A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Energy Harvesting Wireless Communications: A Review of Recent Advances

This article summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed as well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications (Special Issue: Wireless Communications Powered by Energy Harvesting and Wireless Energy Transfer

Serial-batch scheduling – the special case of laser-cutting machines

The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning

Analysis and Design of Communication Policies for Energy-Constrained Machine-Type Devices

This thesis focuses on the modelling, analysis and design of novel communication strategies for wireless machine-type communication (MTC) systems to realize the notion of Internet of things (IoT). We consider sensor based MTC devices which acquire physical information from the environment and transmit it to a base station (BS) while satisfying application specific quality-of-service (QoS) requirements. Due to the wireless and unattended operation, these MTC devices are mostly battery-operated and are severely energy-constrained. In addition, MTC systems require low-latency, perpetual operation, massive-access, etc. Motivated by these critical requirements, this thesis proposes optimal data communication policies for four different network scenarios. In the first two scenarios, each MTC device transmits data on a dedicated orthogonal channel and either (i) possess an initially fully charged battery of finite capacity, or (ii) possess the ability to harvest energy and store it in a battery of finite capacity. In the other two scenarios, all MTC devices share a single channel and are either (iii) allocated individual non-overlapping transmission times, or (iv) randomly transmit data on predefined time slots. The proposed novel techniques and insights gained from this thesis aim to better utilize the limited energy resources of machine-type devices in order to effectively serve the future wireless networks. Firstly, we consider a sensor based MTC device communicates with a BS, and devise optimal data compression and transmission policies with an objective to prolong the device-lifetime. We formulate joint optimization problems aiming to maximize the device-lifetime whilst satisfying the delay and bit-error-rate constraints. Our results show significant improvement in device-lifetime. Importantly, the gain is most profound in the low latency regime. Secondly, we consider a sensor based MTC device that is served by a hybrid BS which wirelessly transfers power to the device and receives data transmission from the device. The MTC device employs data compression in order to reduce the energy cost of data transmission. Thus, we propose to jointly optimize the harvesting-time, compression and transmission design, to minimize the energy cost of the system under given delay constraint. The proposed scheme reduces energy consumption up to 19% when data compression is employed. Thirdly, we consider multiple MTC devices transmit data to a BS following the time division multiple access (TDMA). Conventionally, the energy-efficiency performance in TDMA is optimized through multi-user scheduling, i.e., changing the transmission time allocated to different devices. In such a system, the sequence of devices for transmission, i.e., who transmits first and who transmits second, etc., does not have any impact on the energy-efficiency. We consider that data compression is performed before transmission. We jointly optimize both multi-user sequencing and scheduling along with the compression and transmission rate. Our results show that multi-user sequence optimization achieves up to 45% improvement in the energy-efficiency at MTC devices. Lastly, we consider contention resolution diversity slotted ALOHA (CRDSA) with transmit power diversity where each packet copy from a device is transmitted at a randomly selected power level. It results in inter-slot received power diversity, which is exploited by employing a signal-to-interference-plus-noise ratio based successive interference cancellation (SIC) receiver. We propose a message passing algorithm to model the SIC decoding and formulate an optimization problem to determine the optimal transmit power distribution subject to energy constraints. We show that the proposed strategy provides up to 88% system load performance improvement for massive-MTC systems

Ordonnancement de camions dans une plateforme logistique : complexité, méthodes de résolution et incertitudes

La problématique dite de crossdocking a été source de beaucoup d'attention ces dernières années dans la littérature. Un crossdock est une plateforme logistique favorisant, par une synchronisation efficace des camions entrants et sortants, une rotation rapide des produits, le volume de produits stockés devant être le plus faible possible. Le crossdocking soulève de nombreux problèmes logistiques, dont notamment celui de l'ordonnancement des camions entrants et sortants sur les quais de la plateforme. L'objectif classiquement considéré dans la littérature pour ce problème est la minimisation du makespan, critère très répandu en d'ordonnancement. Pour le crossdocking néanmoins, minimiser la date de départ du dernier camion ne garantie pas nécessairement une bonne synchronisation des camions et le makespan ne semble donc pas être l'objectif le plus pertinent. Pour répondre au besoin de synchronisation et favoriser les rotations rapides, notre travail propose alternativement de minimiser la somme des temps de séjour des palettes dans le stock. Nous étudions d'abord la version déterministe de ce problème d'ordonnancement. Sa complexité est détaillée selon différentes hypothèses pour identifier les éléments menant à sa NP-difficulté. Différentes méthodes de résolutions sont proposées. Une méthode classique de programmation linéaire en nombres entiers utilisant des variables de décision indexées par le temps. Une famille d'inégalités valides est également proposée et exploitée dans un algorithme avec ajout itératif de coupes. Des méthodes basées sur la programmation par contraintes sont enfin proposées. Une analyse comparative de ces différentes méthodes est proposée. Dans un deuxième temps, nous étudions une version non-déterministe de notre problème d'ordonnancement dans laquelle des incertitudes sur les dates d'arrivée des camions sont introduites sous la forme d'intervalles de temps équiprobables. Une méthode d'ordonnancement proactive-réactive utilisant le concept de groupes d'opérations permutables est proposée pour faire face aux incertitudes. Des groupes de camions permutables sont séquencés et affectés aux quais puis, durant l'exécution d'ordonnancement, en fonction de la réalisation des dates d'arrivée, un ordre est choisi dans chaque groupe à l'aide d'un algorithme réactif.Crossdocking has received a lot of attention in the literature in recent years. A crossdock is a logistic platform that promotes rapid product turnover through efficient synchronization of incoming and outgoing trucks, with the volume of products stored being kept as low as possible. Crossdocking raises many logistical problems, including the scheduling of incoming and outgoing trucks on the platform's docks. The classical objective considered in the literature for this problem is the minimization of the makespan, a very common criterion in scheduling. However, for crossdocking, minimizing the departure date of the last truck does not necessarily guarantee a good synchronization of the trucks and the makespan does not seem to be the most relevant objective. In order to meet the need for synchronization and to help fast rotations, our work proposes alternatively to minimize the sum of the pallets' sojourn times in the warehouse. We first study the deterministic version of this scheduling problem. Its complexity is detailed under different assumptions to identify the elements leading to its NP-hardness. Different solution methods are proposed. A classical integer linear programming method using time-indexed decision variables. A family of valid inequalities is also proposed and exploited in an algorithm with iterative addition of cuts. Finally, methods based on constraint programming are proposed. A comparative analysis of these different methods is proposed. In a second step, we study a non-deterministic version of our scheduling problem in which uncertainties on truck arrival dates are introduced in the form of equiprobable time intervals. A proactive-reactive scheduling method using the concept of permutable operation groups is proposed to cope with the uncertainties. Groups of permutable trucks are sequenced and assigned to the docks and then, during the scheduling run, based on the realization of arrival dates, an order is chosen in each group using a reactive algorithm