This paper deals with the problem of optimal control at an isolated oversaturated intersection equipped with a signa1-group-oriented controller. The problem we consider is to determine elements of the signal plan (sta...
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This paper deals with the problem of optimal control at an isolated oversaturated intersection equipped with a signa1-group-oriented controller. The problem we consider is to determine elements of the signal plan (stages, their duration and sequence) so as to maximize the number of vehicles that can pass through the intersection during one cycle. The concents of signal plan structure, admissible stage sequences, and structural constraints are introduced, and the optimization problem is stated as the problem of finding the best closed path through the graph of possible sequences. The optimization technique is of branch-and -bound type. The proposed method gives an exact procedure for simultaneous determination of all signal plan elements, except for the cycle time. An illustrative example is also included.
Amsterdam, a growing city of over 800,000 people in the Netherlands, is struggling to collect waste. While residents in most districts of the city use underground bins to deposit their garbage, the historic Centrum di...
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Amsterdam, a growing city of over 800,000 people in the Netherlands, is struggling to collect waste. While residents in most districts of the city use underground bins to deposit their garbage, the historic Centrum district continues to rely on curbside collection. As such, the streets around the UNESCO-World-Heritage canals are lined with garbage bags as trash trucks rumble down roads centuries old and ill-fitted for vehicles of such size. This paper assesses the viability of moving Centrum trash collection to the canals with a fleet of tug boats and floating dumpsters. It does so by using a combination of GIS tools and integerprogramming to determine the quantity and optimal collection locations while ensuring that an average Centrum resident walks no farther than denizens of the other Amsterdam districts. Additionally, it proposes a schedule for emptying floating dumpsters based on one comparable to the current truck system. The results of this paper suggest that mobile trash collection using the canals is a viable solution that could reduce noise, pollution, and congestion, thus improving the quality of Amsterdam’s historic cityscape.
This paper studies the strategic problem of finding a cost optimal fleet of vessels to support maintenance operations at offshore wind farms. A dual-level stochastic model is formulated, taking into account both long-...
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This paper studies the strategic problem of finding a cost optimal fleet of vessels to support maintenance operations at offshore wind farms. A dual-level stochastic model is formulated, taking into account both long-term strategic uncertainty and short-term operational uncertainty in a single optimization model. The model supports wind farm owners in making strategic decisions regarding the number, placement, charter length, and types of vessels to charter, to meet maintenance demands throughout the lifetime of a wind farm. To evaluate the quality of strategic fleet size and mix decisions, the model also considers the operational decisions of how to utilize the fleet to support maintenance operations. The model accounts for strategic uncertainties that have not been considered in previously developed optimization models for offshore wind, such as uncertainty related to long-term trends in electricity prices and subsidy levels, the stepwise development of wind farms, and technology development in the vessel industry. To solve the proposed stochastic programming model we have developed an ad hoc integer L-shaped method, with customized optimality cuts. The computational experiments show that the proposed method outperforms solving the deterministic equivalent using a commercial MIP solver.
Synthesis of sustainable processing pathway is an important initial step in deciding investments in carbon capture, utilization, and sequestration (CCUS). For a best decision, it is necessary to analyze a very large n...
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Synthesis of sustainable processing pathway is an important initial step in deciding investments in carbon capture, utilization, and sequestration (CCUS). For a best decision, it is necessary to analyze a very large number of potential processing pathways at once in terms of their economics and net carbon emission. Such analysis may also reveal which parts of a pathway incur significant portions of the costs and carbon emissions, suggesting hotspots for improvement. Frameworks used should also be flexible enough to accommodate varying feed conditions and market/emission data as they tend to vary according to the sources of CO 2 and geographical locations. The superstructure method along with a state-task network (STN) representation offers such flexibility in analyzing a CCUS system. This work proposes to use a STN representation of a process in a superstructure composed of feeds, processes, and products, to represent and optimize among various options of CCUS pathways through mathematical programming. A case study is conducted to illustrate the utility of the STN representation in the CCUS superstructure optimization.
Socio-natural disasters pose a significant risk to healthcare systems, particularly in areas with high volcanic and seismic activity, combined with high poverty rates, reflecting vulnerable communities facing severe i...
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Socio-natural disasters pose a significant risk to healthcare systems, particularly in areas with high volcanic and seismic activity, combined with high poverty rates, reflecting vulnerable communities facing severe impacts on societal well-being and the economy. This is the case in the territories of Coquimbo and La Serena in Chile, which are constantly affected by natural phenomena, such as wildfires. Between 2020 and 2021, a total of 58 wildfires were recorded. This study presents a quantitative risk analysis of the healthcare network in response to the recurrence of wildfires in Coquimbo and La Serena cities, employing mixed-integer programming models for optimal location, relating fixed costs to the increased demand for care from the affected population. The methodology applied was quantitative, where the first phase involved identifying the areas affected by wildfires in La Serena and Coquimbo. Subsequently, we have formulated the mathematical model, including the objective function, definition of variables and parameters, definition of constraints, and finally, the implementation of the mathematical model's code.
Rapid urbanization has led to increasing fire incidents and false alarms, increasing the response time of fire departments. When a call arrives, the current technology deploys and relocates the vehicles based on their...
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Rapid urbanization has led to increasing fire incidents and false alarms, increasing the response time of fire departments. When a call arrives, the current technology deploys and relocates the vehicles based on their immediate impact on the system's preparedness. However, the unavailability of the relocated vehicles is often ignored during the relocation, thus the system's preparedness is overestimated. This paper presents a novel mixed-integer programming (MIP) model developed for the relocation and deployment of emergency/fire vehicles. The proposed model incorporates the unavailability element, and estimates system preparedness for future incidents more accurately than current models. To confirm the efficiency of the proposed approach, the required simulations were conducted in Mashhad, Iran. The results demonstrated the ability of the proposed model to improve the performance of the fire department in several performance metrics. We also provide sensitivity analysis over the critical parameters to demonstrate the robustness of the model.
This study examines the problem of minimizing the amount and distribution of time delays or latencies experienced by data as they travel from one point to another within a software-defined network (SDN). For this purp...
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This study examines the problem of minimizing the amount and distribution of time delays or latencies experienced by data as they travel from one point to another within a software-defined network (SDN). For this purpose, a model is proposed that seeks to represent the minimization of the distances between network switches in proportion to the total nodes in a network. The highlights of this study are the proposal of two mixed-integer quadratic models from a fractional initial version. The first is obtained by transforming (from the original fractional model) the objective function into equivalent constraints. The second one is obtained by splitting each term of the fraction with an additional variable. The two developed models have a relationship between switches and controllers with quadratic terms. For this reason, an algorithm is proposed that can solve these problems in a shorter CPU time than the proposed models. In the development of this research work, we used real benchmarks and randomly generated networks, which were to be solved by all the proposed models. In addition, a few additional random networks that are larger in size were considered to better evaluate the performance of the proposed algorithm. All these instances are evaluated for different density scenarios. More precisely, we impose a constraint on the number of controllers for each network. All tests were performed using our models and the computational power of the Gurobi solver to find the optimal solutions for most of the instances. To the best of our knowledge, this work represents a novel mathematical representation of the latency density management problem in an SDN to measure the efficiency of the network. A detailed analysis of the test results shows that the effectiveness of the proposed models is closely related to the size of the studied networks. Furthermore, it can be noticed that the performance of the second model compared to the first one presents better behavior in terms of CPU
The paper investigates the Electric Vehicle Routing Problem with a non-linear concave and strictly monotonic increasing charging function. In the literature, the non-linear charging function is typically approximated ...
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The paper investigates the Electric Vehicle Routing Problem with a non-linear concave and strictly monotonic increasing charging function. In the literature, the non-linear charging function is typically approximated by a piecewise linear charging function which does not overestimate the real charging function in any point. As the piecewise linear charging function underestimates the real state -of -charge in some points, such an approximation excludes feasible solutions from the solution space. To overcome this drawback we introduce a new method to determine a piecewise linear charging function overestimating the real charging function in a way that the area between both functions is minimized as well as an adaptation of a known linearization to include the piecewise linear charging function in a branch -and -cut approach. Thereby, we include infeasible solutions in the solution space. To declare them infeasible again we check every integer solution obtained in the branch -and -cut procedure and add an infeasible path cut if the solution is infeasible for the real charging function such that the procedure terminates with an optimal solution for the real charging function. Our approach is evaluated in a computational study in which instances with up to 100 customers were solved to optimality. Moreover, we evaluate the trade-off between a more complex model formulation due to more binary variables if the number of supporting points for the piecewise linear approximation is increased and the higher approximation error if fewer supporting points are used.
Carsharing services aim to offer short-term car rentals, including round-trip and one-way alternatives. Round-trip clients must deliver the rented car at the same station where the rental has started. One-way clients ...
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Carsharing services aim to offer short-term car rentals, including round-trip and one-way alternatives. Round-trip clients must deliver the rented car at the same station where the rental has started. One-way clients can return the vehicle in a different station. This work proposes a mixed-integer Linear programming Model to optimize the fleet-sizing of a carsharing service for the one-way and round-trip alternatives, seen as utilization scenarios. The proposed model aims to maximize the company’s profit, finding the best number of vehicles to be allocated to each carsharing station. Different scenarios were analyzed for the one-way and round-trip settings, varying service costs, rental prices, number of clients, rental duration and driven distance. Simulations were performed using real spatial data from the city of São Paulo, Brazil. Results showed that round-trip profits can benefit from rentals with higher durations, and that one-way profits can overcome the profits from round-trip if user demand and number of available vehicles are enough.
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