Restaurant dynamic orders selection problem is an important issue in operational profits. Restaurant managers face many pressures, i.e. cook facilities and customer satisfaction, etc., therefore, they schedule orders in a manual (First-come-first-serve) way and do not willing pay more attention to orders selection problem. In order to satisfy customer’s needs, restaurants always loose profits day by day due to add more manpower and equipments. The main purpose of this research is to construct a dynamic order selection approach to maximize a restaurant profits. This study followed cloud computing approach for solving applied mathematic research topics. The proposed new approach, it hybrid Apache server and PHP coding techniques; furthermore, it contained the optimization tools, which were coding by this study using PHP language to precede revised simplex methods mixed branch-and-bound algorithm. Through validations, we found that the proposed approaches would help to increased 214% profits in practical restaurant. Future research suggests applied this approach to others industries’ dynamic orders selection problems.

A dynamic carbon footprints management system is an important issue in future economics. This research applied geographical information to calculate carbon footprints. It also formulated an orders and trucks assignment problem with capacities of carbon footprints constraints, arrival time constraints and recycling missions constraints to maximize cargoes distribution profits. This study adapted the web based structures for programming, and proposed new approach. After validation, it would increase profits by 46% more than the experience of truck drivers, and profits by 61.5% of that of On Call (individual delivery). If a customer does not attend at a specified place and appointed time, the truck would go back and forward, then consume more gases. Through a web-structure dynamic carbon footprints management system, truck drivers leave ample time to complete their jobs. Future research suggests expanding this research to dynamic routes with consideration of traffic jams.

Reducing carbon footprint is a trend within modern green restaurants. A carbon footprint is the total set of greenhouse gas (GHG) emissions caused by an organization, event or product. Food and beverage restaurants have to deliver food using a minimal carbon footprint. Of previous researches, only a small fraction is focused on reducing carbon footprints in a culinary room. Besides, a carbon footprint cost model was hard to solve in economic computation time. Therefore, the main purpose of this research is through a distributed information system to accelerate computing ability of a carbon footprint cost model. Through the distributed computing, our experimental results showed that the proposed approach outperformed the literature approach efficiently. The algorithm improved rate was 68.6%, and low down 82.1% carbon footprint than manual. The proposed approach could contribute to accelerate calculations in others problems due to using multiple machines in future researches.

In a competitive environment, service guarantees would affect customer back-off rate. On time delivery plays an important role on time-based competition. If orders cannot be delivered on time, those orders should be outsourced in order to gain a good reputation. However, if a cargoes distribution company has it own trucks, it would gain more profits if the orders were not outsourced. Thus, JIT orders delivery mixed outsourcing problem (JITMOP) is a difficult decision making problem in a cargoes distribution company. This research constructed a Mixed Integer Programming (MIP) model for maximizing operational profits of the company. The study proposed a Genetic Algorithm with Tabu (GAWT) to solve the model. For validation, the proposed approach was comparing to Random Hybrid Tabu (RHT) and Ransom Search (RS); also found GAWT outperformed RS by an average of 54%. It represented that a cargoes distribution company would gain more than 54% of profits if they schedule orders well, part of orders outsourcing to collaborative companies, and using GAWT heuristics. Future research suggests expanding this research to include more discussion if some assumptions are changed.

Data mining is a hot research topic over the last twenty years or more. In recent decades, network graphs that have represented knowledge of a focus topic have gained increasing attention. These maps include term network, concept map, topic map or knowledge map. A concept map is one of the visualization tools to show the relationships among concepts. It is a graphical tool for organizing and representing knowledge. Global warming poses a grave threat to the world’s ecological system. Economic development has led to a huge increase in energy demand and therefore energy efficiency and saving has become a key issue for most countries. In many countries, they tried hard to find renewable and sustainable energy supplies and sources. This study tries to analyze trends of energy policy literatures from the international literature database within last three years to be visualized in 3-D concept map layouts; besides, measuring keyword relation linkages though control variables of concept maps, such as size of node, linkage, relations and dynamic figure layout, are the main contributions to academics. This research adapts an IP (integer programming) model to maximize relation linkages for each node among a term network. The more linkages, the more useful information offered for mining knowledge from a term network. The 3-D concept map was demonstrated. Future research suggests applying this approach to other research literatures from international literature databases.