Antarctica's unique research environment necessitates innovative project selection and scheduling strategies to maximize scientific output while addressing logistical, environmental, and resource constraints. This...
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Antarctica's unique research environment necessitates innovative project selection and scheduling strategies to maximize scientific output while addressing logistical, environmental, and resource constraints. This study introduces a novel framework for the Research Project Selection and Scheduling in Multiple Antarctic Stations Problem (RPSAP), formulated as a mixed-integer programming model. The model incorporates resource-sharing constraints, station-specific capacities, transportation delays, and sustainability considerations. Given the problem's NP-hard complexity, three metaheuristic methods-Iterated Local Search (ILS), Variable Neighborhood Search (VNS), and Simulated Annealing (SA)-were developed to efficiently solve large-scale instances. Metaheuristics demonstrated robust performance through extensive computational experiments involving 480 test instances across 60 classes. The ILS consistently outperformed others in solution quality and scalability, while SA offered competitive results in execution time. Results reveal that the metaheuristics are superior to exact methods in handling large problem sizes, with optimality achieved only for small instances using the proposed exact model. This research bridges the gap between theoretical optimization models and practical applications, offering decision-making tools for research managers to enhance resource utilization, minimize ecological impacts, and prioritize high-impact projects.
Around the world, efforts are currently underway to implement various decarbonization strategies to meet net-zero emissions objectives. This includes carbon capture and storage (CCS), which involves capturing CO(2 )at...
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Around the world, efforts are currently underway to implement various decarbonization strategies to meet net-zero emissions objectives. This includes carbon capture and storage (CCS), which involves capturing CO(2 )at emitter sites, and transporting it to geological reservoirs, where it is to be injected underground for long-term storage. In this work, we focus on the multi-period strategic planning of a CCS value chain involving pipeline CO2 transportation. From an Operations Research standpoint, this problem exhibits the characteristics of combined facility location and network design. To account for multiple scenarios of input parameters ( e.g. market and geological variability), this problem has to be solved hundreds or thousands of times. Thus, reaching high-quality solutions quickly is crucial. As commercial solvers struggle to provide high quality solutions under these time constraints, we propose a slope scaling heuristic based on previous work on single-period CCS planning and network design. This new heuristic approximates the cost of design variables, generates upper bounds via dynamic programming, uses a long-term memory search strategy, and includes a final improvement phase where a restricted model is solved. Computational experiments show that the proposed heuristic generates better solutions than CPLEX for most instances considered, at a fraction of the computational time.
Disassembly lines are an effective means for the large-scale, industrialized recycling of end-of-life products. Among these, U-shaped disassembly lines are particularly noted for their combination of flexibility and p...
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Disassembly lines are an effective means for the large-scale, industrialized recycling of end-of-life products. Among these, U-shaped disassembly lines are particularly noted for their combination of flexibility and production efficiency. This study addresses the U-shaped disassembly line balancing problem, considering the coexistence of separate stations and spatial limitations within workstations. A mixed-integer nonlinear programming model and a constraint programming model are developed to accurately capture this complex problem. Additionally, a novel hybrid constraint programming with a goal-driven cross-entropy optimization algorithm (CP-GDCE) is introduced. This algorithm combines a multi-objective cross-entropy grouping framework, a constraint programming-based heuristic initialization, a multi-point crossover recombination mechanism, and large neighborhood search techniques, significantly enhancing solution efficiency and accuracy. Extensive benchmarking and experimental validation indicate that the CP-GDCE not only excels in addressing the specific problem of this study but also demonstrates superiority in classic disassembly line balancing issues. In 21 test cases, the CP-GDCE achieved superior hypervolume and inverted generational distance values compared to 11 benchmark algorithms. A practical application using a printer disassembly example shows that the proposed U-shaped configuration is highly flexible and efficient, compatible with both traditional U-shaped and straight disassembly lines. This configuration significantly reduces the total length of the disassembly line, improving space utilization and highlighting its practical potential and advantages.
The emergence of new display devices,such as organic light-emitting diodes,has brought about numerous advantages,including high material utilization,low cost,and high *** devices are manufactured using inkjet printing...
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The emergence of new display devices,such as organic light-emitting diodes,has brought about numerous advantages,including high material utilization,low cost,and high *** devices are manufactured using inkjet printing and possess the potential to become a key technology for display ***,a challenge in achieving this is the display effect that reveals uneven brightness and darkness,which can be avoided by controlling the volume of ink solution in a pixel to within 5%.Currently,the volume difference among the nozzles of commercial printheads does not meet the requirements for volume uniformity,thus challenging the printing ***,designing a suitable printing method that allows for the fusion of different volumes of ink droplets,ultimately reducing the error of the post fusion process,is *** this study,we propose a print display droplet fusion scheduling method comprising two main ***,we use a dichotomous trust domain algorithm to obtain a feasible range of printhead docking point spacings for different nozzle and pixel panel ***,we model the printing process as a droplet fusion scheduling model based on mixedintegerprogramming,with the optimization objective of achieving intra pixel volume uniformity via ensuring the volume uniformity of ink droplets within all *** verified this method through numerical simulations and printing experiments using 394 pixels per inch(ppi)pixel panels and successfully reduced the volume uniformity error among pixels to within 5%.
Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency *** of the primary challenges accompanying with Multi-Beam Satellites(MBS)is an efficient Dynamic Resource Allo...
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Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency *** of the primary challenges accompanying with Multi-Beam Satellites(MBS)is an efficient Dynamic Resource Allocation(DRA)*** paper presents a learning-based Hybrid-Action Deep Q-Network(HADQN)algorithm to address the sequential decision-making optimization problem in *** using a parameterized hybrid action space,HADQN makes it possible to schedule the beam pattern and allocate transmitter power more *** pursue multiple long-term QoS requirements,HADQN adopts a multi-objective optimization method to decrease system transmission delay,loss ratio of data packets and power consumption load *** results demonstrate that the proposed HADQN algorithm is feasible and greatly reduces in-orbit energy consumption without compromising QoS performance.
We present an approach for measuring the vulnerability of a wireless network. Our metric, n-Robustness, measures the change in a network's total signal strength resulting from the optimal placement of n jammers by...
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We present an approach for measuring the vulnerability of a wireless network. Our metric, n-Robustness, measures the change in a network's total signal strength resulting from the optimal placement of n jammers by an attacker. Toward this end, we develop a multi-period mixed-integer programming interdiction model that determines the movement of n jammers over a time horizon so as to minimize the total signal strength of users during a sustained jamming attack. We compared several solution approaches for solving our model including a Lagrangian relaxation heuristic, a genetic algorithm, and a stage decomposition heuristic. We tested our approach on a wireless trace dataset developed as part of the Wireless Topology Discovery project at the University of California San Diego. We found that the Lagrangian approach, which performed best overall, finds a close-to-optimal solution while requiring much less time than solving the MIP directly. We then illustrate the behavior of our model on a small example taken from the dataset as well as a set of experiments. Through our experiments we conclude that the total signal power follows a sigmoid curve as we increase the number of jammers and access points. We also found that increasing access points only improves network robustness initially;after that the benefit levels off. In addition, we found that the problem instances we considered have an n-Robustness of between 39 and 69%, indicating that the value of the model parameters (e.g., number of jammers, number of time periods) has an effect on robustness. (C) 2017 Elsevier Ltd. All rights reserved.
In this paper, a real maintenance workforce-constrained scheduling problem is formulated as a bi-objective mixed-integer programming model with the aim of simultaneously minimizing the workforce requirements and maxim...
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In this paper, a real maintenance workforce-constrained scheduling problem is formulated as a bi-objective mixed-integer programming model with the aim of simultaneously minimizing the workforce requirements and maximizing the equipment availability. The skilled workforce is provided by internal and external resources using regular time, overtime and contracting. The equipment availability is measured by the downtime required for preventive maintenance (scheduled) and failure repair (unscheduled) jobs. We also encounter imminent or potential failures whose priorities depend on the severity of the failure on the system (secondary failure). The total weighted flow time is used as a scheduling criterion to measure the equipment availability;the weight of each job directly depends on the expected downtime resulting from the associated failure. The proposed model is verified using two comprehensive numerical examples and some sensitivity analyses. We conclude by discussing the results. Journal of the Operational Research Society (2011) 62, 1005-1018. doi: 10.1057/jors.2010.51 Published online 9 June 2010
This paper presents a mixed-integer programming model for a multi-floor layout design of cellular manufacturing systems (CMSs) in a dynamic environment. A novel aspect of this model is to concurrently determine the ce...
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This paper presents a mixed-integer programming model for a multi-floor layout design of cellular manufacturing systems (CMSs) in a dynamic environment. A novel aspect of this model is to concurrently determine the cell formation (CF) and group layout (GL) as the interrelated decisions involved in the design of a CMS in order to achieve an optimal (or near-optimal) design solution for a multi-floor factory in a multi-period planning horizon. Other design aspects are to design a multi-floor layout to form cells in different floors, a multi-rows layout of equal area facilities in each cell, flexible reconfigurations of cells during successive periods, distance-based material handling cost, and machine depot keeping idle machines. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. The objective is to minimize the total costs of intra-cell, inter-cell, and inter-floor material handling, purchasing machines, machine processing, machine overhead, and machine relocation. Two numerical examples are solved by the CPLEX software to verify the performance of the presented model and illustrate the model features. Since this model belongs to NP-hard class, an efficient genetic algorithm (GA) with a matrix-based chromosome structure is proposed to derive near-optimal solutions. To verify its computational efficiency in comparison to the CPLEX software, several test problems with different sizes and settings are implemented. The efficiency of the proposed GA in terms of the objective function value and computational time is proved by the obtained results. (C) 2013 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
In this paper, we propose a two-phase approach to solve a combined routing and scheduling problem that occurs in the textile industry: fabrics are dyed by dye-jets and transported by forklifts. The objective is to min...
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In this paper, we propose a two-phase approach to solve a combined routing and scheduling problem that occurs in the textile industry: fabrics are dyed by dye-jets and transported by forklifts. The objective is to minimize the cost of the unproductive activities, i.e., the dye-jet setup times and the forklift waiting time. The first phase solves an integer linear program to assign jobs (fabrics) to dye-jets while minimizing the setup cost;we compare an arc-based and a path-based formulation. The second phase uses a mixed-integer linear program for the dye-jet scheduling and both the routing and scheduling of forklifts. Experiments are performed on real data provided by a major multinational company, and larger test problems are randomly generated to assess the algorithm. The tests were conducted using Cplex 12.6.0 and a column generation solver. The numerical results show that our approach is efficient in terms of both solution quality and computational time.
We consider the problem of decomposing Intensity Modulated Radiation Therapy (IMRT) fluence maps using rectangular apertures. A fluence map can be represented as an integer matrix, which denotes the intensity profile ...
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We consider the problem of decomposing Intensity Modulated Radiation Therapy (IMRT) fluence maps using rectangular apertures. A fluence map can be represented as an integer matrix, which denotes the intensity profile to be delivered to a patient through a given beam angle. We consider IMRT treatment machinery that can form rectangular apertures using conventional jaws, and hence, do not need sophisticated multi-leaf collimator (MLC) devices. The number of,apertures used to deliver the fluence map needs to be minimized in order to treat the patient efficiently. From a mathematical point of view, the problem is equivalent to a minimum cardinality matrix decomposition problem. We propose a combinatorial Benders decomposition approach to solve this problem to optimality. We demonstrate the efficacy of our approach on a set of test instances derived from actual clinical data. We also compare our results with the literature and solutions obtained by solving a mixed-integer programming formulation of the problem. (C) 2011 Elsevier Ltd. All rights reserved.
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