method can be used to guide the optimal design of the refinery hydrogen system with multi-period variable flowrates.
Network security defenses evolve, responding to real-time attack incidents, modifying the underlying topology, or reallocating defense systems across the network. The present work emphasizes reducing the time to compu...
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Network security defenses evolve, responding to real-time attack incidents, modifying the underlying topology, or reallocating defense systems across the network. The present work emphasizes reducing the time to compute new optimal reallocations of defense systems, responding to emerging real-time remote attacks. The proposed heuristic method utilizes parallel processing by slicing the underlying graphical model representing the network topology, solving in parallel multiple mixed-integer programming problems corresponding to the created subgraphs, and producing an estimate of the optimal defense. The parallelized method to compute a new defense enables producing a response, in real-time, before remote attackers compromise a target machine in the network. Our prototype tool to compute a new defense, the high-performance security analyzer, has a speedup of at least 20 over solving the original problem using a serial algorithm, and with an insignificant difference between the performance of the (computed in parallel) approximately optimal defense and the (serially computed) optimal defense. A major conclusion is that further speedups will come from parallel integer programming algorithms rather than from graph partitioning.
The coupling of ever-increasing consumption of fossil fuels around the globe with the decrease in the availability of fossil fuel supplies has led to an increased cost of energy commodities, which together with ever-e...
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The coupling of ever-increasing consumption of fossil fuels around the globe with the decrease in the availability of fossil fuel supplies has led to an increased cost of energy commodities, which together with ever-expanding requirements for reducing the level of environmental pollutions has resulted in an ever-increasing deal of attention to alternative transportation schemes such as electric vehicles (EVs). Since decades ago, national governments and environmental activists have initiated various efforts towards reducing atmospheric pollutions. A part of such effort has been focused on reducing the use of internal combustion vehicles and rather replacing them with EVs. In this research, we attempt to fill in this research gap by presenting a mathematical model for minimizing the sum of traveled distance and recharging cost of EVs per a given period and then solving it by simulated annealing (SA) algorithm. Results of the proposed algorithm were then compared to those of coding in GAMS for 30 different sample problems with different counts of customers, EVs, and charging stations. Numerical results indicated good efficiency of the metaheuristic algorithm in terms of processing time and solution quality. Indeed, with the SA algorithm, the processing time was seen to increase gradually with increasing the problem complexity, while the rate of increase in processing time was much steeper with the GAMS.
The recent evolution and development of urban areas has dramatically transformed the layout of cities and has had a significant impact on mobility. Decision makers have become aware of the problem and have begun to ta...
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The recent evolution and development of urban areas has dramatically transformed the layout of cities and has had a significant impact on mobility. Decision makers have become aware of the problem and have begun to take measures to manage the changing demand for mobility through the diversification and promotion of less aggressive and more efficient transport modes: walking, cycling and using public transport. New standards in sustainable mobility are being incorporated into this new scenario to encourage a reduction in car use. These standards form the basis for the design of planning tools and more efficient management systems, among them, encouraging the use of bicycles as an everyday mode of mobility in urban areas. There are a number of programs aimed at the promotion of cycling in cities. One in particular is for the planning and design of cycle paths through the establishment of networks that allow the use of bicycles in preferential paths with high safety guarantees. This paper presents a mathematical programming model for the optimal design of a network intended for cyclists. Specifically, the model determines which type of infrastructure (type of bike lane) is most appropriate on each link of a road network, based on criteria of cost to users and the investment cost of the infrastructure itself. As an application of the proposed model, several experiments are presented on a testing network based on the known Sioux Falls network. As a result of these experiments a number of useful conclusions are obtained for the design of cycle networks from a social and operational perspective within a pre-defined cost. The model has been developed to be highly versatile and to allow any type of change (different network, different levels of demand, etc ..) and to assure the least consumption of computational resources.
Road vehicle lane changes often initiate traffic disturbances and can therefore impact road networks' energy and time efficiency. Furthermore, unexpected changes in traffic conditions may also render lane changes ...
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Road vehicle lane changes often initiate traffic disturbances and can therefore impact road networks' energy and time efficiency. Furthermore, unexpected changes in traffic conditions may also render lane changes counterproductive for the lane-changing vehicle. Vehicle-to-vehicle connectivity combined with anticipative control could address these challenges via improved lane change decisions by automated vehicles. In a move toward this objective, receding horizon control cast as a mixed-integer quadratic program is used to plan lane changing and acceleration in a coupled optimization. A long-term pacing module, based on Pontryagin's minimum principle from optimal control theory, sets terminal and input references for receding horizon control to target a user's expected travel time. To remove nonlinear vehicle dynamics from the receding horizon controller, lane change commands are passed to a pure pursuit steering module whose response is approximated by a second-order linear model. Comparison against a rule-based reactive algorithm in arterial and highway scenarios shows an 8.9%-13.7% reduction in energy consumption and a 5.2%-10.3% reduction in the travel time, along with navigational improvements.
We present two bilevel programming formulations for the aircraft deconfliction problem: one based on speed regulation in k dimensions, the other on heading angle changes in 2 dimensions. We propose three reformulation...
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We present two bilevel programming formulations for the aircraft deconfliction problem: one based on speed regulation in k dimensions, the other on heading angle changes in 2 dimensions. We propose three reformulations of each problem based on KKT conditions and on two different duals of the lower-level subproblems. We also propose a cut generation algorithm to solve the bilevel formulations. Finally, we present computational results on a variety of instances.
Life cycle assessment (LCA) is a method to evaluate the environmental impacts of technologies from cradle to grave. However, LCAs are commonly defined in terms of the consumption of a single unit of a product and thus...
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Life cycle assessment (LCA) is a method to evaluate the environmental impacts of technologies from cradle to grave. However, LCAs are commonly defined in terms of the consumption of a single unit of a product and thus ignore scaling issues in large-scale deployment of technologies. Such product-level LCAs often do not consider capital manufacturing capacity and supply chain bottlenecks that may hinder the rapid, widespread uptake of emerging technologies entering the market;emerging technologies often require the expansion of existing supply chains or the development of entirely new supply chains, such as the manufacturing of novel materials. As a result, such LCA studies are limited in their ability to realistically assess impacts at the macro-scale and thus to guide large-scale decisions. In this work, we present ECOPT2, a generalized adaptable model that combines these constraints to the LCA approach using a mathematical programming approach and dynamic stock modeling. ECOPT2 combines LCA factors with transition scenarios from energy systems models to determine the environmentally optimal deployment of new technologies while accounting for material circularity constraints and barriers to uptake. We also introduce the structure of the software tool and demonstrate its features using a stylized vehicle electrification scenario.
We present three core principles for engineering-oriented integrated modeling and optimization tool sets-intuitive modeling contexts, systematic computer-aided reformulations, and flexible solution strategies-and desc...
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We present three core principles for engineering-oriented integrated modeling and optimization tool sets-intuitive modeling contexts, systematic computer-aided reformulations, and flexible solution strategies-and describe how new developments in *** for Generalized Disjunctive programming (GDP) advance this vision. We describe a new logical expression system implementation for *** allowing for a more intuitive description of logical propositions. The logical expression system supports automated reformulation of these logical constraints to linear constraints. We also describe two new logic-based global optimization solver implementations built on *** that exploit logical structure to avoid "zero-flow" numerical difficulties that arise in nonlinear network design problems when nodes or streams disappear. These new solvers also demonstrate the capability to link to external libraries for expanded functionality within an integrated implementation. We present these new solvers in the context of a flexible array of solution paths available to GDP models. Finally, we present results on a new library of GDP models demonstrating the value of multiple solution approaches.
This study aims at investigating a scheduling problem for unloading of material ships in a large-scale steel plant. The optimization problem minimizes the cost caused by improperly delayed unloading operations of the ...
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This study aims at investigating a scheduling problem for unloading of material ships in a large-scale steel plant. The optimization problem minimizes the cost caused by improperly delayed unloading operations of the ships for a given time horizon. The problem intends to decide the order of unloading ships and the allocation of conveyors to the ships. The solution for the practical-sized problem is NP-hard because of the dimensionality of the problem. In this paper we resolve the problem using a well-designed differential evolution algorithm (DE). In addition, a mathematical programming model is given as well for comparison. An empirical study with some comparative results from CPLXE solver and other DE variants is carried out, which shows a good potential for real-world applications.(c) 2022 Elsevier Inc. All rights reserved.
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