In this paper, we deal with the unrestricted block relocation problem. We present a new integerprogramming formulation for solving the problem. The initial formulation is improved by tighteningconstraints and a pre-pr...
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In this paper, we deal with the unrestricted block relocation problem. We present a new integerprogramming formulation for solving the problem. The initial formulation is improved by tighteningconstraints and a pre-processing step to fix several variables. We design a exact iterativescheme algorithm based on a fast heuristic for the integer programming formulation (ISA-FH).Computational results show the effectiveness of the improved formulation and algorithm.
Two models and a heuristic algorithm to address the wind farm layout optimization problem are presented. The models are linear integer programming formulations where candidate locations of wind turbines are described ...
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Two models and a heuristic algorithm to address the wind farm layout optimization problem are presented. The models are linear integer programming formulations where candidate locations of wind turbines are described by binary variables. One formulation considers an approximation of the power curve by means of a stepwise constant function. The other model is based on a power-curve-free model where minimization of a measure closely related to total wind speed deficit is optimized. A special-purpose neighborhood search heuristic wraps these formulations with increasing tractability and effectiveness compared to the full model that is not contained in the heuristic. The heuristic iteratively searches for neighborhoods around the incumbent using a branch-and-cut algorithm. The number of candidate locations and neighborhood sizes are adjusted adaptively. Numerical results on a set of publicly available benchmark problems indicate that a proxy for total wind speed deficit as an objective is a functional approach, since high-quality solutions of the metric of annual energy production are obtained when using the latter function as an substitute objective. Furthermore, the proposed heuristic is able to provide good results compared to a large set of distinctive approaches that consider the turbine positions as continuous variables.
cleaning services industry in Malaysia faces significant challenges in effectively managing its workforce. Workforce planning, a critical procedure that aligns employee skills with suitable positions at the right time...
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cleaning services industry in Malaysia faces significant challenges in effectively managing its workforce. Workforce planning, a critical procedure that aligns employee skills with suitable positions at the right time, is becoming increasingly essential across various organizations, including postal delivery and cleaning services. However, the absence of proper workforce planning from management teams has emerged as a primary concern in this sector. This study identifies an opportunity to improve the workforce planning in the cleaning industry by employing an optimization approach that aims to minimize hiring costs. The main objective of this study is to minimize hiring costs in cleaning services operations at a public university in Malaysia. To achieve this, an optimization model based on integer programming was proposed to represent the current situation. Data collection involved interviews and company reports for the purpose of understanding the current conditions comprehensively. Factors influencing hiring costs were meticulously selected, considering the organization's specific situation. Model evaluation was conducted through whatif analysis, which allowed the evaluation of solutions provided by the modified models in three what-if scenarios. The findings indicated that the proposed modified model could assist organizations in improving the workforce planning by optimizing the allocation of resources, reducing hiring costs, and enhancing cleaner performance. This study offers valuable insights for the management of cleaning services, paving the way for more effective and efficient workforce planning practices in the industry.
A crucial role of container shipping is maximizing container uptake onto vessels, optimizing the efficiency of a fundamental part of the global supply chain. In practice, liner shipping companies include block stowage...
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ISBN:
(纸本)9783031719929;9783031719936
A crucial role of container shipping is maximizing container uptake onto vessels, optimizing the efficiency of a fundamental part of the global supply chain. In practice, liner shipping companies include block stowage patterns that ensure that containers in above and below deck partitions of bays have the same destination. Despite preventing restows, increasing free space, and benefits for crane makespan and hydrostatics, this practical planning requirement is rarely included in stowage optimization models. In our paper, we introduce a novel 0-1 IP model that searches in the space of valid paired block stowage patterns, named template planning, which ensures sufficient vessel capacity and limits to crane makespan, trim, and bending moment. Our results show that template planning outperforms traditional allocation planning concerning optimality and runtime efficiency while preserving a sufficiently accurate representation of master planning constraints and objectives.
Prevailing dengue-control strategies in many developing countries yield only limited benefits due to non-optimality of those strategies. In this paper, we demonstrate how the same strategies could be altered using the...
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Prevailing dengue-control strategies in many developing countries yield only limited benefits due to non-optimality of those strategies. In this paper, we demonstrate how the same strategies could be altered using the same amount of resources in order to yield more fruitful results. Accordingly, we develop a binary integer programming model, aimed at minimising the total number of susceptible individuals with high-risk of being infected with dengue, by identifying the most influential dengue-infected individuals who could undergo an epidemiological isolation, subject to the conditions imposed by the topological properties of the epidemiological network and budgetary constraints. Further, we analyse the proposed epidemiological isolation to examine its adequacy in a real-world implementation.
Wireless Sensor Networks (WSNs) are systems with great potential for applications in the most diverse areas such as industry, security, public health, and agriculture. In general, for a WSN to achieve high performance...
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ISBN:
(纸本)9783031232350;9783031232367
Wireless Sensor Networks (WSNs) are systems with great potential for applications in the most diverse areas such as industry, security, public health, and agriculture. In general, for a WSN to achieve high performance, multiple criteria must be considered, such as coverage area, connectivity, and energy consumption. In this work an integer programming (IP) model to solve a Sensor Allocation Problem (SAP) is presented. The IP model considers a heterogeneous WSN and deterministic locations to positioning of sensors. The proposed model was validated using the IBM ILOG CPLEX solver. A several computational experiments were performed, and an analysis through small and mediumsized instances of the problem under study are presented and discussed. The proposed model presents good results given the problem premises, constraints and considered objectives, achieving 0.0099% optimality gap for the best scenarios where networks are fully connected and are feasible to implement. Other suboptimal evaluated scenarios with denser distribution of sensor nodes depict about 0.04% of isolated node positioning, spite maintaining overall balance between energy consumption and coverage. Therefore, the proposed model shows promise for achieving practical solutions, i.e., those with implementation feasibility in most considered heterogeneous network scenarios.
Feasible solutions are crucial for integer programming (IP) since they can substantially speed up the solving process. In many applications, similar IP instances often exhibit similar structures and shared solution di...
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ISBN:
(纸本)9798400704901
Feasible solutions are crucial for integer programming (IP) since they can substantially speed up the solving process. In many applications, similar IP instances often exhibit similar structures and shared solution distributions, which can be potentially modeled by deep learning methods. Unfortunately, existing deep-learning-based algorithms, such as Neural Diving [21] and Predict-and-search framework [8], are limited to generating only partial feasible solutions, and they must rely on solvers like SCIP and Gurobi to complete the solutions for a given IP problem. In this paper, we propose a novel framework that generates complete feasible solutions end-to-end. Our framework leverages contrastive learning to characterize the relationship between IP instances and solutions, and learns latent embeddings for both IP instances and their solutions. Further, the framework employs diffusion models to learn the distribution of solution embeddings conditioned on IP representations, with a dedicated guided sampling strategy that accounts for both constraints and objectives. We empirically evaluate our framework on four typical datasets of IP problems, and show that it effectively generates complete feasible solutions with a high probability (> 89.7 %) without the reliance of Solvers and the quality of solutions is comparable to the best heuristic solutions from Gurobi. Furthermore, by integrating our method's sampled partial solutions with the CompleteSol heuristic from SCIP [19], the resulting feasible solutions outperform those from state-of-the-art methods across all datasets, exhibiting a 3.7 to 33.7% improvement in the gap to optimal values, and maintaining a feasible ratio of over 99.7% for all datasets.
To address challenges inherent in the scheduling of public bus transportation, such as disparities in peak and off-peak operational demands, amalgamated single and double shift operations, this study endeavors to mode...
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ISBN:
(纸本)9798350389913;9798350389906
To address challenges inherent in the scheduling of public bus transportation, such as disparities in peak and off-peak operational demands, amalgamated single and double shift operations, this study endeavors to model and optimize the daily operational schedules for bus routes, grounded in empirical data reflecting actual operational requirements. The formulation of the bus scheduling problem entails the integration of various parameters including planned trip quantities, peak-hour road conditions, station dwell durations, inter-departure intervals, and driver shift change dynamics. In the optimization process, a hybrid algorithm, combining Genetic Algorithm (GA) and Tabu Search (TS), is proposed. GA serves as a heuristic for a global exploration of the solution space, while TS is for a detailed exploration of local regions. Leveraging the operational parameters of actual bus schedules in Nanjing, the proposed hybrid algorithm is applied to refine the scheduling plan for a specific bus route. The actual scheduling results demonstrate that, in comparison to stand-alone implementations of GA, greedy algorithms, and manually crafted schedules, the hybrid GA-Tabu algorithm yields a noteworthy 7.88 percent improvement in the utilization rate of working hours. Furthermore, the departure frequency seamlessly adapts to peak periods, aligning with passenger demand patterns and augmenting the overall system efficiency. The proposed hybrid GA-Tabu algorithm proves efficacious in enhancing system efficiency, catering to passenger demands, and ensuring compliance with driver work hour regulations. Besides, its applicability showcases a degree of generality within the realm of public bus transportation scheduling.
The Geometric Buchberger algorithm is a specialization of the classical Buchberger algorithm to the case of toric ideals, with applications in the solution of families of integer programming problems. In this paper, w...
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ISBN:
(纸本)9798400706967
The Geometric Buchberger algorithm is a specialization of the classical Buchberger algorithm to the case of toric ideals, with applications in the solution of families of integer programming problems. In this paper, we investigate the performance of the Geometric Buchberger algorithm in a variety of combinatorial optimization problems with both integer and binary variables, introducing algorithmic improvements for the latter case in the form of an efficient criterion for detecting S-binomials that can be truncated away. Our experiments show that our implementation, available in the package ***, computes Grobner bases for toric ideals of combinatorial optimization problems more efficiently than the previous state-of-the-art.
Over the years, countries have been exploring options to solve the current global energy crisis. It hinders economic development due to a lack of proper energy management infrastructure and higher energy consumption, ...
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ISBN:
(纸本)9798350371345
Over the years, countries have been exploring options to solve the current global energy crisis. It hinders economic development due to a lack of proper energy management infrastructure and higher energy consumption, resulting in an imbalance between demand and supply. Thus, this paper presents the application of integer programming in home energy management to develop an integer programming model that monitors and controls energy consumption trends in real-time while considering available energy units balance based on which devices are turned on at that particular time. The energy consumption optimization problem was formulated by considering prepaid billing consumers who can only switch their devices as per limited available energy at any given time. The devices were controlled with the help of a mobile application, and real-time power consumption was obtained from the deployed sensors on our system. The results confirm that the proposed model can predict the amount of time it takes for the available electricity balance to run out based on energy consumed by powered-on devices. This interval gets dynamically updated in real-time and also considers device status change. Hence, this will help consumers to make data-driven decisions on energy usage allocation and device activation prioritization while considering the availability of remaining energy units as one of the constraints leading to improved energy usage efficiency and sustainability as well as cost savings.
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