Dr Hamish Waterer

© 2016 INFORMS. Anew mixed-integer linear programming (MILP) formulation for nonpreemptive single machine scheduling problems is presented. The model is a generalisation of the classical time indexed (TI) model to one in which at most two jobs can be processing in each time period. Like the TI model, the new model, called the bucket indexed (BI) model, partitions the planning horizon into periods of equal length, or buckets. Unlike the TI model, the length of a period is a parameter of the BI model and can be chosen to be as long as the processing time of the shortest job. The two models are equivalent if a period is of unit length, but when longer periods are used in the BI model, it can have significantly fewer variables and nonzeros than the corresponding TI model. A computational study using weighted tardiness instances, and weighted completion time instances with release dates, reveals that the BI model significantly outperforms the TI model on instances where the minimum processing time of the jobs is large. Furthermore, the performance of the BI model is less vulnerable to increases in average processing time when the ratio of the largest processing time to the smallest is held constant.

Strategic capacity planning is a core activity for the Hunter Valley Coal Chain Coordinator as demand for coal is expected to double in the next decade. Optimization and simulation models are used to suggest and evaluate infrastructure expansions and operating policy changes. These models require input data in the form of shipping stems, which are arrival streams of ships at the port, together with their cargo types and composition. Creating shipping stems that accurately represent future demand scenarios has been a time-consuming and daunting challenge. We describe an optimization-based decision support tool that facilitates and enhances this process, and which has become an integral part of the company's work flow. The tool embeds sampling to enable the generation of multiple shipping stems for a single demand scenario, employs targets, and desirable and permissable ranges to specify and control the characteristics of the shipping stems, and uses integer programming in a hierarchical fashion to generate shipping stems that best meet the set goals. © 2014 Elsevier Ltd.

In this paper we present an integer programming method for solving the Classroom Assignment Problem in University Course Timetabling. We introduce a novel formulation of the problem which generalises existing models and maintains tractability even for large instances. The model is validated through computational results based on our experiences at the University of Auckland, and on instances from the 2007 International Timetabling Competition. We also expand upon existing results into the computational difficulty of room assignment problems. © 2014 Elsevier Ltd.