240 research outputs found
Optimizing Sensing: From Water to the Web
Where should we place sensors to quickly detect contamination in drinking water distribution networks? Which blogs should we read to learn about the biggest stories on the Web? Such problems are typically NP-hard in theory and extremely challenging in practice. The authors present algorithms that exploit submodularity to efficiently find provably near-optimal solutions to large, complex real-world sensing problems
Bi-objective facility location in the presence of uncertainty
Multiple and usually conflicting objectives subject to data uncertainty are
main features in many real-world problems. Consequently, in practice,
decision-makers need to understand the trade-off between the objectives,
considering different levels of uncertainty in order to choose a suitable
solution. In this paper, we consider a two-stage bi-objective single source
capacitated model as a base formulation for designing a last-mile network in
disaster relief where one of the objectives is subject to demand uncertainty.
We analyze scenario-based two-stage risk-neutral stochastic programming,
adaptive (two-stage) robust optimization, and a two-stage risk-averse
stochastic approach using conditional value-at-risk (CVaR). To cope with the
bi-objective nature of the problem, we embed these concepts into two criterion
space search frameworks, the -constraint method and the balanced box
method, to determine the Pareto frontier. Additionally, a matheuristic
technique is developed to obtain high-quality approximations of the Pareto
frontier for large-size instances. In an extensive computational experiment, we
evaluate and compare the performance of the applied approaches based on
real-world data from a Thies drought case, Senegal
A review on the charging station planning and fleet operation for electric freight vehicles
Freight electrification introduces new opportunities and challenges for
planning and operation. Although research on charging infrastructure planning
and operation is widely available for general electric vehicles, unique
physical and operational characteristics of EFVs coupled with specific patterns
of logistics require dedicated research. This paper presents a comprehensive
literature review to gain a better understanding of the state-of-the-art
research efforts related to planning (charging station siting and sizing) and
operation (routing, charge scheduling, platoon scheduling, and fleet sizing)
for EFVs. We classified the existing literature based on the research topics,
innovations, methodologies, and solution approaches, and future research
directions are identified. Different types of methodologies, such as heuristic,
simulation, and mathematical programming approaches, were applied in the
reviewed literature where mathematical models account for the majority. We
further narrated the specific modeling considerations for different logistic
patterns and research goals with proper reasoning. To solve the proposed
models, different solution approaches, including exact algorithms,
metaheuristic algorithms, and software simulation, were evaluated in terms of
applicability, advantages, and disadvantages. This paper helps to draw more
attention to the planning and operation issues and solutions for freight
electrification and facilitates future studies on EFV to ensure a smooth
transition to a clean freight system.Comment: 43 pages, 4 figures, 2 table
Recommendation & mobile systems - a state of the art for tourism
Recommendation systems have been growing in number over the last fifteen years. To evolve and adapt to the demands of the actual society, many paradigms emerged giving birth to even more paradigms and hybrid approaches. These approaches contain strengths and weaknesses that need to be evaluated according to the knowledge area in which the system is going to be implemented. Mobile devices have also been under an incredible growth rate in every business area, and there are already lots of mobile based systems to assist tourists. This explosive growth gave birth to different mobile applications, each having their own advantages and disadvantages. Since recommendation and mobile systems might as well be integrated, this work intends to present the current state of the art in tourism mobile and recommendation systems, as well as to state their advantages and disadvantages
Network Interdiction under Uncertainty
We consider variants to one of the most common network interdiction formulations: the shortest path interdiction problem. This problem involves leader and a follower playing a zero-sum game over a directed network. The leader interdicts a set of arcs, and arc costs increase each time they are interdicted. The follower observes the leader\u27s actions and selects a shortest path in response. The leader\u27s optimal interdiction strategy maximizes the follower\u27s minimum-cost path.
Our first variant allows the follower to improve the network after the interdiction by lowering the costs of some arcs, and the leader is uncertain regarding the follower\u27s cardinality budget restricting the arc improvements. We propose a multiobjective approach for this problem, with each objective corresponding to a different possible improvement budget value. To this end, we also present the modified augmented weighted Tchebychev norm, which can be used to generate a complete efficient set of solutions to a discrete multi-objective optimization problem, and which tends to scale better than competing methods as the number of objectives grows.
In our second variant, the leader selects a policy of randomized interdiction actions, and the follower uses the probability of where interdictions are deployed on the network to select a path having the minimum expected cost. We show that this continuous non-convex problem becomes strongly NP-hard when the cost functions are convex or when they are concave. After formally describing each variant, we present various algorithms for solving them, and we examine the efficacy of all our algorithms on test beds of randomly generated instances
TeMA: A Tensorial Memetic Algorithm for Many-Objective Parallel Disassembly Sequence Planning in Product Refurbishment
The refurbishment market is rich in opportunities—the global refurbished smartphones market alone will be $38.9 billion by 2025. Refurbishing a product involves disassembling it to test the key parts and replacing those that are defective or worn. This restores the product to like-new conditions, so that it can be put on the market again at a lower price. Making this process quick and efficient is crucial. This paper presents a novel formulation of parallel disassembly problem that maximizes the degree of parallelism, the level of ergonomics, and how the workers' workload is balanced, while minimizing the disassembly time and the number of times the product has to be rotated. The problem is solved using the Tensorial Memetic Algorithm (TeMA), a novel two-stage many-objective (MaO) algorithm, which encodes parallel disassembly plans by using third-order tensors. TeMA first splits the objectives into primary and secondary on the basis of a decision-maker's preferences, and then finds Pareto-optimal compromises (seeds) of the primary objectives. In the second stage, TeMA performs a fine-grained local search that explores the objective space regions around the seeds, to improve the secondary objectives. TeMA was tested on two real-world refurbishment processes involving a smartphone and a washing machine. The experiments showed that, on average, TeMA is statistically more accurate than various efficient MaO algorithms in the decision-maker's area of preference
Best effort QoS support routing in mobile ad hoc networks
In the past decades, mobile traffic generated by devices such as smartphones, iphones,
laptops and mobile gateways has been growing rapidly. While traditional direct
connection techniques evolve to provide better access to the Internet, a new type of
wireless network, mobile ad hoc network (MANET), has emerged. A MANET differs
from a direct connection network in the way that it is multi-hopping and self-organizing
and thus able to operate without the help of prefixed infrastructures. However,
challenges such dynamic topology, unreliable wireless links and resource constraints
impede the wide applications of MANETs.
Routing in a MANET is complex because it has to react efficiently to unfavourable
conditions and support traditional IP services. In addition, Quality of Service (QoS)
provision is required to support the rapid growth of video in mobile traffic. As a
consequence, tremendous efforts have been devoted to the design of QoS routing in
MANETs, leading to the emergence of a number of QoS support techniques. However,
the application independent nature of QoS routing protocols results in the absence of a
one-for-all solution for MANETs. Meanwhile, the relative importance of QoS metrics
in real applications is not considered in many studies.
A Best Effort QoS support (BEQoS) routing model which evaluates and ranks
alternative routing protocols by considering the relative importance of multiple QoS
metrics is proposed in this thesis. BEQoS has two algorithms, SAW-AHP and FPP for
different scenarios. The former is suitable for cases where uncertainty factors such as
standard deviation can be neglected while the latter considers uncertainty of the
problems.
SAW-AHP is a combination of Simple Additive Weighting and Analytic Hierarchical Process in which the decision maker or network operator is firstly required to assign
his/her preference of metrics with a specific number according to given rules. The
comparison matrices are composed accordingly, based on which the synthetic weights
for alternatives are gained. The one with the highest weight is the optimal protocol
among all alternatives. The reliability and efficiency of SAW-AHP are validated
through simulations. An integrated architecture, using evaluation results of SAW-AHP
is proposed which incorporates the ad hoc technology into the existing WLAN and
therefore provides a solution for the last mile access problems. The protocol selection
induced cost and gains are also discussed. The thesis concludes by describing the
potential application area of the proposed method.
Fuzzy SAW-AHP is extended to accommodate the vagueness of the decision maker and
complexity of problems such as standard deviation in simulations. The fuzzy triangular
numbers are used to substitute the crisp numbers in comparison matrices in traditional
AHP. Fuzzy Preference Programming (FPP) is employed to obtain the crisp synthetic
weight for alternatives based on which they are ranked. The reliability and efficiency of
SAW-FPP are demonstrated by simulations
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