We propose a general framework for the formulation of superstructure-based optimization models for holistic process synthesis. First, we redefine the fundamental problems of reactor, separation, and heat exchanger net...
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We propose a general framework for the formulation of superstructure-based optimization models for holistic process synthesis. First, we redefine the fundamental problems of reactor, separation, and heat exchanger network synthesis, by presenting generalized problem statements, to make them amenable to seamless integration with each other. Second, we describe the general forms of models that can be developed to address these generalized problems and identify some key characteristics. Notably, for each system, we identify internal variables used only within the system, cost variables used in the objective function, and, importantly, coupling variables for the coupling between systems. Third, we outline some literature models that can be used to address the generalized problems and present new models to couple the three systems. Finally, we show how the individual components (systems and coupling models) can be integrated to formulate a single simultaneous reactor, separation, and heat exchanger network synthesis. (C) 2019 Elsevier Ltd. All rights reserved.
In this paper, we propose an algorithm for constrained global optimization of mixed-integer nonlinear programming (MINLP) problems. The proposed algorithm uses the Bernstein polynomial form in a branch-and-bound frame...
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In this paper, we propose an algorithm for constrained global optimization of mixed-integer nonlinear programming (MINLP) problems. The proposed algorithm uses the Bernstein polynomial form in a branch-and-bound framework. Ingredients such as continuous relaxation, branching for integer decision variables, and fathoming for each subproblem in the branch-and-bound tree are used. The performance of the proposed algorithm is tested and compared with several state-of-the-art MINLP solvers, on two sets of test problems. The results of the tests show the superiority of the proposed algorithm over the state-of-the-art solvers in terms of the chosen performance metrics.
This paper addresses the problem of optimal conductor selection in direct current (DC) distribution networks with radial topology. A nonlinearmixed-integerprogramming model (MINLP) is developed through a branch-to n...
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This paper addresses the problem of optimal conductor selection in direct current (DC) distribution networks with radial topology. A nonlinearmixed-integerprogramming model (MINLP) is developed through a branch-to node incidence matrix. An important contribution is that the proposed MINLP model integrates a set of constraints related to the telescopic structure of the network, which allows reducing installation costs. The proposed model also includes a time-domain dependency that helps analyze the DC network under different load conditions, including renewable generation and battery energy storage systems, and different voltage regulation operative consigns. The objective function of the proposed model is made up of the total investment in conductors and the total cost of energy losses in one year of operation. These components of the objective function show multi-objective behavior. For this reason, different simulation scenarios are performed to identify their effects on the final grid configuration. An illustrative 10-nodes medium-voltage DC grid with 9 lines is used to carry out all the simulations through the General Algebraic Modeling System known as GAMS.
The multi-mode resource-constrained project scheduling problem under uncertain activity cost (MRCPSP-UAC) has a wide range of applications in production planning and project management. We first build a new mixed inte...
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The multi-mode resource-constrained project scheduling problem under uncertain activity cost (MRCPSP-UAC) has a wide range of applications in production planning and project management. We first build a new mixedintegernonlinearprogramming (MINLP) model with the objective of minimizing the risk of project cost overrun, which provides a vehicle to obtain optimal solutions. To overcome the computational challenge of exact method for solving large instances, we devise a construction heuristic (CH) with a multi-pass greedy improvement procedure to obtain a feasible solution efficiently. To further improve solution quality, a hybrid CH and genetic algorithm (CH-GA) is developed with a custom fitness function to properly calibrate the quality of an individual. A comprehensive computational study is performed to examine the impact of various problem parameters on the optimal solutions, and the performance of our algorithms. Our hybrid CH-GA performs well for large instances with significantly less computational time than the exact method.
作者：
Wang, GangUniv Massachusetts
Charlton Coll Business Dept Decis & Informat Sci 285 Old Westport Rd N Dartmouth MA 02747 USA
This paper considers an integrated scheduling problem in an e-commerce supply chain. The supply chain consists of heterogeneous suppliers, consumer-goods manufacturers that offer online channels, and a network of reta...
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This paper considers an integrated scheduling problem in an e-commerce supply chain. The supply chain consists of heterogeneous suppliers, consumer-goods manufacturers that offer online channels, and a network of retailers. Suppliers provide raw materials or semi-finished products to manufacturers. Then, manufacturers produce and deliver end items to retailers. The retailers require that the manufac-turers maintain a given fill rate, and, otherwise, a penalty will apply. Missing the fill rate can not only result in penalties, but also retailers' switching, operational issues, and loss of image, and market value. Therefore, the total penalty costs are nonlinear with retailers' unsatisfied quantities and the fill rate, which exhibits spillover effects. The problem is to select a subset of suppliers, assign retailers' orders, and identify a production schedule with the manufacturers while minimizing the total costs that includes shipping and penalty. In this paper, we formulate a mixed-integernonlinear program and develop a hybrid particle swarm optimization algorithm that can find a suitable solution within a reasonable time. The algorithm incorporates augmented Lagrangian relaxation and particle swarm optimization. We then perform computational testing on randomly generated cases and evaluate the performance of the proposed algorithm. Our numerical experiments show that the proposed algorithm is robust and efficient. Moreover, we provide a real case study, which demonstrates that the proposed model can lead to a substantial reduction of both the total and penalty costs. (C) 2020 Elsevier Ltd. All rights reserved.
We develop a trust region filter strategy for simultaneous optimal design of heat exchanger networks that includes detailed design of shell-and-tube heat exchangers. The strategy first solves a mixed-integernonlinear...
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We develop a trust region filter strategy for simultaneous optimal design of heat exchanger networks that includes detailed design of shell-and-tube heat exchangers. The strategy first solves a mixed-integer nonlinear programming (MINLP) formulation with shortcut models to generate candidate network topologies, which are then used in a non-isothermal mixing nonlinearprogramming (NLP) suboptimization with detailed optimal exchanger design models embedded using a modified trust region filter (TRF) algorithm. An integer cut based strategy is used to bound the solutions from MINLP and the NLP which aids in convergence to the solution of the overall simultaneous design problem. Under assumptions, the TRF based strategy can guarantee convergence to near optimal solutions of the overall design problem. The presented solution strategy is thus able to find optimal heat exchanger network designs based on the simultaneous optimization of the network topology and mass and energy balances, together with detailed shell-and-tube heat exchanger optimization, including the number of shell and tube passes, pressure drops, and tubes, tube lengths, etc. The proposed strategy is tested on three literature based case studies and their results are compared with previous studies to showcase its performance. (c) 2021 Elsevier Ltd. All rights reserved.
Automated vehicles (AV) have the potential to provide cost-effective mobility options along with overall system-level benefits in terms of congestion and vehicular emissions. Additional resource allocation at the netw...
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Automated vehicles (AV) have the potential to provide cost-effective mobility options along with overall system-level benefits in terms of congestion and vehicular emissions. Additional resource allocation at the network level, such as AV-exclusive lanes, can further foster the usage of AVs rendering this mode of travel more attractive than legacy vehicles (LV). However, it is necessary to find the crucial locations in the network where providing these dedicated lanes would reap the maximum benefits. In this study, we propose an integrated mixed-integerprogramming framework for optimal AV-exclusive lane design on freeway networks which accounts for commuters' demand split among AVs and LVs via a logit model incorporating class-based utilities. We incorporate the link transmission model (LTM) as the underlying traffic flow model due to its computational efficiency for system optimum dynamic traffic assignment. The LTM is modified to integrate two vehicle classes namely, LVs and AVs with a lane-based approach. The presence of binary variables to represent lane design and the logit model for endogenous demand estimation results in a nonconvex mixed-integernonlinear program formulation. We propose a Benders' decomposition approach to tackle this challenging optimization problem. Our approach iteratively explores possible lane designs in the Benders' master problem and, at each iteration, solves a sequence of system-optimum dynamic traffic assignment problems in an attempt to find fixed-points representative of logit-compatible demand splits. The proposed approach is implemented on three hypothetical freeway networks with single and multiple origins and destinations. Our numerical results reveal that the optimal lane design of freeway network is non-trivial while accounting for endogenous demand of each mode.
The presence of smart buildings (SBs) is expected to grow manifold in the next decade, leading to both challenges and opportunities in managing increasingly "active" distribution systems. SBs are host to a n...
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The presence of smart buildings (SBs) is expected to grow manifold in the next decade, leading to both challenges and opportunities in managing increasingly "active" distribution systems. SBs are host to a number of devices and technologies capable of providing residential flexibility in order to maintain acceptable operating conditions within the distribution system. A prevalent class of devices with significant potential for flexibility provision are shiftable loads (SLs), which represent a large amount of potential SB-provided flexibility. However, the accurate modelling of SLs within the multi-period optimal power flow (MP-OPF) framework results in complex non-convex mixed-integer nonlinear programming (MINLP) problems. For realistic feeders, MINLP problems are intractable and hence require some heuristic form of circumvention. This work evaluates the limitations of the MINLP formulation and proposes new practical alternatives, namely a flexible NLP approximation and a multi-faceted heuristic algorithm. The proposed approaches are generic and applicable for any problem size or type of SL, while they are shown to work efficiently, both speed-wise and solution-quality-wise, outperforming state-of-the-art MINLP solvers. A simple scoring and ranking scheme is also proposed in order to make a direct comparison between the different approaches.
Many industrial optimization problems are sparse and can be formulated as block-separable mixed-integer nonlinear programming (MINLP) problems, where low-dimensional sub-problems are linked by a (linear) knapsack-like...
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ISBN:
(纸本)9783030588083;9783030588076
Many industrial optimization problems are sparse and can be formulated as block-separable mixed-integer nonlinear programming (MINLP) problems, where low-dimensional sub-problems are linked by a (linear) knapsack-like coupling constraint. This paper investigates exploiting this structure using decomposition and a resource constraint formulation of the problem. The idea is that one outer approximation master problem handles sub-problems that can be solved in parallel. The steps of the algorithm are illustrated with numerical examples which shows that convergence to the optimal solution requires a few steps of solving sub-problems in lower dimension.
The paper presents the Generalized Benders Decomposition (GBD) method, which is now one of the basic approaches to solve big mixed-integernonlinear optimization problems. It concentrates on the basic formulation with...
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The paper presents the Generalized Benders Decomposition (GBD) method, which is now one of the basic approaches to solve big mixed-integernonlinear optimization problems. It concentrates on the basic formulation with convex objectives and constraints functions. Apart from the classical projection and representation theorems, a unified formulation of the master problem with nonlinear and linear cuts will be given. For the latter case the most effective and, at the same time, easy to implement computational algorithms will be pointed out.
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