3,736 research outputs found
On the use of biased-randomized algorithms for solving non-smooth optimization problems
Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines
The two-echelon capacitated vehicle routing problem: models and math-based heuristics
Multiechelon distribution systems are quite common in supply-chain and logistics. They are used by public administrations in their transportation and traffic planning strategies, as well as by companies, to model own distribution systems. In the literature, most of the studies address issues relating to the movement of flows throughout the system from their origins to their final destinations. Another recent trend is to focus on the management of the vehicle fleets required to provide transportation among different echelons. The aim of this paper is twofold. First, it introduces the family of two-echelon vehicle routing problems (VRPs), a term that broadly covers such settings, where the delivery from one or more depots to customers is managed by routing and consolidating freight through intermediate depots. Second, it considers in detail the basic version of two-echelon VRPs, the two-echelon capacitated VRP, which is an extension of the classical VRP in which the delivery is compulsorily delivered through intermediate depots, named satellites. A mathematical model for two-echelon capacitated VRP, some valid inequalities, and two math-heuristics based on the model are presented. Computational results of up to 50 customers and four satellites show the effectiveness of the methods developed
Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU
A parallel genetic algorithm (GA) implemented on GPU clusters is proposed to
solve the Uncapacitated Single Allocation p-Hub Median problem. The GA uses
binary and integer encoding and genetic operators adapted to this problem. Our
GA is improved by generated initial solution with hubs located at middle nodes.
The obtained experimental results are compared with the best known solutions on
all benchmarks on instances up to 1000 nodes. Furthermore, we solve our own
randomly generated instances up to 6000 nodes. Our approach outperforms most
well-known heuristics in terms of solution quality and time execution and it
allows hitherto unsolved problems to be solved
Genetic algorithm for the continuous location-routing problem
This paper focuses on the continuous location-routing problem that comprises of the location of multiple depots from a given region and determining the routes of vehicles assigned to these depots. The objective of the problem is to design the delivery system of depots and routes so that the total cost is minimal. The standard location-routing problem considers a finite number of possible locations. The continuous location-routing problem allows location to infinite number of locations in a given region and makes the problem much more complex. We present a genetic algorithm that tackles both location and routing subproblems simultaneously.Web of Science29318717
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
Una comparación de algoritmos basados en trayectoria granular para el problema de localización y ruteo con flota heterogénea (LRPH)
Indexación: Scopus.We consider the Location-Routing Problem with Heterogeneous Fleet (LRPH) in which the goal is to determine the depots to be opened, the customers to be assigned to each open depot, and the corresponding routes fulfilling the demand of the customers and by considering a heterogeneous fleet. We propose a comparison of granular approaches of Simulated Annealing (GSA), of Variable Neighborhood Search (GVNS) and of a probabilistic Tabu Search (pGTS) for the LRPH. Thus, the proposed approaches consider a subset of the search space in which non-favorable movements are discarded regarding a granularity factor. The proposed algorithms are experimentally compared for the solution of the LRPH, by taking into account the CPU time and the quality of the solutions obtained on the instances adapted from the literature. The computational results show that algorithm GSA is able to obtain high quality solutions within short CPU times, improving the results obtained by the other proposed approaches.https://revistas.unal.edu.co/index.php/dyna/article/view/55533/5896
Towards the fast and robust optimal design of Wireless Body Area Networks
Wireless body area networks are wireless sensor networks whose adoption has
recently emerged and spread in important healthcare applications, such as the
remote monitoring of health conditions of patients. A major issue associated
with the deployment of such networks is represented by energy consumption: in
general, the batteries of the sensors cannot be easily replaced and recharged,
so containing the usage of energy by a rational design of the network and of
the routing is crucial. Another issue is represented by traffic uncertainty:
body sensors may produce data at a variable rate that is not exactly known in
advance, for example because the generation of data is event-driven. Neglecting
traffic uncertainty may lead to wrong design and routing decisions, which may
compromise the functionality of the network and have very bad effects on the
health of the patients. In order to address these issues, in this work we
propose the first robust optimization model for jointly optimizing the topology
and the routing in body area networks under traffic uncertainty. Since the
problem may result challenging even for a state-of-the-art optimization solver,
we propose an original optimization algorithm that exploits suitable linear
relaxations to guide a randomized fixing of the variables, supported by an
exact large variable neighborhood search. Experiments on realistic instances
indicate that our algorithm performs better than a state-of-the-art solver,
fast producing solutions associated with improved optimality gaps.Comment: Authors' manuscript version of the paper that was published in
Applied Soft Computin
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