Decision Variables Research Articles

Optimization of urban transport vehicle tasks for large, mixed fleet of vehicles and real-world constraints

Planning the operation of urban public transport vehicles is the first stage of operational planning and consists in combining timetable tips, which are input data, into blocks that constitute the daily tasks of vehicles. For a large mixed fleet of vehicles of various types, especially those with battery power that requires recharging, operating from many depots, with numerous requirements and rolling stock constraints, the problem is a major engineering challenge, even for an experienced team of planners. IT solutions based on realistic, mathematical decision-making models and fast optimization algorithms can be of great assistance. For the problem formulated this way, a mathematical decision model with a multi-criteria objective function was built, taking into account technical, economic, and ecological criteria, natural, and binary decision variables. The model takes into account the real requirements and constraints, a mixed fleet of different types vehicles, including electric buses, multiple depots, technical trips (dead heads), and battery charging. The considered problem is an NP-hard combinatorial optimization problem. The use of classical, exact algorithms to solve this problem is not possible for schedules with many thousands of line trips and fleets of hundreds or thousands of vehicles. This research proposes an original, dedicated heuristic, enabling to obtain an acceptable, but still suboptimal solution, in a very short time. The tests of the proposed heuristic algorithm were carried out on real databases of public transport systems of the two selected medium and large Polish cities. In particular, multiple depots, a mixed fleet of different types of vehicles, and real-world constraints were taken into account. The results of computer experiments carried out using the developed heuristic were compared with the results obtained manually by a team of experienced and expert planners. For the developed multi-criteria decision-making model results comparable to and better than those prepared manually by experts were obtained in a very short time using the proposed heuristics. It is the basis for further development works on expanding the model and improving the optimization algorithm.

Prescriptive analytics for low and high water management in German waterways

Abstract The real-time management of multi-purpose storage reservoirs aims at an efficient operation of existing hydraulic infrastructure. This management process can be structured as a prescriptive analytics setup that considers both current and predicted system states to recommend actions and outline potential implications. In application to a reservoir and river system, it combines hydrological modelling components for the system schematization, observations and data assimilation for the identification of the current system state, meteorological and hydrological predictions as well as optimization-based techniques to support decision-making regarding reservoir operations. In this paper, we present the application of such a framework to the short-term management of the Eder and Diemel storage reservoirs. These reservoirs are operated by the German Federal Waterways and Shipping Administration (WSV) with the primary goal to support navigation in the River Weser during low flow periods. In addition, partially conflicting objectives such as flood protection, energy generation and recreation are considered. The implementation includes an explicit consideration of forecast uncertainty and its impact on the decision-making by using probabilistic forecasts in combination with a multi-stage stochastic optimization approach. We demonstrate the applicability of the approach based on low and high water use cases. Special attention is paid on the benefits of the probabilistic forecast in combination with the multi-stage stochastic optimization versus a deterministic setup. It provides an explicit translation of the forecast uncertainty in the decision variables, in this case the reservoir releases helping the operators to better anticipate the range of future release decisions. Furthermore, the stochastic approach is expected to provide more stable decisions in an operational setting, based on more stable forecasts by considering various possible realizations of the future instead of picking a single one, which gets random after 4–5 days.

Semi-supervised soft sensor development based on dynamic dimensionality reduction-assisted large-scale pseudo label optimization and sample-weighted quality-relevant deep learning

Data-driven soft sensors have become popular tools for estimating critical quality variables in the process industry. However, in practical applications, it is very common that the unlabeled data are abundant but the labeled data are scarce, which poses a great challenge for developing high-performance data-based soft sensors. Thus, a dynamic dimensionality reduction-assisted large-scale pseudo label optimization method (DDR-LSPLO) is proposed for achieving sample expansion. This method repeatedly converts the LSPLO issue into a reduced-dimension pseudo label optimization problem with the low-confidence pseudo labels as new decision variables during the evolutionary optimization process. Meanwhile, to tackle the sample imbalance problem resulting from the inclusion of large-scale pseudo-labeled samples, a sample expansion and weighting-based quality-relevant autoencoder (SEWQAE) is developed for semi-supervised soft sensor modeling. The effectiveness and superiority of the proposed DDR-LSPLO and SEWQAE methods are verified through an industrial chlortetracycline (CTC) fermentation process and a simulated Tennessee Eastman (TE) chemical process.

An improved problem transformation algorithm for large-scale multi-objective optimization

Solving Large-Scale Multi-Objective Optimization Problems (LSMOPs) is the major challenge in evolution computation. Due to the large number of decision variables involved, it is not easy for existing multi-objective evolutionary algorithms to search the entire decision variable space with limited computation resources. To address the above problem, a large-scale multi-objective evolutionary algorithm based on problem transformation, called LSMOEA/PT, is presented in this paper. LSMOEA/PT uses an improved problem transformation strategy to transform LSMOPs into small-scale multi-objective problems to accelerate convergence speed and reduce computational resources. Specifically, The transformation strategy selects reference solutions to initialize a weight population by applying the opposite individual method. After optimizing the weight population, a dual line strategy is implemented to combine the weight vectors with the decision variables from the original population, thereby generating a new population. The new population is combined with the original population for environment selection. In LSMOEA/PT, a double learning swarm optimizer is introduced to speed up the convergence of the population. A uniform distribution strategy is conducted to update transformed solutions after the problem transformation process, increasing the diversity of the population. Experimental results show that LSMOEA/PT is competitive with eleven state-of-the-art algorithms on large-scale multi-objective optimization test problems with up to 2000 decision variables.

An Energy-Efficient Logistic Drone Routing Method Considering Dynamic Drone Speed and Payload

Unmanned aerial vehicles (UAVs), or drones, are recognized for their potential to improve efficiency in last-mile delivery. Unlike the vehicle routing problem, drone route design is challenging due to several operational signatures, such as speed optimization, multi-trip operation, and energy consumption estimation. Drone energy consumption is a nonlinear function of both speed and payload. Moreover, the high speed of drones can significantly curtail the drone range, thereby limiting the efficiency of drone delivery systems. This paper addresses the trade-off between speed and flight range in a multi-trip drone routing problem with variable flight speeds (DRP–VFS). We propose a new model to specifically consider energy constraints using a nonlinear energy consumption model and treat drone speeds as decision variables. The DRP–VFS is initially formulated using mixed-integer linear programming (MILP) to minimize energy consumption. To solve large-scale instances, we propose a three-phase adaptive large neighborhood search (ALNS) algorithm and compare its performance with a commercial MIP solver. The experimental results demonstrate that the proposed method is capable of effectively identifying suboptimal solutions in practical scenarios. Furthermore, results indicate that operating drones at variable speeds leads to about 21% energy savings compared to fixed speeds, with advantages in cost savings and range extension.

A Metaheuristic Framework with Experience Reuse for Dynamic Multi-Objective Big Data Optimization

Dynamic multi-objective big data optimization problems (DMBDOPs) are challenging because of the difficulty of dealing with large-scale decision variables and continuous problem changes. In contrast to classical multi-objective optimization problems, DMBDOPs are still not intensively explored by researchers in the optimization field. At the same time, there is lacking a software framework to provide algorithmic examples to solve DMBDOPs and categorize benchmarks for relevant studies. This paper presents a metaheuristic software framework for DMBDOPs to remedy these issues. The proposed framework has a lightweight architecture and a decoupled design between modules, ensuring that the framework is easy to use and has enough flexibility to be extended and modified. Specifically, the framework now integrates four basic dynamic metaheuristic algorithms, eight test suites of different types of optimization problems, as well as some performance indicators and data visualization tools. In addition, we have proposed an experience reuse method, speeding up the algorithm’s convergence. Moreover, we have implemented parallel computing with Apache Spark to enhance computing efficiency. In the experiments, algorithms integrated into the framework are tested on the test suites for DMBDOPs on an Apache Hadoop cluster with three nodes. The experience reuse method is compared to two restart strategies for dynamic metaheuristics.

Optimization of a combined gas turbine and steam Rankine cycle utilizing the chemical looping combustion (CLC) reactor for CO2 capture and Storage: Towards enhanced efficiency and Cost-Effectiveness

In recent years, CO2 released by fossil fuel power plants, through the process of capture and storage, has been tried to be separated from other combustion products and directed to the underground to prevent it from entering the environment. While various methods exist for this purpose, they are typically expensive and consume a significant amount of power. In this research, a proposed solution to study and optimize the capture and storage process involves a combined cycle power plant with two gas turbines and four steam turbines, equipped with a carbon dioxide capture and storage system using the chemical looping combustion (CLC) method. The storage of released CO2 is facilitated by four compressors operating in four stages. To begin, the developed model is validated in terms of energy, exergy, and economic factors. Subsequently, the system is optimized using an NSGA genetic algorithm, with two objective functions: exergy efficiency and total cost rate. Sensitivity analysis is conducted to define the decision variables. The results demonstrate that the optimization process has led to a 4.37 % increase in the system’s power output, from 379.75 MW to 396.37 MW. Moreover, the optimization has improved both the exergy and energy efficiency values, with these parameters rising from 51.97 % and 54.23 % to 54.24 % and 56.61 %, respectively. Furthermore, the total cost rate has slightly decreased from 46,267 to 46,020 $/hr as a result of system optimization.

Machine learning-based optimization and 4E analysis of renewable-based polygeneration system by integration of GT-SRC-ORC-SOFC-PEME-MED-RO using multi-objective grey wolf optimization algorithm and neural networks

This study introduces a new solar-methane-driven setup that recovers waste heat from flue gases to produce electricity, hydrogen, oxygen, fresh water (FW), and domestic hot water (DHW) while reducing the environmental impact of gas turbines. This integration includes the Brayton cycle and solid oxide fuel cell as motive cycles, the steam and organic Rankine cycle, a parabolic trough solar collector (PTC), a proton exchange membrane electrolyzer, multi-effect distillation, and reverse osmosis. Using Engineering Equation Solver software, analyses in energy, exergy, exergoeconomic, and exergoenvironment (4E) are conducted to identify critical system evaluation parameters in thermodynamics, economics, and the environment. Additionally, the proposed system is optimized using an artificial neural network and the multi-objective grey wolf optimizer algorithm, utilizing decision variables identified through sensitivity analysis to enhance system performance. The results of six-objective optimization show energy and exergy efficiencies at 49.48 % and 47.21 %, with a net power production of 133 MW and a total cost rate of $7903/h. Among all the components, the combustion chamber and PTC have the maximum exergy destruction, with values of 58.87 MW and 17.29 MW, respectively. Also, hydrogen, FW, and DHW production rates are 201.6 kg/h, 43.88 kg/s, and 40.98 kg/s, respectively. Moreover, the costs of hydrogen, FW, and DHW are $953.64/h, $57.35/h, and $82.48/h, respectively. Finally, Bloemfontein, Las Vegas, and Tunis are the most favorable regions economically, with a low levelized cost of electricity of $0.05931, $0.06006, and $0.05932 per kWh, along with payback periods of 0.6144, 0.5524, and 1.069 years, respectively.

Analysis of multi-objective optimization applied to the simulation of a natural gas separation plant

This study concentrated on simulating and optimizing a natural gas separation plant using Unisim Design® software. The optimization procedure aimed to maximize gas production and the higher-molecular-weight hydrocarbons (yC5+) in the produced gas. The maximum water dew point of −45 °C of the produced gas was specified. The Nelder Mead/Weight Sum Method (NM/WSM) algorithm was employed in Python to optimize five decision variables involving the temperature and pressure of the main process streams. Results indicated that the relative deviations below 1%, compared to reference values, were obtained for the main process variables. The multi-objective optimization (MOO) results showed an increase in gas production of up to 5.58% (maximum value of 62,736.10 kg/h) and a rise in the molar fraction of yC5+ by up to 32.2% compared to the reference values. However, it was noted that maximizing gas production indirectly maximizes the yC5+, with only a single decision variable differing in the optimal direction, represented by the intermediate temperature of one compression stage. A Pareto curve was built to display the range of optimal solutions, showing variations of 0.035% in optimal gas production and 0.11% in yC5+. Furthermore, various simulation scenarios were optimized with different thermodynamic conditions at the unit's inlet. In all cases, the optimization resulted in an average gain of 1.57% in gas production regarding the reference (non-optimal case).

Optimization of Above-ground Environmental Factors in Greenhouses Using a Multi-objective Adaptive Annealing Genetic Algorithm

The greenhouse environment represents a dynamic, nonlinear system characterized by hysteresis and is influenced by a myriad of interacting environmental parameters, posing a complex multi-variable optimization challenge. This study proposes a multi-objective adaptive annealing genetic algorithm to optimize above-ground environmental factors in greenhouses, addressing the challenges of variable environmental conditions and extensive heating and humidity infrastructure. Initially, after analyzing the multi-objective model of greenhouse above-ground environmental factors, including temperature, relative humidity, and CO2 concentration, a comprehensive multi-objective, multi-constraint model was developed to encapsulate these factors in greenhouse environments. Subsequently, the model optimization incorporated multi-parameter coding of decision variables, a fitness function, and an annealing dynamic penalty factor. Validation conducted at Yangling Agricultural Demonstration Park revealed that the application of multi-objective adaptive annealing genetic algorithms (schemes 1 and 2) significantly outperformed the single-objective genetic algorithm (scheme 3) and the traditional genetic algorithm (scheme 4). Specifically, the improvements included a reduction in average temperature rise by 2.64°C and 5.29°C for schemes 1 and 2, respectively, equating to 20% and 34% decreases. Additionally, average humidification reductions of 2.39% and 3.9% were observed, alongside decreases in the total lengths of heating and humidification pipes by up to 2.99 km and 0.443 km, respectively, with a maximum reduction of 14% in heating pipes. The integration of an annealing dynamic penalty factor enhanced the adaptive climbing ability of schemes 1 and 2, improving static stability and robustness. Furthermore, the number of iterations required to achieve convergence was reduced by approximately 170 to 240 times compared to schemes 3 and 4. This reduction in iterations also resulted in a significant decrease in running time by 5 to 13 minutes, corresponding to time savings of 31% to 56%, thereby achieving further optimization.

Multi-resource allocation and care sequence assignment in patient management: a stochastic programming approach.

To mitigate outpatient care delivery inefficiencies induced by resource shortages and demand heterogeneity, this paper focuses on the problem of allocating and sequencing multiple medical resources so that patients scheduled for clinical care can experience efficient and coordinated care with minimum total waiting time. We leverage highly granular location data on people and medical resources collected via Real-Time Location System technologies to identify dominant patient care pathways. A novel two-stage Stochastic Mixed Integer Linear Programming model is proposed to determine the optimal patient sequence based on the available resources according to the care pathways that minimize patients' expected total waiting time. The model incorporates the uncertainty in care activity duration via sample average approximation.We employ a Monte Carlo Optimization procedure to determine the appropriate sample size to obtain solutions that provide a good trade-off between approximation accuracy and computational time. Compared to the conventional deterministic model, our proposed model would significantly reduce waiting time for patients in the clinic by 60%, on average, with acceptable computational resource requirements and time complexity. In summary, this paper proposes a computationally efficient formulation for the multi-resource allocation and care sequence assignment optimization problem under uncertainty. It uses continuous assignment decision variables without timestamp and position indices, enabling the data-driven solution of problems with real-time allocation adjustment in a dynamic outpatient environment with complex clinical coordination constraints.

Triple-layer joint optimization of capacity and operation for integrated energy systems by coordination on multiple timescales

The introduction of energy conversion and storage devices, such as power-to-gas (P2G) and seasonal energy storage, can realize the multi-timescale electricity, heat and natural gas complementarity for integrated energy systems (IESs), promoting renewable energy consumption. However, this brings a large number of decision variables, strong coupling, and nonlinearity, increasing the complexity and difficulty of the optimal design for IESs. Therefore, this study proposes a generic triple-layer optimal design framework for IESs via coordination on multiple timescales. The proposed framework decomposes the optimal design of IESs into long-, medium-, and short-timescale optimization layers that are tractable. The long-timescale optimization layer realizes the seasonal source–load matching by incorporating a refined electricity–gas complementarity model; medium-timescale optimization layer utilizes the inter-day energy transfer to deal with extreme loads caused by rare weather conditions; and short-timescale optimization layer aims to realize real-time energy balances and gain economic benefits by intraday peak shaving. Each layer not only optimizes related capacity and operation variables jointly for its own timescale but also exchanges coupling information with adjacent layers via designed interactive iteration mechanisms. The implementation of the proposed framework is further investigated in the design of an electricity–gas–heating–cooling coupled IES. Finally, a case study is performed to verify the feasibility and effectiveness of the proposed optimal design method. Simulation results show that the proposed method realizes source–load matching across multiple timescales, reducing the annualized total cost of the IES by 2.4 % and 3.3 % compared to the conventional single-timescale optimal design method under the grid connection and off-grid modes, respectively. The renewable energy consumption and device utilization rate of the optimized IES exhibit obvious improvements. In particular, the wind energy consumption of the IES increases by 15 times after introducing P2G and seasonal energy storage. Furthermore, sensitivity analyses indicate that the natural gas price considerably affects the optimization of the capacity configuration and economic performance of the IES, far exceeding that of the electricity price.

A Multi-Objective Approach for Optimal Sizing and Placement of Distributed Generators and Distribution Static Compensators in a Distribution Network Using the Black Widow Optimization Algorithm

This paper presents a new optimization technique for the locations and sizes of Distributed Generators (DGs) and distribution static compensators (DSTATCOMs) in a radial system of a distribution network based on a multi-objective approach. It uses black widow optimization to improve voltage profile and power loss reduction. The black widow optimization simulates the mating behaviour of black widow spiders. The optimum size and placement of DGs and DSTATCOMs are deemed to be decision variables that are defined by using black widow optimization. The proposed technique is implemented in selected IEEE bus systems to evaluate its performance. The simulation results indicate reduced power losses and voltage profile enhancement as sizes and locations of integrated DGs and DSTATCOMs are adjusted based on optimization. The number of DGs and DSTATCOMs required to achieve the objectives is reduced. Furthermore, the results of the black widow algorithm are compared to existing techniques in the literature.

An integrated optimization model for procurement and production lot sizing and scheduling problems

Abstract Lot sizing is a prevalent issue within manufacturing companies, where determining the optimal procurement and production lot sizes is crucial for maximizing profits. This problem has become more complex, given that numerous suppliers can provide the same raw materials with different prices and quantity discount schemes. A company should also determine optimal carriers to deliver materials to the company’s warehouse. In a manufacturing process, the company should determine the optimal production lot size and its schedules. In this paper, a model was developed to solve simultaneously procurement and production lot sizing, as well as production scheduling problems. The model encompasses multiple suppliers offering quantity discounts, aiming to maximize company profit by accounting for various costs, including procurement, production, inventory, and quality costs. A case study is taken from a company producing noodles and its related derivative products to illustrate the application of the model. Based on the optimization results, the company obtained a total profit of IDR. 14,656,550,000 or $950,921.30 (the exchange rate of $1 at IDR. 15,413). The sensitivity analysis results show that the objective function is sensitive to changes in the purchase cost, sale revenue, and discount rate parameters. The decision variables for accepted product demand, product quantity, and the starting and completion time of product family are only sensitive to changes in certain parameters. Meanwhile, the decision variables for product inventory, product backlog, raw material inventory, and purchased raw material quantity are sensitive to the changes in all the analyzed parameters.

An optimal sizing framework of a microgrid system with hydrogen storage considering component availability and system scalability by a novel approach based on quantum theory

This paper introduces an innovative framework for optimizing a hybrid photovoltaic-wind system with integrated hydrogen storage, taking into account component availability and system scalability. The primary objective is to minimize the total net present cost (TNPC) while enhancing system reliability based on the Loss of Power Supply probability (LPSP). The decision variables include the determination of the number of photovoltaic (PV) panels, wind turbines (WTs), hydrogen storage mass, fuel cell capacity, electrolyzer capacity, and inverter capacity. To achieve this optimization, the Quantum Beluga Whale Optimization (QBWO) algorithm is employed, which is an improved version of the Beluga Whale Optimization (BWO) algorithm. QBWO overcomes some limitations of BWO by incorporating principles from quantum theory and addressing challenges related to the scarcity of population diversity and stagnation in local optima. Simulations were conducted for scenarios involving WT/FC, PV/FC, and PV/WT/FC systems. The results indicate that within a microgrid context, the PV/WT/FC configuration proves to be the most cost-effective approach for supplying local loads. Under certain conditions, it achieved the best TNPC with 0.8176 M$, Levelized Cost of Energy (LCOE) with 0.264 $/kWh, and LPSP with 0.04687. However, considering components availability and system scalability changes indicate a significant effect on the system cost and reliability. Overall, this framework provides a precise method for designing energy systems, considering factors such as electricity generation costs and reliability enhancements in the face of uncertainties. QBWO outperforms original BWO, Particle Swarm Optimization (PSO), and Crow Search Algorithm (CSA) by achieving lower design costs and improved reliability. This study contributes to developing environmentally friendly electrification plans and provides valuable insights for power investments in energy-deprived Southwest Morocco.

Balancing economic and environmental goals: A novel strategy for growing items acquisition in livestock farming under an incremental discount and a power demand pattern

The human food supply chain is significantly influenced by growing items (GIs) like beef, sheep, hog, chicken, turkey, and other species, as these provide major contributions to food security worldwide. Industrial farming businesses carry out their operations by purchasing a certain species of baby GIs from a supplier, feeding and caring for them until they reach an acceptable living weight, and then selling them after they have been slaughtered and processed. In addition, the production of livestock worldwide accounts for 14.5% of all anthropogenic greenhouse gas (GHG) emissions, according to the Food and Agriculture Organization (FAO), making it a substantial contributor to GHG emissions. Thereby, to regulate emissions, governments from several countries have imposed a tax scheme on the farm’s emission volume. Due to the discrete nature of purchase numbers, using a differentiation-based optimization approach is not appropriate for determining the optimal GI procurement for an industrial farm, making the decision significantly more challenging. The primary goal of this study is to identify the optimal number of GIs for a farm to enhance financial and environmental performance when the cost per weight unit of procurement depends on the volume of purchases and the consumption of processed commodities follows a power demand pattern. A comparison tactic is employed only for the discrete decision variable, while the classical optimization tactic, based on differentiation, is adopted for the remaining continuous decision variable of the derived problem. Our method, which is quite straightforward in its operation, enables the creation of a rule to distinguish between instances in which there is only one optimum solution and those in which there are two. The results from the execution of the novel solution technique reveal that the farm reduces the overall cost of the order while keeping the integrality of the purchased GIs by taking into account the regulation for carbon emissions, the discount, and the power demand pattern.

Optimal sizing of off-grid microgrid building-integrated-photovoltaic system with battery for a net zero energy residential building in different climates of Morocco

An optimal sizing of an off-grid microgrid system composed of photovoltaic (PV)/building integrated photovoltaic (BIPV)/battery energy storage installation is undergone for Net Zero Energy Residential Building blocks across six different climates of Morocco. The Particle Swarm Optimization algorithm is used to find the optimal sizing of the system, by considering the hourly spatiotemporal variations in both solar energy availability and energy demand, with the lowest Total Annualized Cost as the objective function and capacities of BIPV and battery as decision variables. The methodology adopted focuses on main load fulfillment through direct PV and BIPV power supply, backed by battery energy storage technology, to continually guarantee self-sufficiency. A key metric, the load cover factor, is introduced to quantify the ratio by which the load demand is satisfied by the solar PV and BIPV systems. The findings show that the optimal sizing of the BIPV system can help to improve the load cover factor by 0.68–2.58 %. Moreover, integrating BIPV system with PV system and Battery leads to a reduction in the Levelized Cost of Energy with approximately 8.7–20.72 %, as opposed to utilizing only the PV system and battery. Depending on the local climate, the levelized cost of energy ranges from $0.366/kWh in Ouarzazate city up to $0.664/kWh in Ifrane city. Lastly, this holistic approach aims to transform the building from its traditional role as an energy consumer to a carbon-free energy prosumer.

A Sustainable Supply Chain Model with a Setup Cost Reduction Policy for Imperfect Items under Learning in a Cloudy Fuzzy Environment

The present paper deals with an integrated sustainable supply chain model with the effect of learning for an imperfect production system under a cloudy fuzzy environment where the demand rate is treated as a cloudy triangular fuzzy (imprecise) number, which means that the demand rate of the items is not constant, and shortages and a warranty policy are allowed. The vendor governs the manufacturing process to serve the demand of the buyer. When the vendor supplies the demanded lot after the production of items, it is also considered that the delivery lots have some defective items that follow an S-shape learning curve. After receiving the lot, the buyer inspects the whole lot, and the buyer classifies the whole lot into two categories: one is the defective-quality items and the other is the imperfect-quality items. The buyer returns the defective-quality items to the seller after a screening process, for which a warranty cost is included. During the transportation of the items, a lot of carbon units are emitted from the transportation, damaging the quality of the environment. The seller includes carbon emission costs to achieve sustainability as per considerations. A one-time discrete investment is also included for the minimizing of the setup cost of the seller for the next cycles. We developed models for the scenario of the separate decision and for the integrated decision of the players (seller/buyer) under the model’s consideration. Our aim is to jointly optimize the integrated total fuzzy cost under a cloudy fuzzy environment sustained by the seller and buyer. Numerical examples, sensitivity, analysis limitations, future scope and conclusions have been provided for the justification of the proposed model, and the impact of the input parameters on the decision variables and integrated total fuzzy cost for the supply chain are provided for the validity and robustness of this proposed model. The effect of learning in a cloudy fuzzy environment was positive for this proposed model.

Understanding the Impact of Seismic Hazard and Climate Conditions on Multi Criteria–Based Retrofitting of Existing Buildings

A large share of the reinforced concrete (RC) building stock in Mediterranean countries faces a dual challenge of seismic vulnerability and energy inefficiency, calling for urgent renovation efforts. While energy upgrades have been the focus of previous renovation policies, recent research highlights the critical need for integrated retrofitting solutions that address both structural integrity and energy performance. Multi-criteria decision-making (MCDM) approaches are a promising tool for optimizing the combined choice of these integrated interventions, considering various decision variables (DVs) of economic, social, environmental, and technical nature. To understand the impact of climate and seismic hazard conditions on multi-criteria-based retrofitting assessment, a case-study RC school building is selected and assumed to be located in three distinct climate conditions, cold, mild, and warm, and three seismic hazard levels, low, medium and high. Moreover, given the complexity and challenges of quantifying seismic performance metrics for practitioners, an available simplified (practice-oriented) approach is compared herein with a more thorough research-based one for quantifying the seismic performance of RC buildings within the MCDM framework. Both approaches are applied to the case-study building, considering twelve possible combinations of energy and seismic interventions. The accuracy of the practice-oriented approach and its impact on the retrofitting rankings is evaluated, emphasizing the importance of accessible and efficient evaluation methods in facilitating informed decision-making for building renovation.

Whale Optimization for Cloud-Edge-Offloading Decision-Making for Smart Grid Services.

As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services' computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.