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This study focused on enhancing the balance between sufficient daylighting and heat mitigation in lecture halls, set within the challenging hot-arid climate of Saudi Arabia, through the optimization of window designs and the incorporation of light-shelves. To achieve this, a combination of in-detail Parametric Sensitivity Analyses (PSA) was performed in the first stage to find the best configuration of windows, while a multi-objective optimization (MOO) strategy was applied in the second stage considering multiple objectives in each stage. Before running both analyses, the simulation model was validated by field measurement. The optimization results show that the optimal window configurations obtained through PSA and MOO offer significant potential for improving daylighting performance, energy efficiency and comfort. In terms of the overall best solutions from PSA and MOO, the PSA demonstrated substantial improvements, with Useful Daylight Illuminance (UDI) rising by 46.31% to 55.36%, glare decreasing by 52.17% to 82.61% and Energy Use Intensity (EUI) improving by 0.89% to 2.63%. Additionally, MOO solutions yielded even more significant enhancements, increasing UDI by 43.58% to 58.37%, reducing glare by 95% to 100% and enhancing EUI by 1.81% to 3.88%. This has resulted in a more evenly distributed and efficient daylighting throughout the space. [ABSTRACT FROM AUTHOR]

Wind turbines and solar photovoltaic (PV) systems are intermittent and uncertain energy sources that disturb grid planning operations. In this paper, we establish a multi-objective distributionally robust optimization model for optimal locations of wind turbines and solar photovoltaics (PV) that minimize the variance of renewable energy sources and maximize power production. Moreover, this paper evaluates the accuracy of the Autoregressive Moving Average (ARMA), Deep Learning Gated Recurrent unit (GRU), and Deep Learning Long Short-Term Memory (LSTM) as forecasting models for wind speed and solar irradiation and compares their root mean square errors (RMSE). Using the forecasting error information, we characterize the uncertain variables in the ambiguity set, incorporating the bounds, means, and covariance values. Furthermore, we propose a modified multi-objective non-dominated sorting genetic algorithm (NSGA-II) approach to achieve a tractable Pareto front solution. To verify the effectiveness of the model, we use the actual candidate sites for wind turbines and solar photovoltaic (PV) systems in Saudi Arabia. The results demonstrate that our proposed model is an attractive and less conservative solution than a multi-objective robust optimization model when considering forecasting uncertainties. [ABSTRACT FROM AUTHOR]

Routing and parking problems are considered a real challenge in the populated cities. An intelligent routing and parking algorithm is needed to facilitate locating the most appropriate parking spots to vehicles in a way that efficiently utilizes parking resources, reduces the traffic congestion, and minimizes trip driving time. This work proposes a hierarchical optimal algorithm to solve the routing and parking problem (HOPRA) using multi-criteria decision concept and historical traffic data. The problem is divided into three parts: finding the preferred parking lot, identifying the parking entrance (in case there are multiple), and then selecting the preferred route to the destination point. The optimal parking lot is defined based on an objective function that depends on the availability rates of the parking lots. After finding the parking lot and the parking entrance, a multi-objective function is used to specify the preferred route to the selected parking lot. This function depends on the travel distance, the traffic rate of a specific route, and the driver's preferences. We use the Open Street Maps from the AnyLogic software in the implementation stage. We compare HOPRA against the Shortest Route (SR) algorithm that was implemented also in AnyLogic with historical traffic data obtained from HERE technologies for a sample city (Al-Madinah City in Saudi Arabia). The results show that the HORPA algorithm outperforms the SR algorithm in balancing the traffic congestion on the roads and reducing the driving time of the trip. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective mixed-integer linear programming model is developed to design a hybrid PV-hydrogen renewable energy system considering two objective functions; minimizing total life costs and loss probability of power supply. The decisions of the hybrid system include the number of PV panels, the number of hydrogen tanks, the number of electrolyzers, the number of fuel cells, and quantity of hydrogen stored over time. An exact method embedded in GAMS software is used to solve the developed model. The model is validated using an electrical testing lab in Saudi Arabia with hourly power demand. Different plans are chosen from the obtained optimal Pareto solutions. For example, one of the plans found that a hybrid system with 212 PV panels, 617 hydrogen tanks, 30 electrolyzers, and 21 fuel cells is sufficient to satisfy the electrical testing lab load with an annual cost of $61663, the loss of power supply probability is 10%, and the CO 2 saving of 214,882 kg of CO 2. The results indicated the feasibility of combining an electrolyzer, hydrogen tank storage, and fuel cell with a renewable energy system; however, the cost of energy generated is still high because of the high investment cost. Furthermore, the findings revealed that hydrogen technologies are appealing as an energy storage solution for intermittent renewable energy systems and in other applications such as transportation, residential, and industrial sectors. In addition, the findings demonstrated the possibility of using renewable energy as a source of energy, namely, in Saudi Arabia. However, weather conditions, geomorphological conditions, and climatic dependence on hydrogen fuel cell technology can harm the energy yields produced by renewable energy systems. • Design a multi-objective hybrid PV-Hydrogen storage renewable energy system. • Evaluate the performance of the proposed model based on a real case study. • Help the decision-maker in analyzing alternative trade-offs. • The LCOE ranges between 0.143 and 0.160 $/kWh for generated plans. • The annual CO 2 savings range between 197,351 and 227,905 kg of CO 2. [ABSTRACT FROM AUTHOR]

The hydrocarbon supply chain (HCSC) is a significant part of the world's energy sector. The energy market has experienced erratic behavior over the last few years results in financial risks such as exceeding certain limits of the budget or not achieving the desired levels of cash in-flow, i.e., revenue. In this work, robust optimization and multi-objective mathematical programming are used to develop a model that eliminates or at least mitigates the impact of uncertain market behavior. Robust optimization provides tactical plans that are feasible and robust over market scenarios. The model assesses the trade-offs between alternatives and guides the decision-maker towards the effective management of the HCSC. The economic objectives are to minimize total cost and maximize revenue, while the non-economic objective is to minimize the depletion rate. The model considers the environmental aspect by limiting the emission of CO2 and the sustainability aspect by reducing the depletion rate of natural resources. Uncertain behavior of the oil market is modeled on scenario representation. A case study based on real data from Saudi Arabia HCSC is provided to demonstrate the model's practicality, and a sensitivity analysis is conducted to provide some managerial insights. The results indicate that Saudi Arabia can cover its entire expenditure, break-even-point, by producing oil at 7.18 MMbbld and gas at 3,543.48 MMcftd. Besides, the robust approach provides a preferred plan with the highest cash inflow and the lowest sustainability over other approaches, e.g., deterministic, stochastic, and risk-based. The differences show that the robust model increases oil production to compensate for the variability of the scenario. [ABSTRACT FROM AUTHOR]

The oil and gas networks are overlapped because of the inclusion of associated gas in crude oil. This necessitates the integration and planning of oil and gas supply chain together. In recent years, hydrocarbon market has experienced high fluctuation in demands and prices which leads to considerable economic disruptions. Therefore, planning of oil and gas supply chain, considering market uncertainty is a significant area of research. In this regard, this study develops a multi-objective stochastic optimization model for tactical planning of downstream segment of oil and natural gas supply chain under uncertainty of price and demand of petroleum products. The proposed model was formulated based on a two-stage stochastic programming approach with a finite number of realizations. The proposed model helps to assess various trade-offs among the selected goals and guides decision maker(s) to effectively manage oil and natural gas supply chain. The applicability and the utility of the proposed model has been demonstrated using the case of Saudi Arabia oil and gas supply chain. The model is solved using the improved augmented ε-constraint algorithm. The impact of uncertainty of price and demand of petroleum products on the obtained results was investigated. The Value of Stochastic Solution (VSS) for total cost, total revenue, and service level reached a maximum of 12.6%, 0.4%, and 6.2% of wait-and see solutions, respectively. Therefore, the Value of the Stochastic Solution proved the importance of using stochastic programming approach over deterministic approach. In addition, the obtained results indicate that uncertainty in demand has higher impact on the oil and gas supply chain performance than the price. [ABSTRACT FROM AUTHOR]

An electric arc furnace exhibits highly nonlinear characteristics and can cause power quality issues. Its performance is a vital issue for electric utilities. In this paper, a novel multiobjective optimization technique has been proposed for the estimation of the electric arc furnace parameters to predict the voltage and current waveforms and evaluate the performance of the furnace under various conditions. The multiobjective optimization technique minimizes the extinction voltage error and arc resistance error simultaneously to enhance the performance of the electric arc furnace model. The proposed optimization model employs a genetic algorithm and the stochastic variation of the arc length to estimate the parameters of a nonlinear time-variant resistance model of the electric arc furnace by using actual current and voltage data. These data have been obtained from a steel plant in Saudi Arabia. Optimization has been carried out in MATLAB/SIMULINK environment and the model of the electric arc furnace with the estimated parameters has been developed in PSCAD/EMTDC. The results obtained from the PSCAD model have been validated with actual data from the steel industry. The validation shows that the proposed method is efficient and accurate in the estimation of the electric arc furnace parameters. [ABSTRACT FROM PUBLISHER]

Cement manufacturing is a major industry in Saudi Arabia. As a highly polluting sector, it may be part of CO 2 abatement policy for the country. This paper presents a multi-criteria analysis to examine how two competing objectives affect the performance of the cement industry in Saudi Arabia. Namely, these criteria are profit and CO 2 emissions. We examine the joint effects of economic costs and biases of the industry’s senior management on decisions. Relevant decisions include fuel use, and investment in carbon capture and storage and more energy efficient kilns. The analysis adopts an operational model that we have developed for the industry, where 2015–2020 is the period of interest. It allows us to account for events that are not anticipated by the industry, such as the rise in energy prices that took place in 2016. Allowing imports and deeming the reduction of CO 2 emissions as highly important, the industry would choose to import clinker and bypass the pyroprocessing stage in manufacturing; however, the country has broader local content and economic diversification requirements that make this infeasible. By not permitting imports to observe the actual operation of the industry at all levels, we find: ● In environmental regulations and depending on the CO 2 price, behavioral considerations have a major impact on the decision-making process of cement manufacturers. ● For a low carbon price of up to 1 $/ton, the industry would have to care for emissions considerably in order to mitigate it. At 45 $/ton or above, behavioral considerations have a limited impact in the wake of profits. ● An example of a policy that could induce a reaction is one that consists of a carbon price of 27 $/ton. At that price, the industry’s decision-makers do not have to weigh pollutants highly relative to the industry’s profits. The policy would garner $4.9 billion in government revenue and reduce emissions by 181 million tons in the six-year period. The deal would generate $1.3 billion in profit in 2020, compared to $2.5 billion without government intervention. [ABSTRACT FROM AUTHOR]

A multi-objective goal programming model was developed for water distribution from multiple sources to multiple users. The model was applied in Riyadh, Saudi Arabia, for the period of 2015-2050. In Riyadh, water sources are groundwater (GW), desalinated water (DW) and treated wastewater (TWW), while the users are domestic, agricultural and industrial sectors. The model was applied to: (1) satisfy water demands and quality; (2) maximize TWW reuse and GW conservation; and (3) minimize overproduction of DW and overall cost. In 2015, the required allocations of GW, DW and TWW are 3286, 662 and 609 MCM, respectively, which are projected to be 4345, 1554 and 1305 MCM in 2050, respectively. GW source is likely to satisfy the predicted withdrawal of GW till 2035, while probabilities of non-satisfaction of full demands of GW in 2040, 2045 and 2050 were 0.04, 0.23 and 0.51, respectively. Supply of DW and reuse of TWW are needed to be increased to satisfy the predicted quantities during 2015-2050. [ABSTRACT FROM AUTHOR]

Utilizing fossil fuel for powering desalination plants is associated with release of greenhouse gases emissions to the atmosphere. Alternately, renewable energy sources are clean and environmentally friendly. This paper develops a multi-objective model based on mixed-integer programming approach to size a grid-connected Photovoltaic-wind system. The model objectives consider the economical aspect by minimizing the total life cycle cost, and the non-economical aspects by reducing the greenhouse gas emissions and the grid contribution share. The hybrid system covers the energy demand of a Reverse Osmosis desalination plant that is used to supply fresh water to a residential area in Saudi Arabia. The model generates a set of Pareto-optima solutions from which decision-makers can choose according to their preferences. Three plans are selected from the Pareto-optima solutions to show the exchangeability between the renewable energy system and the grid. For instance, with 100 photovoltaic modules and 94 wind turbines, the system can supply 18% of the plant's energy requirements while emitting the least amount of carbon dioxide (90,899 kgCO2-eq/yr). Furthermore, the energy cost is 0.0557 $/kWh, which is less than the cost of kWh purchased from the grid. The main findings and managerial insights are analyzed from economic and environmental perspectives. • A hybrid solar-wind-grid system to power a reverse osmosis plant is proposed. • Multi-objective mixed-integer optimization model is formulated to size the system. • Pareto-optima solution is generated under different perspectives. • Using renewable energy in reverse osmosis is economical and environmentally viable. • Greenhouse gase emissions can be cut by 71% compared to current practice. [ABSTRACT FROM AUTHOR]

This paper presents an optimal design for a nanogrid/microgrid for desert camps in the city of Hafr Al-Batin in Saudi Arabia. The camps were designed to operate as separate nanogrids or to operate as an interconnected microgrid. The hybrid nanogrid/microgrid considered in this paper consists of a solar system, storage batteries, diesel generators, inverter, and load components. To offer the designer/operator various choices, the problem was formulated as a multi-objective optimization problem considering two objective functions, namely: the cost of electricity (COE) and the loss of power supply probability (LPSP). Furthermore, various component models were implemented, which offer a variety of equipment compilation possibilities. The formulated problem was then solved using the multi-objective evolutionary algorithm, based on both dominance and decomposition (MOEA/DD). Two cases were investigated corresponding to the two proposed modes of operation, i.e., nanogrid operation mode and microgrid operation mode. The microgrid was designed considering the interconnection of four nanogrids. The obtained Pareto front (PF) was reported for each case and the solutions forming this front were discussed. Based on this investigation, the designer/operator can select the most appropriate solution from the available set of solutions using his experience and other factors, e.g., budget, availability of equipment and customer-specific requirements. Furthermore, to assess the quality of the solutions found using the MOEA/DD, three different methods were used, and their results compared with the MOEA/DD. It was found that the MOEA/DD obtained better results (nondominated solutions), especially for the microgrid operation mode. [ABSTRACT FROM AUTHOR]

The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems. [ABSTRACT FROM AUTHOR]