Semester 1, 2012.

  Lecturer/Coordinator: Dr Otto Konstandatos This assignment has several parts of unequal value. Some parts are technical, and are designed to help you understand the connection between Mathematics and Econometric Regression Analysis. You must type or neatly hand-write such answers in the spaces provided. Other parts are applied, and are designed to help you develop practical econometric skills. You will apply regression analysis as done by economists when estimating production functions to real world data. These parts will test your econometric modelling skills using multiple regression and Eviews.

  For these parts you may be required to report your Eviews output in the spaces below. This assignment may be done in groups of up to five students who either are in the same formal tutorial group as you, http://www.ttc-switch.com/Assignment_Writing/ or who have the same tutor but are from another tutorial group. When you hand in your assignment you must include this cover sheet. No names may be added onto the group lists apart from the names that appear below at hand-in.

  Due Time/Date: 12:00pm (noon) on Wednesday 30-05-2012

  Deposit your assignment script into your assigned tutor’s assignment box on Level 3, Building 5 in Finance and Economics on the due date by the indicated time. http://www.ttc-switch.com/Assignment_Writing/2590.html Late assignments will require you to lodge a Special Consideration application and will otherwise not be accepted.

  Name Student Number Tutor’s Name: Tutorial Day and Time: Date stamp or tutor’s signature and date
   

    A recurring problem in project management involves the allocation of scarce resources to the individual jobs comprising the project. In many situations such as audit scheduling, the resources correspond to individuals (skilled labour). This naturally leads to an assignment type project scheduling problem, i.e. a project has to be processed by assigning one of several individuals (resources) to each job. In this paper we consider the nonpreemptive variant of a resource-constrained project job-assignment problem, where job durations as well as costs depend upon the assigned resource. Regarding precedence relations as well as release dates and deadlines, the question arises, to which jobs resources should be assigned in order to minimize overall costs. For solving this time-resource-cost-tradeoff problem we present a hybrid brand and bound/dynamic programming algorithm with a (rather efficient Monte Carlo type) heuristic upper bounding technique as well as various relaxation procedures for determining lower bounds. Computational results are presented as well.
 

  Managers of construction projects, maintenance activities, auditing contracts, software shops, etc. are frequently faced with the task of establishing a new project's due date, which must compete with other projects already in progress or expected (forecasted) to start in the future. The study reported here addresses the problem of establishing due dates for projects which require limited resources, in an environment where new projects arrive continuously and randomly over time. A set of procedures is developed which set each project's due date when it arrives using information about the new project, current projects, and available resources. The due date setting procedures are tested via simulation with four activity scheduling heuristics that control the assignment of resources to specific activities of available projects. A second test demonstrates the performance of the due date procedures, where a portion of arriving projects have their due dates established by external forces beyond management's control. Performance measures of project mean completion time, project mean lateness, project standard deviation of lateness, and total tardiness (sum of all projects' tardy time) were collected for evaluation.

  This study presents a number of important results for managers interested in scheduling projects and setting due dates. First, using more information concerning the current work in progress, available resources, and activity precedent relationships provides a better due date estimate for a new project. Second, a finite scheduling procedure (called SFT) consistently gives better due date estimates than simpler aggregate procedures. Third, when some project due dates are set externally, due date performance deteriorates. However, when SFT is combined with a due date oriented activity scheduling rule, due dale performance deterioration is less. Fourth, the effort, measured by CPU time, for SFT to estimate a good due date depends upon the ratio of activity resources required to total resources available, rather than the number of activities across all projects. And fifth, similarities and differences between the results observed in this study and past due date job shop scheduling research are reviewed.