8,149 research outputs found
Fair Knapsack
We study the following multiagent variant of the knapsack problem. We are
given a set of items, a set of voters, and a value of the budget; each item is
endowed with a cost and each voter assigns to each item a certain value. The
goal is to select a subset of items with the total cost not exceeding the
budget, in a way that is consistent with the voters' preferences. Since the
preferences of the voters over the items can vary significantly, we need a way
of aggregating these preferences, in order to select the socially best valid
knapsack. We study three approaches to aggregating voters' preferences, which
are motivated by the literature on multiwinner elections and fair allocation.
This way we introduce the concepts of individually best, diverse, and fair
knapsack. We study the computational complexity (including parameterized
complexity, and complexity under restricted domains) of the aforementioned
multiagent variants of knapsack.Comment: Extended abstract will appear in Proc. of 33rd AAAI 201
An integrated approach for requirement selection and scheduling in software release planning
It is essential for product software companies to decide which requirements should be included in the next release and to make an appropriate time plan of the development project. Compared to the extensive research done on requirement selection, very little research has been performed on time scheduling. In this paper, we introduce two integer linear programming models that integrate time scheduling into software release planning. Given the resource and precedence constraints, our first model provides a schedule for developing the requirements such that the project duration is minimized. Our second model combines requirement selection and scheduling, so that it not only maximizes revenues but also simultaneously calculates an on-time-delivery project schedule. Since requirement dependencies are essential for scheduling the development process, we present a more detailed analysis of these dependencies. Furthermore, we present two mechanisms that facilitate dynamic adaptation for over-estimation or under-estimation of revenues or processing time, one of which includes the Scrum methodology. Finally, several simulations based on real-life data are performed. The results of these simulations indicate that requirement dependency can significantly influence the requirement selection and the corresponding project plan. Moreover, the model for combined requirement selection and scheduling outperforms the sequential selection and scheduling approach in terms of efficiency and on-time delivery. \u
Coefficients of Sylvester's Denumerant
For a given sequence of positive integers, we consider
the combinatorial function that counts the nonnegative
integer solutions of the equation , where the right-hand side is a varying
nonnegative integer. It is well-known that is a
quasi-polynomial function in the variable of degree . In combinatorial
number theory this function is known as Sylvester's denumerant.
Our main result is a new algorithm that, for every fixed number , computes
in polynomial time the highest coefficients of the quasi-polynomial
as step polynomials of (a simpler and more explicit
representation). Our algorithm is a consequence of a nice poset structure on
the poles of the associated rational generating function for
and the geometric reinterpretation of some rational
generating functions in terms of lattice points in polyhedral cones. Our
algorithm also uses Barvinok's fundamental fast decomposition of a polyhedral
cone into unimodular cones. This paper also presents a simple algorithm to
predict the first non-constant coefficient and concludes with a report of
several computational experiments using an implementation of our algorithm in
LattE integrale. We compare it with various Maple programs for partial or full
computation of the denumerant.Comment: minor revision, 28 page
Network-Assisted Resource Allocation with Quality and Conflict Constraints for V2V Communications
The 3rd Generation Partnership Project (3GPP) has recently established in
Rel. 14 a network-assisted resource allocation scheme for vehicular broadcast
communications. Such novel paradigm is known as vehicle--to--vehicle (V2V)
\textit{mode-3} and consists in eNodeBs engaging only in the distribution of
sidelink subchannels among vehicles in coverage. Thereupon, without further
intervention of the former, vehicles will broadcast their respective signals
directly to their counterparts. Because the allotment of subchannels takes
place intermittently to reduce signaling, it must primarily be conflict-free in
order not to jeopardize the reception of signals. We have identified four
pivotal types of allocation requirements that must be guaranteed: one quality
of service (QoS) requirement and three conflict conditions which must be
precluded in order to preserve reception reliability. The underlying problem is
formulated as a maximization of the system sum-capacity with four types of
constraints that must be enforced. In addition, we propose a three-stage
suboptimal approach that is cast as multiple independent knapsack problems
(MIKPs). We compare the two approaches through simulations and show that the
latter formulation can attain acceptable performance at lesser complexity
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