The extensive use of energy worldwide, as well as a reduction in non-renewable energy sources, has led to increased greenhouse gas emissions and global warming. Therefore, the optimal use of energy has recently become...
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The extensive use of energy worldwide, as well as a reduction in non-renewable energy sources, has led to increased greenhouse gas emissions and global warming. Therefore, the optimal use of energy has recently become very important. A decrease in energy consumption in manufacturing environments, as one of the major energy consumers, has attracted the attention of different researchers. In addition, considering a variety of tariffs in different periods is regarded as one of the strategies which the governments apply to control energy consumption. The present study aimed to minimize the cost of consuming energy in a production environment with unrelated parallel machines. So far, various studies have evaluated the unrelated parallel machine scheduling with energy tariffs. Based on the study objective, a mixed-integer linear programming model was presented for the problem. Further, a number of dominance rules and valid inequalities were developed to improve the computational time of the model due to the assumptions for this problem. The results of the study indicated that the proposed model was better than that of the other related studies in the literature. Furthermore, a heuristic fix and relax algorithm was proposed for large-size instances, which could solve the instances up to 1000 jobs and 20 machines in size. Finally, this algorithm had a low gap compared to the lower bound of the problem. (C) 2019 Elsevier Ltd. All rights reserved.
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 ...
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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-integerlinearprogramming (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 similar to 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.
Medical waste generated in healthcare facilities is categorized as hazardous due to its infectious, toxic, or radioactive properties, posing substantial risks to human health and the environment. This research propose...
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Medical waste generated in healthcare facilities is categorized as hazardous due to its infectious, toxic, or radioactive properties, posing substantial risks to human health and the environment. This research proposes a bi-objective mixed-integer linear programming model for designing reverse logistics networks that enable both economical and safe medical waste management. Said model determines optimal quantity, depot locations, and routes for transporting waste from its generating points (hospitals) to the treatment and disposal sites (depots). This research addresses a bi-objective location routing problem, with multiple depots, time windows, capacity constraints, and variated waste types, across a multi-period horizon. It is innovative because no previous works have successfully dealt with such a combination of pragmatic features. The Location Routing Problem is an already well-known NP-Hard problem, with the additional aforementioned features further adding to its complexity. Thus, a genetic algorithm to solve large-sized realistic instances within reasonable computing times had been developed. Numerical experiments considered 20 random instances contrasting exact and approximate solution approaches. Also, a medical waste collection case study of a public hospital network in Atlantico Department, Colombia, delivering a Pareto frontier solution set, was introduced. The results demonstrated the efficacy of the proposed genetic algorithm in successfully addressing small instances of the problem, delivering outcomes comparable to the mixed-integer linear programming model. Furthermore, it yields good-quality solutions in a reduced computational time for larger instances deemed unfeasible for the mixed-integer linear programming model. This article contributes to both scientific literature and practitioners by presenting a decision-making tool designed to address the medical waste reverse logistics challenge under realistic scenarios. It approaches the
The method described for production scheduling in this study is a simultaneous use of a clustering algorithm with a genetic algorithm (GA). The aggregating algorithm presented in this study aims to control the concent...
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The method described for production scheduling in this study is a simultaneous use of a clustering algorithm with a genetic algorithm (GA). The aggregating algorithm presented in this study aims to control the concentration of operations and the cluster size, which is evaluated using the Silhouette criterion. The fitness function and the chromosome length in the GA have differences from the usual one. The results showed the number of binary variables in a mixed-integer linear programming model was reduced by 78.5% based on the created clusters. Although the aggregated model's net present value (NPV) is decreased by 7%, the solution time significantly dropped from 3 h to 43.1 s. Also, compared to the non-clustering block model, the aggregated block model's NPV, obtained by GA, was improved.
Increasing level of wind power integration imposes challenges on the power grid due to the variable and intermittent characteristics of the wind resources. Concentrated Solar Power (CSP) plant with low-cost Thermal En...
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Increasing level of wind power integration imposes challenges on the power grid due to the variable and intermittent characteristics of the wind resources. Concentrated Solar Power (CSP) plant with low-cost Thermal Energy Storage, is schedulable and controllable, and therefore is an ideal technology to hybridize with wind resources for generation smoothing. Furthermore, an Electric Heater, which converts redundant wind power into thermal energy, can be coupled with CSP plants to both reduce wind curtailment and provide more operational flexibility into power systems. Thus, this paper proposes a new hybrid power generation system integrating wind resources and CSP with an Electric Heater. A mixed-integer linear programming model is established to maximize the daily profit of the hybrid system. In this work, the effects of Electric Heater on the system considering different weather conditions are studied. The results show that the system proposed can effectively mitigate the wind power fluctuation, reduce the wind curtailment, and increase the power dispatchability of the hybrid system.
The growth of the global economy is accompanied by significant energy consumption, and greenhouse gas emissions create various problems such as global warming and environmental degradation. To protect the environment,...
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The growth of the global economy is accompanied by significant energy consumption, and greenhouse gas emissions create various problems such as global warming and environmental degradation. To protect the environment, governments are seeking to reduce carbon emissions. Production systems that operate solely based on economic factors in the workshop only consider problems such as production speed, cost, and processing time. Two aspects can be effective in saving energy and reducing emissions at the production planning level: using routing to find the shortest path for collecting workpieces to the workshop, and turning off machines with long idle times and restarting them at the appropriate time. If the workshop production problem is combined with vehicle routing, a new problem arises. According to the research conducted so far, an integrated mathematical model for production routing has not been designed in a situation where the routing is before the production workshop. In this research, this bi-objective model is introduced, and it is solved using the augmented epsilon-constraint (AEC) method. The proposed mixed-integer linear programming model of this research includes three dimensions: environmental, social (customer satisfaction), and economic simultaneously. Given the high complexity of the mathematical model, MATLAB software and MOPSO and NSGA-II algorithms were used to solve it at higher dimensions. Seven evaluation criteria were used to compare the two proposed algorithms, and the results show that the MOPSO algorithm performs better. The findings suggest that minimizing pollution may involve sacrificing on-time delivery to customers. Consequently, decision-makers must carefully weigh the trade-off between reducing environmental impact and maintaining satisfactory delivery performance, ultimately deciding on an acceptable pollution level.
Efficient operation of the gas field supply chain is an important guarantee for oil and gas energy security, and it needs to dynamically adapt to upstream and market fluctuations. This paper proposes an innovative des...
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Efficient operation of the gas field supply chain is an important guarantee for oil and gas energy security, and it needs to dynamically adapt to upstream and market fluctuations. This paper proposes an innovative design and operation optimization mixedinteger nonlinearprogramming (MILP) method for distributed supply chain based on skid equipment. Unlike the traditional methods, the proposed MILP method can simultaneously obtain upstream production planning, midstream modular equipment and processing capacity allocation, and downstream transportation allocation schemes for various natural gas products such as liquefied natural gas (LNG), compressed natural gas (CNG) and pipeline natural gas (PNG) of each time periods by making the maximum net present value (NPV) of the full development cycle as the target. In order to prove the superiority and usability of the proposed method, four operation scheduling modes are compared through two comprehensive case analysis. Additionally, the effects of gas well productivity, market demand and product price are investigated through sensitivity analysis. The results show that comparing with the traditional method, the proposed method can effectively improve the loading rate of the processing equipment, increase the overall revenue of gas field development, integrate the operation in the upstream, midstream and downstream of the supply chain, dynamically adapt to the gas production and marketing fluctuations. This study provides a creative way to obtain better profit, reduce energy utilization and promote cleaner production for sustainable supplies management in gas industry.
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