Simulated Annealing Approach To Flow Shop Scheduling

Flow Shop Scheduling refers to the process of allotting various jobs to the machines given, such that every job starts to process on a machine n only after it has finished processing on machine n-1, with each job having n operations to be performed one per machine. To find a schedule that leads to the optimal utilization of resources, expects the schedule to finish in a minimum span of time, and also satisfy the optimality criterion set for the related scheduling problem is NP-Hard, if n \u3e 2. In this thesis, we have developed an algorithm adopting a heuristic called Simulated Annealing, to act as a support to the Flow Shop Scheduling. This algorithm tries to deliver good/near optimal solutions to the given scheduling problem, in a reasonable time. We also carry out various tests to determine the behavior of the algorithm as well as to evaluate its effectiveness

A Branch and Bound Method for Sum of Completion Permutation Flow Shop

We present a new branch and bound algorithm for solving three machine permutation flow shop problem where the optimization criterion is the minimization of sum of completion times of all the jobs. The permutation flow shop problem (F||∑Ci ) belongs to the class of NP-hard problems; finding the optimal solution is thus expected to be highly computational. For each solution our scheme gives an approximation ratio and finds near optimal solutions. Computational results for up to 20 jobs are given for 3 machine flow shop problem when the objective is minimizing the sum of completion times. The thesis also discusses a number of related but easier flow shop problems where polynomial optimization algorithms exist

Experimental user interface design toolkit for interaction research (IDTR).

The research reported and discussed in this thesis represents a novel approach to User Interface evaluation and optimisation through cognitive modelling. This is achieved through the development and testing of a toolkit or platform titled Toolkit for Optimisation of Interface System Evolution (TOISE). The research is conducted in two main phases. In phase 1, the Adaptive Control of Thought Rational (ACT-R) cognitive architecture is used to design Simulated Users (SU) models. This allows models of user interaction to be tested on a specific User Interface (UI). In phase 2, an evolutionary algorithm is added and used to evolve and test an optimised solution to User Interface layout based on the original interface design. The thesis presents a technical background, followed by an overview of some applications in their respective fields. The core concepts behind TOISE are introduced through a discussion of the Adaptive Control of Thought “ Rational (ACT-R) architecture with a focus on the ACT-R models that are used to simulate users. The notion of adding a Genetic Algorithm optimiser is introduced and discussed in terms of the feasibility of using simulated users as the basis for automated evaluation to optimise usability. The design and implementation of TOISE is presented and discussed followed by a series of experiments that evaluate the TOISE system. While the research had to address and solve a large number of technical problems the resulting system does demonstrate potential as a platform for automated evaluation and optimisation of user interface layouts. The limitations of the system and the approach are discussed and further work is presented. It is concluded that the research is novel and shows considerable promise in terms of feasibility and potential for optimising layout for enhanced usability