In this paper we describe a complex optimization application arising in maintenance scheduling, developed in close collaboration with an industrial partner. We have to plan and schedule preventive and corrective maintenance activities at customer sites by a group of traveling repair technicians. A specific property of the problem considered here is a mix of customers in both urban centers and rural areas. This means that travel times between customers must be considered when balancing overall workload for each agent. We discuss a problem decomposition compatible with current management practice, describe different solvers for the individual problem steps, and show results on real-world data from the industrial partner.

Energy consumed by computation and cooling represents the greatest percentage of the average energy consumed in a data centre. As these two aspects are not always coordinated, energy consumption is not optimised. Data centres lack an integrated system that jointly optimises and controls all the operations in order to reduce energy consumption and increase the usage of renewable sources. GENiC is addressing this through a novel scalable, integrate energy management and control platform for data centre wide optimisation. We have implemented and prototype of the platform together with workload and thermal management algorithms. We evaluate the algorithms in a simulation based model of a real data centre. Results show significant energy savings potential, in some cases up to 40%, by integrating workload and thermal management.

Energy consumption in data centres accounts for a significant proportion of national energy usage in many countries. One approach for reducing energy consumption is to improve the server usage efficiency via workload consolidation. However, there are two primary reasons why this is not done to a large extent. The first reason is that greater consolidation could result in violations of Service Level Agreements (SLAs) if resources are over-utilised. The second reason is that users specify the requirements of a virtual machine (VM) based on the maximum estimated usage for each resource over the whole life span of the VM, and usually over-estimate these maximum values to avoid possible contract violations. Typically, the VM will have significantly lower resource usage in most time periods. Recently, a number of methods have been proposed to predict resource usage of VMs. We show that although these prediction techniques are efficient when their performances are measured using well known metrics, a low prediction error can still result in significant violations of SLAs if not handled properly during workload allocation. Our results emphasise the importance of analysing workload prediction in conjunction with workload allocation techniques. We examine the impact of using predicted resource usage for optimal server consolidation. We investigate the occurrences of over-utilised resources on servers due to under-predicted resource usage. We propose methods to reduce the likelihood of such occurrences, both through the enforcement of safety capacities on the server side, and through biasing towards over-prediction on the VM side. The results indicate that an appropriate balance can be found between energy savings and non-violation of SLAs.

The electric rail sector, like many sectors, is looking for means to reduce its energy consumption and energy cost. In this work we consider the scenario where the utility provider charges based on the maximum consumption over a period. Therefore one wishes to schedule the departure of trains such that the aggregate load is balanced across time periods while satisfying timetabling and resource restrictions. We present an approach which combines the strengths of a number of research areas such as constraint programming, linear programming, mixed-integer programming, and large neighbourhood search. The empirical performance on instances from an ongoing research challenge demonstrates the approach's ability to dramatically reduce the overall energy cost. In addition, we are able to close a number of the instances for which we prove optimality.

We introduce a simple technique for disjunctive machine scheduling problems and show that this method can match or even outperform state-of-the-art algorithms on a number of problem types. Our approach combines a number of generic search techniques such as restarts, adaptive heuristics, and solution-guided branching on a simple model based on a decomposition of disjunctive constraints and on the reification of these disjuncts. This paper describes the method and its application to variants of the job shop scheduling problem (JSP). We show that our method can easily be adapted to handle additional side constraints and different objective functions, often outperforming the state-of-the-art and closing a number of open problems. Moreover, we perform in-depth analysis of the various factors that make this approach efficient. We show that, while most of the factors give moderate benefits, the variable and value ordering components are key.