Facility location decisions within integrated forward/reverse logistics under uncertainty

In this paper, a stochastic mixed integer linear programming (SMILP) model is proposed to optimize the location and size of facilities and service centres in integrated forward and reverse streams under uncertainty. The objective of the model is to minimize establishment, transportation and inventory management costs and simultaneously maximize customer satisfaction with sustainable perspective. The model incorporates different elements and features of distribution networks including inventory management, transportation and establishment of new facilities as well as existing centres. The presented model is the streamlined approach for multi-objective, multi-period, multi-commodity distribution system, and it is supported by a real case study in automobile after sales network. Genetic algorithm is implemented to solve the model in reasonable time. The performance of the model and the effects of uncertainty on provided solution are studied under different cases. Competitive result of the stochastic model compared to deterministic model ensures that the proposed approach is valid to be applied for decision making under uncertainty.Scopu

Optimal design and operation of conventional, solar electric, and solar thermal district cooling systems

This research investigates the integration of solar energy with traditional cooling technologies using solar electric cooling systems. A holistic optimization process is introduced to enable the cost-effective design of such technology. Two mixed-integer linear programming (MILP) models are developed, one for a baseline conventional cooling system and the other for a solar electric cooling system. The MILP models determine the optimal system design and the hourly optimal quantities of electricity and cold water that should be produced and stored while satisfying the cooling demand. The models are tested and analyzed using real-world data, and multiple sensitivity analyses are conducted. Finally, an economic comparison of solar thermal and solar electric cooling systems against a baseline conventional cooling system is performed to determine the most cost-effective system. The findings indicate that the photovoltaic panels used in solar electric cooling cover 42% of the chiller demand for electricity. Moreover, the solar electric cooling system is found to be the most cost-effective, achieving ~5.5% and 55% cost savings compared with conventional and solar thermal cooling systems, respectively. A sensitivity analysis shows that the efficiency of photovoltaic panels has the greatest impact on the annual cost of solar electric cooling systems-their annual cost only increases by 10% when the price of electricity increases by 20%, making solar electric the most economical system. 2021 The Authors. Energy Science & Engineering published by Society of Chemical Industry and John Wiley & Sons Ltd.The publication of this article was funded by the Qatar National Library. This publication was made possible by the NPRP award [NPRP 10-0129-170280] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Optimization of design and operation of solar assisted district cooling systems

The demand for air conditioning and cooling services is rapidly increasing worldwide. As cooling demand has high coincidence to occur in countries with high solar irradiation, the combination of solar thermal energy and cooling appears to be an exciting alternative to replace traditional electricity-driven cooling systems where electricity is generated from fossil fuels. Nevertheless, solar assisted cooling is not yet widely deployed because of many barriers amongst them the presumed high investment cost of solar cooling technology. This research aims at making this technology more affordable by providing a holistic optimization design of solar assisted district cooling systems. Toward this end, a mixed-integer linear programming model (MILP) is proposed that captures the key design and operation variables of a solar-assisted district cooling system. Hence, the proposed model aims at finding the optimal system design (i.e., the system's main components along with their optimal capacities) together with the optimal hourly policies for production and storage of hot and cold water while satisfying the expected cooling demand. The model was validated using collected real data of different case studies. The optimal system design of some cases showed that solar collectors covered about 46% of the chiller's heat demand. Moreover, the existence of the cold-water TES in the system depends on the chosen chiller capacity and the cooling demand of the case study. Furthermore, a sensitivity analysis was carried out to study the model robustness. The sensitivity analysis shows that the chiller COP had the highest impact on the annual total system cost, where increasing COP by 20% of its initial value, will decrease the annual total system cost by 4.4%. 2020 The AuthorsThis publication was made possible by the NPRP award [NPRP 10-0129-170280] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Impact of COVID-19 Pandemic on Qatar Electricity Demand and Load Forecasting: Preparedness of Distribution Networks for Emerging Situations

The COVID-19 pandemic has brought several global challenges, one of which is meeting the electricity demand. Millions of people are confined to their homes, in each of which a reliable electricity supply is needed, to support teleworking, e-commerce, and electrical appliances such as HVAC, lighting, fridges, water heaters, etc. Furthermore, electricity is also required to operate medical equipment in hospitals and perhaps temporary quarantine hospitals/shelters. Electricity demand forecasting is a crucial input into decision-making for electricity providers. Without an accurate forecast of electricity demand, over-capacity or shortages in the power supply may result in high costs, network bottlenecks, and instability. Electricity demand can be divided, typically, into two sectors: domestic and industrial. This paper discusses the impact of the COVID 19 pandemic on Qatar’s electricity demand and forecasting. It is noted that students’ and employees’ attendance are the restrictions with the highest impact on electricity demand. There was an increase of nearly 28% in the domestic peak due to the attendance of 30% of school students. Furthermore, in this study, historical data on Qatar’s electricity demand, population, and GDP were collected, along with information on COVID-19 restrictions. Statistical analysis was used to unfold the impact of the COVID-19 pandemic. The results and findings will help decision-makers and planners manage future electricity demand, and support distribution networks’ preparedness for emerging situations.Open Access funding provided by the Qatar National Library

Solar Technology and District Cooling System in a Hot Climate Regions: Optimal Configuration and Technology Selection

With the increasing need for cooling and the concerns for pollution due to fossil fuel-based energy use, renewable energy is considered an add-on to cooling technologies. The climatic condition in the Middle East, analyzed in this paper, provides the potential to integrate solar energy with the cooling system. Due to the availability of various solar energy and cooling technologies, multiple configurations of solar-cooling systems can be considered to satisfy the cooling demand. The research presented in this paper aims to assess and compare these configurations by considering the energy prices and the installation area. The proposed model is formulated in Mixed-Integer Linear Programming and optimizes the holistic system design and operation. The economic, renewable energy use, and environmental performances of the optimal solution for each configuration are analyzed and compared to the base grid-DCS configuration. Results show that the electricity tariff and the available installation area impact the economic competitiveness of the solar energy integration. When electricity tariff is subsided (low), the conventional grid-based DCS is the most competitive. The PV-DCS configuration is economically competitive among the solar assisted cooling systems, and it can contribute to reducing the environmental impact by 58.3%. The PVT-DCS configuration has the lowest operation cost and the highest environmental performance by decreasing the global warming potential by 89.5%. The T-DCS configuration becomes economically competitive only at high electricity tariffs. 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This publication was made possible by the [NPRP10-0129-170280] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. The publication of this article was funded by the Qatar National Library.Scopus2-s2.0-8512847810

A Bibliometric Analysis and Visualization of Decision Support Systems for Healthcare Referral Strategies

Background: The referral process is an important research focus because of the potential consequences of delays, especially for patients with serious medical conditions that need immediate care, such as those with metastatic cancer. Thus, a systematic literature review of recent and influential manuscripts is critical to understanding the current methods and future directions in order to improve the referral process. Methods: A hybrid bibliometric-structured review was conducted using both quantitative and qualitative methodologies. Searches were conducted of three databases, Web of Science, Scopus, and PubMed, in addition to the references from the eligible papers. The papers were considered to be eligible if they were relevant English articles or reviews that were published from January 2010 to June 2021. The searches were conducted using three groups of keywords, and bibliometric analysis was performed, followed by content analysis. Results: A total of 163 papers that were published in impactful journals between January 2010 and June 2021 were selected. These papers were then reviewed, analyzed, and categorized as follows: descriptive analysis (n = 77), cause and effect (n = 12), interventions (n = 50), and quality management (n = 24). Six future research directions were identified. Conclusions: Minimal attention was given to the study of the primary referral of blood cancer cases versus those with solid cancer types, which is a gap that future studies should address. More research is needed in order to optimize the referral process, specifically for suspected hematological cancer patients.This article was made possible by the National Priorities Research Program-Standard (NPRP-S) Twelfth (12th) Cycle grant# NPRP12S-0219-190108, from the Qatar National Research Fund (a member of Qatar Foundation)

A dynamic MOPSO algorithm for multiobjective optimal design of hybrid renewable energy systems

In this paper, a dynamic multiobjective particle swarm optimization (DMOPSO) method is presented for the optimal design of hybrid renewable energy systems (HRESs). The main goal of the design is to minimize simultaneously the total net present cost (NPC) of the system, unmet load, and fuel emission. A DMOPSO-simulation based approach has been used to approximate a worthy Pareto front (PF) to help decision makers in selecting an optimal configuration for an HRES. The proposed method is examined for a case study including wind turbines, photovoltaic (PV) panels, diesel generators, batteries, fuel cells, electrolyzer, and hydrogen tanks. Well-known metrics are used to evaluate the generated PF. The average spacing and diversification metrics obtained by the proposed approach are 1386 and 4656, respectively. Additionally, the set coverage metric value shows that at least 67% of Pareto solutions obtained by DMOPSO dominate the solutions resulted by other reported algorithms. By using a sensitivity analysis for the case study, it is found that if the PV panel and wind turbine capital cost are decreased by 50%, the total NPC of the system would be decreased by 18.8 and 3.7%, respectively.Scopu

Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach

Recently, the increasing energy demand has caused dramatic consumption of fossil fuels and unavoidable raising energy prices. Moreover, environmental effect of fossil fuel led to the need of using renewable energy (RE) to meet the rising energy demand. Unpredictability and the high cost of the renewable energy technologies are the main challenges of renewable energy usage. In this context, the integration of renewable energy sources to meet the energy demand of a given area is a promising scenario to overcome the RE challenges. In this study, a novel approach is proposed for optimal design of hybrid renewable energy systems (HRES) including various generators and storage devices. The ?-constraint method has been applied to minimize simultaneously the total cost of the system, unmet load, and fuel emission. A particle swarm optimization (PSO)-simulation based approach has been used to tackle the multi-objective optimization problem. The proposed approach has been tested on a case study of an HRES system that includes wind turbine, photovoltaic (PV) panels, diesel generator, batteries, fuel cell (FC), electrolyzer and hydrogen tank. Finally, a sensitivity analysis study is performed to study the sensibility of different parameters to the developed model.Scopu

An efficient hybrid algorithm for the two-machine no-wait flow shop problem with separable setup times and single server

We consider the two-machine no-wait flow shop problem with separable setup times and single server side constraints, and makespan as the performance measure. This problem is strongly NP-hard. A mathematical model of the problem is developed and a number of propositions are proven for the special cases. Furthermore, a hybrid algorithm of variable neighbourhood search (VNS) and Tabu search (TS) is proposed for the generic case. For evaluation, a number of test problems with small instances are generated and solved to optimality. Computational results show that the proposed algorithm is able to reproduce the optimal solutions of all of the small-instance test problems. For larger instances, proposed solutions are compared with the results of the famous two-opt algorithm as well as a lower bound that we develop in this paper. This comparison demonstrates the efficiency of the algorithm to find good-quality solutions. [Received 25 November 2009; Revised 26 February 2010; Revised 19 March 2010; Accepted 20 March 2010]two machine flow shops; no-wait; separable setup times; makespan; single server; variable neighbourhood search; tabu search.