281 research outputs found
Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach
Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution
Optimizing fire station locations for the Istanbul metropolitan municipality
Copyright @ 2013 INFORMSThe Istanbul Metropolitan Municipality (IMM) seeks to determine locations for additional fire stations to build in Istanbul; its objective is to make residences and historic sites reachable by emergency vehicles within five minutes of a fire station’s receipt of a service request. In this paper, we discuss our development of a mathematical model to aid IMM in determining these locations by using data retrieved from its fire incident records. We use a geographic information system to implement the model on Istanbul’s road network, and solve two location models—set-covering and maximal-covering—as what-if scenarios. We discuss 10 scenarios, including the situation that existed when we initiated the project and the scenario that IMM implemented. The scenario implemented increases the city’s fire station coverage from 58.6 percent to 85.9 percent, based on a five-minute response time, with an implementation plan that spans three years
Streaming Algorithms for Submodular Function Maximization
We consider the problem of maximizing a nonnegative submodular set function
subject to a -matchoid
constraint in the single-pass streaming setting. Previous work in this context
has considered streaming algorithms for modular functions and monotone
submodular functions. The main result is for submodular functions that are {\em
non-monotone}. We describe deterministic and randomized algorithms that obtain
a -approximation using -space, where is
an upper bound on the cardinality of the desired set. The model assumes value
oracle access to and membership oracles for the matroids defining the
-matchoid constraint.Comment: 29 pages, 7 figures, extended abstract to appear in ICALP 201
Satisfiability Modulo Transcendental Functions via Incremental Linearization
In this paper we present an abstraction-refinement approach to Satisfiability
Modulo the theory of transcendental functions, such as exponentiation and
trigonometric functions. The transcendental functions are represented as
uninterpreted in the abstract space, which is described in terms of the
combined theory of linear arithmetic on the rationals with uninterpreted
functions, and are incrementally axiomatized by means of upper- and
lower-bounding piecewise-linear functions. Suitable numerical techniques are
used to ensure that the abstractions of the transcendental functions are sound
even in presence of irrationals. Our experimental evaluation on benchmarks from
verification and mathematics demonstrates the potential of our approach,
showing that it compares favorably with delta-satisfiability /interval
propagation and methods based on theorem proving
On Budget-Feasible Mechanism Design for Symmetric Submodular Objectives
We study a class of procurement auctions with a budget constraint, where an
auctioneer is interested in buying resources or services from a set of agents.
Ideally, the auctioneer would like to select a subset of the resources so as to
maximize his valuation function, without exceeding a given budget. As the
resources are owned by strategic agents however, our overall goal is to design
mechanisms that are truthful, budget-feasible, and obtain a good approximation
to the optimal value. Budget-feasibility creates additional challenges, making
several approaches inapplicable in this setting. Previous results on
budget-feasible mechanisms have considered mostly monotone valuation functions.
In this work, we mainly focus on symmetric submodular valuations, a prominent
class of non-monotone submodular functions that includes cut functions. We
begin first with a purely algorithmic result, obtaining a
-approximation for maximizing symmetric submodular functions
under a budget constraint. We view this as a standalone result of independent
interest, as it is the best known factor achieved by a deterministic algorithm.
We then proceed to propose truthful, budget feasible mechanisms (both
deterministic and randomized), paying particular attention on the Budgeted Max
Cut problem. Our results significantly improve the known approximation ratios
for these objectives, while establishing polynomial running time for cases
where only exponential mechanisms were known. At the heart of our approach lies
an appropriate combination of local search algorithms with results for monotone
submodular valuations, applied to the derived local optima.Comment: A conference version appears in WINE 201
A Submodular Approach for Reference Recommendation
© 2020, Springer Nature Singapore Pte Ltd. Choosing appropriate references for a given topic is an important, yet challenging task. The pool of potential candidates is typically very large, in the order of tens of thousands, and growing by the day. For this reason, this paper proposes an approach for automatically providing a reference list for a given manuscript. The approach is based on an original submodular inference function which balances relevance, coverage and diversity in the reference list. Experiments are carried out using an ACL corpus as a source for the references and evaluated by MAP, MRR and precision-recall. The results show the remarkable comparative performance of the proposed approach
Bus Frequency Optimization: When Waiting Time Matters in User Satisfaction
Reorganizing bus frequency to cater for the actual travel demand can save the
cost of the public transport system significantly. Many, if not all, existing
studies formulate this as a bus frequency optimization problem which tries to
minimize passengers' average waiting time. However, many investigations have
confirmed that the user satisfaction drops faster as the waiting time
increases. Consequently, this paper studies the bus frequency optimization
problem considering the user satisfaction. Specifically, for the first time to
our best knowledge, we study how to schedule the buses such that the total
number of passengers who could receive their bus services within the waiting
time threshold is maximized. We prove that this problem is NP-hard, and present
an index-based algorithm with approximation ratio. By exploiting the
locality property of routes in a bus network, we propose a partition-based
greedy method which achieves a approximation ratio. Then we
propose a progressive partition-based greedy method to further improve the
efficiency while achieving a approximation ratio.
Experiments on a real city-wide bus dataset in Singapore verify the efficiency,
effectiveness, and scalability of our methods
Decomposition algorithms for submodular optimization with applications to parallel machine scheduling with controllable processing times
In this paper we present a decomposition algorithm for maximizing a linear function over a submodular polyhedron intersected with a box. Apart from this contribution to submodular optimization, our results extend the toolkit available in deterministic machine scheduling with controllable processing times. We demonstrate how this method can be applied to developing fast algorithms for minimizing total compression cost for preemptive schedules on parallel machines with respect to given release dates and a common deadline. Obtained scheduling algorithms are faster and easier to justify than those previously known in the scheduling literature
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