79 research outputs found
An Efficient Hybrid Ant Colony System for the Generalized Traveling Salesman Problem
The Generalized Traveling Salesman Problem (GTSP) is an extension of the
well-known Traveling Salesman Problem (TSP), where the node set is partitioned
into clusters, and the objective is to find the shortest cycle visiting each
cluster exactly once. In this paper, we present a new hybrid Ant Colony System
(ACS) algorithm for the symmetric GTSP. The proposed algorithm is a
modification of a simple ACS for the TSP improved by an efficient GTSP-specific
local search procedure. Our extensive computational experiments show that the
use of the local search procedure dramatically improves the performance of the
ACS algorithm, making it one of the most successful GTSP metaheuristics to
date.Comment: 7 page
A Memetic Algorithm for the Generalized Traveling Salesman Problem
The generalized traveling salesman problem (GTSP) is an extension of the
well-known traveling salesman problem. In GTSP, we are given a partition of
cities into groups and we are required to find a minimum length tour that
includes exactly one city from each group. The recent studies on this subject
consider different variations of a memetic algorithm approach to the GTSP. The
aim of this paper is to present a new memetic algorithm for GTSP with a
powerful local search procedure. The experiments show that the proposed
algorithm clearly outperforms all of the known heuristics with respect to both
solution quality and running time. While the other memetic algorithms were
designed only for the symmetric GTSP, our algorithm can solve both symmetric
and asymmetric instances.Comment: 15 pages, to appear in Natural Computing, Springer, available online:
http://www.springerlink.com/content/5v4568l492272865/?p=e1779dd02e4d4cbfa49d0d27b19b929f&pi=1
Solving the Workflow Satisfiability Problem using General Purpose Solvers
The workflow satisfiability problem (WSP) is a well-studied problem in access
control seeking allocation of authorised users to every step of the workflow,
subject to workflow specification constraints. It was noticed that the number
of steps is typically small compared to the number of users in the
real-world instances of WSP; therefore is considered as the parameter in
WSP parametrised complexity research. While WSP in general was shown to be
W[1]-hard, WSP restricted to a special case of user-independent (UI)
constraints is fixed-parameter tractable (FPT). However, restriction to the UI
constraints might be impractical.
To efficiently handle non-UI constraints, we introduce the notion of
branching factor of a constraint. As long as the branching factors of the
constraints are relatively small and the number of non-UI constraints is
reasonable, WSP can be solved in FPT time.
Extending the results from Karapetyan et al. (2019), we demonstrate that
general-purpose solvers are capable of achieving FPT-like performance on WSP
with arbitrary constraints when used with appropriate formulations. This
enables one to tackle most of practical WSP instances. While important on its
own, we hope that this result will also motivate researchers to look for
FPT-aware formulations of other FPT problems.Comment: Associated data: http://doi.org/10.17639/nott.711
Conditional Markov Chain Search for the Generalised Travelling Salesman Problem for Warehouse Order Picking
The Generalised Travelling Salesman Problem (GTSP) is a well-known problem
that, among other applications, arises in warehouse order picking, where each
stock is distributed between several locations -- a typical approach in large
modern warehouses. However, the instances commonly used in the literature have
a completely different structure, and the methods are designed with those
instances in mind. In this paper, we give a new pseudo-random instance
generator that reflects the warehouse order picking and publish new benchmark
testbeds. We also use the Conditional Markov Chain Search framework to
automatically generate new GTSP metaheuristics trained specifically for
warehouse order picking. Finally, we report the computational results of our
metaheuristics to enable further competition between solvers
Valued Workflow Satisfiability Problem
A workflow is a collection of steps that must be executed in some specific
order to achieve an objective. A computerised workflow management system may
enforce authorisation policies and constraints, thereby restricting which users
can perform particular steps in a workflow. The existence of policies and
constraints may mean that a workflow is unsatisfiable, in the sense that it is
impossible to find an authorised user for each step in the workflow and satisfy
all constraints. In this paper, we consider the problem of finding the "least
bad" assignment of users to workflow steps by assigning a weight to each policy
and constraint violation. To this end, we introduce a framework for associating
costs with the violation of workflow policies and constraints and define the
\emph{valued workflow satisfiability problem} (Valued WSP), whose solution is
an assignment of steps to users of minimum cost. We establish the computational
complexity of Valued WSP with user-independent constraints and show that it is
fixed-parameter tractable. We then describe an algorithm for solving Valued WSP
with user-independent constraints and evaluate its performance, comparing it to
that of an off-the-shelf mixed integer programming package
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