A Quadratic Programming for Truck-to-Door Assignment Problem

Cross-docking includes receiving products supplied by a set of suppliers, unloading them from inbound trucks (ITs) at strip doors, consolidating and handling these products to stack doors based on their destinations, loading them into outbound trucks (OTs); then, delivering these products to customers. An effective assignment of the trucks to the doors would enhance the advantages of the cross-docking (e.g. reduction of the handling costs). This paper addresses the truck-to-door assignment problem in a cross-dock in which assignment of the ITs to the strip doors as well as assignment of the OTs to the stacks doors is determined so that total material handling cost in the cross-dock is minimized. Capacity constraints are applied for the ITs, OTs, strip doors, and stack doors. We develop a Quadratic Programming (QP) to formulate the problem. To solve it, the model is coded in LINGO software to specify the best assignment of the trucks to the doors.

Solving Fuzzy Multi-Objective Linear Programming Problems with Fuzzy Decision Variables

In this paper, a method is proposed for solving Fuzzy Multi-Objective Linear Programming problems (FMOLPP) with fuzzy right hand side and fuzzy decision variables. To illustrate the proposed method, it is applied to the problem of selecting suppliers for an automotive parts producer company in Iran in order to find the number of optimal orders allocated to each supplier considering the conflicting objectives. Finally, the obtained results are discussed.

Dynamic Programming Based Algorithm for the Unit Commitment of the Transmission-Constrained Multi-Site Combined Heat and Power System

High penetration of intermittent renewable energy sources (RES) such as solar power and wind power into the energy system has caused temporal and spatial imbalance between electric power supply and demand for some countries and regions. This brings about the critical need for coordinating power production and power exchange for different regions. As compared with the power-only systems, the combined heat and power (CHP) systems can provide additional flexibility of utilizing RES by exploiting the interdependence of power and heat production in the CHP plant. In the CHP system, power production can be influenced by adjusting heat production level and electric power can be used to satisfy heat demand by electric boiler or heat pump in conjunction with heat storage, which is much cheaper than electric storage. This paper addresses multi-site CHP systems without considering RES, which lay foundation for handling penetration of RES. The problem under study is the unit commitment (UC) of the transmission-constrained multi-site CHP systems. We solve the problem by combining linear relaxation of ON/OFF states and sequential dynamic programming (DP) techniques, where relaxed states are used to reduce the dimension of the UC problem and DP for improving the solution quality. Numerical results for daily scheduling with realistic models and data show that DP-based algorithm is from a few to a few hundred times faster than CPLEX (standard commercial optimization software) with good solution accuracy (less than 1% relative gap from the optimal solution on the average).

Integer Programming Model for the Network Design Problem with Facility Dependent Shortest Path Routing

We consider a network design problem which has shortest routing restriction based on the values determined by the installed facilities on each arc. In conventional multicommodity network design problem, a commodity can be routed through any possible path when the capacity is available. But, we consider a problem in which the commodity between two nodes must be routed on a path which has shortest metric value and the link metric value is determined by the installed facilities on the link. By this routing restriction, the problem has a distinct characteristic. We present an integer programming formulation containing the primal-dual optimality conditions to the shortest path routing. We give some computational results for the model.

Competence-Based Human Resources Selection and Training: Making Decisions

Human Resources (HR) selection and training have various implementation possibilities depending on an organization’s abilities and peculiarities. We propose to base HR selection and training decisions about on a competence-based approach. HR selection and training of employees are topical as there is room for improvement in this field; therefore, the aim of the research is to propose rational decision-making approaches for an organization HR selection and training choice. Our proposals are based on the training development and competence-based selection approaches created within previous researches i.e. Analytic-Hierarchy Process (AHP) and Linear Programming. Literature review on non-formal education, competence-based selection, AHP form our theoretical background. Some educational service providers in Latvia offer employees training, e.g. motivation, computer skills, accounting, law, ethics, stress management, etc. that are topical for Public Administration. Competence-based approach is a rational base for rational decision-making in both HR selection and considering HR training.

Supplier Selection by Considering Cost and Reliability

Supplier selection problem is one of the important issues of supply chain problems. Two categories of methodologies include qualitative and quantitative approaches which can be applied to supplier selection problems. However, due to the complexities of the problem and lacking of reliable and quantitative data, qualitative approaches are more than quantitative approaches. This study considers operational cost and supplier’s reliability factor and solves the problem by using a quantitative approach. A mixed integer programming model is the primary analytic tool. Analyses of different scenarios with variable cost and reliability structures show that the effectiveness of this approach to the supplier selection problem.

A Hybrid Expert System for Generating Stock Trading Signals

In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy.

Evaluation of Introductory Programming Course for Non-Computer Science Majored Students

Although students’ interest level in pursuing Computer Science and related degrees are lower than previous decade, fundamentals of computers, specifically introductory level programming courses are either listed as core or elective courses for a number of non-computer science majors. Universities accommodate these non-computer science majored students either via creating separate sections of a class for them or simply offering mixed-body classroom solutions, in which both computer science and non-computer science students take the courses together. In this work, we demonstrated how we handle introductory level programming course at Sam Houston State University and also provide facts about our observations on students’ success during the coursework. Moreover, we provide suggestions and methodologies that are based on students’ major and skills to overcome the deficiencies of mix-body type of classes.

A Scalable Media Job Framework for an Open Source Search Engine

This paper explores efficient ways to implement various media-updating features like news aggregation, video conversion, and bulk email handling. All of these jobs share the property that they are periodic in nature, and they all benefit from being handled in a distributed fashion. The data for these jobs also often comes from a social or collaborative source. We isolate the class of periodic, one round map reduce jobs as a useful setting to describe and handle media updating tasks. As such tasks are simpler than general map reduce jobs, programming them in a general map reduce platform could easily become tedious. This paper presents a MediaUpdater module of the Yioop Open Source Search Engine Web Portal designed to handle such jobs via an extension of a PHP class. We describe how to implement various media-updating tasks in our system as well as experiments carried out using these implementations on an Amazon Web Services cluster.

Ubiquitous Life People Informatics Engine (U-Life PIE): Wearable Health Promotion System

Since Google launched Google Glass in 2012, numbers of commercial wearable devices were released, such as smart belt, smart band, smart shoes, smart clothes ... etc. However, most of these devices perform as sensors to show the readings of measurements and few of them provide the interactive feedback to the user. Furthermore, these devices are single task devices which are not able to communicate with each other. In this paper a new health promotion system, Ubiquitous Life People Informatics Engine (U-Life PIE), will be presented. This engine consists of People Informatics Engine (PIE) and the interactive user interface. PIE collects all the data from the compatible devices, analyzes this data comprehensively and communicates between devices via various application programming interfaces. All the data and informations are stored on the PIE unit, therefore, the user is able to view the instant and historical data on their mobile devices any time. It also provides the real-time hands-free feedback and instructions through the user interface visually, acoustically and tactilely. These feedback and instructions suggest the user to adjust their posture or habits in order to avoid the physical injuries and prevent illness.

Timetabling Communities’ Demands for an Effective Examination Timetabling Using Integer Linear Programming

This paper explains the educational timetabling problem, a type of scheduling problem that is considered as one of the most challenging problem in optimization and operational research. The university examination timetabling problem (UETP), which involves assigning a set number of exams into a set number of timeslots whilst fulfilling all required conditions, has been widely investigated. The limitation of available timeslots and resources with the increasing number of examinations are the main reasons in the difficulty of solving this problem. Dynamical change in the examination scheduling system adds up the complication particularly in coping up with the demand and new requirements by the communities. Our objective is to investigate these demands and requirements with subjects taken from Universiti Malaysia Terengganu (UMT), through questionnaires. Integer linear programming model which reflects the preferences obtained to produce an effective examination timetabling was formed.

Towards Developing a Self-Explanatory Scheduling System Based on a Hybrid Approach

In the study, we present a conceptual framework for developing a scheduling system that can generate self-explanatory and easy-understanding schedules. To this end, a user interface is conceived to help planners record factors that are considered crucial in scheduling, as well as internal and external sources relating to such factors. A hybrid approach combining machine learning and constraint programming is developed to generate schedules and the corresponding factors, and accordingly display them on the user interface. Effects of the proposed system on scheduling are discussed, and it is expected that scheduling efficiency and system understandability will be improved, compared with previous scheduling systems.

Feasibility Study of MongoDB and Radio Frequency Identification Technology in Asset Tracking System

Taking into consideration the real time situation specifically the higher academic institutions, small, medium to large companies, public to private sectors and the remaining sectors, do experience the inventory or asset shrinkages due to theft, loss or even inventory tracking errors. This happening is due to a zero or poor security systems and measures being taken and implemented in their organizations. Henceforth, implementing the Radio Frequency Identification (RFID) technology into any manual or existing web-based system or web application can simply deter and will eventually solve certain major issues to serve better data retrieval and data access. Having said, this manual or existing system can be enhanced into a mobile-based system or application. In addition to that, the availability of internet connections can aid better services of the system. Such involvement of various technologies resulting various privileges to individuals or organizations in terms of accessibility, availability, mobility, efficiency, effectiveness, real-time information and also security. This paper will look deeper into the integration of mobile devices with RFID technologies with the purpose of asset tracking and control. Next, it is to be followed by the development and utilization of MongoDB as the main database to store data and its association with RFID technology. Finally, the development of a web based system which can be viewed in a mobile based formation with the aid of Hypertext Preprocessor (PHP), MongoDB, Hyper-Text Markup Language 5 (HTML5), Android, JavaScript and AJAX programming language.

A Survey on the Requirements of University Course Timetabling

Course timetabling problems occur every semester in a university which includes the allocation of resources (subjects, lecturers and students) to a number of fixed rooms and timeslots. The assignment is carried out in a way such that there are no conflicts within rooms, students and lecturers, as well as fulfilling a range of constraints. The constraints consist of rules and policies set up by the universities as well as lecturers’ and students’ preferences of courses to be allocated in specific timeslots. This paper specifically focuses on the preferences of the course timetabling problem in one of the public universities in Malaysia. The demands will be considered into our existing mathematical model to make it more generalized and can be used widely. We have distributed questionnaires to a number of lecturers and students of the university to investigate their demands and preferences for their desired course timetable. We classify the preferences thus converting them to construct one mathematical model that can produce such timetable.

Network-Constrained AC Unit Commitment under Uncertainty Using a Bender’s Decomposition Approach

In this work, the system evaluates the impact of considering a stochastic approach on the day ahead basis Unit Commitment. Comparisons between stochastic and deterministic Unit Commitment solutions are provided. The Unit Commitment model consists in the minimization of the total operation costs considering unit’s technical constraints like ramping rates, minimum up and down time. Load shedding and wind power spilling is acceptable, but at inflated operational costs. The evaluation process consists in the calculation of the optimal unit commitment and in verifying the fulfillment of the considered constraints. For the calculation of the optimal unit commitment, an algorithm based on the Benders Decomposition, namely on the Dual Dynamic Programming, was developed. Two approaches were considered on the construction of stochastic solutions. Data related to wind power outputs from two different operational days are considered on the analysis. Stochastic and deterministic solutions are compared based on the actual measured wind power output at the operational day. Through a technique capability of finding representative wind power scenarios and its probabilities, the system can analyze a more detailed process about the expected final operational cost.

A Paradigm Shift towards Personalized and Scalable Product Development and Lifecycle Management Systems in the Aerospace Industry

Integrated systems for product design, manufacturing, and lifecycle management are difficult to implement and customize. Commercial software vendors, including CAD/CAM and third party PDM/PLM developers, create user interfaces and functionality that allow their products to be applied across many industries. The result is that systems become overloaded with functionality, difficult to navigate, and use terminology that is unfamiliar to engineers and production personnel. For example, manufacturers of automotive, aeronautical, electronics, and household products use similar but distinct methods and processes. Furthermore, each company tends to have their own preferred tools and programs for controlling work and information flow and that connect design, planning, and manufacturing processes to business applications. This paper presents a methodology and a case study that addresses these issues and suggests that in the future more companies will develop personalized applications that fit to the natural way that their business operates. A functioning system has been implemented at a highly competitive U.S. aerospace tooling and component supplier that works with many prominent airline manufacturers around the world including The Boeing Company, Airbus, Embraer, and Bombardier Aerospace. During the last three years, the program has produced significant benefits such as the automatic creation and management of component and assembly designs (parametric models and drawings), the extensive use of lightweight 3D data, and changes to the way projects are executed from beginning to end. CATIA (CAD/CAE/CAM) and a variety of programs developed in C#, VB.Net, HTML, and SQL make up the current system. The web-based platform is facilitating collaborative work across multiple sites around the world and improving communications with customers and suppliers. This work demonstrates that the creative use of Application Programming Interface (API) utilities, libraries, and methods is a key to automating many time-consuming tasks and linking applications together.

An Investigation of Community Radio Broadcasting in Phutthamonthon District, Nakhon Pathom, Thailand

This study aims to explore and compare the current condition of community radio stations in Phutthamonthon district, Nakhon Pathom province, Thailand, as well as the challenges they are facing. Qualitative research tools including in-depth interviews; documentary analysis; focus group interviews; and observation, are used to examine the content, programming, and management structure of three community radio stations currently in operation within the district. Research findings indicate that the management and operational approaches adopted by the two non-profit stations included in the study, Salaya Pattana and Voice of Dhamma, are more structured and effective than that of the for-profit Tune Radio. Salaya Pattana – backed by the Faculty of Engineering, Mahidol University, and the charity-funded Voice of Dhamma, are comparatively free from political and commercial influence, and able to provide more relevant and consistent community-oriented content to meet the real demand of the audience. Tune Radio, on the other hand, has to rely solely on financial support from political factions and business groups, which heavily influence its content.

A Fuzzy Linear Regression Model Based on Dissemblance Index

Fuzzy regression models are useful for investigating the relationship between explanatory variables and responses in fuzzy environments. To overcome the deficiencies of previous models and increase the explanatory power of fuzzy data, the graded mean integration (GMI) representation is applied to determine representative crisp regression coefficients. A fuzzy regression model is constructed based on the modified dissemblance index (MDI), which can precisely measure the actual total error. Compared with previous studies based on the proposed MDI and distance criterion, the results from commonly used test examples show that the proposed fuzzy linear regression model has higher explanatory power and forecasting accuracy.

Design of a Pulse Generator Based on a Programmable System-on-Chip (PSoC) for Ultrasonic Applications

This paper describes the design of a pulse generator based on the Programmable System-on-Chip (PSoC) module. In this module, using programmable logic is possible to implement different pulses which are required for ultrasonic applications, either in a single channel or multiple channels. This module can operate with programmable frequencies from 3-74 MHz; its programming may be versatile covering a wide range of ultrasonic applications. It is ideal for low-power ultrasonic applications where PZT or PVDF transducers are used.

Smart Power Scheduling to Reduce Peak Demand and Cost of Energy in Smart Grid

This paper discusses the simulation and experimental work of small Smart Grid containing ten consumers. Smart Grid is characterized by a two-way flow of real-time information and energy. RTP (Real Time Pricing) based tariff is implemented in this work to reduce peak demand, PAR (peak to average ratio) and cost of energy consumed. In the experimental work described here, working of Smart Plug, HEC (Home Energy Controller), HAN (Home Area Network) and communication link between consumers and utility server are explained. Algorithms for Smart Plug, HEC, and utility server are presented and explained in this work. After receiving the Real Time Price for different time slots of the day, HEC interacts automatically by running an algorithm which is based on Linear Programming Problem (LPP) method to find the optimal energy consumption schedule. Algorithm made for utility server can handle more than one off-peak time period during the day. Simulation and experimental work are carried out for different cases. At the end of this work, comparison between simulation results and experimental results are presented to show the effectiveness of the minimization method adopted.