...Computing Science, Poznań University of Technology Piotrowo 3A, 60-965 Poznań, Poland Malgorzata.Sterna@cs.put.poznan.pl Faculty of Mathematics, Otto-von-Guericke-University PSF 4120, 39016 Magdeburg, Germany Frank.Werner@mathematik.uni-magdeburg.de 1) 2) 3) 1 A Comparison of Solution Procedures for the Flow Shop Scheduling Problem with Late Work Criterion Abstract In this paper, we analyze different solution procedures for the two-machine flow shop scheduling problem with a common due date and the weighted late work criterion, i.e. for problem F2 | dj = d | Yw, which is known to be binary NP-hard. In computational experiments, we compare the practical efficiency of a dynamic programming approach, an enumerative method and a heuristic list scheduling procedure. Test results show that each solution method has its advantages and none of them can be rejected from the consideration a priori. Keywords: flow shop, late work, dynamic programming, enumerative method, list scheduling 2 1. Introduction The process of investigating every new optimization problem follows the same well known scheme (cf. e.g. (Błażewicz, Ecker, Pesch, Schmidt & Węglarz, 2001), (Brucker, 1998)). If researchers have failed in constructing an optimal polynomial-time method solving the problem under consideration, they attempt to prove the hardness of the problem....
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...knowledge about the social world around themes or subjects and that influence the information people notice, think about, and remember. 4. Accessibility- The extent to which schemas and concepts are at the forefront of people's minds and are therefore likely to be used when making judgments about the social world. 5. Priming- The process by which recent experiences increase the accessibility of a schema, trait, or concept. 6. Self-Fulfilling Prophecy- the case whereby people have an expectation about what another people is like which influences how they act toward that person, which causes that person to behave consistently with people's original expectations come true. 7. Judgment Heuristics- Mental shortcuts people use to make judgments quickly and efficiently. 8. Availability Heuristic- A mental rule of thumb whereby people base a judgment on the ease with which they can bring something to mind. 9. Representativeness- A mental shortcut whereby people classify something according to how similar it is to a typical case. 10. Analytic Thinking Style- A type of thinking in which people focus on the properties of objects without considering their surrounding context; this type of thinking is common in Western cultures. 11. Holistic Thinking Style- A type of thinking in which people focus on the overall context, particularly the ways in which objects relate to each other; this type of thinking is common in East Asian cultures. 12. Controlled Thinking- Thinking that is...
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...influence judgments, feelings, goals, and behaviors. Minimal thought, impulse purchase contrast effect participants who were primed with inexpensive brands rated the moderately priced target (with a clearly visible brand name) as expensive, while participants who were primed with expensive brands rated the target as inexpensive (141) Implicit Association Test (IAT) a new procedure for measuring sensitive beliefs, including those held without awareness or intention (143) mindset priming effect the cognitive activity performed during the first session tends to be performed again in the second, even if the products considered during the two sessions are completely different (143) procedural priming effect the effect that occurs when situations are linked to cognitive or motor processes via “if X, then Y” linkages, where X refers to a specific situation and Y refers to a cognitive or behavioral activity (142) truth effect as familiarity increases, a brand name seems more famous, liking for the brand increases, judgments about the brand are held with greater confidence, and product claims seem more likely to be true (144) Chapter 8 accounts excuses and justifications. The former reduce or deny one’s responsibility for inappropriate actions; the latter acknowledge responsibility but rationalize the behavior as appropriate, given the circumstances (161) aligning activities consist of comments that attempt to realign our behavior with norms (161) brand anthropomorphism both...
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...May 2011, pp. XX–XX A Marketing Science Perspective on Recognition-Based Heuristics (and the Fast and Frugal Paradigm) John Hauser∗ Abstract Marketing science seeks to prescribe better marketing strategies (advertising, product development, pricing, etc.). To do so we rely on models of consumer decisions grounded in empirical observations. Field experience suggests that recognition-based heuristics help consumers to choose which brands to consider and purchase in frequently-purchased categories, but other heuristics are more relevant in durable-goods’ categories. Screening with recognition is a rational screening rule when advertising is a signal of product quality, when observing other consumers makes it easy to learn decision rules, and when firms react to engineering-design constraints by offering brands such that a high-level on one product feature means a low level on another product feature. Experience with applications and field experiments suggests four fruitful research topics: deciding how to decide (endogeneity), learning decision rules by self-reflection, risk reduction, and the difference between utility functions and decision rules. These challenges also pose methodological cautions. Keywords: Consideration Sets, Ecological Rationality, Evaluation Cost Model, Fast and Frugal Heuristics, Selfreflection Learning, Non-compensatory Decision Rules, Product Development, Recognition Heuristic. 1 A Marketing Science Perspective Marketing science provides a valuable...
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...The Giant Pool of Money Analysis The housing crisis that occurred less than a decade ago is a great example, and has become an extensively covered case study, of how dangerous certain biases and heuristics can become if left unchecked on a massive scale. Alex Blumberg and Adam Davidson, in collaboration with NPR News, put together a special program titled “The Giant Pool of Money,” where they explore just how the phenomenon occurred and the underlying factors that contributed through sound bites of those directly involved, affected, or simply aware of the situation as it unfolded. Based on descriptions of various biases and heuristics in Judgment in Managerial Decision Making (Bazerman, 2009), the two biases that prolonged and strengthened the housing crisis in a significant manner can be seen in the “Ease of recall” bias stemming from the “Availability Heuristic,” as well as the “Anchoring” bias coming from the “Confirmation Heuristic.” Bazerman and Moore define the “ease of recall” bias as one where “individuals judge events that are more easily recalled from memory, based on vividness or recency, to be more numerous than events of equal frequency whose instances are less easily recalled.” The housing market and real estate had been doing quite well since the “Dot-com” boom and bust had rattled markets in the late 1990’s. Housing prices were continually increasing, and “lots of people in the mortgage industry had this faith that housing prices in the US simply never go down...
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...Heuristics Nerissa Jones Professor Mattos Lithonia Campus CIS 111 Intro to Relation Dbase Mgt System Feb 28, 2013 Strayer University Heuristics We are finally approaching the end of this proposal but before I conclude this research data I would like to tell you about heuristics and how it is used to access your database information. This report explains the implementation of an algorithm to optimize a QT with heuristic optimization rules. These rules were taken from [1] chapter 16 and [2] chapter 11. Heuristic optimization rules are based on properties of operations as mathematical operations in the relational algebra. Summaries of these properties can be found both in [1] and [2] also. These properties give the following heuristic rules for query optimization: 1. Perform SELECT operations as far down the tree as possible. This has the effect of reducing the number of tuples in later binary operations which are highly expensive. 2. Perform PROJECT operations as far down the tree as possible. This has the effect of reducing the number of attributes in each tuple and reduces the memory requirements, attempting to cut down on secondary storage usage. 3. Combine successive SELECT operations into one composite SELECT operation and successive PROJECT...
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...FINANCE HONOURS 2011-2012 Topic: Behavioural corporate finance Lecturer: Yue (Lucy) Liu E-mail: Yue.Liu@ed.ac.uk Outline Psychological phenomena Biases Heuristics Framing effects Impact on corporate finance Valuation Capital Budgeting Capital structure Dividend policy Mergers and acquisitions Yue (Lucy) Liu 2011/2012 Corporate Finance 2 Psychological phenomena Bias Excessive optimism Overconfidence Confirmation bias Illusion of control Heuristics Representativeness Availability Anchoring and adjustment Affect Heuristic Framing effects Loss aversion Aversion to a sure loss Yue (Lucy) Liu 2011/2012 Corporate Finance 3 Biases Bias A predisposition toward error. – Excessive optimism People overestimate how frequently they will experience favorable outcomes and underestimate how frequently they will experience unfavorable outcomes. – Overconfidence People make mistakes more frequently than they believe and view themselves as better than average. – Confirmation bias People attach too much importance to information that supports their views relative to information that runs counter to their views. – Illusion of control People overestimate the extent to which they can control events. Yue (Lucy) Liu 2011/2012 Corporate Finance 4 Heuristics Heuristic A rule of thumb used to make a decision. – Representativeness People make judgments based on stereotypic thinking, asking how representative an object or idea...
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...REPORT ON HEURISTICS SOLUTION FOR COMPLEX CHEMICAL CARGOES Contents CHAPTER 1- INTRODUCTION 5 1.1 Background 5 CHAPTER 2 - LITERATURE REVIEW 7 2.1 Classification Scheme 7 2.2 Modes of shipping 7 CHAPTER 3 - APPLICATION AND MODEL 9 3.1 Description of the project 9 3.2 Objective of this project 10 3.3 Model Formulation 11 3.4 Mixed-integer Linear Programming Formulation 14 CHAPTER 4 - SOLUTION ALGORITHM 15 4.1 Problem complexity 15 4.2 Heuristic Algorithm 15 4.3 Test case 18 CHAPTER 5 - COMPUTATIONAL RESULT 21 CHAPTER 6 - CONCLUSION 24 6.1 Conclusion and Recommendation 24 6.2 Future Research 24 REFERENCES 26 APPENDIX B – Various ship routing and scheduling problem 29 APPENDIX C – Basic notation for this project 33 CHAPTER 1- INTRODUCTION 1.1 Background Logistics plays an integral role in companies dealing with tangible goods, both finished and semi-finished products. This ranges from electronic products to computer software and even liquid chemicals. Companies need to ensure their products reach their customers in the right quantity, at the right time and the lowest cost to maximize their profits. Logistics component of a company’s operation can be kept in house, if it is considered a key function; otherwise can be out sourced to an established logistics company, such as Federal Express (FedEx). Goods can be delivered via air, sea or land freight. Long distance deliveries use mainly air or sea fright, with land fright...
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...* An assembly line with 17 tasks is to be balanced. The longest task is 2.4 minutes, and the total time for all tasks is 18 minutes. The line will operate for 450 minutes per day. 1. What are the minimum and maximum cycle times? Minimum is 2.4 minutes, maximum is 18 minutes. 2. What range of output is theoretically possible for the line? 25 units to 187.5 units. 3. What is the minimum number of workstations needed if the maximum output rate is to be sought? Eight. 4. What cycle time will provide an output rate of 125 units per day? 3.6 minutes. 5. What output potential will result if the cycle time is (1) 9 minutes? (2) 15 minutes? (1) 50 units. (2) 30 units. * A manager wants to assign tasks to workstations as efficiently as possible, and achieve an hourly output of 33⅓ units. Assume the shop works a 60-minute hour. Assign the tasks shown in the accompanying precedence diagram (times are in minutes) to workstations using the following rules: 1. In order of most following tasks. Tiebreaker: greatest positional weight. 2. In order of greatest positional weight. 3. What is the efficiency? * A manager wants to assign tasks to workstations as efficiently as possible, and achieve an hourly output of 4 units. The department uses a working time of 56 minutes per hour. Assign the tasks shown in the accompanying precedence diagram (times are in minutes) to workstations using the following rules: 1. In order of most following...
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...Availability heuristic provides us with the ability to quickly judge the likeliness of an event by the most outstanding memories we have associated with it. The Daily Show’s segment, “I Know What You Did Last Summer of the Shark” depicts how we can turn everyday occurrences into rare epidemics if the idea is influenced enough by the media or one’s own beliefs through their past memories of an event. The summer of 2001 was dubbed the “Summer of the Shark” because many new channels relentlessly covered the stories of shark attacks, making the viewer believe that sharks were much more dangerous that summer than any other. The Daily Show pokes fun of how easy it is for people to become terrified of ‘normal occurrences’ through availability heuristics. By overweighting easy-to-imagine instances of shark attacks, death from fallen coconuts, or even stair injuries, people will often remember these vivid experiences and will judge the likelihood of these events on how available they are in their memory. Repeatedly hearing about shark attacks will cause a person to believe that shark attacks happen much more frequent then they statistically do because the false information is readily available in their memory to call upon. Thinking about it, I believe that availability heuristics often influences my behavior and the people around me. A few days ago when I was talking to my mother, I mentioned to her that I was going into the city to meet a few of my friends for dinner and I would be...
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...and a height, into the minimum number of bins. The weight corresponds to a ‘‘hard’’ constraint that cannot be violated while the height is a ‘‘soft’’ constraint. The objective is to find a trade-off between the number of bins and the maximum height of a bin. This problem has various real-world applications (computer science, production planning and logistics). Based on the special structure of its Pareto front, we propose two iterative resolution approaches for solving the Mo2-DBPP. In each approach, we use several lower bounds, heuristics and metaheuristics. Computational experiments are performed on benchmarks inspired from the literature to compare the effectiveness of the two approaches. Ó 2013 Elsevier Ltd. All rights reserved. Article history: Received 21 May 2012 Received in revised form 2 March 2013 Accepted 26 May 2013 Available online 4 June 2013 Keywords: 2-Dimensional vector packing problem Multi-objective optimization Lower bounds Heuristics Metaheuristics 1. Introduction In this paper, we...
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...difficult to retrieve. Given that foresight knowledge seems to be more difficult to retrieve once outcome knowledge has been provided and that, in many cases, foresight knowledge may actually conflict with hindsight knowledge, one method suggested by Fischhoff (1975) to assimilate outcome data makes use of heuristics as proposed by Tversky and Kahneman (1973, 1974). Of particular relevance to our study, Fischhoff argued: THE HINDSIGHT BIAS 441 A second heuristic leads judges to evaluate an outcome's likelihood by the relative "availability" of scenarios leading to its occurrence and nonoccurrence. The judge who knows what happened, and has adjusted his perceptions in the light of that knowledge, may well find it difficult to imagine how things could have turned out otherwise. (1975, p. 298) Our purpose was to investigate the role of the availability heuristic with regard to outcome knowledge. If hindsight knowledge facilitates the use of heuristics because it inhibits access to the foresight state-of-mind as suggested by Fischhoff (1975), then accompanying biases or distortion in probability estimates should be present in hindsight judgments. THE AVAILABILITY HEURISTIC One such distortion that may be evident in hindsight can be inferred from the work of Slovic, Fischhoff, and Lichtenstein (1980), who found that people greatly...
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...Lauren Wang MKTG 211 002 Response 6 My biggest takeaway from this lecture was that given the number of times we use heuristics to make both everyday and large decisions, and how many errors we make when using heuristics, us as consumers must make many mistakes when using peripheral routes of processing. For example, using frequency heuristics is often inaccurate and highly subjective based on the person and the information available to them. This is definitely apparent to me when making minor decisions such as where to eat for dinner. When thinking about risk, incidences that are more interesting or shocking to consumers are a lot more known and have a lot more prevalence in media, creating the availability bias that these incidences occur more than they do. This could be hazardous for risks that are less “sexy” and that people do not think a lot about. For example, in the example of estimates of deaths from accident vs. diabetes, the actual result where diabetes is four times more likely to occur than an accident is frightening. Consumers should be more cautious of preventing diabetes. Without this awareness due to selective media coverage, consumers are focusing on the wrong things that could be detrimental to their health or wellbeing. I have learnt about the law of large numbers from my statistics classes, but learning about the law of small numbers showed me how people tend to exaggerate the degree to which a small sample will resemble...
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...26/11/2012 Coursework 2 - overview G54IHC Introduction to Human-Computer Interaction Coursework 2 – ‘Evaluate your interface’ Dr. Gail Hopkins • Evaluate your interface • Worth 50% of your overall mark for this course • Consists of: – Group report – 60% of your coursework mark – Individual report – 40% of your coursework mark • Working in same groups as previously • Using the interface you have developed for CW1 – So I hope it’s ready! Coursework 2 – Group Activity • You must evaluate your prototype in 2 ways 1. 2. Using a method that utilises actual users, the ‘cooperative evaluation’ process discussed in today’s online lecture Using a ‘cognitive walkthrough’ (each member of the group should do this individually) Coursework 2 – Group Activity • You can do the cognitive walk-through evaluation in your own time – or using the lecture time we are not using at the end of term • You can also do the cooperative evaluation with live users in your own time – but you will have to find your own participants – it’s probably easier to get it done in next week’s lecture. • Next week we have 2 lecture slots available. – You must attend both sessions – Monday 9am Groups 1-12 will evaluate their interfaces (others act as participants) – Monday 12noon Groups 13-25 will evaluate their interfaces (others act as participants) Coursework 2 – Cooperative Evaluation days • Use the participants made available to you • I would suggesting running at least 3 participants through...
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...Practical Search Techniques in Path Planning for Autonomous Driving Sebastian Thrun Dmitri Dolgov AI & Robotics Group Toyota Research Institute Ann Arbor, MI 48105 ddolgov@ai.stanford.edu Michael Montemerlo James Diebel Computer Science Department Computer Science Department Computer Science Department Stanford University Stanford University Stanford University Stanford, CA 94305 Stanford, CA 94305 Stanford, CA 94305 diebel@stanford.edu mmde@ai.stanford.edu thrun@ai.stanford.edu Abstract We describe a practical path-planning algorithm that generates smooth paths for an autonomous vehicle operating in an unknown environment, where obstacles are detected online by the robot’s sensors. This work was motivated by and experimentally validated in the 2007 DARPA Urban Challenge, where robotic vehicles had to autonomously navigate parking lots. Our approach has two main steps. The first step uses a variant of the well-known A* search algorithm, applied to the 3D kinematic state space of the vehicle, but with a modified state-update rule that captures the continuous state of the vehicle in the discrete nodes of A* (thus guaranteeing kinematic feasibility of the path). The second step then improves the quality of the solution via numeric non-linear optimization, leading to a local (and frequently global) optimum. The path-planning algorithm described in this paper was used by the Stanford Racing Teams robot, Junior, in the Urban Challenge. Junior demonstrated...
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