Cable Tree Wiring -- Benchmarking Solvers on a Real-World Scheduling Problem with a Variety of Precedence Constraints

Cable trees are used in industrial products to transmit energy and information between different product parts. To this date, they are mostly assembled by humans and only few automated manufacturing solutions exist using complex robotic machines. For these machines, the wiring plan has to be translated into a wiring sequence of cable plugging operations to be followed by the machine. In this paper, we study and formalize the problem of deriving the optimal wiring sequence for a given layout of a cable tree. We summarize our investigations to model this cable tree wiring Problem (CTW) as a traveling salesman problem with atomic, soft atomic, and disjunctive precedence constraints as well as tour-dependent edge costs such that it can be solved by state-of-the-art constraint programming (CP), Optimization Modulo Theories (OMT), and mixed-integer programming (MIP) solvers. It is further shown, how the CTW problem can be viewed as a soft version of the coupled tasks scheduling problem. We discuss various modeling variants for the problem, prove its NP-hardness, and empirically compare CP, OMT, and MIP solvers on a benchmark set of 278 instances. The complete benchmark set with all models and instance data is available on github and is accepted for inclusion in the MiniZinc challenge 2020

Operations research modeling language for an ERP System

Estágio realizado na MicrosoftTese de mestrado integrado. Engenharia Informátca e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt

In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search (L2S) solver for routing problems. It learns to perform flexible k-opt exchanges based on a tailored action factorization method and a customized recurrent dual-stream decoder. As a pioneering work to circumvent the pure feasibility masking scheme and enable the autonomous exploration of both feasible and infeasible regions, we then propose the Guided Infeasible Region Exploration (GIRE) scheme, which supplements the NeuOpt policy network with feasibility-related features and leverages reward shaping to steer reinforcement learning more effectively. Additionally, we equip NeuOpt with Dynamic Data Augmentation (D2A) for more diverse searches during inference. Extensive experiments on the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) demonstrate that our NeuOpt not only significantly outstrips existing (masking-based) L2S solvers, but also showcases superiority over the learning-to-construct (L2C) and learning-to-predict (L2P) solvers. Notably, we offer fresh perspectives on how neural solvers can handle VRP constraints. Our code is available: https://github.com/yining043/NeuOpt.Comment: Accepted at NeurIPS 202

Anteater analysis, The: a comparison of traveling salesman tour construction methods and their global frequencies

2022 Summer.Includes bibliographical references.For the Traveling Salesman Problem (TSP), many algorithms have been developed. These include heuristic solvers, such as nearest neighbors and ant colony optimization algorithms. In this work, the ATT48 and EIL101 instances are examined to better understand the difference between biased and unbiased methods of tour construction algorithms when combined with the 2-opt local search operator. First, a sample of tours are constructed. Then, we examine the frequencies of global edges of different sizes using n-grams. Using 2-opt as the tour improvement algorithm, we analyze randomly initialized local optima compared to nearest neighbors local optima as well as ant colony solutions with and without 2-opt. This comparison serves to better understand the nature of these different methods in their relation to the global optimum. We also provide some ways the algorithms may be adapted to take advantage of the global frequencies, particularly the ant colony optimization algorithm

The In-Transit Vigilant Covering Tour Problem of Routing Unmanned Ground Vehicles

The routing of unmanned ground vehicles for the surveillance and protection of key installations is modeled as a new variant of the Covering Tour Problem (CTP). The CTP structure provides both the routing and target sensing components of the installation protection problem. Our variant is called the in-transit Vigilant Covering Tour Problem (VCTP) and considers not only the vertex cover but also the additional edge coverage capability of the unmanned ground vehicle while sensing in-transit between vertices. The VCTP is formulated as a Traveling Salesman Problem (TSP) with a dual set covering structure involving vertices and edges. An empirical study compares the performance of the VCTP against the CTP on test problems modified from standard benchmark TSP problems to apply to the VCTP. The VCTP performed generally better with shorter tour lengths but at higher computational cost

Augmented tour construction heuristics for the travelling salesman problem

Tour construction heuristics serve as fundamental techniques in optimizing the routes of a traveling salesman. These heuristics remain significant as foundational methods for generating initial solutions to the Traveling Salesman Problem (TSP), facilitating subsequent applications of tour improvement heuristics. These heuristics effectively comprise the iterative application of city node selection and insertion. However, thus far, no attempts have been made to enhance the basic structure of tour construction heuristics to bring a better initial solution for the advanced heuristics. This study aims to enhance tour construction heuristics without compromising their theoretical complexity. Specifically, an iterative step of partial tour deconstruction has been introduced to the existing heuristics. This additional step has been implemented and evaluated with three highly performing tour construction heuristics: the farthest insertion heuristic, the max difference insertion heuristic, and the fast max difference insertion heuristic. The results demonstrate that augmenting these heuristics with the partial tour deconstruction step improves the best, worst, and average solutions while preserving their theoretical complexit

Optimizations of a Multi-Agent System for a Real-World Warehouse Problem

In recent years, many warehouses applied mobile robots to move products from one location to another. We focus on a traditional warehouse where agents are humans, and they are engaged with tasks to navigate to the next destination one after the other. The possible destinations are determined at the beginning of the daily shift. Our real-world warehouse client asked us to minimize the total wage cost, and to minimize the irritation of the workers because of conflicts in their tasks. We define a heuristic for the optimizations for splitting the orders into warehouse carts, defining the sequence of the products within the carts, and the assignment of the carts to workers. We extend Multi-Agent Path Finding (MAPF) solution techniques. Furthermore, we have implemented our proposal in a simulation software, and we have run several experiments. According to the experiments, the make-span and the wage cost cannot be reduced with the heuristic optimization, however the heuristic optimization considerably reduces the irritation of the workers. We conclude our work with a guideline for the warehouse