Production planning is a critical component in supply chain management. The goal of production planning is to meet market demand while minimizing operational costs. There is inherent uncertainty in manufacturing systems due to unscheduled shutdowns and variable production rates. Additionally, actual demand levels cannot be predicted accurately. As a result, there is value in creating a production plan that considers these uncertainties. Scheduling is also a critical component in supply chain management, but at a smaller level of time granularity. Industrially sized scheduling problems are often on such a large scale that the problem is computationally difficult to solve. Consequently, there is value in creating a mathematical model and selecting a solution algorithm that minimizes this burden. This work aims to determine the benefit of a stochastic production planning model over its deterministic counterpart. The problem utilizes a multi-period, multi-product aggregated planning model with a finite horizon in a steel manufacturing environment. The production and demand uncertainty is modelled as a two-stage stochastic mixed integer linear program. The problem utilizes a Monte Carlo simulation technique to create the scenarios used in the optimization. The objective of the optimization is to determine the production volume and inventory levels for each discrete time interval while minimizing the weighted cost of production and surplus. The production decisions must be non-anticipative, immediately implementable, and are subjected to production capacity and inventory holding constraints. This work also investigates the advantages a cost-based model has over its goal-programming counterpart. Finally, this thesis develops several mathematical batch scheduling models that use different modelling paradigms in an effort to compare their computational complexity. With the selection of an appropriate model, model extensions are added to replicate an industrially relevant steel mill scheduling problem for a finishing line using data from a facility located in Ontario, Canada. / Thesis / Master of Applied Science (MASc)

This thesis investigates energy fairness in an environment where multiple mobile cloud computing users are attempting to utilize both a shared channel and a shared server to offload jobs to remote computation resources, a technique known as mobile computation offloading. This offloading is done in an effort to reduce energy consumption at the mobile device, which has been demonstrated to be highly effective in previous work. However, insufficient resources are available for all mobile devices to offload all generated jobs due to constraints at the shared channel and server. In addition to these constraints, certain mobile devices are at a disadvantage relative to others in their achievable offloading rate. Hence, the shared resources are not necessarily shared fairly, and an effort must be made to do so. A method for improving offloading fairness in terms of total energy is derived, in which the state of the queue of jobs waiting for offloading is evaluated in an online fashion, at each job arrival, in order to inform an offloading decision for that newest arrival; no prior state or future predictions are used to determine the optimal decision. This algorithm is evaluated by comparing it on several criteria to standard scheduling methods, as well as to an optimal offline (i.e., non-causal) schedule derived from the solution of a min-max energy integer linear program. Various results derived by simulation demonstrate the improvements in energy fairness achieved. / Thesis / Master of Applied Science (MASc)

The Mississippi River Valley Alluvial Aquifer (MRVAA) is declining precipitously due to irrigation withdrawal for row-crops. The development of scientific irrigation scheduling techniques and for soybean (Glycine max L.) will reduce withdrawal from the MRVAA. The objective of this research was to determine if soybean grain yield, irrigation water use efficiency (IWUE), and net return above irrigation cost could be optimized using a static irrigation threshold or if the irrigation threshold should be changed as a function of plant growth stage.

The flow shop scheduling problem is a classical combinatorial problem being studied for years. The focus of this research is to study two variants of the flow shop scheduling problem in order to minimize makespan by scheduling n jobs on m machines. A solution approach is developed for the modified flow shop problem with due dates and release times. This algorithm is an attempt to contribute to the limited literature for the problem. Another tabu search-based solution approach is developed to solve the classical flow shop scheduling problem. This meta-heuristic (called 3XTS) allows an efficient search of the neighboring solutions leading to a fast solution procedure. Several control parameters affecting the quality of the algorithm are experimentally tested, and certain rules are established for different problem instances. The 3XTS is compared to another tabu search method (that seems to be a champion) in terms of solution quality and computation time.

This study provides heuristic approaches, including an ant colony optimization (ACO) inspired heuristic, to solve a crane scheduling problem that exists in most shipyards, where cranes are a primary means of processing and handling materials. Cranes move on a network of tracks, thus, blocking of crane movements is an issue. The crane scheduling problem consists of two major sub-problems: scheduling problem that determines the best overall order in which jobs are to be performed; the assignment problem that assigns cranes to jobs. The proposed heuristic consists of an Earliest Due Date sorting procedure in combination with an ACO assignment procedure that aims to satisfy the objectives of minimizing makespan while maximizing crane utilization. Test data sets of various sizes are generated and the results of the proposed approach are compared to other developed heuristics. The proposed approach outperforms others in both objective measures and obtains solutions in a timely manner.

No description available.

No description available.

No description available.

No description available.

No description available.