An Inventory Model of Production with Level Dependent Demand Allowing Few Defective Items

This paper considers a model regarding the products with finite life which allows defective items in reproduction and causes a small amount of decay. The market demand is assumed to be level dependent linear type. The model has also considered the constant production rate which stops after a desired level of inventories and that is the highest level of it. Due to the market demand, defective item and product’s decay, the inventory reduces to the zero level where again the production cycle starts. With a numerical search procedure, the proof of the proposed model has been shown. The objective of the proposed model is to find out the total optimum inventory cost, optimum ordering cost and optimum ordering cycle.

Genetic Algorithm for Scattered Storage Assignment in Kiva Mobile Fulfillment System

Scattered storage means an item can be stored in multiple inventory bins. The scattered storage assignment problem based on association rules in Kiva mobile fulfillment system is investigated, which aims to decide the pods for each item to put on so as to minimize the number of pods to be moved when picking a batch of orders. This problem is formulated into an integer programming model. A genetic algorithm is developed to solve the large-sized problems. Computational experiments and comparison between the scattered storage strategy and random storage strategy are conducted to evaluate the performance of the model and algorithm.

Multiobjective Stochastic Linear Programming: An Overview

Many Optimization problems in engineering and economic involve the challenging task of pondering both conflicting goals and random data. In this paper, we give an up-to-date overview of how important ideas from optimization, probability theory and multicriteria decision analysis are interwoven to address situations where the presence of several objective functions and the stochastic nature of data are under one roof in a linear optimization context. In this way users of these models are not bound to caricature their problems by arbitrarily squeezing different objective functions into one and by blindly accepting fixed values in lieu of imprecise ones.

Using Return and Risk Model for Choosing Perfect Portfolio Applied Study in Cairo Stock Exchange

Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whenever there is an imperfect correlation between returns risk is reduced by maintaining only a portion of wealth in any asset, or by selecting a portfolio according to expected returns and correlations between returns. The major improvement of the portfolio approaches over prior received theory is the incorporation of 1) the riskiness of an asset and 2) the addition from investing in any asset. The theme of this paper is to discuss how to propose a new mathematical model like that provided by Markowitz, which helps in choosing a nearly perfect portfolio and an efficient input/output. Besides applying this model to reality, the researcher uses game theory, stochastic and linear programming to provide the model proposed and then uses this model to select a perfect portfolio in the Cairo Stock Exchange. The results are fruitful and the researcher considers this model a new contribution to previous models.

A New Approach of Solving Single Objective Unbalanced Assignment Problem

In this paper, we discuss a new approach for solving an unbalanced assignment problem. A Lexi-search algorithm is used to assign all the jobs to machines optimally. The results of new approach are compared with existing approaches, and this approach outperforms other methods. Finally, numerical example (Table 1) has been given to show the efficiency of the proposed methodology.

Fuzzy Geometric Programming in Multivariate Stratified Sample Surveys in Presence of Non-Response with Quadratic Cost Function

In this paper, the problem of non-response with significant travel costs in multivariate stratified sample surveys has been formulated of as a Multi-Objective Geometric Programming Problem (MOGPP). The fuzzy programming approach has been described for solving the formulated MOGPP. The formulated MOGPP has been solved with the help of LINGO Software and the dual solution is obtained. The optimum allocations of sample sizes of respondents and non respondents are obtained with the help of dual solutions and primal-dual relationship theorem. A numerical example is given to illustrate the procedure.

Fuzzy Synthetic Evaluation Model for Smart Washing Machine

Generally fuzzy control system (FCS) is worked in washing machine. For the fuzzy set theory, membership functions are the building blocks. In a fuzzy set, fuzziness is determined by its membership functions. The shapes of membership functions are important, because it has an effect on fuzzy inference system. The shapes of membership functions can be triangular, trapezoidal and gaussian. The most widely used triangular membership function is used in this paper, because it can capture the short time period. In washing machine, open loop control system is found. This paper applies a fuzzy synthetic evaluation method (FSEM) for washing cloth in washing machine as FSEM can handle the multiple criteria with the help of evaluation matrix generated from membership function and weight matrix generated by Analytical Hierarchy Process (AHP). The purpose of this research is to minimize the wash time. By applying FSEM, we get a wash time which is less than that wash time got from applying the Mamdani approach in FCS. An example is given for illustration. For more reduction of wash time, statistical averaging method is also used. To reduce the wash time, statistical averaging method can be used in Mamdani approach also.

Advanced Transformation Technique to Solve Multi-Objective Optimization Problems

Multi-objective optimization problem (MOOP) is an important class of optimization problem that ensures users to model a large variety of real world applications. In this paper an advanced transformation technique has been proposed to solve MOOP. An algorithm is suggested and the computer application of algorithm has been demonstrated by a flow chart. This method is comparatively easy to calculate. Applying on different types of examples, the result indicates that the proposed method gives better solution than other methods and it is less time consuming. Physical presentation and data analysis represent the worth of the method more compactly.

Stochastic Design of Enhanced Network Management Architecture and Algorithmic Implementations

The paper is focused on available server management in Internet connected network environments. The local backup servers are hooked up by LAN and replace broken main server immediately and several different types of backup servers are also considered. The remote backup servers are hooked up by VPN (Virtual Private Network) with high-speed optical network. A Virtual Private Network (VPN) is a way to use a public network infrastructure and hooks up long-distance servers within a single network infrastructure. The remote backup servers also replace broken main severs immediately under the different conditions with local backups. When the system performs a mandatory routine maintenance of main and local backup servers, auxiliary servers from other location are being used for backups during idle periods. Analytically tractable results are obtained by using several mathematical techniques and the results are demonstrated in the framework of optimized networked server allocation problems. The operational workflow give the guidelines for the actual implementations.

Optimal Ordering Policy of Deteriorating Items with Mixed Cargo Transportation Over a Finite Planning Horizon

In this paper, we propose a deteriorating items inventory model with constant demand and deterioration rates, and mixed cargo transportation modes. The transportation modes are full container load (FCL) and less than container load (LCL). Deteriorating items, such as specialty gases which are applied in semiconductor fabrication, deteriorate owing to environmental variation. Exact algorithms are proposed to determine the optimal inventory policies over a finite and an infinite planning horizon. Numerical examples are given to illustrate the proposed solution procedures. In addition, when the deterioration rate is large, the results of the proposed model perform better compared to the inventory model proposed by Rieksts and Ventura (2008).

Shedding Light on Non Binding Constraints in Linear Programming: An Industrial Application

In Linear Programming (LP) applications, unexpected non binding constraints are among the “why” questions that can cause a great deal of debate. That is, those constraints that are expected to have been active based on price signals, market drivers or manager’s experiences. In such situations, users have to solve many auxiliary LP problems in order to grasp the underlying technical reasons. This practice, however, is cumbersome and time-consuming in large scale industrial models. This paper suggests a simple solution-assisted methodology, based on known concepts in LP, to detect a sub set of active constraints that have the most preventing impact on any non binding constraint at the optimal solution. The approach is based on the marginal rate of substitutions that are available in the final simplex tableau. A numerical example followed by a real-type case study is provided for illustration.

Efficient Heuristic Based Methods for Two-Stage Transshipment Problem

In this article, we propose efficient methods for solving two stage transshipment problems. Transshipment problem is the special case of Minimum cost flow problem in which arc capacities are infinite. We start by proposing a novel problem formulation for a two stage transshipment problem. Later, special structure of our problem formulation is utilized to devise two dual based heuristics solutions with computational complexity of O (n2), and O (n3) respectively. These methods are motivated by the methods developed by Sharma and Saxena [1], Sinha and Sharma [2]. Our methods differ in the initialization and the subsequent variation of the dual variables associated with the transshipment nodes along the shortest path. Lastly, a method is proposed to extract a very good primal solution from the given dual solutions with a computational complexity of O (n2). Efficacy of these methods is demonstrated by our numerical analysis on 200 random problems.

Combining Lean Concepts &Tools with the DMAIC Framework to Improve Processes and Reduce Waste

Six Sigma DMAIC methodology has been applied to systematically apply lean manufacturing concepts and tools in order to improve productivity in a local company specialized in the manufacturing of safety and fire resistance metal doors, windows, and frames. In-depth analysis of the plant processes unfolded different critical processes, specifically foam injection process and sheet metal cutting process. Throughout the different project phases, various improvements had been implemented to reduce production cycle time from 216 min to 161 min;non-value added activities in the different processes were identified and eliminated. Plant layout and machine reconfiguration reduced backtracking and unutilized space. Percentage of defective doors (needing rework) dropped from 100% to only 15%. The successful implementation of this project is largely due to top management active involvement and participation of workers and operators in all stages of the project. Finally, new policies and mentoring programs are introduced to maintain improvements.

Dynamic Promotional Resource Allocation for Segment Specific and Spectrum Effect of Promotion for a Product Line Incorporating Repeat Purchase Behavior

Promotion is an essential element in the marketing mix. It is used by businesses to inform, influence and persuade customers to adopt the products and services they offer. Without promotion, business would be stagnant and lack substantial growth because the brands would have low visibility in the market. Moreover, today’s vast and assorted markets comprise of customers with different needs and varied behavior. So it is rarely possible for companies to satisfy all customers by treating them alike. Thus there arises a need to divide the market into segments having customers with similar traits/characteristics. After identifying appropriate market segments, firms can design differentiated promotional campaigns for each segment. At the same time there can be a mass market promotional campaign that reaches different segments with a fixed spectrum. Also since promotional effort resources are limited, one must use them judiciously. In this paper, we formulate mathematical programming problem under repeat purchase scenario, which optimally allocates mass promotional effort resources and differentiated promotional effort resources across the segments dynamically in order to maximize the overall sales obtained from multiple products of a product line under budgetary and minimum sales aspiration level constraint on each product under consideration in each segment. The planning horizon is divided into multi periods, the adoption pattern of each product in each segment is observed in every subinterval and accordingly promotional effort allocations are determined for the next period till we reach the end of planning period. The optimization model has been further extended to incorporate minimum aspiration level constraints on total sales for each product under consideration from all the segments taken together. The non linear programming problem so formulated is solved using differential evolution approach. A numerical example has been discussed to illustrate applicability of the model.

Limited Re-Sequencing for Mixed-Models with Multiple Objectives, Part II: A Permutation Approach

This research presents an approach to solving the limited re-sequencing problem for a JIT system when two objectives are considered for multiple processes. One objective is to minimize the number of setups;the other is to minimize the material usage rate [1]. For this research effort, each unique permutation of the problem’s demand structure is noted, and used as a mechanism for finding subsequent sequences. Two variants of this permutation approach are used: one employs a Monte-Carlo simulation, while the other employs a modification of Ant-Colony Optimization to find sequences satisfying the objectives of interest. Problem sets from the literature are used for assessment, and experimentation shows that the methodology presented here outperforms methodology from an earlier research effort [3].

Location Optimization of a Coal Power Plant to Balance Costs against Plant’s Emission Exposure

The goal of the research is to develop a methodology to minimize the public’s exposure to harmful emissions from coal power plants while maintaining minimal operational costs related to electric distribution losses and coal logistics. The objective is achieved by combining EPA Screen3, ISC3 and Japanese METI-LIS model equations with minimum spanning tree (MST) algorithm. Prim’s MST algorithm is used to simulate an electric distribution system and coal transportation pathways. The model can detect emission interaction with another source and estimate the ground level concentrations of emissions up to distances of 25 kilometers. During a grid search, the algorithm helps determine a candidate location, for a new coal power plant, that would minimize the operational cost while ensuring emission exposure is below the EPA/NIOSH thresholds. The proposed methodology has been coded in form of a location analysis simulation. An exhaustive search strategy delivers a final candidate location for a new coal power plant to ensure minimum operational costs as compared to the random or greedy search strategy. The simulation provides a tool to industrial zone planners, environmental engineers, and stakeholders in coal-based power generation. Using operational and emissions perspectives, the tool helps ascertain a list of compromise locations for a new coal power plant facility.

Solving the Traveling Salesman Problem Using Hydrological Cycle Algorithm

In this paper, a recently developed nature-inspired optimization algorithm called the hydrological cycle algorithm (HCA) is evaluated on the traveling salesman problem (TSP). The HCA is based on the continuous movement of water drops in the natural hydrological cycle. The HCA performance is tested on various geometric structures and standard benchmarks instances. The HCA has successfully solved TSPs and obtained the optimal solution for 20 of 24 benchmarked instances, and near-optimal for the rest. The obtained results illustrate the efficiency of using HCA for solving discrete domain optimization problems. The solution quality and number of iterations were compared with those of other metaheuristic algorithms. The comparisons demonstrate the effectiveness of the HCA.

The Role of Technology in the Process of Learning Mathematics

The research was achieved by applying several questionnaires and evaluation tests that have shown the efficacy of using the computer in building active thought and skills in the graphical representation of functions , within a Cartesian reference system xOy. Our research was based on the following hypothesis: if we systematically use, in lessons of algebra and mathematical analysis, the GeoGebra software for drawing the graph of a function, then we shall contribute to building attitudes and competences in using the computer for the graphical representation of functions , within a Cartesian reference system xOy. The purpose of our study is to present a practical computer use model for the students from the Pre- and In-Service Teacher Training Department, “Vasile Alecsandri” University of Bacau, during their pedagogical practice stage of continuous training, aimed at building practical skills in supporting the teaching-learning process with computers. To deal with these issues, we are trying to find the best teaching-learning strategies by using the calculus technique. The research was conducted at the “Stefan cel Mare” National Pedagogical College from Bacau, by assisting 20 lessons of mathematics and 20 lessons of Information and Communication Technology, involving a group of 104 students in the 11th grade. In order to verify the hypothesis, we have established several objectives that orient and guide our activity: knowing the initial level of mathematical training in terms of graphical representation of functions;identifying the frame and reference objectives of the curriculum for mathematical education regarding the graphical representation of functions;designing and conducting a teaching process focused on the use of the computer and the GeoGebra software in learning the graphical representation of functions;final evaluation of the students’ level of training regarding the graphical representation of functions in a plane.

The Effect of Reputation to the Moral Hazard in C2C E-Market with Game Theory

It is well known that the reputation is the basis of a seller to survive and gain trust from customers in a competitive business environment. But as the existence of information asymmetry between buyer and seller, the moral hazard problem is the key obstacle that impedes the benefits of related shareholders and reduces the efficiency of total market. It is crucial to design a control mechanism to avoid the negative impact of moral hazard. This paper studies the principal and agent relationship between buyer and seller in C2C e-market;because of the influence of information asymmetry, many customers suffered from being cheated by sellers with defective products in practice. These frequent cases will deteriorate long term relationship between sellers and buyers. Here we focus on the analysis of the causes of moral risks and the effect of reputation on oral risk with repeated game theory. The purpose of this paper is to help both firms and customers effectively avoid morality risk and realize a win-win situation.

Solving the Multi Observer 3D Visual Area Coverage Scheduling Problem by Decomposition

This paper presents two solution methodologies for the Visual Area Coverage Scheduling problem. The objective is to schedule a number of dynamic observers over a given 3D terrain such that the total visual area covered (viewed) over a planning horizon is maximal. This problem is a more complicated extension of the Set Covering Problem, known to be Np-Hard. We present two decomposition based heuristic methods each containing three stages. The first methodology finds a set of area covering points, and then partitions them into routes (cover first, partition second). The second methodology partitions the area into a region for each observer, and then finds the best covering points and routes (partition first, cover second). In each, a last stage determines dwell (view) times so as to maximize the visible coverage smoothly over the terrain. Comparative tests were made for the two methods on real terrains for several scenarios. When comparing the best solutions of both methods the CF-PS method was slightly better. However, because of the increased computation time we suggest that the PF-CS method with a fine terrain approximation be used. This method is faster as partitioning the terrain into separate regions for each observer results in smaller coverage and routing problems. A sensitivity analysis of the number of observation points to the total number of terrain points covered depicted the classical notion of decreasing returns to scale, increasing in a convex manner as the number of observation points was increased. The best method achieved 100 percent coverage of the terrain by using only 2.7 percent of its points as observation points. Experts stated that the computer based solutions can save precious time and help plan observation missions with satisfying results.