Minimization Of Objective Function Research Articles

Characterization of the temporal profile of the antinociceptive effects of an intravenous bolus of ketamine using the analgesia nociception index in no-anesthetized adult patients.

An effect-site target-controlled infusion (TCI) would allow a more precise titration of intravenous analgesics effect. The analgesia nociception index (ANI) continuously monitors the analgesia/nociception balance during general anesthesia. This study aims to derive a PKPD model of ketamine antinociceptive effect using the Domino PK parameter set and the ANI response data in awake patients without other drugs affecting the ANI response. Twenty awake adult patients were prospectively studied before general anesthesia. Patients received a single intravenous bolus of ketamine 0.1mg·kg- 1, and the subsequent ANI values were recorded. An effect compartment model incorporating the Domino PK parameter set was used to characterize the time lag between ketamine plasma concentrations and the ANI response. The model was parameterized with a single parameter Ke0. An Emax pharmacodynamic model was used to fit the ANI response data. Model parameters were estimated with NONMEM® 7.5. The minimum objective function value guided the model construction. After the ketamine administration, basal ANI values increased from 38.5 ± 4.95 to a maximum of 53.5 ± 4.95 with an observed time-to-peak effect of 1.83 ± 0.74min. Modeling analysis revealed hysteresis between predicted plasma concentrations from the Domino model and observed ANI data. Hysteresis was characterized, incorporating an estimated Keo of 0.238 (CI95% 0.20-0.28) min-1 to the described PK parameters set. The developed PKPD model, using Domino's PK parameters and the ANI response data, adequately characterized the temporal profile of ketamine's antinociceptive effect. The current estimated model parameters can be used to perform an effect-site TCI of ketamine for analgesic purposes.

Physics-informed neural networks approach for pollutant dispersion flow caused by magnetic dipole

Abstract Research on the liquid flow and distribution of waste discharge concentration over cylindrical surfaces might lead to the growth of efficient waste management and pollution controlling strategies. In view of this, the pollutant concentration and magnetic dipole impact on the liquid flow through the cylinder with a porous medium are explored in the present analysis. Moreover, Fourier’s and Fick’s laws are considered to explore heat transport features. The governing partial differential equations (PDEs) of the present study are converted into non-dimensional ordinary differential equations (ODEs) utilizing similarity variables. Also, the current study proposes a novel implementation of neural networks based on physics knowledge for the liquid flow past a cylinder. The self-updating weights and bias facilitate the network to satisfy the minimization objective function and provide accurate results. Additionally, the solution obtained by Runge Kutta Fehlberg's fourth-fifth order (RKF-45) method is utilized to validate the physics-informed neural network (PINN) results, presenting mean squared errors from 10-9 to 10-11. Furthermore, it is found that the increase in temperature relaxation time parameter decreases the Nusselt number gradually at the rate of 1 to 5%. An upsurge in the values of the pollutant external source parameter and the pollutant external source variation parameter decreases the concentration profile.

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

AbstractA self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

Voyage scheduling and energy management co-optimization in hydrogen-powered ships

The maritime sector is striving to lessen its dependence on fossil fuels, motivated by both economic factors related to fuel efficiency and the policies established by the International Maritime Organization (IMO). Consequently, there has been a growing focus on developing and deploying all-electric ships (AES) as a more sustainable alternative to conventional fossil fuel-powered vessels. AES presents several advantages over conventional ships, including zero emissions, enhanced energy efficiency, and greater sustainability. However, the implementation of AES also brings new challenges, such as the necessity to manage a hybrid microgrid and optimize voyage scheduling. Hybrid fuel cell (FC)-battery energy storage (BES) technology can significantly contribute to addressing these requirements. To tackle these challenges, this article introduces a comprehensive framework for the optimal planning of hybrid microgrids and voyage scheduling in AES. The framework incorporates multi-objective optimization techniques based on genetic algorithms to achieve a balance between cost and voyage time. The proposed framework is applied to a case study of a ship operating on a fixed route between two ports. The simulation results indicated that the total cost per voyage, associated with the objective function of cost minimization and voyage time reduction, has decreased by 12% and increased by 40%, respectively, when compared to the regular ship speed profile. Additionally, the travel time has been reduced by half an hour and 2 h, respectively, in comparison to the regular ship speed profile. To verify the results, the optimization problem for cost and voyage scheduling is also solved by Mayfly Algorithm (MA) separately. Thus, it can be illustrated the effectiveness of the framework in enhancing energy efficiency and fuel economy while preserving the ship's performance.

Wear behaviour analysis of thermo-mechanically processed AA7075 and AA7075/SiC/Graphite composite

The current work focuses on investigating the tribological performance of AA7075 alloy and AA7075 hybrid composite with SiC and graphite reinforcements of 2 and 0.5 wt percentages, respectively. The alloy and hybrid composite are fabricated through stir casting and thermo-mechanically processed through hot rolling routes. Dry sliding wear characterization has been carried out using a pin-on-disc wear testing setup for alloy and composite under various processing conditions using experiments designed according to Taguchi's L9 orthogonal array. The technique for order preference by similarity to ideal solution (TOPSIS) method has been adopted to achieve a single multi-response index (MRI) considering the minimization of multiple objective functions for specific wear rate (SWR) and coefficient of friction (COF). The analysis of variance (ANOVA) results of the MRI indicated that sample processing condition and load were the most significant parameters. The wear mechanism at different experimental conditions was studied using Scanning electron microscope (SEM) micrographs, energy dispersive spectroscopy analysis and 3D surface profile of wear track. The AA7075 composites are found to be comparatively good in all experimental conditions compared to the base alloy due to load-bearing SiC particles and solid lubricant graphite as reinforcing elements. The mechanically mixed layer (MML) formation in hybrid composites helped achieve good tribological properties. The AA7075 alloy and hybrid composites cross rolled at 350 °C yielded better results than other samples. The wear mechanism was abrasive and adhesive at low temperature and low load conditions in both alloy and hybrid composites. Whereas, at higher temperatures and high loads oxidative wear mechanism with delamination was prevalent. The transition of SWR from mild to severe value occurs at 250 °C in all experimental cases. The specific wear rate was improved by 85.64 % between worst and best conditions, i.e., annealed alloy and cross rolled composite.

A deterministic design approach of tilt integral derivative controller for integer and fractional-order system with time delay

A tilt integral derivative (TID) controller modifies the proportional integral derivative (PID) controller in the fractional domain. It converts the proportional gain as a function of frequency and is thereby capable of achieving optimal system response. The usual practice for the parameter estimation of the TID controller is by minimization of the error-based objective functions using optimization techniques. Although precise results can be achieved, these nature-inspired algorithms are stochastic and hence produce different solutions during different iterations. Therefore, a comparative statistical study is usually necessary to validate the best possible result. This study shows a deterministic analytical procedure for the paramssseter estimation of TID controllers. The magnitude and phase angle criteria, along with the frequency-domain loop shaping specifications, are used for the explicit evaluation of the TID parameters. Because of its model-independent nature, this tuning strategy can be used for a variety of integral and nonintegral order systems with different plant structures. In this article, the authenticity of the applied procedure is demonstrated through suitable numerical examples. The complexity of the design problem is enhanced by using it for both integer and non-integer (fractional) order plus time-delay systems. Further, the robustness of the control system in the presence of a TID controller was examined under the influence of external parameters and input reference changes. Simulation studies validate the supremacy of TID controllers over PID controllers in terms of reference tracking and disturbance rejection capabilities.

An Obstacle Avoidance Trajectory Planning Methodology Based on Energy Minimization (OTPEM) for the Tilt-Wing eVTOL in the Takeoff Phase

Electric tilt-wing flying cars are an efficient, economical, and environmentally friendly solution to urban traffic congestion and travel efficiency issues. This article addresses the high energy consumption and obstacle interference during the takeoff phase of the tilt-wing eVTOL (electric Vertical Takeoff and Landing), proposing a trajectory planning method based on energy minimization and obstacle avoidance. Firstly, based on the dynamics analysis, the relationship between energy consumption, spatial trajectory, and obstacles is sorted out and the decision variables for the trajectory planning problem with obstacle avoidance are determined. Secondly, based on the power discretization during the takeoff phase, the energy minimization objective function is established and the constraints of performance limitations and spatial obstacles are derived. Thirdly, by integrating the optimization model with the SLSQP (Sequential Least Squares Quadratic Programming algorithm), the second-order sequential quadratic programming model and decision variable update equations are derived, establishing the solution process for the trajectory planning problem of the tilt-wing eVTOL takeoff with obstacle avoidance. Finally, the Airbus Vahana A3 is taken as an example to verify and validate the effectiveness, stability, and robustness of the model and optimization algorithm proposed. The validation results show that the OTPEM (obstacle avoidance trajectory planning methodology based on energy minimization) can effectively handle changes in the takeoff end state and exhibits good stability and robustness in different obstacle environments. It can provide a certain reference for the three-dimensional obstacle avoidance trajectory planning of Airbus Vahana A3 and other tilt-wing eVTOL trajectory planning problems.

Comparative study of PI-controller and neurocontroller performances in optimal by settling time control problems

When developing control systems, an important issue arises of choosing an operator, which forms the control function. The standard approach is to use a PI- or PID-controller, a more advanced approach involves ANNs training for this purpose. A comparative analysis of the PI- and neurocontroller performances makes it possible to establish the disadvantages and advantages of each of the compared controllers, which is an important scientific and applied problem. The purpose of the work was to conduct a comparative analysis of the performance of the PI-controller and the neurocontroller based on a set of evaluation indicators for plants of the second and third orders. Such a comparison was carried out by using an approach to the synthesis of both controllers, which involved the minimization of a complex objective function. The latter is obtained as a result of reducing the problem of optimal control with constraints to the problem of unconstrained optimization. The analysis showed that according to the settling time indicator (optimization criterion), the neurocontroller has an advantage of 6.1...96.2% for the modelled plants. At the same time, according to other indicators of the control quality, the PI-controller has an advantage. In addition, the synthesis of a neurocontroller in terms of finding the minimum of the objective function is a more difficult problem. For its solution, a bigger number of iterations of the VCT-PSO optimization algorithm is required. It is rationally to set more than 1000 iterations and swarm population in the range 30…50 particles. A comparative analysis by the settling time of the neurocontroller and PI-controller, which is tuned according to engineering methods, showed significant reserves for improving this indicator. Thus, if the requirements for settling time minimization are quite strict, then it is advisable to use a neurocontroller. The obtained results will make it possible to develop recommendations for the rational choice of the control operator when solving practical problems of the control systems synthesis

Virtual synchronous generator control with parameters optimization based on new WaOA algorithm for grid-feeding inverters

The widespread integration of renewable energy sources (RESs) within traditional power systems causes a considerable impact, such as a decrease in total inertia, damping properties, and large frequency deviation. To tackle this issue, numerous research have been proposed to introduce the virtual synchronous generator (VSG) control strategy as an effective solution applied to power electronics inverters operating in grid-forming or grid-feeding control modes. However, the proper tunning of the inertia and damping parameters is considered as a significant challenge to ensure the system stability and reliability. In this paper, an efficient meta-heuristic Walrus Optimization Algorithm (WaOA) -based VSG control intended for grid-feeding inverters within a weak grid is proposed. The objective of this work is to offer a proper tuning of the VSG parameters, thereby improving the dynamic characteristics of the microgrid (MG) and guaranteeing a stable operation. In addition, it highlights the adopted optimization technique which is introduced on VSG control while ensuring minimum objective function value. Through offline simulations in MATLAB/Simulink, the superiority of the suggested WaOA method is first confirmed, in which it offers the lowest Integral Square Error (ISE) fitness function value of 0.026, compared to the Mountain Gazelle Optimizer (MGO) and Tunicate Swarm Algorithm (TSA) with 0.027 9 and 0.0281, respectively. Second, the performance of the WaOA-based VSG control is compared to the ones based on MGO and TSA techniques under active and reactive power variations. The obtained results demonstrate that the proposed VSG control ensures a better transient performance, especially in terms of under and overshoots.

High‐throughput in silico workflow for optimization and characterization of multimodal chromatographic processes

AbstractWhile high‐throughput (HT) experimentation and mechanistic modeling have long been employed in chromatographic process development, it remains unclear how these techniques should be used in concert within development workflows. In this work, a process development workflow based on HT experiments and mechanistic modeling was constructed. The integration of HT and modeling approaches offers improved workflow efficiency and speed. This high‐throughput in silico (HT‐IS) workflow was employed to develop a Capto MMC polishing step for mAb aggregate removal. High‐throughput batch isotherm data was first generated over a range of mobile phase conditions and a suite of analytics were employed. Parameters for the extended steric mass action (SMA) isotherm were regressed for the multicomponent system. Model validation was performed using the extended SMA isotherm in concert with the general rate model of chromatography using the CADET modeling software. Here, step elution profiles were predicted for eight RoboColumn runs across a range of ionic strength, pH, and load density. Optimized processes were generated through minimization of a complex objective function based on key process metrics. Processes were evaluated at lab‐scale using two feedstocks, differing in composition. The results confirmed that both processes obtained high monomer yield (>85%) and removed of aggregate species. Column simulations were then carried out to determine sensitivity to a wide range of process inputs. Elution buffer pH was found to be the most critical process parameter, followed by resin ionic capacity. Overall, this study demonstrated the utility of the HT‐IS workflow for rapid process development and characterization.

A Novel Classification Algorithm Based on the Synergy Between Dynamic Clustering with Adaptive Distances and K-Nearest Neighbors

This paper introduces a novel supervised classification method based on dynamic clustering (DC) and K-nearest neighbor (KNN) learning algorithms, denoted DC-KNN. The aim is to improve the accuracy of a classifier by using a DC method to discover the hidden patterns of the apriori groups of the training set. It provides a partitioning of each group into a predetermined number of subgroups. A new objective function is designed for the DC variant, based on a trade-off between the compactness and separation of all subgroups in the original groups. Moreover, the proposed DC method uses adaptive distances which assign a set of weights to the variables of each cluster, which depend on both their intra-cluster and inter-cluster structure. DC-KNN performs the minimization of a suitable objective function. Next, the KNN algorithm takes into account objects by assigning them to the label of subgroups. Furthermore, the classification step is performed according to two KNN competing algorithms. The proposed strategies have been evaluated using both synthetic data and widely used real datasets from public repositories. The achieved results have confirmed the effectiveness and robustness of the strategy in improving classification accuracy in comparison to alternative approaches.

A novel method based on the calculus of variations to optimize the cooling passage configuration in thermal protection structure

A novel method based on the calculus of variations to obtain the optimization of cooling structure was developed in this work. The optimization of heat sink designs for better heat-dissipating effects has been researched substantially. However, the optimization of cooling passage configurations in thermal protection structures to improve the comprehensive heat transfer performance has remained a long-term interest and unsolved problem for researchers. Due to the intrinsic complexity, the laminar flow is usually considered before while the turbulent flow is seldom treated. It is valuable to provide timely and effective solutions to optimize the cooling structure, where the turbulent flow is involved, for practical use. A novel method to optimize cooling structure based on the calculus of variations has been developed to meet this need. The average temperature, temperature inhomogeneity, and coolant flow pressure drop were chosen as objective functions. The cooling channels can be established depending on the geometric and thermal boundary conditions of the structure, and then the quasi-three-dimensional simulation of the fluid/solid coupling field was calculated to get the temperature distribution of the cooling structure, the pressure drop, etc. Hence, this work explores the feasibility of combining coupled heat transfer and the calculus of variations to realize optimal cooling channels. By the novel method, the minimum objective function can be obtained.

공액경사법을 이용한 LPG 운반선 화물창 내부구조물 중량최소화 설계

Since the cargo tank of LPG carriers is made of expensive low-temperature steel that can maintain its function even at -50oC, the reduction of cargo tank materials has been a major concern for shipyards to improve price competitiveness. The purpose of this study is to minimize the weight of the web frames and stringers that form the interior of the cargo tank of the LPG carriers. A total of 92 design variables including 6 shape-related design variables were selected for the minimum weight design. In order to reduce the time required for the optimization process, the conjugate gradient method, which is a gradient-based optimization technique, was adopted. In addition, for the structural analysis, the cargo tank located at the second position in the bow was selected as the structural analysis target, and in order to improve the reliability of the analysis, a boundary condition considering the deformation of the entire ship for each loading condition was applied to the partial model. As a result of the study, it was confirmed that the error rate did not exceed the maximum 3% by comparing the maximum stress value generated in all internal structures for the entire model structure analysis and the partial model one. A total of 92 dimensions with the minimum objective function were determined, and the weight was reduced by about 39 tons per cargo tank.

Optimizing the Carbon Footprint of Performance-Engineered Concrete Mixtures

This paper describes a process to optimize concrete mixture proportions to meet specific performance criteria through the minimization of a user-defined objective function that considers material cost and CO2 cost. The performance criteria impose constraints in the form of required mechanical properties (e.g., compressive strength), microstructural parameters (e.g., porosity or calcium hydroxide content), transport properties (i.e., the formation factor), and durability predictions (e.g., time to corrosion initiation or time to critical saturation). A thermodynamics-based model is used to predict reaction products and these results are used to predict performance. A feasible region of mixture proportions satisfying these constraints is identified after varying the water-to-binder ratios, cement replacement levels using limestone or supplementary cementitious materials, and air contents. The objective function is minimized to identify the optimal mixture proportions that meet all constraints while minimizing the material cost and CO2 footprint.

Size Optimization of Truss Structures Based on Genetic Algorithm by Developing Single-point Crossover with Bit-wise Mutation Using FORTRAN Programming

A truss is a collection of axially loaded structural elements connected through pin joints. Optimization of truss structures indicates the determination of best possible conditions that are necessary for achieving the most economical design in terms of lowest possible weights of the truss elements. According to recent studies, Size, Topology and Shape based optimizations are the three possible independent optimization scopes for finding the optimum weight of a truss. The current study focuses on the size optimization of truss structure and aims to identify the most cost-effective sections of the truss using a powerful evolutionary optimization technique named as Genetic Algorithm (GA). Here single point cross over method and bit wise mutation operator are adopted for expanding the search space with the assigned displacement and stress constraints. Fortran based sub routines are developed for each individual steps of GA such as fitness evaluation, selection, cross-over and mutation. Finaly the complete program is run for validation of its outcome. While comparing with the solution of an existing literature regarding a minimization of objective function having two design variables the outcomes tend to show a good correlation. Further, the study is extended for the weight minimization of two widely used truss having 10 members and 17 members respectively with standard Finite Element Method of analysis. Minimum weight of each truss is obtained via convergence plot and compared with the existing literatures. With this simple but efficient global optimization technique optimum weight of truss structure can easily be achieved.

Multiobjective route finding in a multimode transportation network by NSGA-II

Route finding is an everyday challenge for urban residents. While many route planner applications exist, they cannot find suitable routes based on user preferences. According to user preferences, routing in a multimode urban transportation network can be considered a multiobjective optimization problem. Different objectives and modes for transportation, along with many routes as decision elements, give rise to the complexity of the problem. This study uses an elitism multiobjective evolutionary algorithm and the Pareto front concept to solve the problem. The data of a simulated multimode network consisting of 150 vertexes and 2600 edges are used to test and evaluate the proposed method. Four transport modes are considered: the metro, bus, taxi, and walking. Also, three minimization objective functions are considered: expense, discomfort, and time. The results show the competence of the algorithm in solving such a complex problem in a short run time. The optimal setting for the algorithm parameters is found by considering the algorithm run time, diversity of solutions, and convergence trend by running sensitivity analyses. A repeatability test is applied using the optimal setting of the algorithm, which shows a high level of repeatability. While NSGA-II (Non-dominated Sorting Genetic Algorithm II) may be a well-established algorithm in the literature, its application in multiobjective route finding in multimode transport networks is unique and novel. The outcomes of the proposed method are compared with existing methods in the literature, proving the better performance of the NSGA-II algorithm.

A minimum objective function trim procedure for VTOLs noise reduction

The present paper proposes a novel approach for multi-rotor acoustic nuisance mitigation based on the identification of optimal control settings. This strategy is possible thanks to the peculiarity of multi-rotor systems (like, for instance, those involved in urban air mobility applications) to have redundant controls. The idea is to take full advantage of the control redundancy by defining the control settings that guarantee the desired steady-state flight conditions while, at the same time, optimizing selected target functions such as performance or noise emissions. To this aim, the trim problem is recast into a constrained cost function minimization problem. Since the objective is to reduce as much as possible the noise disturbance of trimmed VTOL, an aeroacoustic tool based on the chordwise-compact form of the Farassat 1A formulation for the evaluation of noise radiation is suitably coupled with a trim solver. The numerical investigations consider quadcopter and hexacopter configurations in level flight at several advancing speeds. Comparisons of the proposed minimum-noise trim strategy with two other trim strategies based on the minimization of rotor torque and control effort prove its capability to provide an overall reduction of noise throughout the entire velocity range examined.

A Systematic Load Frequency Regulation in Two Area Renewable Micro-grid Cluster Utilizing Bald Eagle Search Algorithm

This paper works on the systematic load frequency and tie-lie power transfer regulation within the prescribed limit. The proposed two area renewable energy based micro-grid cluster comprises of dish stirling solar generator, micro hydro turbine, biogas generator as energy generators in area 1. Also, wind turbine, tidal generator, biogas generator as energy generators in area 2. The Flywheel and battery act as mechanical and electrical energy storage devices in their respective areas. Moreover, to make the frequency oscillation die out quickly super magnetic storage devices are connected in the system. A comparison of performance of system with respect to objective function error is made to determine superior controller with minimum error and most stability. The controller parameters is adjusted with Bald eagle search optimizer, Black widow optimizer algorithm, Giza pyramid construction, Teaching learning based optimizer and Genetic algorithm. Lastly, best controller-algorithm i.e. (tilt integral tilt derivative with filter - Bald eagle search optimizer) combination with minimized objective function integral square error is examined under different scenarios.

Integrating the stochastic multiproject scheduling and material ordering problems for offshore projects: multiagent optimization methodology

Purpose The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO2 emission of all projects.Design/methodology/approach A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.Findings The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.Practical implications A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.Originality/value Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO2 detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

Evolutionary optimization for risk-aware heterogeneous multi-agent path planning in uncertain environments.

Cooperative multi-agent systems make it possible to employ miniature robots in order to perform different experiments for data collection in wide open areas to physical interactions with test subjects in confined environments such as a hive. This paper proposes a new multi-agent path-planning approach to determine a set of trajectories where the agents do not collide with each other or any obstacle. The proposed algorithm leverages a risk-aware probabilistic roadmap algorithm to generate a map, employs node classification to delineate exploration regions, and incorporates a customized genetic framework to address the combinatorial optimization, with the ultimate goal of computing safe trajectories for the team. Furthermore, the proposed planning algorithm makes the agents explore all subdomains in the workspace together as a formation to allow the team to perform different tasks or collect multiple datasets for reliable localization or hazard detection. The objective function for minimization includes two major parts, the traveling distance of all the agents in the entire mission and the probability of collisions between the agents or agents with obstacles. A sampling method is used to determine the objective function considering the agents' dynamic behavior influenced by environmental disturbances and uncertainties. The algorithm's performance is evaluated for different group sizes by using a simulation environment, and two different benchmark scenarios are introduced to compare the exploration behavior. The proposed optimization method establishes stable and convergent properties regardless of the group size.