International Journal of Industrial Engineering: Theory, Applications and Practice
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A TWO-STAGE BI-LEVEL DECISION APPROACH FOR GREEN SUPPLY CHAIN DESIGN AND CLOUD VIRTUAL MACHINE PLACEMENT
No full textThis study aims to establish an integrated model of green supply chain network design in cloud computing environments. In this study, two levels are defined: supply chain design and virtual machine allocation. In the proposed two-stage model, the supply chain network design decision (leader level) is solved in the first stage by considering three objectives: minimizing the total costs, minimizing the carbon emission, and maximizing the satisfaction level of service (minimizing the transportation lead time). In the second stage, considering the information demand from the leader level, the placement of virtual machines to the supply chain servers (follower level) is established by minimization of energy consumption and maximization of the effectiveness (minimization of the power wastage) of the physical machine. A case study and experiments are conducted to verify the performance of the proposed methods
A COMPREHENSIVE STUDY ON INTEGRATED OPTIMIZATION OF FLEXIBLE MANUFACTURING SYSTEM LAYOUT AND SCHEDULING FOR NYLON COMPONENTS PRODUCTION: A study on the integrated optimization of the FMS layout and schedule scheme for production of nylon components
No full textThis study proposes an integrated optimization approach to the layout and scheduling of a hybrid-flow flexible manufacturing system to produce nylon components. The approach minimizes energy consumption and makespan simultaneously. In this approach, a flexible hybrid-flow manufacturing system for the layout and scheduling of nylon components production is established via traditional hybrid-flow shop scheduling based on the loading sequence, machine number, machine type, distance between two machines, multiple buffers, and robot manipulator transport. The traditional non-dominated sorting genetic algorithm II and improved multi-population NSGA-II were compared to obtain the best layout and scheduling to minimize energy consumption and makespan under various impact factors. The improved multi-population NSGA-II leads to optimizing the hybrid flow FMS layout and schedule according to the influencing factors. The energy consumption and makespan predictions are consistent with experimental observations. A case study was used to demonstrate and validate the proposed approach
ENERGY MANAGEMENT AND HARMONIC MITIGATION OF HYBRID RENEWABLE ENERGY MICROGRID USING COORDINATED CONTROL OF MULTI-AGENT SYSTEM
Get PDFIn this paper, a novel energy management method that is based on a Multi-Agent System (MAS) is presented for hybrid Distributed Energy Sources (DES) in a microgrid. These DESs include Photovoltaic (PV), wind energy systems, and Fuel Cell (FC) in the Microgrid (MG). The MG is responsible for supplying both active and reactive powers, allowing it to serve variable linear and non-linear loads. The MAS that has been proposed and is based on a decentralized control structure offers control not only for the energy management of the Distributed Generation (DG) but also for the management of power flow between the MG and the power grid that is connected to the MG. This control is offered by the MAS. The main objective of the control strategy is to manage the amount of energy that is transferred between the power grid and the MG concerning the supply conditions of the required internal energy via DES, which will ultimately result in a reduction in the dependence on the MG on the grid. For current harmonic compensation, a Static Compensator (STATCOM) with a Fuzzy Logic (FL) based Instantaneous Reactive Power control scheme is used. On the other hand, a discrete controller is utilized to manage the energy of the MG. The findings of the simulation and the experiments demonstrated that the implementation of the suggested Energy Management System (EMS) has good performance as a novel energy management solution for a hybrid distributed power generating system and harmonic compensation
Hybrid Taguchi-Harmony Search Algorithm for Solving Engineering Optimization Problems
Get PDFHarmony search algorithm have recently gained a lot of attention from the optimization research community. In this paper, an improved harmony search algorithm is introduced to solve engineering optimization problems. To demonstrate the effectiveness and robustness of the proposed approach, it is applied to an engineering design and manufacturing optimization problem taken from the literature. The results obtained by the new hybrid harmony search approach for the case studies are compared with a hybrid genetic algorithm, scatter search algorithm, genetic algorithm, feasible direction method and handbook recommendation. The results of case studies show that the proposed optimization approach is highly competitive and that can be considered a viable alternative to solve design and manufacturing optimization problems
EFFECTS OF ESTIMATORS OF PROCESS STANDARD DEVIATION ON THE DETECTIVE CAPACITY OF X-BAR CHARTS
No full textThe effect of the biased and unbiased estimators of process standard deviation on the performance of X charts is studied, with reference to false alarm rates. The false alarm rate function of X-bar charts is derived on the subgroup and the effect of the coefficients of the process standard deviation (denoted as k/) on X control charts is studied. The probability that a subgroup mean falls outside the control limits approaches its theoretical value as the number of subgroups m exceeds 500, with sample sizes n = 5.The conclusion of this study is that the traditional estimators, such as R/d2 {n) andS/c4{n), are better than biased ones in constructing X chart limits because of their ease of use and their lower false alarm rates. This study also presents an example of the wire pull strength of the BGA product to demonstrate the application of these estimators in the statistical process control (SPC) arena. It provides suggestions on the choice of suitable estimators for practical use
APPLICATION OF MULTIPLE FUZZY GOALS PROGRAMMING TO PROJECT MANAGEMENT DECISIONS
No full textIn most practical situations, the project decision maker (DM) must handle conflicting objectives that govern the use of the organization's resources. These conflicting objectives are required to be optimized simultaneously by the DMin the framework of fuzzy aspiration levels. This work develops a novel multiple fuzzy goals programming (MFGP) model for solving project management (PM) decision problems in a fuzzy environment. The proposed model attempts to minimize total project costs, total completion time and total crashing costs with reference to direct costs, indirect costs, contractual penalty costs, duration of activities and budget constraint. An industrial case shows the feasibility of applying the proposed model to a PM decision problem. The proposed model yields an efficient compromise PM plan and the OM's overall level of satisfaction. In particular, several significant characteristics of the proposed model, which contrast with those of the major PM decision models, are presented
Offsetting Inventory Cycles using Mixed Integer Programming and Genetic Algorithm
Get PDFWe propose a mixed integer programming model to minimize the maximum storage space requirement over an infinite time horizon by offsetting the inventory cycles of items. We also develop a genetic algorithm to find the near-optimal solution. The mixed integer programming model and the genetic algorithm produce better results than the existing heuristic. We also develop a mixed integer programming model for the finite time horizon; this model is more general and realistic than that for the infinite time horizon. A warehouse management system is designed based on the algorithms we developed
Selection of Curricular Topics Using Framework for Enhanced Quality Function Deployment
Get PDFBased on the fact that products and services require an interdisciplinary approach, there is a growing conviction that engineering disciplines are converging rather than diverging. The curricula must, therefore, be designed to ensure that a student will posses all the qualities of a generalist and all the competence of a specialist with a capacity for life long learning. Decision science can be an effective tool for enhancing organizational participation during strategic and complex decision making. The challenge was to develop a set of curricular topics that not only directly contributed to the identified goals but that did so proportionately to the respective importance level of each goal. This paper describes an application of Quality Function Deployment (QFD) to define curricular topics that meet program objectives. The inputs for the QFD’s were obtained through several brainstorming sessions of students, faculty members, administrators, and business people. Based on the ability of QFD to establish relationships, the model identifies the most important topics and quantifies their impact on meeting program goals. The model was developed to support restructuring of a masters of technology programme. It provides a framework using enhanced quality function deployment for selection of curricular topics. The model provided a practical methodology for developing faculty consensus in the selection of curricular topics with a strategic focus. The decision problem is structured for assessment of impact of curricular topics on the program goals keeping checks on consistencies
A PRACTICAL CONSIDERATION OF THE LEAD TIME DEMAND
No full textDue to the importance of lead time demand in the design of inventory management systems, researchers and practitioners have paid continuous attention to it and a few analytic models using the compound distribution approach have been reported However, since the nature of compound distributions arises a heavy analytic burden, the analytic models have been developed by non-recognition of the compound nature of some components to reduce the analytic task. Through the theoretic examination of the analytic model approach, this paper clarifies the assumptions implicitly made by the analytic models and provides some precautions in using the analytic models. An illustrative example is also presented
AN EFFICIENT HEURISTIC METHOD FOR DETERMINING MULTI-ECHELON DISTRIBUTION QUANTITIES AND VEHICLE ROUTES
No full textThis paper formulates a novel mixed-integer programming model for determining multi-echelon distribution quantities and vehicle routes simultaneously, and then develops an efficient two-phase heuristic method for solving large-scale problems. A good initial solution can be generated in the first phase heuristic, in which three important criteria are involved. The second phase heuristic, based on Luger's best-first search method, is developed to improve the solution quality. The excellent performance ofthe proposed two-phase heuristic method is verified through two experiments