How meta-heuristic algorithms contribute to deep learning in the hype of big data analytics

Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and hierarchical layers. DL is believed to be a suitable tool so far for extracting insights from very huge volume of so-called big data. Nevertheless, one of the three “V” or big data is velocity that implies the learning has to be incremental as data are accumulating up rapidly. DL must be fast and accurate. By the technical design of DL, it is extended from feed-forward artificial neural network with many multi-hidden layers of neurons called deep neural network (DNN). In the training process of DNN, it has certain inefficiency due to very long training time required. Obtaining the most accurate DNN within a reasonable run-time is a challenge, given there are potentially many parameters in the DNN model configuration and high dimensionality of the feature space in the training dataset. Meta-heuristic has a history of optimizing machine learning models successfully. How well meta-heuristic could be used to optimize DL in the context of big data analytics is a thematic topic which we pondered on in this paper. As a position paper, we review the recent advances of applying meta-heuristics on DL, discuss about their pros and cons and point out some feasible research directions for bridging the gaps between meta-heuristics and DL

Determine planning method in the storage area

ABSTRACT Ji Yuan Hai Wan Shi Ye AB started in the December of 2011, is co-funded by the Xiamen Hai Wan AB and Luo Yang Shi Hua. It has integrated business such as scientific research, production and trade together. The company locates in the industrial manufactory area at Ji Yuan, Henan Province. The main products are verity of unsaturated polyester resin. Their products are widely used in the fields like industry, agricultural, transportation and construction. This shows its wide coverage status quo and broad future (Company´s website). Inventory is the network-node among goods flow, information flow and cash flow, and is of great importance to economic development nowadays. The rational management in transportation, packaging, loading/unloading, processing, delivery, can efficiently alleviate working strength, damage ratio, Inventory turnover and distribution costs. The storage area is not only the most important but also the most difficult part to plan. According to the statistics, unloading, picking, sorting and loading take 40% time of the whole process, and the rest are consumed by staff’s patrolling. As a result, a cost effective design in storage area can effectively lower down internal transportation cost, and highly improve the performance of the whole inventory. In order to increase the efficiency inside storage area, and create a best possible environment for material handlers in the inventory; this article intended to generate a directive description by studying the design inside storage area of the inventory. Based on the case study, author has raised a recommendation to the inventory´s equipment and slot planning. MATLAB is the main programming tool in the slot planning and it contains aspects below: 1, A brief introduction to company and their main activities. 2, Storage area planning and design theories: The definition, types, functions of storage area were introduced in the beginning. Then storage area and several ways of storage were briefly described. At last, author described the classical EIQ analysis and its applications in the logistics system. Page IV 3, Selection of equipment in storage area Author elaborated basic principles of the equipment choice; and introduced types, choice methods in storing and material handling equipment. Finally the EIQ analysis was applied and its influence in equipment choice was proved. 4, Planning and design of slots in the storage area This part mainly included following contents A. Coding The article began with methodology introduction for the coding of both location and goods, and the principles of choice methodology for different goods. B. Spacing plan and corresponding information Principles and concepts of location planning as well as corresponding benefits were introduced, which are the reasons of location planning. C. Modeling The author has presented the aim of the location planning; and according to that, a model has been introduced. There are two things has to be concerned, one is the total transporting distance for the goods and the other one is the stability of the racking. The aim of the location planning is to increase picking efficiency by storing the fastest-moving goods in the most convenient positions which is gate in this case. To increase racking ´s stability, lighter goods have to put in higher than heavier things. D. The algorithm Location planning is a multi-objective optimization problem. Author has analyzed and compared several algorithms; finally genetic algorithm was chosen to solve the problem. Author introduced the principles of the methodology about using genetic algorithm to solve a multi-objective optimization problem. After that, according to the model introduced in the previously step, author has decided to use parallel selection method, multi-Gender evolutionary method and constraint method. To get an optimal solution of location planning, MATLAB has been used as the tool. Page V 5, Applied and analyzed the methods and programs, mentioned in the article, to the design of inventory´s storage area. 6, Summary and prospect Author has summarized the article’s result and raised future research topics. This article has given some new points in the following aspect： 1．By thinking of velocity of goods circulation and goods weigh, a multi-objective optimization model for location planning has been made. Model has been solved, via using Genetic Algorithm’s paratactic selection, multi-gender evolution and constraint methods. 2．By using MATLAB, author programmed the location plan which generates solutions to optimization problems using techniques inspired by natural evolution. Programming has been used crossover, mutation methods and the algorithm terminated when the 500 generations has been produced, then a satisfactory fitness solution has been reached for the location