Puzznic is a tile-matching video game published by Taito in 1989 and ported to many platforms. The player manipulates blocks in a given grid until they match when two or more blocks of the same pattern are adjacent an...
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ISBN:
(纸本)9798331527242;9798331527235
Puzznic is a tile-matching video game published by Taito in 1989 and ported to many platforms. The player manipulates blocks in a given grid until they match when two or more blocks of the same pattern are adjacent and are removed from play. The goal is to match all patterned blocks in the grid. Puzznic is rich in structure: levels have internal platforms and the blocks are affected by gravity, leading to complex state changes and the possibility of a cascaded series of matches following each move by the player. The puzzle is therefore a significant challenge to model, motivating our study. We study Puzznic from both constraint modelling and AI Planning perspectives, identifying their complementary strengths and weaknesses for this problem. We further exploit our constraint model to produce an automated tool for instance generation, parameterised on the grid, the combination of patterned blocks, and the steps required.
Purpose The purpose of this research is to develop a time-cost optimization model to schedule repetitive projects while considering limited resource availability. Design/methodology/approach The model is based on the ...
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Purpose The purpose of this research is to develop a time-cost optimization model to schedule repetitive projects while considering limited resource availability. Design/methodology/approach The model is based on the constraint programming (CP) framework;it integrates multiple scheduling characteristics of repetitive activities such as continuous or fragmented execution, atypical activities and coexistence of different modes in an activity. To improve project performance while avoiding inefficient hiring and firing conditions, the strategy of bidirectional acceleration is presented and implemented, which requires keeping regular changes in the execution modes between successive subactivities in the same activity. Findings Two case studies involving a real residential building construction project and a hotel refurbishing project are used to demonstrate the application of the proposed model based on four different scenarios. The results show that (1) the CP model has great advantages in terms of solving speed and solution quality than its equivalent mathematical model, (2) higher project performance can be obtained compared to using previously developed models and (3) the model can be easily replicated or even modified to enable multicrew implementation. Originality/value The original contribution of this research is presenting a novel CP-based repetitive scheduling optimization model to solve the multimode resource-constrained time-cost tradeoff problem of repetitive projects. The model has the capability of minimizing the project total cost that is composed of direct costs, indirect costs, early completion incentives and late completion penalties.
Driven by the demand to preserve the existing road pavement condition, the issue of selecting maintenance action at the appropriate time under budget limitation has attracted great attention from highway agencies. Thi...
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Driven by the demand to preserve the existing road pavement condition, the issue of selecting maintenance action at the appropriate time under budget limitation has attracted great attention from highway agencies. This study focuses on the strategy of how to manage pavement maintenance budget effectively on road network level based on life cycle cost analysis. The framework of resource-constrained project scheduling problem (RCPSP) is implemented to establish a maintenance action decision-making mechanism for allocating pavement maintenance budgets on the planning and controlling phases. In the RCPSP environment, a two-stage optimization model based on constraint programming techniques is developed to meet two different management goals such as 1. annual budget evaluation from planning points of view, and 2. actual budget adjustment from the controlling point of view, by considering the factor of road usability. Model-I, the life cycle lifespan evaluation model solves the problem of annual budget evaluation to satisfy the maintenance requirements of all road sections. The optimal maintenance plan then can be made to maintain road performance and evaluate annual budget requirement for future years to maximize total maintenance benefits, in terms of the overall maximum pavement lifespan. Based on the suggested results of budget evaluation from Model-I, Model-II, the actual budget adjustment model deals with the actual budget allocation problem of how to keep up the original maintenance budget plan when the actual budget is always awarded insufficiently each year. Finally, the proposed two-stage integrated models provide an optimal maintenance strategy to respond to actual maintenance status and pavement deterioration as feedbacks to the actual budget adjustment model, and recursively make pavement maintenance strategy closer to actual conditions by budget adjustment yearly.
The no-idle permutation flowshop scheduling problem (NIPFSP) extends the well-known permutation flowshop scheduling problem, where idle time is not allowed on the machines. This study proposes a new mixed-integer line...
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The no-idle permutation flowshop scheduling problem (NIPFSP) extends the well-known permutation flowshop scheduling problem, where idle time is not allowed on the machines. This study proposes a new mixed-integer linear programming (MILP) model and a new constraint programming (CP) model for the NIPFSP with makespan criterion. To the best of our knowledge, this study presents a CP model for the NIPFSP for the first time in the literature. We also compare the performance of the proposed MILP and CP models with a well-known MILP model from the literature. Since the studied problem is NP-hard, we also develop a new iterated greedy algorithm with restart and learning mechanisms (IG_RL) and a new iterated local search with restart and learning mechanisms (ILS_RL) as metaheuristics for the problem. In the proposed algorithms, all the parameters are determined by a learning mechanism in a self-adaptive way. Furthermore, a restart mechanism is employed in the proposed IG_RL and ILS_RL algorithms to guarantee the variety of the initial solutions and to assist the algorithm in avoiding the local optima. A variable neighborhood descent procedure is also embedded in the proposed algorithms. We use two well-known benchmark sets, i.e., VRF and Ruiz benchmark suites, to evaluate the performance of proposed solution methods. For almost half of the 240 small VRF instances, optimal results are reported by the MILP and CP models, whereas time-limited model results are reported for the rest. The results on small instances show that the proposed MILP and CP models outperform the MILP model from literature, where the CP model performs better than both MILP models. We compare the performance of the proposed IG_RL and ILS_RL algorithms with the state-of-the-art metaheuristics from the literature on both large VRF instances and Ruiz benchmark instances. The computational results show the effectiveness and superiority of the proposed ILS_RL and IG_RL algorithms for solving the NIPFSP. Primar
Nowadays, constellations of satellites have to deal with heterogeneous and complex observation requests, such as one-shot, video, stereoscopic, and periodic requests. In this paper, we consider the problem of scheduli...
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ISBN:
(纸本)9783031332708;9783031332715
Nowadays, constellations of satellites have to deal with heterogeneous and complex observation requests, such as one-shot, video, stereoscopic, and periodic requests. In this paper, we consider the problem of scheduling these requests in order to maximize a measure of global utility. To solve this problem, we propose two Large Neighborhood Search algorithms that exploit problem decompositions. These algorithms explore large neighborhoods respectively based on heuristic search and constraint programming. The experiments performed on instances generated from real constellation features and weather data show that the approaches improve the state of the art.
We present the Multi-Route Weighted Package Delivery Problem (MRWPDP) and a scalable solution methodology as a major step towards enabling an airspace deconfliction service for drone delivery operations. The problem i...
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ISBN:
(纸本)9798350333572
We present the Multi-Route Weighted Package Delivery Problem (MRWPDP) and a scalable solution methodology as a major step towards enabling an airspace deconfliction service for drone delivery operations. The problem is motivated by Strategic deconfliction under the FAA's "Unmanned Aircraft Systems Traffic Management" Concept of Operations. MRWPDP falls under a class of vehicle routing and scheduling problems, and as such is NP-Hard. In MRWPDP, a graph network is given which consists of depots, drop-off sites, and multiple routes connecting the two. In addition, routes are weighted by the associated ground risk and total travel distance for package delivery. The goal is to optimally schedule the departure time and assign routes to a known set of vehicles at the depot. We propose a heuristic solution to the problem by borrowing techniques from Mixed Integer Linear programming (MILP), constraint programming, and Monte Carlo Tree Search (MCTS). The resulting hybrid framework is MCTS with Bound-and-Prune (BP) and rapid simulated updates (U), or MCTS-BP-U. This approach is able to quickly provide a feasible solution for MRWPDP, even for large problem instances up to 1000 vehicles. We provide a MILP formulation of MRWPDP and compare its performance against MCTS-BP-U in terms of solution quality. An agent-based model simulation is conducted as a final step to validate the efficacy of our approach.
In project scheduling, calendar considerations can increase the duration of a task when its execution overlaps with holidays. On the other hand, the use of overtime may decrease the task's duration. We introduce t...
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This paper describes an implementation of a constraint programming approach to the problem of multi-criteria forest management optimization. The goal is to decide when to harvest each forest unit while striving to opt...
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ISBN:
(纸本)9783031464393;9783031464386
This paper describes an implementation of a constraint programming approach to the problem of multi-criteria forest management optimization. The goal is to decide when to harvest each forest unit while striving to optimize several criteria under spatial restrictions. With a large number of management units, the optimization problem becomes computationally intractable. We propose an approach for deriving a set of efficient solutions for the entire region. The proposed methodology was tested for Vale do Sousa region in the North of Portugal.
Side-channel attacks impose a serious threat to cryptographic algorithms, including widely employed ones, such as AES and RSA. These attacks take advantage of the algorithm implementation in hardware or software to ex...
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ISBN:
(纸本)9798350321920
Side-channel attacks impose a serious threat to cryptographic algorithms, including widely employed ones, such as AES and RSA. These attacks take advantage of the algorithm implementation in hardware or software to extract secret information via side channels. Software masking is a mitigation approach against power side-channel attacks aiming at hiding the secret-revealing dependencies from the power footprint of a vulnerable implementation. However, this type of software mitigation often depends on general-purpose compilers, which do not preserve non-functional properties. Moreover, microarchitectural features, such as the memory bus and register reuse, may also leak secret information. These abstractions are not visible at the high-level implementation of the program. Instead, they are decided at compile time. To remedy these problems, security engineers often sacrifice code efficiency by turning off compiler optimization and/or performing local, post-compilation transformations. This paper proposes Secure by Construction Code Generation (SecCG), a constraint-based compiler approach that generates optimized yet protected against power side channels code. SecCG controls the quality of the mitigated program by efficiently searching the best possible low-level implementation according to a processor cost model. In our experiments with twelve masked cryptographic functions up to 100 lines of code on Mips32 and ARM Thumb, SecCG speeds up the generated code from 77% to 6.6 times compared to non-optimized secure code with an overhead of up to 13% compared to non-secure optimized code at the expense of a high compilation cost. For security and compiler researchers, this paper proposes a formal model to generate power side channel free low-level code. For software engineers, SecCG provides a practical approach to optimize performance critical and vulnerable cryptographic implementations that preserve security properties against power side channels.
A binary constraint satisfaction problem (BCSP) consists in determining an assignment of values to variables that is compatible with a set of constraints. The problem is called binary because the constraints involve o...
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A binary constraint satisfaction problem (BCSP) consists in determining an assignment of values to variables that is compatible with a set of constraints. The problem is called binary because the constraints involve only pairs of variables. The BCSP is a cornerstone problem in constraint programming (CP), appearing in a very wide range of real-world applications. In this work, we develop a new exact algorithm which effectively solves the BCSP by reformulating it as a k-clique problem on the underlying microstructure graph representation. Our new algorithm exploits the cutting-edge branching scheme of the stateof-the-art maximum clique algorithms combined with two filtering phases in which the domains of the variables are reduced. Our filtering phases are based on colouring techniques and on heuristically solving an associated boolean satisfiability (SAT) problem. In addition, the algorithm initialization phase performs a reordering of the microstructure graph vertices that produces an often easier reformulation to solve. We carry out an extensive computational campaign on a benchmark of almost 20 0 0 instances, encompassing numerous real and synthetic problems from the literature. The performance of the new algorithm is compared against four SAT-based solvers and three general purpose CP solvers. Our tests reveal that the new algorithm significantly outperforms all the others in several classes of BCSP instances. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )
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