We propose domain-independent dynamic programming (DIDP) and constraint programming (CP) models to exactly solve type-1 and type-2 assembly line balancing problem with sequence-dependent setup times (SUALBP). The goal...
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Drones are currently seen as a viable way of improving the distribution of parcels in urban and rural environments, while working in coordination with traditional vehicles, such as trucks. In this paper, we consider t...
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Drones are currently seen as a viable way of improving the distribution of parcels in urban and rural environments, while working in coordination with traditional vehicles, such as trucks. In this paper, we consider the parallel drone scheduling traveling salesman problem, where a set of customers requiring a delivery is split between a truck and a fleet of drones, with the aim of minimizing the total time required to serve all the customers. We propose a constraint programming model for the problem, discuss its implementation and present the results of an experimental program on the instances previously cited in the literature to validate exact and heuristic algorithms. We were able to decrease the cost (the time required to serve customers) for some of the instances and, for the first time, to provide a demonstrated optimal solution for all the instances considered. These results show that constraint programming can be a very effective tool for attacking optimization problems with traveling salesman components, such as the one discussed.
Recovery of products has received much attention in the last decade due to the increase in both environmental awareness and regulations enacted by governments. In product recovery, disassembly of a product into its co...
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Recovery of products has received much attention in the last decade due to the increase in both environmental awareness and regulations enacted by governments. In product recovery, disassembly of a product into its constituent parts on a line is one of the most significant operations. This paper deals with a disassembly line balancing and sequencing (DLBS) problem subject to balancing issues, hazardousness of parts, demand quantities and direction changes considered in a lexicographic order. Due to the combinatorial nature of this problem, exact methods, e.g., mixed integer linear programming (MILP), are able to solve only small and medium size problems. Therefore, various metaheuristic algorithms are proposed in literature to find near-optimal solutions. In this paper, constraint programming (CP), which is a suitable technique especially for highly-constrained discrete problems, is used to develop models and solution approaches. To the best of author's knowledge, this study is the first that uses CP for the disassembly line balancing problems. For the DLBS problem, first, a generic CP model is developed. This CP model provides efficient results for small/medium size disassembly problems and benchmark instances. Observing that the generic CP model could not produce even feasible sequence of tasks for some large-sized benchmark instances, a CP-based solution approach is proposed. This approach generates a feasible sequence subject to a fixed assignment of tasks to the workstations by using a CP model and uses this sequence as an initial feasible solution within a warm-start context in CP sequencing models. The computational results show that the proposed CP model improves the several best solutions of medium-sized benchmark instances, while the proposed CP-based solution approach produces excellent results in all large test instances by either improving the best solutions (found so far) or establishing new benchmark solutions. (C) 2020 Elsevier Ltd. All rights reserv
Problems affecting the transport of people or goods are plentiful in industry and commerce and they also appear to be at the origin of much more complex problems. In recent years, the logistics and transport sector ke...
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Problems affecting the transport of people or goods are plentiful in industry and commerce and they also appear to be at the origin of much more complex problems. In recent years, the logistics and transport sector keeps growing supported by technological progress, i.e. companies to be competitive are resorting to innovative technologies aimed at efficiency and effectiveness. This is why companies are increasingly using technologies such as Artificial Intelligence (AI), Blockchain and Internet of Things (IoT). Artificial intelligence, in particular, is often used to solve optimization problems in order to provide users with the most efficient ways to exploit available resources. In this work we present an overview of our current research activities concerning the development of new algorithms, based on constraint Logic programming (CLP) techniques, for route planning problems exploiting the geometric information intrinsically present in many of them or in some of their variants. The research so far has focused in particular on the Euclidean Traveling Salesperson Problem (Euclidean TSP) with the aim to exploit the results obtained also to other problems of the same category, such as the Euclidean Vehicle Routing Problem (Euclidean VRP), in the future.
constraint programming (CP) is a powerful paradigm for solving combinatorial problems. In CP, the user creates a model by declaring variables with their domains and expresses the constraints that need to be satisfied ...
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ISBN:
(纸本)9781577358350
constraint programming (CP) is a powerful paradigm for solving combinatorial problems. In CP, the user creates a model by declaring variables with their domains and expresses the constraints that need to be satisfied in any solution. The solver is then in charge of finding feasible solutions-a value in the domain of each variable that satisfies all the constraints. The discovery of solutions is done by exploring a search tree that is pruned by the constraints in charge of removing impossible values. The CP framework has the advantage of exposing a rich high-level declarative constraint language for modeling, as well as efficient purpose-specific filtering algorithms that can be reused in many problems. In this work, we harness this flexibility and efficiency for the Block Modeling problem. It is a variant of the graph clustering problem that has been used extensively in many domains including social science, spatio-temporal data analysis and even medical imaging. We present a new approach based on constraint programming, allowing discrete optimization of block modeling in a manner that is not only scalable, but also allows the easy incorporation of constraints. We introduce a new constraint filtering algorithm that outperforms earlier approaches. We show its use in the analysis of real datasets.
We propose Nester, a method for injecting neural networks into constrained structured predictors. Nester first uses a neural network to compute an initial prediction that may or may not satisfy the constraints, and th...
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We propose Nester, a method for injecting neural networks into constrained structured predictors. Nester first uses a neural network to compute an initial prediction that may or may not satisfy the constraints, and then applies a constraint-based structured predictor to refine the raw predictions according to hard and soft constraints. Nester combines the advantages of its two components: the network can learn complex representations from low-level data while the constraint program on top reasons about the high-level properties and requirements of the prediction task. An empirical evaluation on handwritten equation recognition shows that Nester achieves better performance than both the either component in isolation, especially when training examples are scarce, while scaling to more complex problems than other neuro-programming approaches. Nester proves especially useful to reduce errors at the semantic level of the problem, which is particularly challenging for neural network architectures.
This paper addresses the incompatible case of parallel batch scheduling, where compatible jobs belong to the same family, and jobs from different families cannot be processed together in the same batch. Existing const...
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In the liner shipping business, shipping ports represent the main nodes in the maritime transportation network. These ports have a collection of terminals where container vessels can load and discharge containers. How...
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The design of efficient and generic algorithms for solving combinatorial optimization problems has been an active field of research for many years. Standard exact solving approaches are based on a clever and complete ...
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ISBN:
(纸本)9783030782306;9783030782290
The design of efficient and generic algorithms for solving combinatorial optimization problems has been an active field of research for many years. Standard exact solving approaches are based on a clever and complete enumeration of the solution set. A critical and non-trivial design choice with such methods is the branching strategy, directing how the search is performed. The last decade has shown an increasing interest in the design of machine learning-based heuristics to solve combinatorial optimization problems. The goal is to leverage knowledge from historical data to solve similar new instances of a problem. Used alone, such heuristics are only able to provide approximate solutions efficiently, but cannot prove optimality nor bounds on their solution. Recent works have shown that reinforcement learning can be successfully used for driving the search phase of constraint programming (CP) solvers. However, it has also been shown that this hybridization is challenging to build, as standard CP frameworks do not natively include machine learning mechanisms, leading to some sources of inefficiencies. This paper presents the proof of concept for SeaPearl, a new CP solver implemented in Julia, that supports machine learning routines in order to learn branching decisions using reinforcement learning. Support for modeling the learning component is also provided. We illustrate the modeling and solution performance of this new solver on two problems. Although not yet competitive with industrial solvers, SeaPearl aims to provide a flexible and open-source framework in order to facilitate future research in the hybridization of constraint programming and machine learning.
The Traveling Salesperson Problem (TSP) is a well-known problem addressed in the literature through various techniques, including Integer Linear programming, constraint programming (CP) and Local Search. Many real lif...
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ISBN:
(数字)9783030770914
ISBN:
(纸本)9783030770914;9783030770907
The Traveling Salesperson Problem (TSP) is a well-known problem addressed in the literature through various techniques, including Integer Linear programming, constraint programming (CP) and Local Search. Many real life instances belong to the subclass of Euclidean TSPs, in which the nodes to be visited are points in the Euclidean plane, and the distance between them is the Euclidean distance. A well-known property of the Euclidean TSP is that no crossings can exist in an optimal solution. In a previous publication, we exploited this property to speed-up the solution of Euclidean instances in CP, by imposing a quadratic number of so-called no-overlapping constraints. In this work, we observe that not all the no-overlapping constraints are equally useful: by experimental analysis, some of them provide a speed-up, while others only introduce overhead. Thus, it is important to define a way to classify useful constraints. To do so, we use machine learning approaches with the objective to impose only those no-overlapping constraints that have been classified as effective. We compare two classifiers based on Random Forest and Neural Networks, which show to be effective, with a slight prevalence for Random Forest.
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