The traditional aim in transportation planning is to maximise gains associated with vehicular travel distances or times, indirectly prioritising populations that live near existing or proposed roads-remote populations...
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The traditional aim in transportation planning is to maximise gains associated with vehicular travel distances or times, indirectly prioritising populations that live near existing or proposed roads-remote populations that first require hours of walking to reach roads are overlooked. In this paper, rural roads optimisation is performed using a new model that estimates proposed roads' accessibility gains, considering reductions in vehicular travel time and reductions in walking time required by remote populations to reach them. This ensures that even the most remote populations that may benefit from new roads are included in their evaluation. When presented with a large number of proposed roads and the requirement of determining a plan within a suitable budget, it is often infeasible to construct all proposed roads. In such instances, subsets of well-performing road-combinations that are evaluated with respect to multiple objectives need to be identified for analysis and comparison-for which multi-objective optimisation approaches can be employed. Traditional optimisation approaches return a small number of road-combination plans only, limited to user-specified budget levels and objective weight sets. This paper presents an innovative heuristic solution approach that overcomes such limitations by returning thousands of well-performing solutions scattered across a budget span, and not limited in number to user-specified objective weight sets at fixed budget levels. The heuristic is employed along with a more traditional weighted-sum integer-linear programming approach to determine high-quality road-combination plans selected from 92 roads recently proposed for construction in Nepal's remote Karnali province. Using these two approaches with inputs from the new multi-modal accessibility model, it is illustrated how rural roads planning can be performed to the benefit of rural populations regardless of their proximity to roads. New planning and analysis benefits of the h
This letter details two novel algorithms for computing asset allocations given dynamic requests for support. Using spatial and temporal discretization, the first algorithm casts the allocation problem as a lexicograph...
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This letter details two novel algorithms for computing asset allocations given dynamic requests for support. Using spatial and temporal discretization, the first algorithm casts the allocation problem as a lexicographic integer-linear program (ILP) that is efficiently solved using an ILP solver. Improving computational efficiency, the second algorithm replaces the ILP solver with a novel dual-tactic linear program solver. Provided proofs, complexity analyses, and numerical analyses demonstrate the computational efficiency and convergence of both algorithms to performant solutions. Discussion of application to broad domains such as defense, conservation, and resource management for businesses is provided.
This paper focuses on a dynamic embedding of client-constrained heterogeneous Virtual Network (VN) requests with multiple nodes and links affinity and anti-affinity requirements. For this Virtual Network Embedding (VN...
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ISBN:
(纸本)9798350300741;9798350300734
This paper focuses on a dynamic embedding of client-constrained heterogeneous Virtual Network (VN) requests with multiple nodes and links affinity and anti-affinity requirements. For this Virtual Network Embedding (VNE) problem, we formulate an integer-linear programming (ILP)-based model that achieves joint mapping of virtual nodes and links of each VN onto the dynamically updated Substrate Network (SN). This model not only meets the clients expressed isolation constraints but also includes VN request arrivals and departures to update SN information. Numerical experiments illustrate the efficiency of the proposed methods and their ability to find optimal solutions. Performance reports provide cloud service providers with insights into additional investments in nodes and links they should make to serve clients with anti-affinity requirements.
Most knowledge-intensive industries, especially companies developing software engineering projects such as Enterprise Resource Planning (ERP) implementation projects, generally necessitate finding the optimal trade-of...
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Most knowledge-intensive industries, especially companies developing software engineering projects such as Enterprise Resource Planning (ERP) implementation projects, generally necessitate finding the optimal trade-off between the project duration and total usage cost of the renewable resource costs (e.g., human resource expertise costs). Therefore, the MRC-DTCTP, which integrates classical multi-mode resource-constrained project scheduling (MRCPSP) and discrete time-cost trade-off problems (DTCTP), can be seen as a more applicable problem since it better reflects the objectives and requirements of today's real-life software project applications. The MRC-DTCTP is a much more complex variant of the MRCPSP since it aims to minimize total direct/indirect costs of the resources simultaneously under a pre-specified project deadline. Based on this motivation, a new explicit integer-linear programming (ILP) model of the MRC-DTCTP was first developed based on the implicit non-linearprogramming model of Wuliang and Chengen (2009). Due to its NP-hard nature, we also proposed a constraint programming (CP) model that includes several search strategies to solve large-sized problem instances within reasonable computation time. In addition, a genetic algorithm (GA) approach in combination with a Modified Serial Schedule Generation scheme (SSGS) is implemented to make further comparisons on several benchmark instances, which are generated based on the existing MRCPSP data sets taken from the project scheduling problem library (PSPLIB) by considering additional problem characteristics. A comprehensive experimental study has shown that the proposed CP model and GA approach can provide superior results in shorter run times for large-sized benchmark instances. Finally, an international Enterprise Resource Planning (ERP) Software Company's real-life application is presented. The ERP projects generally necessitate finding the optimal trade-off between project makespan and human resource
The possibilities of enabling travelers to walk a short distance to reach their prescribed pickup locations or final destinations in ridesharing services have received a lot of attention in recent years. This thesis c...
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The possibilities of enabling travelers to walk a short distance to reach their prescribed pickup locations or final destinations in ridesharing services have received a lot of attention in recent years. This thesis classifies the existing literature on ridesharing with walking trip legs into three different classes. Additionally, this thesis presents a variant of the single-vehicle pickup and delivery problem with time windows (PDPTW), called the pickup and delivery problem with time windows and walking legs (PDPTWWL), that explicitly incorporates the access and egress walking trips of travelers into the decision space. The proposed model targets a ridesharing service that can pool together two to three requests, and it is solved to optimality using a commercial mixed integerprogramming (MIP) solver. The candidate pickup/drop-off (PUDO) locations for each request are either the road intersections or street centerline midpoints within a pre-defined maximum allowable walking time. The study applies the model to two road network datasets—Isla Vista and Chicago downtown—reflecting different real-world application scenarios, and it presents various performance metrics based on a large number of randomly generated 2-request, 3-request, and 4-requesst PDPTWWL instances. The results indicate that a relatively short allowable walking time has a comparatively high vehicle driving time reduction per second of walking (DTRPSW) relative to a long allowable walking time. Moreover, pooling a larger number of requests (e.g., four requests) in one vehicle can lead to a much larger reduction in vehicle driving time and a higher DTRPSW, compared to pooling only two requests in one vehicle.
Embedded multi-core systems are increasingly in use. As established single-core design methodologies are often not applicable out of the box, novel design-time optimization methods are required in order to manage real...
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ISBN:
(纸本)9781728148823
Embedded multi-core systems are increasingly in use. As established single-core design methodologies are often not applicable out of the box, novel design-time optimization methods are required in order to manage real-time characteristics, predictability, or tight constraints with respect to energy consumption or system performance. With focus on the memory subsystem in a multi-core embedded system, this paper proposes an optimization workflow for the application-specific optimal binding of code and data to memory instances, efficient handling and scheduling of available memory low-power modes, and the automated and transparent integration of these optimization results on the software level. Presented optimization algorithms are realized as integerlinear programs;code modification and generation are implemented on the basis of LLVM. Experimental results for an ARM-based quad-core platform with SRAM memory subsystem, consisting of core-local scratchpad memories and global shared memory, prove the efficiency of our method in terms of energy consumption when compared to a system using direct-mapped caches, but also in comparison with a state-of-the-art scratchpad mapping heuristic.
Current compilers lack precise timing models guiding their built-in optimizations. Hence, compilers apply ad-hoc heuristics during optimization to improve code quality. One of the most important optimizations is regis...
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ISBN:
(纸本)9780769544427
Current compilers lack precise timing models guiding their built-in optimizations. Hence, compilers apply ad-hoc heuristics during optimization to improve code quality. One of the most important optimizations is register allocation. Many compilers heuristically decide when and where to spill a register to memory, without having a clear understanding of the impact of such spill code on a program's runtime. This paper presents an integer-linear programming (ILP) based register allocator that uses precise worst-case execution time (WCET) models. Using this WCET timing data, the compiler avoids spill code generation along the critical path defining a program's WCET. To the best of our knowledge, this paper is the first one to present a WCET-aware ILP-based register allocator. Our results underline the effectiveness of the proposed techniques. For a total of 55 realistic benchmarks, we reduced WCETs by 20.2% on average and ACETs by 14%, compared to a standard graph coloring allocator. Furthermore, our ILP-based register allocator outperforms a WCET-aware graph coloring allocator by more than a factor of two for the considered benchmarks, while requiring less runtime.
Optimization of leakage power is essential for nanoscale CMOS (nano-CMOS) technology based integrated circuits for numerous reasons, including improving battery life of the system in which they are used as well as enh...
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Optimization of leakage power is essential for nanoscale CMOS (nano-CMOS) technology based integrated circuits for numerous reasons, including improving battery life of the system in which they are used as well as enhancing reliability. Leakage optimization at an early stage of the design cycle such as the register-transfer level (RTL) or architectural level provides more degrees of freedom to design engineers and ensures that the design is optimized at higher levels before proceeding to the next and more detailed phases of the design cycle. In this paper, an RTL optimization approach is presented that targets leakage-power optimization while performing simultaneous scheduling, allocation and binding. The optimization approach uses a nature-inspired firefly algorithm so that large digital integrated circuits can be effectively handled without convergence issues. The firefly algorithm optimizes the cost of leakage delay product (LDP) under various resource constraints. As a specific example, gate-oxide leakage is optimized using a 45 nm CMOS dual-oxide based pre-characterized datapath library. Experimental results over various architectural level benchmark integrated circuits show that average leakage optimization of 90% can be obtained. For a comparative perspective, an integerlinearprogramming (ILP) based algorithm is also presented and it is observed that the firefly algorithm is as accurate as ILP while converging much faster. To the best of the authors' knowledge, this is the first ever paper that applies firefly based algorithms for RTL optimization. (C) 2015 Elsevier B.V. All rights reserved.
A key problem in post-silicon validation is to identify a small set of traceable signals that are effective for debug during silicon execution. Most signal selection techniques rely on a metric based on circuit struct...
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ISBN:
(纸本)9781479939466
A key problem in post-silicon validation is to identify a small set of traceable signals that are effective for debug during silicon execution. Most signal selection techniques rely on a metric based on circuit structure. Simulation-based signal selection is promising but have major drawbacks in computation overhead and restoration quality. In this paper, we propose an efficient simulation-based signal selection technique to address these bottlenecks. Our approach uses (1) bounded mock simulations to determine state restoration effectiveness, and (2) an ILP-based algorithm for refining selected signals over different simulation runs. Experimental results demonstrate that our algorithm can provide significantly better restoration ratio (up to 515%, 51% on average) compared to the state-of-the-art techniques.
In urban emergency evacuation, a potentially large number of evacuees may depend either on transit or other modes, or need to walk a long distance, to access their passenger cars. In the process of approaching the des...
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In urban emergency evacuation, a potentially large number of evacuees may depend either on transit or other modes, or need to walk a long distance, to access their passenger cars. In the process of approaching the designated pick-up points or parking areas for evacuation, the massive number of pedestrians may cause tremendous burden to vehicles in the roadway network. Responsible agencies often need to contend with congestion incurred by massive vehicles emanating from parking garages, evacuation buses generated from bus stops, and the conflicts between evacuees and vehicles at intersections. Hence, an effective plan for such evacuation needs to concurrently address both the multi-modal traffic route assignment and the optimization of network signal controls for mixed traffic flows. This paper presents an integrated model to produce the optimal distribution of vehicle and pedestrian flows, and the responsive network signal plan for massive mixed pedestrian-vehicle flows within the evacuation zone. The proposed model features its effectiveness in accounting for multiple types of evacuation vehicles, the interdependent relations between pedestrian and vehicle flows via some conversion locations, and the inevitable conflicts between intersection turning vehicle and pedestrian flows. An illustrating example concerning an evacuation around the M&T stadium area has been presented, and the results indicate the promising properties of our proposed model, especially on reflecting the complex interactions between vehicle and pedestrian flows and the favorable use of high-occupancy vehicles for evacuation operations. (C) 2014 Elsevier Ltd. All rights reserved.
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