Reinforcement learning based local search for grouping problems: A case study on graph coloring

Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints. Grouping problems cover a large class of important combinatorial optimization problems that are generally computationally difficult. In this paper, we propose a general solution approach for grouping problems, i.e., reinforcement learning based local search (RLS), which combines reinforcement learning techniques with descent-based local search. The viability of the proposed approach is verified on a well-known representative grouping problem (graph coloring) where a very simple descent-based coloring algorithm is applied. Experimental studies on popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves competitive performances compared to a number of well-known coloring algorithms

A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification

Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years. However, they are unable to construct the state-of-the-art convolutional neural networks due to their intrinsic architectures. In this regard, we propose a flexible convolutional auto-encoder by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional convolutional auto-encoder. We also design an architecture discovery method by using particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed flexible convolutional auto-encoder with much less computational resource and without any manual intervention. We use the designed architecture optimization algorithm to test the proposed flexible convolutional auto-encoder through utilizing one graphic processing unit card on four extensively used image classification datasets. Experimental results show that our work in this paper significantly outperform the peer competitors including the state-of-the-art algorithm.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems, 201

A general solution framework for component commonality problems

Component commonality, the use of the same version of a component across multiple products, is increasingly considered as a promising way to offer high external variety while retaining low internal variety in operations. However, increasing commonality has both positive and negative cost effects, so that optimization approaches are required to identify an optimal commonality level. As a more or less of components influences nearly every process step along the supply chain, it is not astounding that a multitude of diverging commonality problems is investigated in literature, each of which developing a specific algorithm designed for the respective commonality problem considered. The paper on hand aims at a general framework, flexible and effcient enough to be applied to a wide range of commonality problems. Such a procedure basing on a two-stage graph approach is presented and tested. Finally, flexibility of the procedure is shown by customizing the framework to account for different types of commonality problems.Product variety, Component commonality, Optimization, Graph approach

Heuristics and policies for online pickup and delivery problems

Master ThesisIn the last few decades, increased attention has been dedicated to a speci c subclass of Vehicle Routing Problems due to its signi cant importance in several transportation areas such as taxi companies, courier companies, transportation of people, organ transportation, etc. These problems are characterized by their dynamicity as the demands are, in general, unknown in advance and the corresponding locations are paired. This thesis addresses a version of such Dynamic Pickup and Delivery Problems, motivated by a problem arisen in an Australian courier company, which operates in Sydney, Melbourne and Brisbane, where almost every day more than a thousand transportation orders arrive and need to be accommodated. The rm has a eet of almost two hundred vehicles of various types, mostly operating within the city areas. Thus, whenever new orders arrive at the system the dispatchers face a complex decision regarding the allocation of the new customers within the distribution routes (already existing or new) taking into account a complex multi-level objective function. The thesis thus focuses on the process of learning simple dispatch heuristics, and lays the foundations of a recommendation system able to rank such heuristics. We implemented eight of these, observing di erent characteristics of the current eet and orders. It incorporates an arti cial neural network that is trained on two hundred days of past data, and is supervised by schedules produced by an oracle, Indigo, which is a system able to produce suboptimal solutions to problem instances. The system opens the possibility for many dispatch policies to be implemented that are based on this rule ranking, and helps dispatchers to manage the vehicles of the eet. It also provides results for the human resources required each single day and within the di erent periods of the day. We complement the quite promising results obtained with a discussion on future additions and improvements such as channel eet management, tra c consideration, and learning hyper-heuristics to control simple rule sequences.The thesis work was partially supported by the National ICT Australia according to the Visitor Research Agreement contract between NICTA and Martin Damyanov Aleksandro

Ridepooling and public bus services: A comparative case-study

This case-study aims at a comparison of the service quality of time-tabled buses as compared to on-demand ridepooling cabs in the late evening hours in the city of Wuppertal, Germany. To evaluate the service quality of ridepooling as compared to bus services, and to simulate bus rides during the evening hours, transport requests are generated using a predictive simulation. To this end, a framework in the programming language R is created, which automatically combines generalized linear models for count regression to model the demand at each bus stop. Furthermore, we use classification models for the prediction of trip destinations. To solve the resulting dynamic dial-a-ride problem, a rolling-horizon algorithm based on the iterative solution of Mixed-Integer Linear Programming Models (MILP) is used. A feasible-path heuristic is used to enhance the performance of the algorithm in presence of high request densities. This allows an estimation of the number of cabs needed depending on the weekday to realize the same or a better general service quality as the bus system

Heuristics and policies for online pickup and delivery problems

Master ThesisIn the last few decades, increased attention has been dedicated to a speci c subclass of Vehicle Routing Problems due to its signi cant importance in several transportation areas such as taxi companies, courier companies, transportation of people, organ transportation, etc. These problems are characterized by their dynamicity as the demands are, in general, unknown in advance and the corresponding locations are paired. This thesis addresses a version of such Dynamic Pickup and Delivery Problems, motivated by a problem arisen in an Australian courier company, which operates in Sydney, Melbourne and Brisbane, where almost every day more than a thousand transportation orders arrive and need to be accommodated. The rm has a eet of almost two hundred vehicles of various types, mostly operating within the city areas. Thus, whenever new orders arrive at the system the dispatchers face a complex decision regarding the allocation of the new customers within the distribution routes (already existing or new) taking into account a complex multi-level objective function. The thesis thus focuses on the process of learning simple dispatch heuristics, and lays the foundations of a recommendation system able to rank such heuristics. We implemented eight of these, observing di erent characteristics of the current eet and orders. It incorporates an arti cial neural network that is trained on two hundred days of past data, and is supervised by schedules produced by an oracle, Indigo, which is a system able to produce suboptimal solutions to problem instances. The system opens the possibility for many dispatch policies to be implemented that are based on this rule ranking, and helps dispatchers to manage the vehicles of the eet. It also provides results for the human resources required each single day and within the di erent periods of the day. We complement the quite promising results obtained with a discussion on future additions and improvements such as channel eet management, tra c consideration, and learning hyper-heuristics to control simple rule sequences.The thesis work was partially supported by the National ICT Australia according to the Visitor Research Agreement contract between NICTA and Martin Damyanov Aleksandro