group assignment with an individual element | My Assignment Tutor

June 2019 v1Page 1 of 6ASSESSMENT TASKThis is a group assignment with an individual element. Your group should have 4-5 members, and youmust enrol to your group on Brightspace by 30/10/2020. After this deadline you will be assigned to agroup randomly.BackgroundIn this assignment you will address a real-world problem of delivery planning for the LPG (LiquefiedPetroleum Gas) distributor. In the areas where mains gas for heating purposes is not available, LPG isthe closest alternative. It has the lowest carbon emissions per kWh out of all fossil fuels available in ruralareas, does not pose any ground or water pollution hazards and can be used for both heating andcooking. LPG distribution companies source the fuel from major oil refineries and deliver it to the bulkcustomers by a fleet tanker lorries like the one in Figure 1.Figure 1. Tanker lorry.You have been employed by SaO Gas Ltd as data scientists. You are to produce delivery schedules forthe fleet of 25 tanker lorries operating from 4 depots in the country of Optilandia (which surprisingly use £as their currency), minimising the overall cost of delivery for the distributor under certain constraints.SaO Gas Ltd operates three types of tanker as shown in Table 1. Tanker typeCapacity [tonnes]Cost per mile [£]Cost per mile pertonne [£]Small51.001.50Medium121.501.00Large222.000.50 Table 1. Tanker lorries operated by SaO Gas Ltd.As an example, a large tanker can be loaded with up to 22 tonnes of LPG at one of the depots (you canassume that depots never run out of gas). It costs the distributor £2 per mile to use the large tanker (evenif it is empty) plus £0.50 per mile for every tonne of LPG the tanker carries. So, a large tanker with 10tonnes of LPG travelling 20 miles costs: 20 [miles] x (2.00 [£] + 10 [tonnes] x 0.50 [£]) = 140 [£]. However,Faculty of Science and Technology – Department of Computing & Informatics Unit Title: Search and OptimisationAssessment Title: Algorithm Design and Comparison for an Optimisation ProblemUnit Level: 7Assessment Number: 1 of 1Credit Value of Unit: 20Date Issued: 13/10/2020Marker(s): Kevin Wilson, IftekharMahmud Towhid, Marcin BudkaSubmission Due Date: 15/01/2021 Time: 12.30pmQuality Assessor:Prof. Hamid BouchachiaSubmission Location: Video submission (Panopto)Jupyter Notebook (Brightspace)Report in PDF (Turnitin)Solution files (Brightspace)Peer assessment (Brightspace)Feedback method: Brightspace This is group assignment which carries 100% of the final unit markJune 2019 v1Page 2 of 6after dropping 2 tonnes of LPG at a customer, the next 20 miles travelled will cost less as the lorry is nowlighter: 20 [miles] x (2.00 [£] + 8 [tonnes] x 0.50 [£]) = 120 [£].SaO Gas Ltd operates from four depots as shown in Table 2. The road network of Optilandia has beendepicted in Figure 2, with the red dots denoting locations of the four depots, and the green dots denotinglocations of SaO Gas customers. DepotLocation IDSmall tankersMedium tankersLarge tankers1#5232212#1243323#3733434#116110 Table 2. SaO Gas depots.Figure 2. Road network of Optilandia.Optimisation problemsThere are two problems you should address:1. Schedule LPG delivery to all customers in order to fully fill their tanks while minimising theoverall cost of delivery. There are no additional constrains apart from the tanker lorry capacity –a lorry can for example visit any of the depots multiple times in order to load additional gas.2. Schedule LPG delivery in order to maximise the amount of gas delivered to the customers,while minimising the overall cost of delivery (including any potential penalties! – see below)and while observing the following constraints in addition to tanker lorry capacity:a. Each lorry can travel up to 250 milesb. Each lorry can only stop up to 5 times, this includes customer deliveries and anyadditional visits to the depotc. Each lorry must end its journey in one of the depots (this doesn’t count towards the 5stop limit and doesn’t have to be the depot from which the lorry started its journey)d. If you don’t deliver to customers who have less than 15% of gas in their tanks, SaO GasLtd will incur a penalty of £1,000 for each such customerYou are required to design and implement at least two optimisation schemes to address the LPGdelivery scheduling problems (that is two schemes in total, not two per each problem). One of theseschemes can be simple e.g. a random or greedy scheduler, but you need to make sure that the approachis able to generate valid solutions i.e. not violating the given constraints. You can use any combination ofmethods covered in class (e.g. Genetic Algorithms, Ant Colony Optimisation) as well as methods youhave found during your independent study or which you came up with yourself.Your implementation should be in Python. You are allowed to use existing Python optimisation libraries orimplementations if you need to, but you should aim to implement as much as possible from scratchJune 2019 v1Page 3 of 6yourself. You must apply your implementations to the two LPG delivery scheduling problems and criticallyevaluate the results, comparing and contrasting performance, strengths and weaknesses of theapproaches you have used in terms of quality of the solution, running time etc.DatasetThe dataset for this assignment consists of three files:1. SaO_Optilandia_locations.csv2. SaO_Optilandia_links.csv3. SaO_Optilandia_depot_lorries.jsonThe SaO_Optilandia_locations.csv file contains the following columns:id – Location IDx,y – coordinates of a location; Optilandia lives on a flat version of Earth, so the Euclideandistance between two locations is what you should use (no need to use Haversine for example);the Euclidean distance is expressed in milesis_depot – True if a location is one of the four depotsis_customer – True if a location is one of the customerscapacity – capacity of the tank in tonnes (only for locations where is_customer ==True)level – current gas level in the tank in tonnes (only for locations where is_customer ==True)A small sample of the SaO_Optilandia_locations.csv file has been given in Table 3. You can think ofthe locations as nodes of a graph.Table 3. Locations dataset sample.The SaO_Optilandia_links.csv file contains the following columns:id1,id2 – Location IDs connected by a road segmentA small sample of the SaO_Optilandia_links.csv file has been given in Table 4. You can think of thelocations as edges of a graph.Table 4. Links dataset sample.June 2019 v1Page 4 of 6The SaO_Optilandia_ depot_lorries.json file contains information about the types and capacities oftanker lorries in each depot as given in Table 1 and Table 2.Solution formatThe file SaO_Optilandia_example_solution.json contains an example solution. A solutions file containsa list of lorry journeys, where each journey includes the lorry identifier and a list of tuples consisting oflocation identifier and the amount of gas loaded to the tanker (positive number) or dropped off at acustomer (negative number) at this location.An example journey is given below:{ ‘lorry_id’: ‘124-0’, ‘loc’: [(124,5),(14,-0.33),(36,-0.81),(124,0)] }where the interpretation of loc is as follows:(124,5) – load 5 tonnes of LPG at location 124 (depot)(14,-0.33) – go to next location (14 – customer) and drop off 0.33 tonnes of LPG(36,-0.81) – go to next location (36 – customer) and drop off 0.81 tonnes of LPG(124,0) – go back to depot (location 124 – customer)SUBMISSION FORMATYour submission must consist of:1. Report in the shape of a Jupyter notebook (Brightspace, Group), that contains the followingsections: Front matter, Problem definition, Methodology (all steps), Experiments & discussion,Conclusion and References. The report must be a combination of text and working code, relevantfigures (e.g. evolution of the objective function values over time), tables and anything else you deemuseful in communicating your work (e.g. interactive visualisations or animations). You must makesure that your notebook executes from top to bottom without any intervention in the Google Colabenvironment.2. PDF version of your Jupyter notebook (Brightspace/Turnitin, Group). This can be produced bysimply printing your notebook to a .pdf file. The feedback will be provided via Turnitin on this verydocument.3. Two solution files (Brighspace, Group) representing your best solutions to the two optimisationproblems. The files should follow the format of the SaO_Optilandia_example_solution.json file.4. Peer-assessment of the group members (Brightspace, Individual)Each member of each group will be asked to anonymously assess the contributions to the task of allthe other members in their respective group via Brightspace. It is very important that you’re honest inyour assessment. The final mark will be weighted by your contribution. For example, if in a group of 4you all contributed equally, your final mark will be the same. We will use the uGrade system for thepeer assessment, details will be posted on Brightspace.5. Video presentation (Panopto, Individual) of your group’s submission, which should be between 7and 10 minutes in total. The video should include a short walkthrough of the report including screencapture rather than just a “talking head”. This element is mandatory, and marks will only be awardedto those who submit the video.Important: if your video is longer than 10 minutes, your mark will be based on the first 10 minutes ofthe video only. You should hence carefully plan the content of your video and rehearse and time yourpresentation. You can also use video editing software when putting it together.June 2019 v1Page 5 of 6MARKING CRITERIAThe following criteria will be used to assess the assignment: CriteriaMark %ILO(s)Quality of the report and code:– Complexity of the approach– Clarity of presentation– Critical evaluation– Conclusions and future improvements– Completeness– Correct execution of the code60%1,2,3,4Quality of the solutions– Improvement over simple benchmark approach– Coverage of the constraints15%3, 4Quality of the video presentation– Delivery– Demo25%1, 2, 3, 4 The following sections describe what are the expectations for each level of achievement:To achieve a Pass:Attempt to solve both optimisation problems, exceeding benchmark approach in at least one of them.Produce a reasonable report and fully running code which takes advantage of existing implementations orlibraries. Deliver a video presentation showing decent understanding of most aspects of the project. Activelyparticipate in the development of the project.To achieve a higher mark:Solve both optimisation problems, exceeding benchmark approach in both of them. Produce an excellentreport with details and fully running high quality code implemented by yourselves. Deliver a videopresentation showing very good or excellent understanding of all aspects of the project. Actively participatein the development of the project.INTENDED LEARNING OUTCOMES (ILOs)This assignment tests your ability to:1. Demonstrate knowledge and understanding of search and optimisation techniques and theirapplications.2. Demonstrate critical awareness of the strengths and limitations of various stochastic search andoptimisation techniques.3. Implement search and optimisation solutions to real-world problems using modern algorithms andsoftware libraries.4. Design search and optimisation experiments and conduct rigorous statistical analysis of theresults.QUESTIONS ABOUT THE BRIEFYou are encouraged to ask questions about the brief as early as possible, giving you the opportunity toachieve the best marks possible without any delay. The best way to ask questions is via Microsoft Teams.Signature Marker: Marcin BudkaJune 2019 v1Page 6 of 6HELP AND SUPPORT• If a piece of coursework is not submitted by the required deadline, the following will apply:1. If coursework is submitted within 72 hours after the deadline, the maximum mark that can beawarded is 50%. If the assessment achieves a pass mark and subject to the overall performanceof the unit and the student’s profile for the level, it will be accepted by the Assessment Board asthe reassessment piece. The unit will count towards the reassessment allowance for the level;This ruling will apply to written coursework and artefacts only; This ruling will apply to the firstattempt only (including any subsequent attempt taken as a first attempt due to exceptionalcircumstances).2. If a first attempt coursework is submitted more than 72 hours after the deadline, a mark of zero(0%) will be awarded.3. Failure to submit/complete any other types of coursework (which includes resubmissioncoursework without exceptional circumstances) by the required deadline will result in a mark ofzero (0%) being awarded.The Standard Assessment Regulations can be found on Brightspace.• If you have any valid exceptional circumstances which mean that you cannot meet an assignmentsubmission deadline and you wish to request an extension, you will need to complete and submit theExceptional Circumstances Form for consideration to your Programme Support Officer (based inC114) together with appropriate supporting evidence (e.g, GP note) normally before thecoursework deadline. Further details on the procedure and the exceptional circumstances form canbe found on Brightspace. Please make sure that you read these documents carefully beforesubmitting anything for consideration. For further guidance on exceptional circumstances please seeyour Programme Leader.• You must acknowledge your source every time you refer to others’ work, using the BU HarvardReferencing system (Author Date Method). Failure to do so amounts to plagiarism which is againstUniversity regulations. Please refer to http://libguides.bournemouth.ac.uk/bu-referencing-harvardstyle for the University’s guide to citation in the Harvard style. Also be aware of Self-plagiarism, thisprimarily occurs when a student submits a piece of work to fulfill the assessment requirement for aparticular unit and all or part of the content has been previously submitted by that student for formalassessment on the same/a different unit. Further information on academic offences can be found onBrightspace and from https://www1.bournemouth.ac.uk/discover/library/using-library/howguides/how-avoid-academic-offences •Students with Additional Learning Needs may contact Learning Support onwww.bournemouth.ac.uk/als Disclaimer: The information provided in this assignment brief is correct at time of publication. In theunlikely event that any changes are deemed necessary, they will be communicated clearly via e-mail andBrightspace and a new version of this assignment brief will be circulated.

QUALITY: 100% ORIGINAL PAPER – NO PLAGIARISM – CUSTOM PAPER

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