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Designing a large neighborhood search method to solve a multi-processor avionics scheduling problem

This thesis introduces a Large Neighborhood Search (LNS) method to solve a multi-processor avionics scheduling problem. In a typical scheduling problem, tasks are scheduled with exact starting times. In this thesis however, tasks will instead be assigned to disjoint time segments, called buckets. For an assignment to be feasible, precedence relations and capacity constraints related to network and computing resources need to be fulfilled. The introduced LNS method relies on solving Mixed-Integer Programming (MIP)-models. To make progress in the search for a feasible assignment, we construct a MIP-model that allows violation of the problem constraints at a cost of increased objective value. The LNS method uses two operators, a destroy operator that chooses a set of tasks that are allowed to change buckets, and a repair operator that through solving the MIP-model creates a new schedule. This thesis develops 11 types of destroy operators and 30 (concrete) variants of them. The MIP-based LNS is evaluated on a set of 60 instances with up to 84 000 tasks and 21 processors. The instances belongs to six categories of varying difficulty. The MIP-based LNS solves 50 instances within our time limit, and the largest instance solved has 77 757 tasks. This is significantly better than solving the complete MIP-model in a single step. With this approach only 36 instances can be solved within our time limit and the largest instance solved has 48554 tasks.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-177878
Date January 2021
CreatorsSvensson, Jesper
PublisherLinköpings universitet, Tillämpad matematik, Linköpings universitet, Tekniska fakulteten
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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