Water allocation network (WAN) and heat exchange network (HEN) are effective optimization techniques in chemical process system engineering (CPSE). This paper reviews the literature about the optimization of water and...
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Water allocation network (WAN) and heat exchange network (HEN) are effective optimization techniques in chemical process system engineering (CPSE). This paper reviews the literature about the optimization of water and heat over the last 20 years. By analyzing the development of CPSE, this review presents a systematic overview on deterministic optimization algorithms and stochastic optimization algorithms. Deterministic opti-mization algorithms, like the conceptual method and mathematical programming method, could obtain a deterministic feasible solution by analyzing diagrams or solving the water-energy equations. Stochastic opti-mization algorithms have a strong search ability in solution space. It doesn't need to solve complicated math-ematical models, and could avoid local optimum effectively and achieve the global optimal solution quickly. This paper summarizes the recent researches on stochastic optimization algorithms like simulated annealing algo-rithm (SA), genetic algorithm (GA), particle swarm algorithm (PSO), and so on. Finally, the application and future research gaps in this field are presented.
In large-scale agriculture, insufficient irrigation water may lead to overpumping of groundwater, increasing the risk of land subsidence. Growing dryland crops can effectively decrease the demand for irrigation water....
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In large-scale agriculture, insufficient irrigation water may lead to overpumping of groundwater, increasing the risk of land subsidence. Growing dryland crops can effectively decrease the demand for irrigation water. However, the previous works on annual crop planning (ACP) focused on maximizing the profit through growing wetland crops and consuming much water. For sustainability, in this article, we propose a mathematical programming model for an ACP that allocates a land area for growing dryland and wetland crops to maximize the total profit and minimize the total irrigation water used for multiple cropping, under practical constraints. The simplified swarm optimization (SSO) improves the particle swarm optimization with four probabilities to determine the operations of updating solutions. We further propose dynamic SSO (DSSO) to solve the concerned ACP in which the four probabilities are adjusted dynamically according to the performance of the operations executed. Through simulation on a case study, the proposed DSSO demonstrates high performance over some classical approaches.
Dial-a-ride (DAR) is a shared-ride service that provides mobility to transportation-disadvantaged individuals who are unable to use public transit. While most DAR studies focus on optimizing operations, our research e...
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Dial-a-ride (DAR) is a shared-ride service that provides mobility to transportation-disadvantaged individuals who are unable to use public transit. While most DAR studies focus on optimizing operations, our research explores the feasibility and benefits of outsourcing outlier trips to transportation network companies (TNCs) to minimize the combined service delivery cost. To achieve this goal, we formulate a multi-vehicle DAR problem (DARP) with trip outsourcing to TNCs, which can be solved optimally for small scale instances. To solve larger instances, we propose a two-stage solution framework to improve DAR routes from commercially available software. Firstly, we develop an integer programming model to re-optimize individual routes with trip outsourcing. Secondly, we design a multi-vehicle heuristic that considers reinserting trips initially designated for outsourcing back into the DAR fleet, as well as reinsertion and exchange of remaining trips among routes. We apply the approach to a medium-sized DAR operator in Maryland and achieve cost reductions of 7%-13% depending on the TNC volume discount negotiated by the DAR company.
This paper introduces a mixed-integer linear programming model to tackle the design and planning problem of Closed Loop Supply Chains in the carpet industry. A distinctive feature of the approach is handling different...
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This paper introduces a mixed-integer linear programming model to tackle the design and planning problem of Closed Loop Supply Chains in the carpet industry. A distinctive feature of the approach is handling different recovery processes involving dissimilar activities and flows of recovered materials. Moreover, the reverse network facilities and logistics are completely integrated with the forward network structure, in order to obtain a high level of customer supply while considering economic goals. In addition, the economies of scale for the recycling facilities are also explicitly handled. The model allows to determine the products and quantities to be manufactured, transported, stored, and recycled to cover the demand of a set of customers. The objective function is to maximize the profit of the whole life-cycle of products. An example based on an industrial case that involves the recovery of raw materials is presented to show the relevance of the method.
Adaptable, low-cost, coils designed by carefully selecting the arrangements and geometries of simple primitive units are used to generate magnetic fields for diverse applications. These extend from magnetic resonance ...
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Adaptable, low-cost, coils designed by carefully selecting the arrangements and geometries of simple primitive units are used to generate magnetic fields for diverse applications. These extend from magnetic resonance and fundamental physics experiments to active shielding of quantum devices including magnetometers, interferometers, clocks, and computers. However, finding optimal arrangements and geometries of multiple primitive structures is time-intensive and it is challenging to account for additional constraints, for example, optical access, during the design process. Here, we demonstrate a general method to find these optimal arrangements. We encode specific symmetries into sets of loops, saddles, and cylindrical ellipses and then solve exactly for the magnetic field harmonics generated by each set. By combining these analytic solutions using computer algebra, we can use numerical techniques to efficiently map the landscape of parameters and geometries which the coils must satisfy. Sets of solutions may be found which generate desired target fields accurately while accounting for complexity and size restrictions. We demonstrate this approach by employing simple configurations of loops, saddles, and cylindrical ellipses to design target linear field gradients and compare their performance with designs obtained using conventional methods. A case study is presented where three optimized arrangements of loops, designed to generate a uniform axial field, a linear axial field gradient, and a quadratic axial field gradient, respectively, are hand-wound around a low-cost, 3-D-printed coil former. These coils are used to null the magnetic background in a typical laboratory environment, reducing the magnitude of the axial field along the central half of the former's axis from (7.8 +/- 0.3) mu T (mean +/- standard deviation) to (0.11 +/- 0.04) mu T.
A surrogate-based multi-objective optimization framework is employed in the design of natural gas liquefaction processes using reliable, black-box process simulation. The conflicting objectives are minimizing both pow...
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A surrogate-based multi-objective optimization framework is employed in the design of natural gas liquefaction processes using reliable, black-box process simulation. The conflicting objectives are minimizing both power consumption and heat exchanger area utilization. The Pareto solutions of the single-mixed refrigerant (SMR) and propane-precooled mixed refrigerant (C3MR) processes are compared to determine the suitability of each process in terms of energy consumption and heat exchanger area. Kriging models and the z-constraint methodology are used to sequentially provide simple surrogate optimization subproblems, whose minimizers are promising feasible and non-dominated solutions to the original black-box problem. The surrogate-based..-constrained optimization subproblems are solved in GAMS using CONOPT. The Pareto Fronts achieved with the surrogate-based framework dominate the results from the NSGA-II, a well-established meta-heuristics of multi-objective optimization. The objective functions of non-dominated solutions go as low as 1045 and 980.3 kJ/kg-LNG and specific UA values of 212.2 and 266.9 kJ/(degrees C kg-LNG) for SMR and C3MR, respectively. The trade-off solutions that present the minimum sum of relative objectives are analyzed as well as the dominance of C3MR over SMR at low power consumption values and conversely at low heat exchanger area utilization.
Biomasses are renewable sources used in energy conversion processes to obtain diverse products through different technologies. The production chain, which involves delivery, logistics, pre-treatment, storage and conve...
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Biomasses are renewable sources used in energy conversion processes to obtain diverse products through different technologies. The production chain, which involves delivery, logistics, pre-treatment, storage and conversion as general components, can be costly and uncertain due to inherent variability. Optimization methods are widely applied for modeling the biomass supply chain (BSC) for energy processes. In this qualitative review, the main aspects and global trends of using geographic information systems (GISs), linear programming (LP) and neural networks to optimize the BSC are presented. Modeling objectives and factors considered in studies published in the last 25 years are reviewed, enabling a broad overview of the BSC to support decisions at strategic, tactical and operational levels. Combined techniques have been used for different purposes: GISs for spatial analyses of biomass;neural networks for higher heating value (HHV) correlations;and linear programming and its variations for achieving objectives in general, such as costs and emissions reduction. This study reinforces the progress evidenced in the literature and envisions the increasing inclusion of socio-environmental criteria as a challenge in future modeling efforts.
The class assignment problem concerns determining student class assignments based on quotas. This problem is also applied in universities to determine student lab assignments as well as in a wide variety of other appl...
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Hybrid manufacturing technology has enabled manufacturers to combine advantages of mainly subtractive and additive manufacturing technologies. A single machine supports producing products with complex geometry, at hig...
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Hybrid manufacturing technology has enabled manufacturers to combine advantages of mainly subtractive and additive manufacturing technologies. A single machine supports producing products with complex geometry, at high quality, and with a high degree of automation. To benefit from these advantages, decisions taken in the process planning stage of such a sophisticated manufacturing system should be optimized. The objective of this paper is to determine the optimal process plan considering both the engineering and manufacturing aspects of the hybrid technology. A comprehensive process planning model is proposed. The model specifies the optimal sequence of additive and subtractive features that minimizes the production cycle time. In addition, the model sets the optimal part orientations such that the time needed for building support structures, performing post-processing and inspection operations, changing cutting tools and printing nozzles, and unclamping the part is minimized. The model is comprehensive as it considers productive and non-productive times, precedence, technological, quality, and manufacturing restrictions imposed on hybrid manufacturing systems. The proposed model is nonlinear;due to this nonlinearity, the model is intractable. A linearization scheme is applied to formulate an equivalent linear model that is solvable to optimality by commercial solvers. Case studies on test and industrial parts are provided to evaluate the computational performance of the proposed model. Integrating the proposed model in hybrid manufacturing (HM) systems ensures adopting the HM technology in its optimal direction. HM technology is an enabler of establishing a smart manufacturing system which is one of the pillars of Industry 4.0.
Decentralized decision-making can be represented as a connected decision network of agents collaboratively optimizing their local objective functions over common coupling constraints. In this setting, solving large-sc...
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Decentralized decision-making can be represented as a connected decision network of agents collaboratively optimizing their local objective functions over common coupling constraints. In this setting, solving large-scale mathematical programming centrally is undesirable or impossible because the data storage and decision au-thority are already decentralized, the communication bandwidth for information exchange is limited, and pri-vacy concerns with information may exist. We introduce a taxonomy of mathematical programming-based decentralized optimization problems and decentralized algorithms based on the degree of information sharing, information exchange and existence of a central coordinator. We synthesize the literature and identify the shortcomings of a decentralized algorithm, the trends, and the potential research directions based on the pro-posed taxonomy.
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