Offline and online variants of the Traveling Salesman Problem

In this thesis, we study several well-motivated variants of the Traveling Salesman Problem (TSP). First, we consider makespan minimization for vehicle scheduling problems on trees with release and handling times. 2-approximation algorithms were known for several variants of the single vehicle problem on a path. A 3/2-approximation algorithm was known for the single vehicle problem on a path where there is a fixed starting point and the vehicle must return to the starting point upon completion. Karuno, Nagamochi and Ibaraki give a 2-approximation algorithm for the single vehicle problem on trees. We develop a Polynomial Time Approximation Scheme (PTAS) for the single vehicle scheduling problem on trees which have a constant number of leaves. This PTAS can be easily adapted to accommodate various starting/ending constraints. We then extended this to a PTAS for the multiple vehicle problem where vehicles operate in disjoint subtrees. We also present competitive online algorithms for some single vehicle scheduling problems. Secondly, we study a class of problems called the Online Packet TSP Class (OP-TSP-CLASS). It is based on the online TSP with a packet of requests known and available for scheduling at any given time. We provide a 5/3 lower bound on any online algorithm for problems in OP-TSP-CLASS. We extend this result to the related k-reordering problem for which a 3/2 lower bound was known. We develop a κ+1-competitive algorithm for problems in OP-TSP-CLASS, where a κ-approximation algorithm is known for the offline version of that problem. We use this result to develop an offline m(κ+1)-approximation algorithm for the Precedence-Constrained TSP (PCTSP) by segmenting the n requests into m packets. Its running time is mf(n/m) given a κ-approximation algorithm for the offline version whose running time is f(n)

Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree

A cross-docking terminal enables consolidating and sorting fast-moving products along supply chain networks and reduces warehousing costs and transportation efforts. The target efficiency of such logistic systems results from synchronizing the physical and information flows while scheduling receiving, shipping and handling operations. Within the tight time-windows imposed by fast-moving products (e.g., perishables), a deterministic schedule hardly adheres to real-world environments because of the uncertainty in trucks arrivals. In this paper, a stochastic MILP model formulates the minimization of penalty costs from exceeding the time-windows under uncertain truck arrivals. Penalty costs are affected by products' perishability or the expected customer’ service level. A validating numerical example shows how to solve (1) dock-assignment, (2) while prioritizing the unloading tasks, and (3) loaded trucks departures with a small instance. A tailored stochastic genetic algorithm able to explore the uncertain scenarios tree and optimize cross-docking operations is then introduced to solve scaled up instaces. The proposed genetic algorithm is tested on a real-world problem provided by a national delivery service network managing the truck-to-door assignment, the loading, unloading, and door-to-door handling operations of a fleet of 271 trucks within two working shifts. The obtained solution improves the deterministic schedule reducing the penalty costs of 60%. Such results underline the impact of unpredicted trucks’ delay and enable assessing the savings from increasing the number of doors at the cross-dock

Federated Robust Embedded Systems: Concepts and Challenges

The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements. While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development. During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs

Mission scheduler for a rail guided vehicle system

A transport system with automatic guided vehicles AGVs, is a fully automatic system that provides logistics services in industrial environments such as warehouses and production plants. These systems have reached such a degree of maturity as to allow, in their daily use, the application of heuristic algorithms for the optimization of the various operations they perform. For instance, find the shortest paths between working stations and storage area, assign movements and strategic positions for idle vehicles, operate efficient and long-life battery management and more. A relevant interesting algorithm, presented and developed in this thesis, concerns the sorting of products in the shipping phase, which affects the scheduling tasks assigned to the autonomous vehicles. The scheduler has the aims of determining which operations have more strict constraints and more priority over others. Studies and practice have shown that the adoption of a valid scheduler implies considerable improvements in the system performance, consequently it is advisable to dedicate time and effort to the research for the right one. The following algorithms obtained a successful outcome and they have been implemented for the production of a modern automated warehouse located in the city of Cesena, Italy. The paper is divided into four chapters, with a further one dedicated to conclusions

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested