When generating layout schemes, both the material usage and practicality of the cutting process should be considered. This paper presents a two-section algorithm for generating guillotine-cutting schemes of rectangula...
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Balancing objectives and constraints is challenging in addressing constrained multiobjective optimization problems (CMOPs). Existing methods may have limitations in handling various CMOPs due to the complex geometries...
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Balancing objectives and constraints is challenging in addressing constrained multiobjective optimization problems (CMOPs). Existing methods may have limitations in handling various CMOPs due to the complex geometries of the Pareto front (PF). And the complexity arises from the constraints that narrow the feasible region. Categorizing problems based on their geometric characteristics facilitates facing this challenge. For this purpose, this article proposes a novel constrained multiobjective optimization framework with detection and supervision phases, called COEA-DAS. The framework categorizes the problems into four types based on the overlap between the obtained approximate unconstrained PF and constrained PF to guide the coevolution of the two populations. In the detection phase, the detection population approaches the unconstrained PF ignoring the constraints. The main population is guided by the detection population to cross infeasible barriers and approximate the constrained PF. In the supervision phase, specialized evolutionary mechanisms are designed for each possible problem type. The detection population maintains evolution to assist the main population in spreading along the constrained PF. Meanwhile, the supervision strategy is conducted to reevaluate the problem types based on the evolutionary state of the populations. This idea of balancing constraints and objectives based on the type of problem provides a novel approach for more effectively addressing the CMOPs. Experimental results indicate that the proposed algorithm performs better or more competitively on 57 benchmark problems and 12 real-world CMOPs compared with eight state-of-the-art algorithms. IEEE
Robust charging schedules for a growing market of battery-electric bus (BEB) fleets are critical to successful adoption. In this paper, we present a BEB charging scheduling framework that considers spatiotemporal sche...
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Robust charging schedules for a growing market of battery-electric bus (BEB) fleets are critical to successful adoption. In this paper, we present a BEB charging scheduling framework that considers spatiotemporal schedule constraints, route schedules, fast and slow charging options, and battery dynamics, modeled as a mixed-integer linear program (MILP). The MILP is based on the berth allocation problem (BAP), a method that optimally assigns vessels for service, and is adapted in a modified form known as the position allocation problem (PAP), which assigns electric vehicles (EVs) for charging. linear battery dynamics are included to model the charging of buses while at the station. To account for the BEB discharges over their respective routes, we assume that each BEB experiences an average kWh charge loss while in transit. The optimization coordinates BEB charging to ensure that each vehicle maintains a state-of-charge (SOC) above a specified level. The model also minimizes the total number of chargers utilized and prioritizes slow charging for battery health. The validity of the model is demonstrated using a set of routes sampled from the Utah Transit Authority (UTA) for 35 buses and 338 visits to the charging station. The model is also compared to a heuristic algorithm based on charge thresholds, referred to as the Qin-modified method. The results show that the MILP framework encourages battery health by assigning slow chargers to BEBs more readily than the Qin-modified method. The MILP utilized one fast charger and six slow chargers, whereas the Qin-modified method utilized four fast chargers and six slow chargers. Moreover, the MILP maintained a specified minimum SOC of 25% throughout the day and achieved the required minimum SOC of 70% at the end of the working day, whereas the Qin-modified method failed to maintain the SOC above 0% without any constraints applied. Furthermore, it is shown that the spatiotemporal constraints are met while considering the batter
Cloud-orchestrated Internet of Things (IoT) facilitates proper utilization of network resources and placating user demands in smart communications. Multiple concurrent access (MCA) techniques designed for cloud-assist...
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This work addresses characteristics of software environments for mathematical modeling and proposes a system for developing and managing models of linear and integer programming (IP) problems. The main features of thi...
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This work addresses characteristics of software environments for mathematical modeling and proposes a system for developing and managing models of linear and integer programming (IP) problems. The main features of this modeling environment are: version control of models and data;client-server architecture, which allows the interaction among modelers and decision makers;the use of a database to store information about the models and data scenarios;and the use of remote servers of optimization, which allows the optimization problems to be solved on different machines. The modeling environment proposed in this work was validated using mathematical programming models that exploit different characteristics, such as the treatment of conditions for generating variables and constraints, the use of calculated parameters derived from other parameters, and the use of integer and continuous variables in mixed IP models among others. This validation showed that the proposed environment is able to treat models found in various application areas of operations research and to solve problems with tens of thousands of variables and constraints.
Surrogate-assisted evolutionary algorithms (SAEAs) rely on the infill criterion to select candidate solutions for expensive evaluations. However, in the context of expensive constrained multi-objective optimization pr...
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Surrogate-assisted evolutionary algorithms (SAEAs) rely on the infill criterion to select candidate solutions for expensive evaluations. However, in the context of expensive constrained multi-objective optimization problems (ECMOPs) with complex feasible regions, guiding the optimization algorithm towards the constrained Pareto optimal front and achieving a balance between feasibility, convergence, diversity, exploration, and exploitation using a single infill criterion pose significant challenges. We propose an ensemble infill criterion-based multi-stage SAEA (EIC-MSSAEA) to tackle these challenges. Specifically, EIC-MSSAEA comprises three stages. In the first stage, we ignore constraints to facilitate the rapid traversal of infeasible obstacles. In the second stage, only one constraint is activated at a time to increase algorithm diversity. Finally, in the last stage, we activate all constraints to improve overall feasibility. In each stage, EIC-MSSAEA first employs NSGA-III as the underlying baseline solver to explore the search space, in which promising solutions are then selected by an ensemble infill criterion that incorporates multiple base-infill criteria to measure the feasibility, convergence, diversity, and uncertainty of candidate solutions. Experimental results demonstrate the competitiveness of EIC-MSSAEA against state-of-the-art SAEAs for ECMOPs. IEEE
While globally optimal solutions to many convex programs can be computed efficiently in polynomial time, this is, in general, not possible for nonconvex optimization problems. Therefore, locally optimal approaches or ...
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While globally optimal solutions to many convex programs can be computed efficiently in polynomial time, this is, in general, not possible for nonconvex optimization problems. Therefore, locally optimal approaches or other efficient suboptimal heuristics are usually applied for practical implementations. However, there is also a strong interest in computing globally optimal solutions of nonconvex problems in offline simulations in order to benchmark the faster suboptimal algorithms. Global solutions often rely on monotonicity properties. A common approach is to reformulate problems into a canonical monotonic optimization problem where the monotonicity becomes evident, but this often comes at the cost of nested optimizations, increased numbers of variables, and/or slow convergence. The framework of mixed monotonic programming (MMP) proposed in this paper avoids such performance-deteriorating reformulations by revealing hidden monotonicity properties directly in the original problem formulation. By means of a wide range of application examples from the area of signal processing for communications (including energy efficiency for green communications, resource allocation in interference networks, scheduling for fairness and quality of service, as well as beamformer design in multiantenna systems), we demonstrate that the novel MMP approach leads to tremendous complexity reductions compared to state-of-the-art methods for global optimization. However, the framework is not limited to optimizing communication systems, and we expect that similar speed-ups can be obtained for optimization problems from other areas of research as well.
This study examined how The World Food Programme's Fill the Nutrient Gap (FNG) situation analysis has facilitated decision-making to support nutrition. Semi-structured interviews were held with 60 'broker'...
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This study examined how The World Food Programme's Fill the Nutrient Gap (FNG) situation analysis has facilitated decision-making to support nutrition. Semi-structured interviews were held with 60 'broker', 'technical analyst' and 'consumer' end users of the FNG in 11 countries. Almost all FNG cases, especially those conducted in 'development' contexts, had objectives of informing government decision-making, with some, especially in 'fragile' settings, also focused on informing WFP's own programming. The FNG was credited with contributing evidence to national or sub-national nutrition strategy development, informing advocacy and building momentum and improving understanding around key nutrition issues. Internally, the FNG helped promote WFP's nutrition-sensitive programming approaches. This article discusses these findings and explores how the FNG's policy contribution could be strengthened in future applications.
In this paper, the effect of temperature on the extraction efficiency of oil and grease from anaerobically fermented kitchen waste was investigated through the analysis of pilot tests. Based on the results of multifac...
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This paper presents a practical method of constructing an implied price surface and, more importantly, risk-neutral transition density functions from market option price data, which is the crucial building block of th...
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