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 construction contractor is concerned with reducing the cost of the project, including reducing unnecessary downtime. This is achieved when resources are fully utilized; this means the crews work continuously moving without interruption from one location to the other. However, any disturbance in the optimally scheduled workflow caused by random events is likely to result in delays, interruptions in the crews work, and productivity losses. There is therefore a need for scheduling methods that allow plans to be more resilient to disruptions and ensure a reduction in downtime and implementation costs. The authors put forward a proactive-reactive approach to the schedule risk management. Proposed method makes it possible to protect schedule deadlines from the impact of risk factors by allocating time buffers (proactive approach). It also takes into account the measures that managers take during execution in response to delays that occur, such as changing construction methods, employing extra resources, or working overtime (reactive approach). It combines both ideas and is based on project simulation technique. The merits of the proposed approach are illustrated by a case of a repetitive project to erect a number of buildings. The presented example proves that the proposed method enables the planner to estimate the scale of delays of processes’ start and consider the impact of measures to reduce duration of processes in particular locations taken in reaction to delays. Thus, it is possible to determine the optimal schedule, at which the costs of losses associated with delays and downtime are minimal.

It is a usual practice for a contractor to deliver several projects at a time. Typically, the projects involve similar types of works and share the same pool of resources (i.e. construction crews). For this reason, the company’s portfolio of orders considered for a particular planning horizon can be modeled as a project with repeatable processes to be performed in heterogeneous units located in a number of construction sites. Its scheduling requires determining the best sequence of the resources’ moving from unit to unit while minding the due dates related with particular orders as well as resource continuity constraints. The authors present a model of this scheduling problem in the form of a mixed-integer linear program. The aim is to schedule a portfolio of projects in a way that minimizes the total of the resource idle time-related costs, the indirect costs, and the delay penalties. The model can be solved by means of a general-purpose solver. The model is applied to schedule a portfolio of multifamily housing projects.