A multiobjective memetic ant colony optimization algorithm (MACS) is proposed to solve the time and space assembly line balancing problem (TSALBP) variant where the objectives are to minimize station area and number of stations. The algorithm combines a global search using MACS with two local search methods. It is compared against a GRASP algorithm on 9 problem instances, with no clear winner between the two approaches. Future work plans to design new evolutionary and memetic algorithms for the problem and add interactive preference methods.

1. A Multiobjective Memetic Ant Colony OptimizationAlgorithm for the 1/3 Variant of the Time andSpace Assembly Line Balancing Problem12th April 2011Manuel Chica, ÓscarCordón, Sergio Damas, Joaquín Bautista

2. SummaryIntroductionSALBP and TSALBPThe memetic MACS proposalGeneral structureThe MACS global searchLocal search operatorsExperimentsConclusions and future work01020304050607

3. Introduction02030405060708The optimization of assembly lines is of great importance in the production and operation research context.The time and space assembly line balancing problem (TSALBP) is a realistic extension of the well-known simple assembly line balancing problem (SALBP).We present a memetic MACS proposal with two multiobjective (MO) local search (LS) methods to solve the 1/3 variant of the TSALBP.The new proposal results are compared with a GRASP algorithm using multiobjective performance indicators in 9 problem instances.

4. SALBP and TSALBP (I)An industrial process is divided into a set V of n tasks. Each task j requires an operation time tjand has a set of direct predecessors (problem constraint).The SALBP involves grouping these tasks in m workstationsminimizing the cycle time (C) or the number of stations (m).03040506070809

5. SALBP and TSALBP (II)Importance of the area in assembly line balancing.TSALBP formulations include: The area of each task, aj ; j=1,…,n The available area for any station, AHence, TSALBP has a multicriteria nature involving three different objectives: The cycle time of the plant (C) The number of stations (m) The available area (A)04050607080910

6. SALBP and TSALBP (III)The existence of these three objectives creates the following problem taxonomy:05060708091011There are 4 TSALBP multiobjective variantsOne of the most realisticvariants in the automotive industry: TSALBP-1/3 (A and m)

7. Memeticmetaheuristicshave demonstrated its good performance because of the combination of global search behaviour and the local optimizer.We present a multiobjective memetic algorithm: With a powerful global search metaheuristic: MACS.A MO local search approach with two local search methods, one per objective.The set of constraints associated to TSALBP encourages the use of constructive memeticmetaheuristics to solve it.06070809101112Thememetic MACS proposal

8. 07080910111213General structure (I)

9. General structure (II)The function to be optimised by the local search is a scalarization of the objective function vector:Weights are created at randomforsolution:λ1, λ2Ifλ1 > λ2then LS operatorforobjectiveAisapplied. Otherwise, LS operatorforobjectivem. If no minimization, theotherisalsolaunchedafterwards.08091011121314

10. A TSALBP solution is an assignment of tasks to different stations satisfying the constraints.We have to give a sequence of tasks and how these tasks are split up into different stations to fully specify the assignment.09101112131415The MACS global search (I)

11. The MACS global search (II)10111213141516Pareto-based MOACO algorithms have shown good performance in several problems.MACS is an extension of the ACS which considers an external Pareto archive. The pseudo-random transition rule is considered:Sametransition rules as ACS

12. The MACS global search (III)11121314151617The pheromone trail information is associated to a pair (task, station). The initial pheromone value 0 is obtained from two single-objective greedy algorithms.No heuristic information used!

13. A new mechanism to close a station is used to induce diversity, following a multi-colony approach:The MACS global search (IV)12131415161718

14. Local search operators (I)13141516171819Both LS operators are based on movements of tasks between their feasible stations. Repeated 20 iterations on each solution obtained by MACS.

15. Local search operators (II)14151617181920

16. Local search operators (III)15161718192021

17. Experiments (I)Nine real-like problem instances with different features have been selected: arc111, barthol2, barthold, scholl…Multiobjective performance indicators: unary HVR, binary C, and graphical representation of the aggregated Pareto fronts.16171819202122

18. Experiments (II)We compare the new proposal with MACS (no memetic). Also against a multiobjectiveGRASP algorithm : Builds the solution with a random selection of the next task to be included in the current station between the candidates using heuristic information. Makes use of an external Pareto archive and a restricted candidate list (RCL) . A mechanism to close stations using different thresholds. Two similar LS operators applied when solution is built.17181920212223

19. Experiments (III)18192021222324Binary C performance indicatorNo cleardominance in P4, P5, P6 and P8MACSMemetic MACSP1 and P9 GRASP isbetterGRASPMemetic MACSClear dominace of Memetic MACSP2, P3 and P7 memetic MACS isbetter

20. Experiments (IV)According to HVR, memetic MACS and GRASP are the best algorithms depending on the problem instance.Memetic MACS is better than GRASP in P2, P3, P7 and P8. But worse in P1, P4, P5, and P6.MACS is obviously worse than GRASP and memetic MACS.192021222324HVR performance indicator

21. Experiments (V)20212223242122

22. A novel memetic MACS was developed and applied to tackle the TSALBP-1/3. Its behaviour is clearly superior to the MACS global search. However, thereis no clearconclusionaboutwhichalgorithmisbetterregardingthecomparisonbetween GRASP and memetic MACS. As future work, we will consider:Designing new EMO and memetic algorithms.Adding interactive procedures to include preferences.Application of Wilcoxon statistical tests to the results.Conclusions and futurework21222021222122

23. Thanks for your attention