1,720,994 research outputs found
Sub-Tree Scheduling for Wireless Sensor Networks with Partial Coverage (STSWSN-PC) Dataset
No full textThis dataset contains new benchmark instances for the optimization problem introduced by Adasme (2019) under the name Sub-Tree Scheduling for Wireless Sensor Networks with Partial Coverage (STSWSN-PC). The instances are those addressed in Bianchessi (2022), in which a complete description of the procedure used to define them is also reported. For more details see the REAMDE.txt file. References: - Adasme, P. (2019). Optimal sub-tree scheduling for wireless sensor networks with partial coverage. Computer Standards & Interfaces, 61, 20-35. - Bianchessi, N. (2022). On optimally solving sub-tree scheduling for wireless sensor networks with partial coverage (Report link: https://hdl.handle.net/2434/934107). Università degli Studi di Milano. Submitted to Networks (ISSN: 0028-3045) as 'On optimally solving sub-tree scheduling for wireless sensor networks with partial coverage: a branch-and-cut algorithm' - second revision
Optimal interval scheduling with a resource constraint
No full textWe consider a scheduling problem where n jobs have to be carried out by m parallel identical machines. The attributes of a job j are a fixed start time sj, a fixed finish time fj, a resource requirement rj, and a value vj. Every machine owns R units of a renewable resource necessary to carry out jobs. A machine can process more than one job at a time, provided the resource consumption does not exceed R. The jobs must be processed in a non-preemptive way.Within this setting, we ask for a subset of jobs that can be feasibly scheduled with the maximum total value. For this strongly NP-hard problem, we first discuss an approximation result. Then, we propose a column generation scheme for the exact solution. Finally, we suggest some greedy heuristics and a restricted enumeration heuristic. All proposed algorithms are implemented and tested on a large set of randomly generated instances. It turns out that the column generation technique clearly outperforms the direct resolution of a natural compact formulation; the greedy algorithms produce good quality solutions in negligible time, whereas the restricted enumeration averages the performance of the greedy methods and the exact technique
Optimal solutions for routing problems with profits
No full textTechnical Report n. 355, Department of Quantitative Methods, University of Bresci
A Column Generation Approach for the Split Delivery Vehicle Routing Problem
No full textTechnical Report n. 329, Department of Quantitative Methods, University of Bresci
A branch-and-price algorithm for the robust graph coloring problem
No full textGiven a graph G, an integer k, and a cost cuv associated with all pairs uv of non-adjacent vertices in G, the robust graph coloring problem is to assign a color in {1,.,k} to every vertex of G so that no edge has both endpoints with the same color, and the total cost of the pairs of vertices having the same color is minimum. We propose a branch-and-price algorithm for the solution of this problem. The pricing problem consists in finding a stable set of minimum total weight, and we propose both an exact and a heuristic algorithm for its solution. Computational experiments are reported for randomly generated and benchmark graph coloring instances
The split delivery capacitated team orienteering problem
No full textTechnical Report n. 349, Department of Quantitative Methods, University of Bresci
Comparison of policies in dynamic routing problems
No full textTechnical Report n. 310, Department of Quantitative Methods, University of Bresci
An Easy Affordable Statistical and Economic (EASE) approach to avoid unnecessary and expensive exams to monitor patients with small AAA (EASE Score)
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