The ongoing transformation of district heating systems (DHSs) aims to reduce emissions and increase renewable energy sources. The objective of this work is to integrate solar thermal (ST) and seasonal aquifer thermal ...
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The ongoing transformation of district heating systems (DHSs) aims to reduce emissions and increase renewable energy sources. The objective of this work is to integrate solar thermal (ST) and seasonal aquifer thermal energy storage (ATES) in various scenarios applied to a large DHS. mixed-integer linear programming (MILP) is used to develop a comprehensive model that minimizes operating costs, including heat pumps (HPs), combined heat and power (CHP) units, electric heat boilers (EHBs), heat-only boilers (HOBs), short-term thermal energy storage (TES), and ATES. Different ATES scenarios are compared to a reference without seasonal TES (potential of 15.3 GWh of ST). An ATES system with an injection well temperature of about 55 degrees C has an overall efficiency of 49.8% (58.6% with additional HPs) and increases the integrable amount of ST by 178% (42.5 GWh). For the scenario with an injection well temperature of 20 degrees C and HPs, the efficiency is 86.6% and ST is increased by 276% (57.5 GWh). The HOB heat supply is reduced by 8.9% up to 36.6%. However, the integration of an ATES is not always economically or environmentally beneficial. There is a high dependency on the configurations, prices, or emissions allocated to electricity procurement. Further research is of interest to investigate the sensitivity of the correlations and to apply a multi-objective MILP optimization.
This study investigated the flexible job shop scheduling problem considering preventive maintenance (FJSP-PM), which can be categorised into two types: FJSP-PM with a fixed maintenance strategy (FJSP-FPM) and FJSP-PM ...
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This study investigated the flexible job shop scheduling problem considering preventive maintenance (FJSP-PM), which can be categorised into two types: FJSP-PM with a fixed maintenance strategy (FJSP-FPM) and FJSP-PM with a periodic maintenance strategy (FJSP-PPM). The objective is to minimize the makespan. To prove the optimality of small-sized instances, for the first time, two mixed-integer linear programming (MILP) models for each problem are established based on different modeling approaches. A Q-learning-based collaborative variable neighbourhood search algorithm (CVNS-Q) is proposed to efficiently obtain approximate optimal solutions for large-sized instances. In the CVNS-Q, eight neighbourhoods are utilized. Two VNS modules are used to guide the evolution of the two individuals, and a Q-learning algorithm guide the selection of neighbourhoods. In addition, a restart strategy is designed to reduce the possibility of sinking into local optima. The effectiveness of the MILP model and CVNS-Q is evaluated using 20 benchmark instances.
This work addresses the capacitated minimum spanning tree problem with time windows (CMSTPTW), which considers capacity constraints for the subtrees and time windows. CMSTPTW belongs to the NP-hard family, making it a...
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This work addresses the capacitated minimum spanning tree problem with time windows (CMSTPTW), which considers capacity constraints for the subtrees and time windows. CMSTPTW belongs to the NP-hard family, making it a computational challenge to find high-quality solutions efficiently. Therefore, this work proposes a novel three-stage matheuristic (3SM) approach that combines modified Prim's algorithm, an iterated local search (ILS), and a mixed-integer linear programming (MILP) model solved with a general-purpose solver. The first stage involves generating an initial solution using Prim's algorithm adapted to the CMSTPTW. Once the initial solution is generated, the second stage consists of solving the MILP by a general-purpose solver considering a given time limit and using the best solution found by 3SM as a warm start. In the last stage, the 3SM approach employs an ILS with various perturbation and local search operators to further refine and optimize the solution. Moreover, the ILS uses two additional strategies: a set of elite solutions to preserve the best solutions throughout the algorithm's execution and a penalization procedure to navigate the infeasible solutions space. These strategies, along with effective parameter tuning, complement each other to increase the algorithm's exploration and enhance the quality of the final solution. The proposed algorithm's performance is evaluated on an existing set of instances and in two new additional sets of larger instances that are proposed. The computational results show that the 3SM approach outperforms the state-of-the-art algorithms and the general-purpose solver in terms of solution quality within a given time limit.
We study mathematical formulations for batch-processing machine scheduling problems (BPMPs), which are the challenging issues in the machine scheduling literature where machines are capable of processing a batch of jo...
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We study mathematical formulations for batch-processing machine scheduling problems (BPMPs), which are the challenging issues in the machine scheduling literature where machines are capable of processing a batch of jobs simultaneously if jobs with non-identical sizes can be packed in a capacitated machine. In this paper, we tackle single- and parallel-machine BPMPs, and other interesting problem variants that aim at minimizing the makespan. We develop novel formulations along with valid inequalities and an algorithm framework that makes use of dual information and bounding techniques to achieve efficiency when instances are intractable. Extensive computational experiments on benchmark instances show that our approaches achieve state-of-the-art results and prove the optimality of intractable instances in the literature.
Ridepooling services play an increasingly important role in modern transportation systems. With soaring demand and growing fleet sizes, the underlying route planning problems become increasingly challenging. In this c...
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Ridepooling services play an increasingly important role in modern transportation systems. With soaring demand and growing fleet sizes, the underlying route planning problems become increasingly challenging. In this context, we consider the dial-a-ride problem (DARP): Given a set of transportation requests with pickup and delivery locations, passenger numbers, time windows, and maximum ride times, an optimal routing for a fleet of vehicles, including an optimized passenger assignment, needs to be determined. We present tight mixed-integer linear programming (MILP) formulations for the DARP by combining two state-of-the-art models into novel location-augmented-event-based formulations. Strong valid inequalities and lower and upper bounding techniques are derived to further improve the formulations. We then demonstrate the theoretical and computational superiority of the new models: First, the linearprogramming relaxations of the new formulations are stronger than existing location-based approaches. Second, extensive numerical experiments on benchmark instances show that computational times are on average reduced by 53.9% compared to state-of-the-art event-based approaches.
Microgrids (MGs) are revolutionizing modern power systems by enabling decentralized energy production, renewable energy integration, and enhanced grid resilience. However, the increasing complexity of MGs, particularl...
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Microgrids (MGs) are revolutionizing modern power systems by enabling decentralized energy production, renewable energy integration, and enhanced grid resilience. However, the increasing complexity of MGs, particularly with the integration of Distributed Energy Resources (DERs), poses significant challenges for traditional protection schemes. This study addresses the coordination of Directional Overcurrent Relays (DOCRs) in MGs through a mixed-integer linear programming (MILP) model. The main contribution is a MILP model that optimizes relay settings, including Time Multiplier Settings (TMS) and standard characteristic curves, to minimize tripping times, while ensuring selectivity. Another key contribution of this work is the integration of both IEC and IEEE standard curves, which enhances coordination performance compared to using a single standard. The model was tested on the IEC benchmark microgrid, and the results demonstrated significant improvements in fault-clearing times across various operational modes. By leveraging advanced optimization techniques and diverse characteristic curves, this study contributes to the development of resilient and efficient protection systems for modern microgrids, ensuring reliable operation under varying fault conditions and DER penetration.
The management of truck arrivals at container terminals is crucial for efficient port operations. Congestions developing both outside and inside the gates can cause logistical problems, while also having a significant...
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The management of truck arrivals at container terminals is crucial for efficient port operations. Congestions developing both outside and inside the gates can cause logistical problems, while also having a significant impact on the environment and the surroundings of the port. Therefore, optimizing truck queues outside the gates of the port, as well as routing of trucks inside the terminals can lead to an improvement in the overall efficiency of the port processes. This paper presents a mixed-integer linear programming formulation to determine these optimal truck routes and schedules. The model considers a port with an external parking lot, multiple gates, internal roadways, and docks. A rolling horizon heuristic is also developed for the solution of instances where the model is otherwise intractable. The developed methods are evaluated on instances simulated based on real-world data.
In this paper, we propose a novel model that is based on a hybrid paradigm composed of a graph convolution network and an integerprogramming solver. The model utilizes the potential of graph neural networks, which ha...
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In this paper, we propose a novel model that is based on a hybrid paradigm composed of a graph convolution network and an integerprogramming solver. The model utilizes the potential of graph neural networks, which have the ability to capture complex relationships and preferences among nodes. While the graph neural network forms node embeddings that are fed as input into the next layer of the model, the introduced MILP solver works to solve the team formation problem. Finally, our experimental work shows that the outcome of the model is balanced teams.
We study a situation where a large number of people visit a popular venue (e.g., an art gallery, a mall, a theme park, or an exhibition) where points of interest are located (e.g., paintings, shops, attractions, or pa...
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We study a situation where a large number of people visit a popular venue (e.g., an art gallery, a mall, a theme park, or an exhibition) where points of interest are located (e.g., paintings, shops, attractions, or pavilions). Visitors have a maximum amount of time available for the overall experience. The points of interest have a limiting capacity and, whenever the turnout of visitors exceeds such capacity, queues and service disruptions occur. Given the maximum time available along with the time spent queuing, a selection of the points of interest may become necessary. Visitors usually act as autonomous decision-makers and do not take into account their interaction with other visitors. This leads to remarkable inefficiencies that could be, to a certain extent, overcome through a coordination of the paths and schedules of the different visitors. The resulting optimization problem is modeled as a mixed-integerlinear Program (MILP), where the goal is to minimize a weighted combination of the points of interest not selected and the time spent queuing. Computational results show the benefits that can be achieved by using the model proposed as a tool to support decision-making.
This study presents a discrete-time mixed-integer linear programming (MILP) model to optimize long-term maintenance turnaround scheduling in an oil refinery focused on fuel production. Refineries are complex networks ...
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This study presents a discrete-time mixed-integer linear programming (MILP) model to optimize long-term maintenance turnaround scheduling in an oil refinery focused on fuel production. Refineries are complex networks of integrated process units, and maintenance turnarounds, involving temporary shutdowns for inspection and repair, can significantly disrupt production and reduce revenues. The MILP model aims to minimize these disruptions by optimizing turnaround schedules while maintaining product supply and maximizing economic performance. The model incorporates flow, labor, resource, and planning constraints, allowing for different unit groupings and scenario simulations. Key outputs include the maintenance schedule, unit utilization rates, intermediate stock levels, production, manpower, and maintenance costs. The model serves as a decision-support tool for refining managers, enabling them to plan maintenance interventions that maximize operating profit while adhering to operational constraints.
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