mps management problem solving

MPS in AEM Curriculum & Concentrations

The Master of Professional Studies (MPS) in Applied Economics and Management program entails 30 credit hours of training in applied economics and management through coursework and a capstone problem-solving project. All students are required to select one of six industry-focused concentrations. Students must meet general MPS AEM degree requirements in addition to concentration-specific coursework.

Core/Pre-requirements

• Statistics, or BTRY 6010 – Statistical Methods I
• Intermediate microeconomics or AEM 5600 – Managerial Economics
• Undergraduate calculus or “Math camp for MBAs” (or Khan Academy calculus)

Required Coursework

• AEM 5111 – Introduction to Econometrics (Fall, 4 credits)
• AEM 5150 – Price Analysis (Fall, 3 credits)
• AEM 5410 – Marketing Research (Spring, 3 credits)
• AEM 5840 – Python for Business Analytics (Fall, 3 credits)
• AEM 5850 – R Programming for Business Analytics and Business Modeling (Spring, 3 credits)
• AEM 6061 – Risk Simulation and Monte Carlo Methods (Spring, 3 credits)
• AEM 6940 – Introduction to Machine Learning (Spring, 3 credits)
• CEE 5102 – Basics of Programming in Python (Spring, 1 credit)
• CRP 5250 – Methods for Spatial Economic and Demographic Analysis (Spring, 4 credits)
• GDEV 5045 – Data and Development (Fall, 3 credits)
• HADM 5760 – Visual Basic for Applications: End-User Programming (Fall/Spring, 3 credits)
• NBA 6200 – Marketing Research (Spring, 3 credits)
• NBA 6550 – Business Data Analysis with SQL (Spring, 1.5 credits)
• NBA 6921 – Machine Learning Applications in Business (Spring, 3 credits)
• STSCI 5010 – Applied Statistical Computation with SAS (Fall, 4 credits)
• STSCI 5040 – R Programming for Data Science (Fall, 4 credits)
• STSCI 5120 – Introduction to R Programming (Fall, 2 credits)
• STSCI 5740 – Data Mining and Machine Learning (Fall/Spring, 4 credits)
• AEM 5305 – Global Citizenship (Fall, 1 credit)**
• AEM 5700 – Management Communications (Fall/Spring, 1.5 credits)
• Problem-solving project (6 credits): AEM 6991 (Fall, 3 credits)** and AEM 6992 (Spring, 3 credits)**
• Core coursework specific to each concentration (9 credits)
• Courses must be relevant to your career goals and approved by your concentration leader. Students can select 5000 level courses from across Cornell University for electives.

* Course number TBD ** CEMS students do not take these courses. See CEMS requirements information.

Applied Economics and Management Concentrations

Behavioral finance.

Associate Professor Scott Yonker Behavioral Finance Concentration Leader

Required Courses (6 credits):

• AEM 6140 – Behavioral Economics and Managerial Decisions (fall, 3 credits)
• AEM 5230 – Contemporary Topics in Behavioral Finance (fall, 3 credits)

Electives (min. 3 credits)

• AEM 5570 – Corporate Finance (fall/spring, 3 credits)
• AEM 5280 – Valuation of Capital Investment (spring, 3 credits)
• AEM 5290 – International Financial Management (spring, 3 credits)
• AEM 5620 – Financial Modeling and Analysis (fall, 2 credits)
• AEM 5670 – Investments in the Global Economy (fall/spring, 3 credits)
• AEM 6940 – Applied Behavioral Economics in Finance and Marketing (spring)
• NBA 5060 – Financial Statement Analysis (fall/spring, 1.5 credits)
• NBA 5090 – Advanced Financial Statement Analysis (fall/spring, 1.5 credits)
• NBA 5111 – Foundations of Financial Modeling (fall/spring, 3 credits)
• NBA 5120 – Applied Portfolio Management (fall/spring, 3 credits)
• NBA 5130 – International Finance Cases (fall, 1.5 credits)
• NBA 5420 – Investment and Portfolio Management (Spring, 3 credits)
• NBA 5550 – Fixed Income Securities and Interest Rate Options (fall, 3 credits)
• NBA 5590 – The Venture Capital Industry and Private Equity Markets (spring, 0.5 credits)
• NBA 6060 – Evaluating Capital Investment Projects (fall, 1.5 credits)
• NBA 6730 – Derivatives Securities Part I (fall, 1.5 credits)
• NBA 6740 – Derivatives Securities Part II (fall, 1.5 credits)

Behavioral Marketing

Assistant Professor Nathan Yang Behavioral Marketing Concentration Leader

Required Courses(6 credits):

• AEM 6440 – Consumer Behavior (spring, 3 credits)

Concentration Electives (min. 3 credits):

• AEM 5435 – Data-Driven Marketing (fall, 1.5 credits)
• AEM 5495 – Consumer Neuroscience (spring, 3 credits)
• NBA 6340 – Customer Strategy and Analytics (spring, 1.5 credits)
• NBA 6090 – Digital Marketing (fall, 1.5 credits)
• NBA 6220 – Marketing strategy (fall, 1.5 credits)
• NBA 6290 – Special Topics in Marketing (fall, 1.5 credits)
• NBA 6620 – Brand Management (fall, 1.5 credits)
• HADM 6430 – Wine Marketing (spring, 3 credits)
• HADM 6435 – Luxury Marketing (spring 3 credits)
• NCC 5530 – Marketing Management* (fall/spring, 3 credits)

**Will not count as a CEMS elective for CEMS students as they were required to take a basic marketing management course as a CEMS prerequisite .

Business of Food

Associate Professor Bradley Rickard Business of Food Concentration Leader

Required Courses (3 credits):

Electives (min. 6 credits):

• AEM 5150 – Price Analysis (fall, 3 credits)
• AEM 5210 – Business and Economics of Food (spring, 3 credits)
• AEM 5270 – Supply Chain Strategy and Supermarket Simulation (fall, 3 credits)
• AEM 6455 – Toward a Sustainable Global Food System (fall, 3 credits)
• AEM 6485 – Economics of Food and Malnutrition (fall, 3 credits)
• AEM 5480 – From Labels to Lab-Grown Meat: Consumer Behavior and the Food Industry (spring, 3 credits)
• AEM 6480 – Food and Consumer Packaged Goods Industry Dynamics (spring, 3 credits)
• AEM 5260 – Cooperative Business Management (spring, 3 credits)
• AEM 6880 – Global Food, Energy, and Water Nexus – Engage the US, China, and India for Sustainability (fall, 3 credits)
• FDSC 5100 – Sensory Evaluation of Food (fall, 2-3 credits)
• PLSCS 5140 – Global Cropping Systems and Sustainable Development (fall, 3 credits)

International and Development Economics (IDE)

Professor Arnab Basu International and Development Economics Concentration Leader

Select 3-4 courses (min. 9 credits):

• AEM 5420 – Emerging Markets (fall, 3 credits)
• AEM 6050 – Agriculture Finance and Development (spring, 1.5 credits)
• AEM 6300 – Policy Analysis: Welfare Theory, Agriculture and Trade (spring, 4 credits)
• AEM 6455 – Toward a Sustainable Global Food System: Food Policy for Developing Countries (fall, 3 credits)
• AEM 6880 – Global Food, Energy, and Water Nexus – Engage the US, China and India for Sustainability (fall, 3-4 credits)
• AEM 6600 – Natural Resources and Economic Development (spring, 3 credits)
• AEM 6940 – Product Design and International Development (spring, 1.5 credits)
• AEM 6410 – Commodity Futures and Options (spring, 3 credits)
• AEM 6960 – Perspectives in Global Development (spring, 1 credit)
• CRP 6720 – International Institutions (spring, 3 credits)
• CRP 6770 – Seminar on Issues in African Development (fall, 2 credits)
• ECON 7660 – Microeconomics of International Development (spring , 3 credits)
• GDEV 6820 – Community Organizing and Development (fall, 3 credits)
• IARD 6960 – Perspectives in International Development (spring, 1 credit)
• INFO 5505 – Computing and Global Development (fall, 3 credits)
• NBA 5255 – Global Macroeconomics News and Events (spring, 1.5 Credits)
• NBA 6030 – Strategies for Sustainability (spring, 1.5 credits)
• NBA 6370 – Current Global Issues for Business: the US, Europe, China and Emerging Markets (spring, 1.5 credits)
• NBA 6380 – Finance and Sustainable Global Enterprise Colloquium (spring, 1 credit)

* Course listing number TBD

Sustainable Business and Economic Policy

Students in the SBEP concentration study the interrelationships among business, the economy, and the environment; examine the role of sustainability in business and economic development; and learn how to design policies and management strategies to promote sustainability, environmental protection, clean technologies, and the optimal and sustainable use of natural resources, taking into account how individuals and businesses respond to these policies.

Professor Catherine Louise Kling Sustainable Business and Economic Policy Concentration Leader

• AEM 5500 – Resource Economics (spring, 3 credits)
• AEM 5510 – Environmental Economics (spring, 3 credits)
• AEM 6960 – Perspectives in Global Development (fall/spring 1 credit)
• BEE 5299 – Sustainable Development (spring, 3 credits)
• NBA 5255 – Global Macroeconomics News and Events(spring, 1.5 credits)
• NBA 6370 – Current Global Issues for Business: the US, Europe, China, and Emerging Markets (spring, 1.5 credits)
• PADM 5717 – Energy Transition: Policy, Financial, and Business Interactions (fall, 3 credits)
• PADM 5418 – Strategic Stakeholder Engagement (spring, 1.5 credits)

Technology Management

Do you have a background in technology? Looking to market your own innovations in technology? With an MPS in Applied Economics and Management concentrating in Technology Management, you will develop the business skills that are required to manage your business ventures in technology.

This concentration allows you to deepen your understanding of the management and strategies related to technology, innovation, and entrepreneurship. Technology management focuses on business processes and practices that facilitate the creation of new technologies; innovation strategies that allow business organizations to successfully launch new technologies in the economy; and the development of an entrepreneurial mindset that is needed to instigate change in organizations and markets. We encourage students to combine the technology management concentration with additional skill-building in data science and analytics to maximize job market opportunities in technology industries.

Professor Aija Leiponen Program Director, MPS and Technology Management Concentration Leader

Required Courses:

• AEM 5220 – Digital Business Strategy (fall, 3 credits)

Electives (min. 6 credits)

• AEM 5225 – Systems and Analytics in Accounting (fall, 3 credits)
• AEM 5615 – Digital Platform Strategy (spring, 1.5 credits)
• AEM 5605 – Predictive Analytics in Business Strategy (spring, 3 credits)
• AEM 5650 – Strategic Management of Innovation (spring, 3 credits)
• INFO 5100 – Visual Data Analytics for the Web (fall, 3 credits)
• INFO 5240 – Designing Technology for Social Impact (fall, 3 credits)
• INFO 5556 – Business Intelligence Systems (fall, 4 credits)
• NBA 5180 – Design and Innovation (fall, 1.5 credits)
• NBA 5410 – Project Management (fall, 1.5 credits)
• NBA 6070 – Designing Data Products (spring, 1.5 credits)
• NBA 6145 – AI Strategy and Applications (fall, 1 credit)
• NBA 6955 – Industrial Organization, Consulting and Business Strategy (fall, 1.5 credits)
• NBA 6500 – Operations Practicum (project; guest speakers; class; site visits – 4 credits)
• Optional electives on operations management

AEM 5700 - MPS Management Communication

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Article • 8 min read

The MPS Process

Discovering work that you love.

By the Mind Tools Content Team

Think of a time when you worked on a project that you really enjoyed.

Chances are, the people were great, the work was meaningful, it brought you pleasure, and it challenged you enough to make you feel that you'd accomplished something really worthwhile.

When a project resonates with us like this, we give it our very best effort. Why? Because doing work like this is pure joy. In fact, it hardly seems like work at all.

When we work on these projects regularly, we're happy, productive, optimistic, and deeply engaged in what we're doing. So, how can you target projects that will give you this type of engagement?

One way to do this is to use the Meaning, Pleasure, Strengths (MPS) Process. In this article, we discuss what MPS is, and see how you can use it to increase the proportion of rewarding work that you do.

What Is the MPS Process?

The MPS Process was created by Harvard professor and bestselling author Dr Tal Ben-Shahar, and was published in his book, " Happier ." The model develops ideas from positive psychologist Mihaly Csikszentmihalyi.

Dr Csikszentmihalyi's groundbreaking work was on the concept of " flow ." When we do work that is both meaningful and challenging, we slip into flow, losing track of time and our sense of self as we focus solely on the task at hand. This can be enormously enjoyable and satisfying.

Dr Ben-Shahar adapted this concept and created the MPS Process as a way for us to seek out jobs, projects, and tasks that challenge and engage us.

With the MPS Process, you ask yourself three crucial questions:

• What gives me meaning?
• What gives me pleasure?
• What are my strengths?

From "Happier" by Tal Ben-Shahar. © 2007 McGraw-Hill Companies, Inc.

When we regularly do work that combines these three elements, we're much more likely to achieve flow and be happy in our careers.

The MPS Process prompts you to look deeply at what brings you meaning and pleasure. It also encourages you to understand and get to know your strengths.

For this reason, it might be best to spend several days thinking about the questions below, before answering them fully. You might also find it helpful to read about the PERMA Model , which helps you define what true happiness means for you.

How to Use the MPS Process

1. answer the mps questions.

The first step in using the tool is to answer the three key questions:

As we mentioned above, it's often helpful to take your time here. There are also many resources that you can use to dive deeper into these fundamental questions.

To help discover what gives you meaning , start by defining your values . These are the guiding principles that shape your behavior and judgments. Often, working on projects that align with your values helps to bring meaning to what you do.

Values can only be a part of what you find meaningful. For instance, you might find meaning through helping others, teaching a new skill, or coaching someone through a crisis. So, think back to other tasks and projects that made you feel good. What were you doing during those times? What was it about those projects that made you feel good?

Next, write down the things that bring you pleasure . These don't have to be work-related; you can also list hobbies, interests, and anything that brings you joy or contentment. For instance, you may include reading, teaching others, traveling, or meeting new people.

Last, list your strengths . This can be difficult, since many of us take our strengths for granted (they come so easily!) You might have strengths that you don't even realize are strengths, such as empathy, a positive attitude, or the ability to learn things quickly.

If you're not sure about your strengths, ask your boss, colleagues, or family members what they think your strengths are. You can also do a Personal SWOT Analysis , or you can use the StrengthsFinder Assessment to help uncover your biggest strengths.

2. Find Overlap

Next, look at your answers in each area and explore elements that are common to each category, or that overlap in some way. These overlapping answers offer valuable insights into the tasks that you'll find most rewarding and engaging.

For example, you might have listed the following in your answers:

These three answers clearly overlap with each other, and from this, you may conclude that you want to focus on tasks or projects that help people solve their problems.

Don't rush this step – at first glance, it may not be obvious which of your answers overlap.

Ideally, you'll have answers that overlap in all three areas, but you may also uncover engaging and rewarding work with elements that overlap in only two.

3. Shape Your Job or Career

You can now use your answers from step 2 as a guide for shaping your current role, or to find a career that you find engaging. Put simply, you want to work on projects and tasks that, in some way, incorporate elements that overlap. This is how you'll get job satisfaction .

You can do this in your current role by using job crafting . This is where you reshape your job to fit you better. For instance, are there any projects that you want to be responsible for, but aren't? Can you do your current work in a way that you'll find more engaging? Or could you do some of your boss's tasks in these areas? There are endless ways that you can reshape your role to fit you better.

You can also use your findings if you want to get a promotion , or if you're exploring possible career options.

MPS Process Example

Ellen has just answered the questions in step 1 of the MPS Process. Here are the answers she came up with.

After comparing each list, Ellen can see that she has overlaps between helping team members, helping others, and being empathic.

Ellen decides to craft her current role so she can incorporate these elements into her job. She approaches her manager and offers to coach and mentor less-experienced team members. She meets them on a regular basis, and helps them overcome their challenges and problems, many of which Ellen experienced earlier in her career.

You can also use tools such as Schein's Career Anchors and Holland's Codes to get an insight into the roles and types of work that are best for you.

You can use the MPS Process with your team when allocating tasks and projects, and when helping them with career development. However, this might not be suitable in all situations, so use your best judgment here.

Discover Work You Love Infographic

See The MPS Process represented in our infographic :

The MPS Process was developed by Dr Tal Ben-Shahar, bestselling author of the book "Happier."

MPS stands for Meaning, Pleasure, and Strengths, and it gives you a useful insight into what truly makes you happy. This, in turn, helps you find tasks and projects that are engaging and rewarding.

To use the MPS Process, think about what gives you meaning and pleasure, and analyze your strengths. Then identify elements that overlap in these three areas.

You can then shape your job or career to incorporate the elements that overlap the most.

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Peer tutoring models in collaborative learning of mathematical problem solving and their effect on group achievement

• Published: 19 November 2022
• Volume 28 , pages 6595–6618, ( 2023 )

Cite this article

• Yangyang Li   ORCID: orcid.org/0000-0001-5361-5058 1 , 2 ,
• Chunlian Jiang   ORCID: orcid.org/0000-0002-2740-1870 3 ,
• Zengzhao Chen   ORCID: orcid.org/0000-0002-4022-9139 1 , 2 ,
• Jing Fang   ORCID: orcid.org/0000-0002-9244-5896 1 , 2 ,
• Chenyang Wang   ORCID: orcid.org/0000-0002-2753-6505 1 , 2 &
• Xiuling He   ORCID: orcid.org/0000-0001-7880-8710 1 , 2  

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This article has been updated

This study was conducted to examine peer tutoring models in collaborative learning of mathematical problem solving (MPS) in flipped classrooms and their effect on group achievement. Quantitative data collected include 32 videos of eight groups of students in four MPS periods that were designed based on a simplified version of Polya’s four-stage model, and their worksheets completed in each period. The video data were coded based on a framework with low-level and high-level cognitive behaviors as well as management behaviors and were analyzed using lag sequence analysis from which three types of peer tutoring models were determined: organization-oriented, cognitive-supported, and cognitive-guided. ANOVA analysis showed that the groups with cognitive-guided tutors performed significantly better than the groups guided by cognitive-supported and organization-oriented tutors, between which the performance difference did not reach a significant level. The results of this study suggest that training provided to peer tutors should focus more on how to stimulate high-level cognitive thinking skills than on organization skills.

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Social Learning Theory—Albert Bandura

The Impact of Peer Assessment on Academic Performance: A Meta-analysis of Control Group Studies

The effects of scaffolding in the classroom: support contingency and student independent working time in relation to student achievement, task effort and appreciation of support, data availability.

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Change history

26 november 2022.

Springer Nature’s version of this paper was updated: In PDF version, Figure 6 has been moved under “Appendix. A worksheet for group discussion” heading.

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Yangyang Li, Zengzhao Chen, Jing Fang, Chenyang Wang & Xiuling He

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yangyang Li, Zengzhao Chen, Jing Fang, and Chenyang Wang. The first draft of the manuscript was written by Yangyang Li. The review and editing of the manuscript was done by Xiuling He and Chunlian Jiang. All authors read and approved the final manuscript. This research was supported by National Natural Science Foundation of China (NSFC) for the Project “A Study on the Perception and Attribution Analysis of Learners' Higher-Order Thinking Activities” (No.: 61807012). We are grateful for the support from NSFC, National Intelligent Society Governance Experiment Base (Education), and Central China Normal University. Any opinions expressed herein are those of the authors and do not necessarily represent the funds' views.

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Li, Y., Jiang, C., Chen, Z. et al. Peer tutoring models in collaborative learning of mathematical problem solving and their effect on group achievement. Educ Inf Technol 28 , 6595–6618 (2023). https://doi.org/10.1007/s10639-022-11429-2

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T1 - Solving Managerial Problems Systematically

AU - Heerkens, Hans

AU - van Winden, Arnold

N1 - Translated into English by Jan-Willem Tjooitink

N2 - Solving Managerial Problems Systematically describes the Seven Steps of the Managerial Problem-Solving Method. With this It helps trouble-shooters arrive at solutions by ticking the boxes on a methodological checklist, and teaches them to differentiate between knowledge and action problems. The Language of Variables ensures that researchers remain concrete, helping them to consciously weigh up alternatives and obtain answers to questions they had not yet thought to ask. The MPSM encourages its users to take advantage of their intuition. Solving Managerial Problems Systematically is meant for higher education where students deal with problems in individual courses, and graduate projects.

AB - Solving Managerial Problems Systematically describes the Seven Steps of the Managerial Problem-Solving Method. With this It helps trouble-shooters arrive at solutions by ticking the boxes on a methodological checklist, and teaches them to differentiate between knowledge and action problems. The Language of Variables ensures that researchers remain concrete, helping them to consciously weigh up alternatives and obtain answers to questions they had not yet thought to ask. The MPSM encourages its users to take advantage of their intuition. Solving Managerial Problems Systematically is meant for higher education where students deal with problems in individual courses, and graduate projects.

UR - http://ho.noordhoff.nl/boek/solving-managerial-problems-systematically

SN - 9789001887957

BT - Solving Managerial Problems Systematically

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DEVELOPING AN MPS

The master scheduler develops a proposed MPS, checks the schedule for feasibility in terms of available capacity, modifies as needed, and authorizes the MPS. The master scheduler starts by creating, revising, and finishing an MPS record for each product. The master scheduler uses the finished MPS records to develop a proposed master production schedule, which he or she then checks for feasibility with a rough-cut capacity planning technique.

Consider the following examples of an MPS record for a product built in a make-to-stock environment with inventory held. The product is built in a fixed-order quantity of 125 units, and there are 110 units in beginning inventory . Table D-1 is an initial MPS record showing the demand forecasts for the next 12 weeks.

TABLE D-1 First MPS Record

The top row of the record shows the time periods (weeks, in our example); the forecast row shows the forecasted demand for the product. The projected available quantity row keeps track of how many units are available at the end of each time period . The MPS row shows when replenishment shipments need to arrive. MPS shipments arrive at the beginning of the time period . To calculate the projected available quantity, we use the following formula:

Projected available = beginning inventory + MPS shipment – forecasted demand

Period 1 To calculate the projected available quantity at the end of ...

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In this article we practice approaches in the use of time fences in MPS. No doubt most of this is known to some in your group. We will cover most of these issues in the MPS policy and time fence procedure. I've attempted to answer some of the questions that might come up such as:

Main Headings:

A. Overview of Time Fences B. Types of Time Fences C. Planning Zones D. Administration of Time Fences

What are time fences?

Time fences are dividing lines on a planning horizon that allow manufacturers to maintain MPS items in zones that are governed by different sets of rules. A time fence is normally identified in the MPS system as a specific location on the planning horizon relative to "today". The length of time between today and the time fences generally remains constant so that as items on the planning horizon, such as requirements and replenishments, move toward today, they also move toward and across the time fences. Consider the example in Figure 1. Any item place, say, in period 9 will move first into period 8, then into period 7, cross the time fence, and move into period 6. This causes an item to be located initially in zone 2 and finally in zone 1.

Figure 1. Planning Horizon Divided by a Time Fence

Time fences are usually located at different times for different product lines. Various types of products may even require different fence locations for specific items within a product grouping. It is important that flexibility be designed into the computer programs to allow the time fence location to vary from item to item or to be easily identified as the same for groups of MPS requirements.

How are time fences used?

Figure 1 illustrates that a time fence actually divides the planning horizon of the MPS into sections or zones. The types of requirements that are permitted within each zone must be identified and the rules for their use defined. Zone 2 might be defined as a mixture of forecast, customer orders and internal orders for MPS items. Zone 1 could be defined as containing only customer orders and internal orders. The time fence is used as a control or barrier to prevent the forecast requirements from moving automatically into zone 1.

Each fence gives the master production scheduler another tool to manage and control the individual items on the schedule. (stabilize requirements). Stability at the MPS level means that mrp will not generate an excessive number of exception messages. These fences are known as the planning time fence and the assembly lead-time time fence. Two additional types of time fences, the competitive lead- time time fence and the customer backlog time fence, provide a measure of flexibility in competitive marketing environments.

The simultaneous use of all four time fences for one item should be avoided. Normally, the planning time fence used in conjunction with one or, at most, two other fences will provide all the control needed to stabilize requirements for an item in the master schedule.

The planning time fence locates a point on the planning horizon when an MPS item comes under the direct manual control of the master production scheduler. It defines the number of time periods in which the scheduler will have total responsibility for scheduling and rescheduling the item. As a general rule, this fence is placed at or near the end of the cumulative lead time for all the purchased and manufactured parts of an item. Figure 2 illustrates the cumulative lead item of product A, and the relative placement of the planning time fence for this product. Components must be ordered or already available when the item crosses over the planning time fence. This is the critical point in the planning horizon for that particular product.

Figure 2. Cumulative Lead time in Relation to Planning Horizon

Computer logic must work differently in each of two zones created by the planning time fence. In the zone from the fence extending into the future, automated mrp logic can be used in planning orders and exploding their requirements. However, in the zone from today to the planning time fence, the computer program should not move or plan orders automatically. Instead, a firm planned order should be manually entered in the first period beyond the fence. Of course, the master production scheduler should receive a message indicating the desired time period for the planned order, but at no time should this desired time period be allowed to automatically offset component requirements. Figure 3 illustrates this action for a hypothetical MPS part Z. Notice that requirements consisting of forecast and orders caused the planned available to go negative in period 5. The placement of a firm planned order for 20 pieces in period 7 will not cause problems at the lower levels of the BOM for part Z because sufficient lead time is available to secure these components. The master production scheduler can analyze the critical components in part Z and determine if it can be scheduled in period 5 without seriously delaying other orders. Perhaps purchase orders can be expedited or overtime arranged to obtain the needed critical components in time to meet the schedule. Only after this analysis is complete and it has been determined that the necessary components can reasonably be expected to be available should the replenishment be firmed and placed in period 5.

Figure 3. Example of Master Production Schedule Netting-Single Time Fence

Assembly lead-time fence

An assembly lead-time time fence is the point in time when the product's final configuration must be identified in the MPS, and the item assigned to either a customer or stock order. When the MPS item reaches this time fence, all components should have been purchased or made and should be available for assembly into a product that can be sold or placed inside the planning time fence at or near the final assembly time of an MPS item. It is the point when a final commitment of what will be made is required.

This time fence is primarily applicable to products whose final assembly time is one or more planning time periods in length; it becomes increasingly more important for products with longer assembly lead time. Normally, the assembly lead-time time fence is not used in process or flow type industries that have relatively short manufacturing cycles. An obvious exception would be in cases where the product must be "cooked" for extended periods of time. The assembly lead time is defined as the length of time to process a specified batch or quantity of an item. This allows some flexibility in scheduling of the batch quantity is selected properly and appropriate assembly lead times are selected for items whose single-quantity lead times are very short.

The logic of the MPS system is different after an item crosses this time fence; only actual demand is used in calculating the planned available balance. Figure 4 illustrates how the assembly lead-time fence changes the order planning logic used in Figure 3. Because the forecast was not considered in the calculation by the computer program, the planned available at the end of period 3 is now 13 compared to 4 in Figure 3. The replenishment identified in Figure 3 is not required and the schedule is stable through period 8. When the assembly lead-time fence is used, the planning horizon is divided into three zones, each with different rules for calculating the planned available and/or the generation and placement of planned orders. The detailed characteristics and control of each zone will be discussed later.

Figure 4. Example of Master Production Schedule Netting-Multiple Time Fence

Using planning time fences divides the planning horizon into three areas or zones.

Planning Zone 1 The final assembly or fabrication of a product occurs during this period, by this time, the final configuration should have been selected and assigned to each item on the MPS in zone 1. Schedule changes must be made by the master production scheduler, because the item is in a portion of the planning horizon and therefore under manual control. When this practice is followed, the modules are removed from the schedule and the end item added. When an MPS item moves into zone 1, other factors must be considered in the scheduling process. Rescheduling items in this zone should be done only after very careful analysis. A good policy is to reschedule only with the authority of top management. An attempt to reschedule the item for later may cause inventory value to increase because all required components have been ordered and received, unless the orders are overdue. Only the cost of final assembly labour can be avoided if the MPS item is canceled or rescheduled for a future date. Another common temptation is the desire to increase the quantity of a production lot in zone 1. This also requires careful analysis by the master production scheduler to determine that sufficient material is available, the available capacity should be examined to determine whether manufactured parts can be completed in time. Items in zone 1 of the MPS should be considered as "frozen" or at the very least "slushy" in nature. They must not be moved to a different time period or be increased in quantity unless executive management considers the impact and approves. Then, and only then, should the master production scheduler make the appropriate change. Planning zone 2 MPS items in zone 2 should consist of only firm planned orders. All planned orders will have been examined by the master production scheduler at the time they are ready to cross the planning time fence to determine that the due date and quantity are reasonable and can be met before they are placed in zone 2. However, it should be noted that these firm planned orders will replenish forecast, customer orders, or internal orders. A number of basic characteristics must be understood when the scheduler moves an order over the planning time fence into this zone. The mrp system will be reasonably tolerant to isolated minor changes in the MPS in zone 2. However, tolerance diminishes very quickly if dates or quantities are changed on several MPS items. The next mrp run will cause computer-planned orders for lower- level items to fluctuate. This fluctuation ripples down through all levels in the BOM, and an excessive number of exception messages is generated. The net effect is the rescheduling of many lower-level items and a symptom known as "nervousness." Unless some stability is maintained in the MPS, planners of lower-level components will be unable to keep up with the recommended reschedules. As an MPS item moves from the planning time fence through zone 2 toward the assembly lead-time time fence, both purchase orders and production orders are released to satisfy the computer-planned order recommendations from mrp. Component items at all levels of the BOM are either in inventory or in the process of being manufactured or purchased. The total inventory value is increasing rapidly as an MPS item approaches the assembly lead-time time fence. Any change to the schedule is almost certain to invalidate some component schedules and cause an increase in inventory. This is especially a problem when unplanned demands are entered in the MPS inside the planning time fence without proper analysis. An MPS that is overloaded will also cause excess inventory to be produced, components or capacity will not be available for the final assembly process. In either case the unassembled components become excess inventory. At the other extreme, under planning can create another serious problem for customer-service levels, because materials will not be available to produce products for customer order demands. Unplanned demands will also be extremely difficult or even impossible to meet. Changes to MPS items in zone 2 can also have disastrous effects on priorities and schedules for components. If the quantity of an MPS item is increased or a due date moved up, at least some planned orders will be recommended for release with less than normal lead time. Expediting these orders can cause other needed items to be delayed, and priorities can be easily destroyed. With careful planning and management, zone 2 can provide some scheduling flexibility. Perhaps its most important characteristic is that it is lead-time sensitive. Changes to the MPS in zone 2 should only be made after careful review and approval by top manufacturing management. Planning zone 3 MPS items in zone 3 are normally computer-planned orders, which cover a mixture of demands such as forecast, customer orders, and internal orders. The only major restriction is available plant capacity. If this restriction is not observed, priorities will inevitably be destroyed in a formal planning system. Lead times present almost no problem in zone 3. Due dates may be moved anywhere within this zone and components and raw materials secured in normal lead time. Excess inventory can be easily avoided because computer-planned orders generated for components have not been released. These orders are beyond their standard lead times; schedule changes will either cause their due dates to change or, in some cases, their requirements to disappear completely. The computer can be given complete control of changes in zone 3.

An easy analogy can be drawn between the MPS and a traffic signal at a busy street intersection. The planning zones are the red, yellow, and green lights of the signal. Time fences are the timing devices that determine which light is displayed and how long it remains illuminated. Zone 3 is green, and changes of MPS items can freely be made with little or no impact, providing capacity constraints are observed. Zone 2 is yellow and caution must be observed if changes are to be made in the schedule. Manufacturing management must approve these changes. Zone 1 is red and changes should only be made with the approval and direction of top management.

The effective administration of time fences requires the functional cooperation of all areas of management. Representatives from finance, marketing, manufacturing, material planning and purchasing must meet with the master production scheduler to establish the initial location of each time fence. The rules governing its use must also be established by this group. Periodic reviews of these fence locations for each product or product line should also be conducted to ensure that they continue to provide the control to effectively manage the MPS.

The following checklist can be used as a guide in establishing the types of time fences discussed in this article:

1. Determine at what level of the BOM the item or product must be incorporated in the master production schedule. 2. Define any special logic required to handle item requirements as they move over this fence when the master production schedule shifts at the end of a time period or if planned orders are generated during the netting calculations of MRP. 3. Modify or write necessary computer programs defined in item 2. 4. Prepare user documentation to explain how the time fence should be used. 5. Identify and enter the assembly or competitive lead time or customer order backlog time for each part. For all items, identify and enter the cumulative lead time. 6. Code the part record to indicate the appropriate time fence logic to be applied to that item. The system should also provide a default record to be set when all products are to use this fence. 7. The specific time fence is now ready for use.

Total commitment by the departments listed above to the observed and use of the time fences is absolutely necessary. Without this commitment, the fences will not be effective in preventing mrp nervousness and providing adequate flexibility in the MPS.

For more information, please email AMGI Management Group Inc. Copyright © AMGI Management Group Inc. All rights reserved.

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Mathematical Problem Solving

Dr. Sasmita Kumari Nayak

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Course Name : Mathematical Problem Solving

Code(credit) : cutm1037(2-2-0), course objectives.

• To understand and analyze algorithms
• To understand efficiency of algorithms and alternative approaches
• To understand data structures and major algorithms and how they together play a role in efficiency
• To apply important algorithm design techniques using a programming language

Course Outcomes

Course syllabus.

Module I: Introduction (4 Hrs)

Mathematics in Computer Science, Problem Solving and Algorithms, Data Structures and Algorithms, Algorithm Efficiency and Importance.

Module II: Problem Types (4 Hrs)

Sorting, Searching, String Processing, Graph and Numerical Problems

Module III: Algorithm Efficiency (6 Hrs)

Orders of Growth, Best-Case, Worst-Case and Average-Case Efficiencies, Analysis of Recursive and Non-Recursive Algorithms

Module IV: Brute Force and Exhaustive Search (10 Hrs)

Selection Sort and Bubble Sort, Sequential Search and Brute-Force String Matching, Exhaustive Search, Depth-First Search and Breadth-First Search.

Module V: Divide and Conquer (10 Hrs)

Merge Sort, Quick Sort, Binary Tree Traversal, Closest-Pair and Convex-Hull Problems

Module VI: Dynamic Programming and Greedy Technique (16 Hrs)

Dynamic programming- Floyd ‘s algorithm, Optimal Binary Search Trees, Knapsack Problem and Memory functions.

Greedy Technique- Prim’sAlgorithm, Kruskal’s Algorithm, Dijkstra’s Algorithm, Huffman Trees.

Module VII: Limitations of Algorithm Power  (4 Hrs)

Lower-Bound Arguments, Decision Trees, P, NP and NP- Complete Problems.

Text Books:

• Fundamentals of Computer Algorithms, 2nd Edition, Ellis Horowitz, Sartaj Sahni and S. Rajasekharan
• Introduction to the Design and Analysis of Algorithms, Anany Levitin

Reference Books:

• Design and Analysis of Algorithms: S. Sridhar
• Design and Analysis of Algorithms: P. H. Dave, H. B. Dave
• Algorithm Design: Foundations, Analysis and Internet Examples: M. T. Goodrich and R. Tomassia, John Wiley and sons
• Algorithms Illuminated (Part 2): Graph Algorithms and Data Structures:Tim Roughgarden

Session Plan

Session 1 & 2 (module i).

Mathematics in Computer Science, Problem Solving and Algorithms: PPT

https://www.youtube.com/watch?v=BP8UzIZj-nU

https://www.youtube.com/watch?v=0IAPZzGSbME&list=PLch-RuUgf43mvNLxnsYjXffEG0fRdQ5KB

Session 3 & 4

Data Structures and Algorithms, Algorithm Efficiency and Importance:  PPT

https://www.youtube.com/watch?v=ovz54KoZo9o

https://www.youtube.com/watch?v=tEI9XrxXhwY

Session 5 & 6 (Module II)

Sorting, Searching, String Processing: PPT

https://www.youtube.com/watch?v=KJWqhaejIn4

https://www.youtube.com/watch?v=SC5IH9GpYVo

Session 7 & 8

Graph and Numerical Problems:  PPT

https://www.youtube.com/watch?v=1n5XPFcvxds&list=PLqM7alHXFySEaZgcg7uRYJFBnYMLti-nh

https://www.youtube.com/watch?v=0u78hx-66Xk&list=PLqM7alHXFySEaZgcg7uRYJFBnYMLti-nh&index=2

Session 9 & 10 (Module III)

Orders of Growth, Best-Case, Worst-Case and Average-Case Efficiencies:  PPT

https://www.youtube.com/watch?v=D9SlLpPQRK8

https://www.youtube.com/watch?v=lj3E24nnPjI

Session 11 & 12

Analysis of Recursive algorithms: Substitution method, recursion tree method and master method: PPT

https://www.youtube.com/watch?v=JPAA1FbM7jk

https://www.youtube.com/watch?v=LC96q_6r3BQ

https://www.youtube.com/watch?v=OynWkEj0S-s

Session 13 & 14

Analysis of Non-Recursive Algorithms with example:  PPT

https://www.youtube.com/watch?v=4HkIai9oiT0

https://www.youtube.com/watch?v=xFRea6xyPgM

Session 15 (Module IV)

Selection Sort: Problem-solving, Algorithm, Find Best, worst, and average case Time complexity:  PPT

https://www.youtube.com/watch?v=8ps1XQigk7U

https://www.youtube.com/watch?v=peLS-S23TvE

Practice 1: 

• Sort the list E, X, A, M, P, L, E in alphabetical order by selection sort.

Bubble Sort: Problem-solving, Algorithm, Find Best, worst, and average case Time complexity: PPT

https://www.youtube.com/watch?v=o4bAoo_gFBU

Practice 2: 

• Sort the list E, X, A, M, P, L, E in alphabetical order by Bubble sort.

Session 19 & 20

Sequential Search and Brute-Force String Matching, Exhaustive Search: PPT

https://www.youtube.com/watch?v=Fit5lRYZAm4

https://www.youtube.com/watch?v=SX5vmCXdg5k

https://www.youtube.com/watch?v=zQyy7s4Lp6E

Practice 3:

You are given a string “s” and s pattern “p”, you need to check if the pattern is there in the string.

S = “prodevelopertutorial”

We need to check if “rial” is present in “prodevelopertutorial” string. Use Brute Force Search Approach

Session 22 & 23

Depth-First Search and Breadth-First Search: problem solving, Find Time complexity: DFS-PPT , BFS-PPT

https://www.youtube.com/watch?v=pcKY4hjDrxk

https://www.youtube.com/watch?v=vf-cxgUXcMk

Practice 4:

• Write down the adjacency matrix and adjacency lists specifying this graph. (Assume that the matrix rows and columns and vertices in the adjacency lists follow in the alphabetical order of the vertex labels.)
• Starting at vertex a and resolving ties by the vertex alphabetical order, traverse the graph by depth-first search and construct the corresponding depth-first search tree. Give the order in which the vertices were reached for the first time (pushed onto the traversal stack) and the order in which the vertices became dead ends (popped off the stack).

Session 25, 26 & 27 (Module V)

Merge Sort: Problem-solving, Algorithm, Find Best, worst, and average case Time complexity: LECTURE NOTES

https://www.youtube.com/watch?v=mB5HXBb_HY8

https://www.youtube.com/watch?v=g1AwUYauqgg

Practice 5:

• Sort the list E, X, A, M, P, L, E in alphabetical order by Merge sort.

Session 28, 29 & 30

Quick Sort: Problem-solving, Algorithm, Find Best, worst, and average case Time complexity: LECTURE NOTES

Practice 6:

• Sort the list E, X, A, M, P, L, E in alphabetical order by Quick sort.

Session 31, 32, 33 & 34

Binary Tree Traversal, Closest-Pair and Convex-Hull Problems: Closest pair-convex hull problem

VIDEO 1 ,  VIDEO 2 ,  VIDEO 3

Session 35, 36 & 37 (Module VI)

Dynamic programming- Floyd ‘s algorithm : Problem-solving, Algorithm, Find Best, worst, and average case Time complexity: PPT

VIDEO 1 , VIDEO 2

Practice 7:

• Solve the all-pairs shortest-path problem for the digraph with the following weight matrix:

Session 38 & 39

Write pseudocode for a linear-time algorithm that generates the optimal binary search tree from the root table.

VIDEO 1 ,  VIDEO 2

Session 40 & 41

knapsack problem:  Problem solving, Algorithm, Time complexity

Practice 8:

a. Apply the bottom-up dynamic programming algorithm to the following instance of the knapsack problem:

b. How many different optimal subsets does the instance of part (a) have? c. In general, how can we use the table generated by the dynamic programming algorithm to tell whether there is more than one optimal subset for the knapsack problem’s instance?

Session 42, 43, 44 & 45

Greedy Technique - Prim’sAlgorithm, Kruskal’s Algorithm: Problem solving, Algorithms, Different cases of Time complexities: LECTURE NOTES

VIDEO 1 ,  VIDEO 2 ,  VIDEO 3 ,  VIDEO 4

Practice 9 & 10:

a. Apply Prim’s and Kruskal's algorithm to the following graph.:

Session 46 & 47

Dijkstra’s Algorithm: Problem solving, Algorithms, Different cases of Time complexities: PPT

Practice 11:

Solve the following instances of the single-source shortest-paths problem with vertex a as the source:

Session 48 & 49

Huffman Trees: Problem solving, Algorithms, Different cases of Time complexities: LECTURE NOTES

Practice 12:

Consider the five-symbol alphabet {A, B, C, D, _} with the following occurrence frequencies in a text made up of these symbols:

Session 50, 51 & 52 (Module VII)

Lower-Bound Arguments, Decision Trees, P, NP and NP- Complete Problems: LECTURE NOTES 1 , LECTURE NOTES 2 ,  VIDEO

https://drive.google.com/file/d/1bMs_xrrNb-zszGUeId_U3JscL1V_5_hv/view?usp=drive_link

Question Bank

https://drive.google.com/file/d/1qfHGpfa9EsQuvTHzT9PV1RwSkkLmayln/view?usp=drive_link

Our Main Teachers

Associate Professor, Department of Computer Science & Engineering

Received M.Tech in Information Technology from BPUT University in the year of 2010 and completed Ph.D. in Computer Science and Engineering at Sambalpur University in 2022. Currently working as an Associate Professor at Centurion University of Technology and Management since 2009.

G.Rama Devi

Assistant professor.

G RAMA DEVI ,MCA,M.TECH(CSE),(Ph.d) working as Assistant professor, Department of CSE, Centurion University of Technology and Management, Andhra Pradesh. Interested to work on Data Structures, Python, Machine Learning, Database Management Systems and Web Development, Problem Solving Methodologies. Programming Skill: Data structures, C programming, python, Web development (Html, CSS,JS), Database Management Systems.

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