1,129 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
Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach
In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version
Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach
In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version
Fast Ejection Chain Algorithms for Vehicle Routing with Time Windows
This paper introduces new ejection chain strategies to effectively target vehicle routing problems with time window constraints (VRPTW). Ejection chain procedures are based on the idea of compound moves that allow a variable number of solution components to be modified within any single iteration of a local search algorithm. The yardstick behind such procedures is the underlying reference structure, which is the structure that is used to coordinate the moves that are available for the local search algorithm. The main contribution of the paper is a new reference structure that is particularly suited in order to handle the asymmetric aspects in a VRPTW. The new reference structure is a generalization of the doubly rooted reference structure introduced by Glover, resulting in a new, powerful neighborhood for the VRPTW. We use tabu search for the generation of the ejection chains. On a higher algorithmic level, we study the effect of different meta heuristics to steer the tabu chain ejection process. Computational results confirm that our approach leads to very fast algorithms that can compete with the current state of the art algorithms for the VRPTW.operations research and management science;
Enhanced Iterated local search for the technician routing and scheduling problem
Most public facilities in the European countries, including France, Germany,
and the UK, were built during the reconstruction projects between 1950 and
1980. Owing to the deteriorating state of such vital infrastructure has become
relatively expensive in the recent decades. A significant part of the
maintenance operation costs is spent on the technical staff. Therefore, the
optimal use of the available workforce is essential to optimize the operation
costs. This includes planning technical interventions, workload balancing,
productivity improvement, etc. In this paper, we focus on the routing of
technicians and scheduling of their tasks. We address for this purpose a
variant of the workforce scheduling problem called the technician routing and
scheduling problem (TRSP). This problem has applications in different fields,
such as transportation infrastructure (rail and road networks),
telecommunications, and sewage facilities. To solve the TRSP, we propose an
enhanced iterated local search (eILS) approach. The enhancement of the ILS
firstly includes an intensification procedure that incorporates a set of local
search operators and removal-repair heuristics crafted for the TRSP. Next, four
different mechanisms are used in the perturbation phase. Finally, an elite set
of solutions is used to extensively explore the neighborhood of local optima as
well as to enhance diversification during search space exploration. To measure
the performance of the proposed method, experiments were conducted based on
benchmark instances from the literature, and the results obtained were compared
with those of an existing method. Our method achieved very good results, since
it reached the best overall gap, which is three times lower than that of the
literature. Furthermore, eILS improved the best-known solution for
instances among a total of while maintaining reasonable computational
times.Comment: Submitted manuscript to Computers and Operations Research journal. 34
pages, 7 figures, 6 table
A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times
Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe
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