Devolpment and Modeling of an Industrial Process Plan, Its Optimization using stochastic search Optimization Technique

Industrial process planning is principally an association between design and development or final production and has vital function in the manufacturing systems. In this paper the under research industry is security vehicle manufacturing industry in Pakistan. First of all a fundamental process plan is developed and then modeled mathematically using progressive closed loop approach. Mathematically modeled process plan is then optimized in order to find optimal or sub optimal solutions. Research then investigates the capability of an innovative optimization technique called stochastic search in handling optimization of manufacturing process plan. This new technique of stochastic, searches the best approximate process planning solution. Finally the research examines the convergence of optimization techniques to an optimal solution for a manufacturing framework.

A stochastic approach to handle resource constraints as knapsack problems in ensemble pruning

Ensemble-based methods are highly popular approaches that increase the accuracy of a decision by aggregating the opinions of individual voters. The common point is to maximize accuracy; however, a natural limitation occurs if incremental costs are also assigned to the individual voters. Consequently, we investigate creating ensembles under an additional constraint on the total cost of the members. This task can be formulated as a knapsack problem, where the energy is the ensemble accuracy formed by some aggregation rules. However, the generally applied aggregation rules lead to a nonseparable energy function, which takes the common solution tools—such as dynamic programming—out of action. We introduce a novel stochastic approach that considers the energy as the joint probability function of the member accuracies. This type of knowledge can be efficiently incorporated in a stochastic search process as a stopping rule, since we have the information on the expected accuracy or, alternatively, the probability of finding more accurate ensembles. Experimental analyses of the created ensembles of pattern classifiers and object detectors confirm the efficiency of our approach over other pruning ones. Moreover, we propose a novel stochastic search method that better fits the energy, which can be incorporated in other stochastic strategies as well.

Exploration and Exploitation Zones in a Minimalist Swarm Optimiser

The trade off between exploration and exploitation is one of the key challenges in evolutionary and swarm optimisers which are led by guided and stochastic search. This work investigates the exploration and exploitation balance in a minimalist swarm optimiser in order to offer insights into the population’s behaviour. The minimalist and vector-stripped nature of the algorithm—dispersive flies optimisation or DFO—reduces the challenges of understanding particles’ oscillation around constantly changing centres, their influence on one another, and their trajectory. The aim is to examine the population’s dimensional behaviour in each iteration and each defined exploration-exploitation zone, and to subsequently offer improvements to the working of the optimiser. The derived variants, titled unified DFO or uDFO, are successfully applied to an extensive set of test functions, as well as high-dimensional tomographic reconstruction, which is an important inverse problem in medical and industrial imaging.

Automatic delimitation of labour market areas based on multi-criteria optimisation: The case of Spain 2011

Labour market areas (LMAs) are a type of functional region (FR) defined on commuting flows and used in many countries to serve as the territorial reference for regional studies and policy making at local levels. Existing methods rely on manual adjustments of the results to ensure high quality, making them difficult to be monitored, hard to apply to different territories, and onerous to produce in terms of required work-hours. We propose an approach to automatise all stages of the delineation procedure and improve the final results, building upon a state-of-the-art stochastic search procedure that ensures optimal allocation of municipalities/counties to LMAs while keeping good global indicators: a pre-processing layer clusters adjoining municipalities with strong commuting flows to constrain the initial search space of the stochastic search, and a multi-criteria heuristic corrects common deficiencies that derive from global maximisation approaches or simple greedy heuristics. It produces high quality LMAs with optimal local characteristics. To demonstrate this methodology and assess the improvement achieved, we apply it to define LMAs in Spain based on the latest commuting data.