Increased travel times are often observed on urban roads, with signalized intersections being the major bottlenecks. The inability of existing static signal timings in accommodating the actual demand fluctuations coul...
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Increased travel times are often observed on urban roads, with signalized intersections being the major bottlenecks. The inability of existing static signal timings in accommodating the actual demand fluctuations could be one of the contributing factors. A traffic-responsive signal control system that changes signal timings according to traffic volume fluctuations may alleviate this problem. However, such problems are conventionally formulated based on the data collected from location-based sensors, which are infrastructure intensive and costly and fail to capture mixed and disordered traffic conditions. Considering these limitations, this paper presents an optimal signal design using sample travel time information collected from mobile data sources such as GPS/Bluetooth/Wi-Fi sensors that work independently of the traffic conditions and are relatively cost-effective. The proposed adaptive signal design minimizes total intersection delay at isolated intersections for every cycle based on the traffic conditions observed in the previous cycle. The mathematical programming-based formulation uses shock waves formed during the red and green phases to estimate optimal-phase durations. Results revealed that the proposed design is capable of handling traffic flow fluctuations without requiring the entire traffic stream data. The system demonstrated that sample data from four probe vehicles per phase is adequate for real-time optimal signal design. Results showed that the proposed model outperformed the existing Webster's signal design procedure with a delay reduction of 11.78% when compared theoretically and 10.41% when implemented in VISSIM.
This study examines a method for deciphering the handwritten dancing men graphic shown in "The Dancing Men." We attempted to reproduce and explain Holmes's reasoning for the graphic cipher by using a mat...
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This study examines a method for deciphering the handwritten dancing men graphic shown in "The Dancing Men." We attempted to reproduce and explain Holmes's reasoning for the graphic cipher by using a mathematical programming approach. Moreover, we studied the validity of Holmes’s reasoning and demonstrated a procedure to decipher the cipher computationally. Herein, we propose a mathematical solution to clarify the ambiguity in Holmes' conjecture. Although the proposed method has not been implemented, its feasibility is confirmed through partial data creation and analysis.
Optimisation-based design is an established methodology that aims to achieve a globally optimal solution to a complex process design task by representing it as an optimisation problem. We propose a hybrid framework fo...
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Optimisation-based design is an established methodology that aims to achieve a globally optimal solution to a complex process design task by representing it as an optimisation problem. We propose a hybrid framework for decomposition-based process design, centred around hierarchical reinforcement learning and mathematical programming. The framework enables the agent to assemble processes, employ mathematical programming, and discover optimal designs without the need for a pre-defined process superstructure. The agent is composed of: (i) a higher level, that learns to construct the overall process by connecting process sections, and (ii) a lower level, that learns to build and solve sections by connecting and initialising unit operations. Such modularity allows for flexible and robust optimisation in constrained, nonlinear and nonconvex spaces. The framework is demonstrated in a case study of an intensified ethylene oxide production plant, yielding improved results compared to baseline designs reported in the open literature. The case study was implemented in Pyomo. Results reveal insights on the agent's learning speed and ability to leverage process models.
A new application of the traveling salesman problem referred to as the Turkish cashier problem (TCP) was recently introduced in literature. The problem revolved around a cashier that must visit several locations and r...
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ISBN:
(数字)9798350312546
ISBN:
(纸本)9798350312553
A new application of the traveling salesman problem referred to as the Turkish cashier problem (TCP) was recently introduced in literature. The problem revolved around a cashier that must visit several locations and return to his office. To complete his visits, he can use taxis or public transportation and the objective is to minimize the total transportation cost. To make this problem more practical, we took time into consideration by adding a soft time interval for each location obligating the cashier to make his visit within. If he fails to visit within the adequate time, a penalty must be paid. We name this problem as the TCP with time windows (TCPwTW). A metaheuristic algorithm known as the Migrating Birds Optimization (MBO) algorithm coupled with mathematical programming was developed to solve TCPwTW. We attempted to find the exact optimum using an exact solver where for complex problems, optimal solutions cannot be found. The quantitative study reveals that for problems having a loose time interval, the Solver serves as the best approach. On the other hand, for problems having tight time intervals, the best solutions can be obtained by the matheuristic.
Advancements in mathematical programming have made it possible to efficiently tackle large-scale real-world problems that were deemed intractable just a few decades ago. However, provably optimal solutions may not be ...
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In this paper, we consider a Markov decision process (MDP) with a Borel state space X ∪ {∆}, where ∆ is an absorbing state (cemetery), and a Borel action space A. We consider the space of finite occupation measures r...
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Finding the correct substitution dosage of levothyroxine after total thyroidectomy can be time consuming using current methods. We present a mixed integer quadratic programming model for determining correct dosage giv...
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ISBN:
(纸本)9783030316358;9783030316341
Finding the correct substitution dosage of levothyroxine after total thyroidectomy can be time consuming using current methods. We present a mixed integer quadratic programming model for determining correct dosage given characteristics of a patient. The model is flexible in terms of determining fixed daily dosage regime, repeating schedule (e.g. weekly), and may include a loading dosage phase to achieve full substitution quicker. A similar model is used in an ongoing research project to determine dosages for patients. One challenge of the most sophisticated dosage regime is that it increases the burden for patients in following a dosage regime with frequent changes and where pills need to be divided to achieve more precise dosage.
In different areas across the U.S., there are utility poles and other critical infrastructure that are vulnerable to flooding damage. The goal of this multidisciplinary research is to assess and minimize the probabili...
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In different areas across the U.S., there are utility poles and other critical infrastructure that are vulnerable to flooding damage. The goal of this multidisciplinary research is to assess and minimize the probability of utility pole failure through conventional hydrological, hydrostatic, and geotechnical calculations embedded to a unique mixed integer linear programming (MILP) optimization framework. Once the flow rates that cause utility pole overturn are determined, the most cost-efficient subterranean pipe network configuration can be created that will allow for flood waters to be redirected from vulnerable infrastructure elements. The optimization framework was simulated using the Julia scientific programming language, for which the JuMP interface and Gurobi solver package were employed to solve a minimum cost network flow objective function given the numerous decision variables and constraints across the network. We implemented our optimization framework in three different watersheds across the U.S. These watersheds are located near Whittier, NC;Leadville, CO;and London, AR. The implementation of a minimum cost network flow optimization model within these watersheds produced results demonstrating that the necessary amount of flood waters could be conveyed away from utility poles to prevent failure by flooding.
Heat exchanger network synthesis (HENS) is an effective tool for heat recovery in chemical and petrochemical industries. This study aims to show a method for HENS with the consideration of different shell-side flow ar...
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Heat exchanger network synthesis (HENS) is an effective tool for heat recovery in chemical and petrochemical industries. This study aims to show a method for HENS with the consideration of different shell-side flow arrangements in shell-and-tube heat exchangers. The proposed MINLP model is modified from the stage-wise superstructure model, incorporating newly developed correlations for shell-side pressure drop calculation for the helical baffle. The objective is to minimise the total annual cost (TAC) with a trade-off between the cost of different shell-side heat exchangers, utility cost, and pumping cost. The selection of baffle design is decided by the saved pumping cost and the increased area cost. The proposed model is tested from different points of view: with/without utility constraints and different statuses of the streams. Three case studies are presented and compared with the results from the literature. The proposed method with different shell-side baffles can reduce the heat transfer area and pressure drop. For fixed utility consumptions, the TAC in a case study is decreased by 8.9% with mixed baffle types. Although the unit per area cost of the helical baffle is higher than the segmental baffle, the increased investment cost could be compensated by the reduced operation cost, especially for plants with high viscosity streams and long-term cost-saving.
The optimal topology and operation of multi-energy systems (MES) can be determined by mathematical programming models, which are commonly difficult to solve due to the complex structures of the models and large-scale ...
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The optimal topology and operation of multi-energy systems (MES) can be determined by mathematical programming models, which are commonly difficult to solve due to the complex structures of the models and large-scale time-series data. The time series aggregation (TSA) strategies are usually adopted to reduce the complication of the data input and thus improve the solution efficiency. Nevertheless, for the optimal design of MES, significant differences exist in the structures and performances of the systems resulting from different TSA strategies. It is crucially important to determine appropriate TSA strategies in the optimal design of MES. In this work, a two-step method was proposed to determine TSA strategies based on a multi-objective optimization framework. In the first step, the preliminary design for the multi-energy system was obtained by different TSA strategies for the optimal design and operation of MES. In the second step, the total annual cost and loss of load probability (LSLP) of the system were taken as two objectives, and a multi-objective optimization approach was established to determine the appropriate TSA strategy for the optimal design of the MES, where the structure and performance of the system were considered simultaneously. The implementation and effectiveness of the proposed method were demonstrated by the optimal design of an MES with energy storage. The deviations of the design scheme and system performance obtained by different TSA strategies were comprehensively compared and analyzed. The results indicate that the feasibility of the optimal design schemes of the MES can be verified by using the original full-time series data, and the deviation of the results obtained by different TSA strategies can be quantified. The reduction of LSLP is at the expense of the increase of the costs. In the multiple time grids strategy (TG1) with the number of time steps (TS) 730, the TAC of the system increases from $9.79 x 10(7) to $1.56 x 10(8) when t
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