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Al waseet automated production

Ruthless way to self-optimize free classifieds magazine insertions and linage design process

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Al waseet automated production

  1. 1. Al Waseet Auto-Layout Optimization A heuristic optimization method Prepared by Yasir Karam yasirkaram@alwaseetintl.com
  2. 2. Agenda • Preamble • Business Problem • Motivation • Proposal – Objectives – Deliverables – Plan – Turnover Analysis • Q & A
  3. 3. Preamble • Interactive Genetic Algorithm is a well known heuristic optimization search method in which gives manual human role to choose offspring results • Defining search space will lead to fine tuned allocation of resources • Few problem elements and with low computational cost resulting efficient solution space • Inspired by Bio-informatics; IGA became leading method for solving search problems • Lifecycle of genotypes from Representation, Selection, Mutation and Re-Production seducing new self-emerged evolved offspring generations
  4. 4. Business Problem • An exaggerated time taken to process post-production phase passed by pre-production and production • Final magazine layout lacks to optimum allocation and style selection due to absence of standard criteria and loose function-to-goal driven Ads among pages • Many Ad preferences taken place (page, position, ..etc) • Hard and soft constraints are managed manually and posted back internally between sales and production depts. • No fine tunings or interactive intelligent methodologies used to elaborate the magazine pages as per customer demands and forced by print issue business rules
  5. 5. Motivation • The need to an intelligent new methodologies capturing business rules as well as customer preferences in order to optimally produce best print issue layout • Need to a frenzy exotic time/effort killer to do issue production automatically with only few definitions of variables and function operators • Bind all process metrics in one container • Save maximum issue space with least space waste and least fillers.
  6. 6. Existing Scenario • Insertions are being processed by production dept after booking confirmation • Artwork job lists disseminated over each designer • Job lists summarized by insertion size, type, price offer and other customer preferences (side, color,.. Etc) based on publication business rules • The proposition of insertion patterns and locations on each page non-provisioned to reflect minimum cost and maximum profit • The layout composition in pre-production phase is totally done manually by production manager • An average of 30 minutes spent on each page except 1st,2nd, 3rd and last.
  7. 7. Layout Optimization • Genetic encoding – Assuming we have n Artworks for n insertions denoted as A1,…An – Artworks represent an insertion with its properties (width X height, page #, color, lead position) – The search space for the IGA (Interactive Genetic Algorithm) is the available space in issue after specifying layout template organics (static components) – After applying publication rules (insertion type, price,.. Etc) – After calculating remaining space from booked insertions – After calculating overall total classified space – 2D genetic algorithm will be applied to optimize space per page – The overall problem is to optimally have best pages overlook by the algorithm user in each selection phase of the GA
  8. 8. Publication Elements • Max number of cols/page = 8 • Col width = 40 mm • Min insertion width = 1 col • Min insertion height = 100 mm • Page spaces – 1st = 84805 square mm – Rest pages = 93100 square mm • If we define an Ad cell sized as cell=1600 sqr mm then a page max cells = 58 cell • If page would be n x m = 58 cells then n <= 8 and m <=7
  9. 9. Gene Representation Gene Description Values Ad Cell 1 X,Y Coord. Of upper left corner of cell ]n] x ]1,8,1] Rowspan, Colspan for the Ad cell Ad cell dimensions ]n] x ]1,8,1] Color Ad insertion type Color, B/W (Ad Cell 2 (same genes as in Ad cell 1
  10. 10. Client Preferences • Client is concerned with some properties usually he pays premium charges to preserve them.
  11. 11. Soft constraints • Page constraints: – Ad cells must represent the correct page layout : • Ads should be within same page section as per Ad section • Ads should fill only free space after reserving all existing ones spaces • No overlap between Ad cells • B/W Ads shouldn’t be inside colour pages, while Colour Ads shouldn’t be inside B/W pages • Publication sectioned pages are page preferences, and similar insertion sections are highly preferred to be together • Overall dimensions included in [1..7]x[1..8] • No more than 2mm space between Ad cells for multiple Ads • Ads constraints: – An Ad should be multiples of Ad cells
  12. 12. Hard Constraints • Generated pages shouldn’t exceed # pages and should be even of multiples of 4 • Margin lead between insertions should be 3 mm • Total issue space shouldn’t exceed A sq mm => total Ads spaces (insertions + classifieds)
  13. 13. Evaluating Fitness • To find best laid area for all issue insertions • Minimize hosted space for insertions • Maximize productivity per issue by taking into consideration profit weight of each rule decision
  14. 14. Objectives Decision Tree Dimensions Colour Page Lead Positioning
  15. 15. Raw Data Art dept job list Insertion attributes Publication page attributes Publication attributes
  16. 16. Revised Scenario • Publication area consumption pricing and booking based on Ad units instead of col x cm, thus this will ensure better reliability on pricing strategy and easily adore the publication to apply IGA optimization to it • Insertions are being processed by production dept after booking confirmation • Artwork job lists disseminated over each designer • Job lists summarized by insertion size, type, price offer and other customer preferences (side, color,.. Etc) based on publication business rules • Hierarchy of insertion placement based on the above insertion attributes • Insertion attributed are weighted each by its price • The algorithm will automatically insert each artwork per insertion over publication layout • Production man can revise residual space of the publication, interactively retain existing genes offspring or continue running IGA until satisfactory situation reached • Final setup of commercial insertions will be followed up by classified filling inside residue space manually
  17. 17. Algorithm Overview • Check and pass into the business rules decision tree and extract rules as customer preferences weighted by price of each rule selected • Reference each constraint from applied business rules • Randomly position insertions per page X and Y • Implement chromosome • Evaluate chromosomes for fittest • Drive into selection phase • Initialization Evaluation Selection Recombination Mutation Evaluation Terminate? Display results
  18. 18. Genetic Operators • Creation – Start with 1st page – Select random number of Ad insertions to put in 1st page – Lets say we have 3 Ad insertions A, B, C, D – Creation operator generates one offspring D and works as follows: • D is initialized to occupy exactly n x 8 Ad cells • Each Ad insertion is randomlly assigned to an Ad cell • Ad cells will get enlarged by repeating merge operations – For each Ad cell C of coordinates X and Y, we compute how far it can be merged horizontally or vertically with the two following values: Colspanmax=m-X+1 and Rowspanmax= n- Y +1. Then for this Ad cell, two desired values for Colspan and Rowspan are randomly generated within [1,Colspanmax] and [1,Rowspanmax] respectively – All Ad cells are considered in a random order and we enlarge each of them according to the desired Colspan and rowspan values. We start by any direction (vertical or horizontal) and enlarge the Ad cell up to the desired value provided it does not tviolate Ad insertions/Ad cell constraints *because when two Ad cells are merged together to fit a big insertion, the resulting larger Ad cell inhertis of the objects contained in each smaller Ad cell), and provided that it does not violate page constraints (no overlap,… etc) – Empty rows or columns are eliminated
  19. 19. Creation Ad cell ADC B AD C B AD C B Offspring
  20. 20. Mutation • Generate offspring D from one parent P with the same algorithm as in the creation operator, except that genes of P are used instead of completely random values. • D is initialized to n x 3 • Ad cells are mutated • Other genes are mutated like desired value of rowspan and colspan • Ad cells may possibly enlarge according to desired colspan or rowspan values • The resulting is that empty rows or columns are eliminated for saving waste space
  21. 21. Mutation Ad cells dimensions.. etc Parents Offspring A B CD A B CD A B C D A B C D A B C D Ad cells locations
  22. 22. Crossover • Combining layouts represented by two parents p1 and P2 in order to create one offspring D • D is initialized to an empty n x 8 page table • P1 and P2 are centred on an n x 8 page table • Insertion Ad cells are placed in D at the location given by P1 and P2 • Other genes of D are inherited either from P1 or P2, like for instance the desired values of rowspan and colspan. • Cells are enlarged as in the creation operator • Empty rows or columns are eliminated
  23. 23. Crossover Parents C A B A B C
  24. 24. Q & A