...Current situation Delays are a very important issue at Boston’s Logan Airport, the fifth most delayed airport in the United States. Approximately 4.75% of total flights at Logan Airport are delayed and with total annual operations projected to increase over the next 15 years, it is important to address this issue today. Delay costs, which include operational and human costs, prove to be extensive and therefore a solution has to be found to ensure the survival of Logan airport. The major causes of these delays include the adverse weather conditions as well as the fact that demand tends to be clustered at certain periods of the day. There are several approaches that can be taken to conquer this delay problem. The first option, which has been determined to be the best alternative, is to build another runway. This would ensure at least two operating runways at all times, regardless of weather conditions. The second option proposed for dealing with the delay issue is that of peak period pricing (PPP), which entails increasing airline fees during periods of high demand in order to spread the demand for flights more evenly throughout the day. Problem analysis Weather is the foremost cause of delays in the case of Logan Airport. The airport’s operational capacity is significantly impacted by these adverse weather conditions (declining from approximately 120 operations per hour in normal conditions to approximately 80 operations per hour). In some cases, entire runways must be...
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...QUANTITATIVE MODULE WAITING LINE MODELS Discussion Questions 1. Three parts of a queuing system: arrivals or inputs to the system; the queue discipline, or the waiting line itself; and the service facility. 2. Qualitative concerns include fairness and the aesthetics of the area in which waiting takes place. 3. Arrivals are governed by: the size of the source population (finite or infinite); the pattern of arrivals at the system (on a schedule or randomly); and the behavior of the arrivals (joining the queue, balking, or reneging). 4. Measures of system performance: ■ the average time each customer or object spends in the queue; ■ the average queue length; ■ the average time each customer or object spends in the system; ■ the average number of customers in the system; ■ the probability that the service facility is idle; ■ the utilization factor for the system; ■ and the probability of a specific number of customers or objects in the system. 5. Assumptions of the “basic” single channel model: 1. Arrivals are served on a first-come, first-served (FIFO) basis, and every arrival waits to be served, regardless of the length of the line or queue. 2. Arrivals are independent of preceding arrivals, but the arrival rate does not change over time. 3. Arrivals are described by a Poisson probability distribution and come from an infinite population. 4. Service times vary from one customer to the next and are independent of...
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...Introduction Being in a queue (waiting line) is an inevitable fact of our daily life, such as waiting for checkout at a supermarket, or waiting to make a bank deposit. Queuing theory, started with research by Agner Krarup Erlang, is used to examine the impact of management decisions on these waiting lines (Anderson et.al, 2009). A basic Queuing Model structure consists of three main characteristics, namely behaviour of arrivals, queue discipline, and service mechanism (Hillier and Lieberman, 2001). In this assignment, New England Foundry’s queuing problem will be solved in Excel, and then, time and cost savings will be identified. First of all, current and new situation will be analysed in order to demonstrate the queuing model by using Kendall’s Notation (for the current queuing problem, queuing model is M/M/s). After that, arrival rate, queue size, and service rate will be defined, and added-in Excel file (Queuing models.xlsx). The results will be discussed at the end. Description New England Foundry (NEF) produces four different types of woodstoves for home use and additional products that are used with these four stoves. Due to the increase in energy prices, George Mathison president of the company wants to change the layout to increase the production of their bestselling type of Warmglo III. NEF has several operations in order to produce woodenstoves which are illustrated as a flow diagram in Figure 1. Current State Analysis Current layout offers one counter...
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...International tourist arrivals are forecast to reach 1.8 billion by 2030 according to the newly released UNWTO long-term forecast, Tourism Towards 2030. International tourism will continue to grow in the period 2010-2030, but at a more moderate pace than the past decades, with the number of international tourist arrivals worldwide increasing by an average 3.3% a year. A large proportion of the arrivals of the next two decades will originate from the countries of Asia and the Pacific, growing at a rate of 5.0% a year and generating an average 17 million additional international arrivals every year. Europe follows with an average 16 million extra arrivals a year, resulting from a much more moderate growth rate (+2.5% a year), but on top of a much larger base. The remaining 10 million additional yearly arrivals are generated by the Americas (5 millions), Africa (3 millions) and the Middle East (2 millions). International arrivals in emerging economy destinations are expected to continue growing at double the pace (+4.4% year) of advanced ones (+2.2% a year). In absolute terms, the emerging economies of Asia, Latin America, Central and Eastern Europe, Eastern Mediterranean Europe, the Middle East and Africa will gain an average 30 million arrivals a year, compared to 14 million in the traditional destinations of the advanced economies of North America, Europe and Asia and the Pacific. By 2015, emerging economies will receive more international tourist arrivals than advanced economies...
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...at a device. Long Term Averages • Queuing theory provides long term average values. • It does not predict when the next event will occur. • Input data should be measured over an extended period of time. • We assume arrival times and service times are random. • • • • Assumptions Independent arrivals Exponential distributions Customers do not leave or change queues. Large queues do not discourage customers. Many assumptions are not always true, but queuing theory gives good results anyway Queuing Model Q W λ Tw Tq S Interesting Values • Arrival rate (λ) — the average rate at which customers arrive. • Service time (s) — the average time required to service one customer. • Number waiting (W) — the average number of customers waiting. • Number in the system (Q) — the average total number of customers in the system. More Interesting Values • Time in the system (Tq) the average time each customer is in the system, both waiting and being serviced. Time waiting (Tw) the average time each customer waits in the queue. Tq = Tw + s Arrival Rate • The arrival rate, λ, is the average rate new customers arrive measured in arrivals per time period. Common units are access/second • The inter-arrival time, a, is the average time between customer arrivals. It is measured in time per customer. A common unit would be seconds/access. • a=1/λ Random Values • We assume that most of the events we are interested in occur randomly. – Time of a request to a device – Time...
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...service. Queue management deals with cases where the customer arrival is random; therefore, service rendered to them is also random. A service organization can reduce cost and thus improve profitability by efficient queue management. A cost is associated with customer waiting in line and there is cost associated with adding new counters to reduce service time. Queue management looks to address this trade off and offer solutions to management. Queuing System To solve problems related to queue management it is important to understand characteristics of the queue. Some common queue situations are waiting in line for service in super-market or banks, waiting for results from computer and waiting in line for bus or commuter rail. General premise of queue theory is that there are limited resources for a given population of customers and addition of a new service line will increase the cost aspect to the business. A typical queue system has the following: Arrival Process: As the name suggests an arrival process look at different components of customer arrival. Customer arrival could in single, batch or bulk, arrival as distribution of time, arrival in finite population or infinite population. Service Mechanism: this looks at available resources for customer service, queue structure to avail the service and preemption of service. Underlining assumption here is that service time of customers is independent of arrival to the queue. Queue Characteristics: this looks at selection...
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...Civil Service Form No. 48 DAILY TIME RECORD -----o0o----- |RONEL B. PIS-AN | (Name) |For the month of |JANUARY 1-15, 2015 | |Official hours for arrival |Regular days | | |and departure | | | | |Saturdays | | |Day |A.M. |P.M. |Undertime | | |Arrival |Depar-ture|Arrival |Depar-ture|Hours |Min-ute| | | | | | | |s | |1 |NEW YEAR’S DAY | |2 |NO WORK | |3 |SATURDAY | |4 |SUNDAY | |5 |7:55 |12:00 |12:54 |5:13 | | | |6 |7:52 |12:05 |12:51 |5:21 | | | |7 |7:57 |12:01 |12:53 |5:23 | | | |8 |7:39 |12:10 |12:56 |5:32 | | | |9 |8:02 |12:04 |12:53 |5:19 | | | |10 |SATURDAY | |11 |SUNDAY | |12 |7:54 |12:13 |12:55 |5:39...
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...Budget Sales Planning Vendors Deliveries in full on time Sales Forecasting. Macro process, players and final goal Forecast Budget Forecast Budget Markets Sales Planning Vendors Deliveries in full on time Sales Forecasting. Process in details Fall Winter 2012 & Spring Summer 2013 Line Plan, CADs, Proto Samples 1 3 Steps Sales Forecast Process 1st Sales Forecast SMS Order Capacity Plan Pre Costing Final Allocation Final Costing Minimum Check Drop 2 3 Photo Samples Arrival 2nd Sales Forecast Sales Meeting & SMS Arrival 3rd Sales Forecast Raw materials Purchasing Plan & Cut Off Schedule Sales Forecasting. Process in details – Fall Winter 2012 1st & 2nd Forecast results Line Plan, CADs, Proto Samples 1 3 Phases Sales Forecast Process 1st Sales Forecast SMS Order Capacity Plan Pre Costing Final Allocation Final Costing Minimum Check Drop 2 3 Photo Samples Arrival 2nd Sales Forecast Sales Meeting & SMS Arrival 3rd Sales Forecast Raw materials Purchasing Plan & Cut Off Schedule...
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...better picking a fight? Bring it on! I’ll play with you all!” The older male taunted with his index finger, wanting us to rush at him like Mike did. The new arrivals and their new roommate did cause noticeable attention. Guards on the outside had reported to the...
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...Wednesday, 27 January 2016 Explain how the global pattern of tourism is the result of a variety of factors (25 Marks) (PLAN) Intro - Global data- in 1950 50 million international arrivals, in comparison in 2010 there was 1,015 million international arrivals - However, this growth in tourism is not uniformly distributed, rather some areas such as Europe have prospered with Europe growing from 25 million in 1950 to 550 million in 2010, but some areas such as South Asia who have grown from 0 million in 1990 to 20 million in 2010. Showing signs of there being spatial patterns and variations. - There are political, social, environmental and economic reasons for this pattern. - Throughout the globe we have two main types of tourism: • organised mass tourism - top-down, high density, ‘package holidays’, all-inclusive, can lead to economic leakages, enclaved tourism (main example- cruise ships), generally ran by TNCs • alternative tourism - bottom-up, less planned, involving communities, eco-tourism, integrating with culture, minimal impact on local environment, niche - Focus on the fact that it is not growth of tourism but the pattern of tourism The global pattern of tourism can result from political factors 1. When a country is stable, tourists are more likely to go there, as it is safer than a less stable country. This could mean that there has been differences in the pattern of tourism between politically stable countries and non stable countries...
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...Cloud computing is a new area of research currently being studied [1]. Basically it’s not a technology it’s a computing model. We can also define Cloud computing as both the applications delivered as services over the internet and the hardware and system software in the data centers that provide those services [2]. OR type of parallel and distributed system consisting of a collection of interconnected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources [3].Cloud computing purely relies on the fact that sharing computing resources rather to have applications on local servers or devices if resources are not equally distributed then this will result in resource wastage. The cloud computing platform guarantees subscribers that it sticks to the service level agreement (SLA) by providing resources as service and by needs based on the broker policy[4]. So in order to get maximum benefit from cloud computing there is need to dynamically balance the load among servers and improve utilization of resources. There are still some areas that are needed to be focused on in cloud computing. Resource Management Task Scheduling The task scheduling goals of Cloud computing is provide optimal tasks scheduling for users, and provide the entire cloud system throughput and QoS at the same time. Scheduling is the process of allocating tasks to available resources on the basis of tasks need [5].The main purpose for scheduling is to...
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...Managerial Report Visitor Arrivals in Fiji Prepared by: |Roshan Singh / S9902083 |GROUP 8 | |Salveendra Singh / S11011093 |MBA 435 QBA | |Alvin P Sharma / S97001213 |Lecturer : Dr. S. Ahmed | |Abdul Ali / S11079449 | | MANAGEMENT REPORT Topic: Visitor Arrivals in Fiji – Analysis for years 2000 to 2010 Executive Summary: The purpose of this report is to present the facts of Visitor arrivals in Fiji, through the use of facts, figures, graphically analyse and interpret results and provide a summary of trends, conclusions and justified recommendations after the 2007 – 2016 review of the Fiji Tourism Development Plan. Introduction: Tourism is Fiji's fastest growing industry in terms of employment creation and foreign exchange earnings. The industry provides employment directly to an estimated 85,000 people. The tourism industry is mainly private sector driven and contributes approximately 25% to the GDP. The sector is private sector driven and has grown substantially over the past few years. The 2007-2016 Fiji Tourism Development Plan provides a framework for the sustainable growth of tourism in Fiji. It is a working...
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...504 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 5, NO. 3, JULY 2008 Stochastic Modeling of an Automated Guided Vehicle System With One Vehicle and a Closed-Loop Path Aykut F. Kahraman, Abhijit Gosavi, Member, IEEE, and Karla J. Oty Abstract—The use of automated guided vehicles (AGVs) in material-handling processes of manufacturing facilities and warehouses is becoming increasingly common. A critical drawback of an AGV is its prohibitively high cost. Cost considerations dictate an economic design of AGV systems. This paper presents an analytical model that uses a Markov chain approximation approach to evaluate the performance of the system with respect to costs and the risk associated with it. This model also allows the analytic optimization of the capacity of an AGV in a closed-loop multimachine stochastic system. We present numerical results with the Markov chain model which indicate that our model produces results comparable to a simulation model, but does so in a fraction of the computational time needed by the latter. This advantage of the analytical model becomes more pronounced in the context of optimization of the AGV’s capacity which without an analytical approach would require numerous simulation runs at each point in the capacity space. Note to Practitioners—This paper presents a model for determining the optimal capacity of an automated guided vehicle (AGV) to be purchased by a manufacturer. This paper was motivated by work...
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...The Manzana Case Manzana Insurance- Fruitvale Branch case study Executive Summary The Fruitvale Branch of Manzana is facing its bitter reality of declining profitability and potential loss of market shares to its main competitor-Golden Gates due to its rising renewal loss rate and expanding turnaround time. In our analysis, we dig into several issues that were possible causes for the problem and tend to provide readers with feasible solutions that may resolve the issues on hand. Examples of such issues as we have identified in the case include, but not limited to, various departments’ deviation from FIFO system, potential capacity and staffing problems, uneven workload among three underwriting teams, outdated SCT for computing TAT, and inaccurate computation process for TAT. In order to lower the number of late renewals and reduce turnaround time, our recommendations include, but not limited to, the following: 1) Making it mandatory for all departments to comply with the FIFO system and implementing monitoring plans for overseeing the entire underwriting process to ensure FIFO is strictly implemented; 2) Revising the incentive scheme for Fruitvale Branch employees to further assist with the successful implementation of FIFO system; 3) Expanding the number of days RERUNs are released to DCs prior to the due dates to ensure that there is sufficient time for relevant departments to complete the requests on or before due dates; 4)Revising and updating the SCT used for...
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...Operations Analysis Operations Analysis Executive Summary Manzana Insurance specialises in commercial insurance, with the Fruitvale branch typically processing 40 requests per day and the product mix being 60% new policies and 40% renewal requests. The operating performance of Fruitvale is deteriorating across five of the company’s six key performance measures. Additionally, competitor Golden Gate has promised efficiency in service and offered a guarantee for time request turnarounds. Our analysis of Fruitvale’s operations, priorities and cost structure shows that: * The “first-in, first-out” processing system is implemented differently across the teams, causing RERUNs to be submitted late and leading to lost renewals. * The increase in ordinary insured losses and declining revenues from renewals is causing a decline in Fruitvale’s profitability. * The current procedure to calculate turnaround time results in a high value and is really a worst case scenario. * There is a large degree of variance in the time it takes to handle requests, which can cause both the distribution and underwriting teams have more work than capacity. This affects responsiveness, as the system has little spare capacity to deal with increased demand due to variation, seasonality or new business. Based on our analysis, we make a number of recommendations for Manzana to implement: * Review employee and agent incentives and change the priorities of the current process, to ensure more focus...
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