Investor bears responsibility for proper preparation of the investment process. One of his tasks is to prepare the project documentation and obtaina building permit. Frequently, during his work, there are situations and events whose im pact interferes with the design solutions. Regardless of reasons, alterations to a project constitute a source of cost risk. In each case, the Investor should be prepared for this type of a risk. Exposure to risk should be taken into account in the planning stage of the investment. Also, a model of investment execution should be chosen at this stage. The type of model is associated with the distribution of risk throughout the project. The aim of this paper is to identify events that generate risk related to alterations to Project Documentation in the context of the selection of the investment executionmodel.

In this work we consider a problem from the field of power- and energy-aware scheduling, in which a set of batteries have to be charged in a minimum time. The formulated problem is to schedule independent and nonpreemptable jobs to minimize the schedule length, where each job requires some amount of power and consumes a certain amount of energy during its processing. We assume that the power demand of each job linearly decreases with time, as it is the case when Li-ion batteries are being charged. For the assumed job model we prove that each next job should be started as soon as the required amount of power is available. Basing on the proven theorem we formulate a procedure generating a minimum-length schedule for an assumed order of jobs. We also analyze the case of identical jobs, and show some interesting properties of this case.

Most scheduling methods used in the construction industry to plan repetitive projects assume that process durations are deterministic. This assumption is acceptable if actions are taken to reduce the impact of random phenomena or if the impact is low. However, construction projects at large are notorious for their susceptibility to the naturally volatile conditions of their implementation. It is unwise to ignore this fact while preparing construction schedules. Repetitive scheduling methods developed so far do respond to many constructionspecific needs, e.g. of smooth resource flow (continuity of work of construction crews) and the continuity of works. The main focus of schedule optimization is minimizing the total time to complete. This means reducing idle time, but idle time may serve as a buffer in case of disruptions. Disruptions just happen and make optimized schedules expire. As process durations are random, the project may be delayed and the crews’ workflow may be severely affected to the detriment of the project budget and profits. For this reason, the authors put forward a novel approach to scheduling repetitive processes. It aims to reduce the probability of missing the deadline and, at the same time, to reduce resource idle time. Discrete simulation is applied to evaluate feasible solutions (sequence of units) in terms of schedule robustness.

A method of creating production schedules regarding production lines with parallel machines is presented. The production line setup provides for intermediate buffers located between individual stages. The method mostly concerns situations when part of the production machines is unavailable for performance of operations and it becomes necessary to modify the original schedule, the consequence of which is the need to build a new schedule. The cost criterion was taken into account, as the schedule is created with the lowest possible costs regarding untimely completion of products (e.g. fines for delayed product completion). The proposed method is relaxing heuristics, thanks to which scheduling is performed in a relatively short time. This was confirmed by the presented results of computational experiments. These experiments were carried out for the rescheduling of machine parts production.

Optimization in mine planning could improve the economic benefit for mining companies. The main optimization contents in an underground mine includes stope layout, access layout and production scheduling. It is common to optimize each part sequentially, where optimal results from one phase are treated as the input for the next phase. The production schedule is based on the mining design. Access layout plays an important role in determining the connection relationships between stopes. This paper proposes a shortest-path search algorithm to design a network that automatically connects each stope. Access layout optimization is treated as a network flow problem. Stopes are viewed as nodes, and the roads between the stopes are regarded as edges. Moreover, the decline location influences the ore transport paths and haul distances. Tree diagrams of the ore transportation path are analyzed when each stope location is treated as an alternative decline location. The optimal decline location is chosen by an enumeration method. Then, Integer Programming (IP) is used to optimize the production scheduling process and maximize the Net Present Value (NPV). The extension sequence of access excavation and stope extraction is taken into account in the optimization model to balance access development and stope mining. These optimization models are validated in an application involving a hypothetical gold deposit, and the results demonstrate that the new approach can provide a more realistic solution compared with those of traditional approaches.

The problem of performing software tests using Testing-as-a-Service cloud environment is considered and formulated as an~online cluster scheduling on parallel machines with total flowtime criterion. A mathematical model is proposed. Several properties of the problem, including solution feasibility and connection to the classic scheduling on parallel machines are discussed. A family of algorithms based on a new priority rule called the Smallest Remaining Load (SRL) is proposed. We prove that algorithms from that family are not competitive relative to each other. Computer experiment using real-life data indicated that the SRL algorithm using the longest job sub-strategy is the best in performance. This algorithm is then compared with the Simulated Annealing metaheuristic. Results indicate that the metaheuristic rarely outperforms the SRL algorithm, obtaining worse results most of the time, which is counter-intuitive for a metaheuristic. Finally, we test the accuracy of prediction of processing times of jobs. The results indicate high (91.4%) accuracy for predicting processing times of test cases and even higher (98.7%) for prediction of remaining load of test suites. Results also show that schedules obtained through prediction are stable (coefficient of variation is 0.2‒3.7%) and do not affect most of the algorithms (around 1% difference in flowtime), proving the considered problem is semi-clairvoyant. For the Largest Remaining Load rule, the predicted values tend to perform better than the actual values. The use of predicted values affects the SRL algorithm the most (up to 15% flowtime increase), but it still outperforms other algorithms.