1. Multi-objective and Multi-mode Assignment and Scheduling Problem for large volume Surveillance Olfa DridiSaoussen Krichen Adel Guitouni Salamanca, Spain, 19-30 September

2. Outline Outline Scheduling Theory 1. Areas of Application 2. Problem Description 3. Literature Review 4. Proposed Model 5. Multi-criteria Genetic approach 6. A bi-level ASP 7. Integration in Inform Lab 8. Conclusion 9.

3. 1. Scheduling Theory The project scheduling and resource management dates from five hundred years: The Egyptian pyramids, the Great wall of China, the temples of Maya by using rudimental tools. Scheduling theory was emerged as an active research area in the early 1950s. In the 1980s, different directions were pursued in academy and industry. Since then, the field has attracted a lot of researcher’s attention and has become an important branch of operations research.

4. Project Scheduling and resource management solutions are in demand throughout the world as a fundamental tools for the survival and success of the compagnies. This is what can happen without effective resources management

5. 2. Areas of Application Production scheduling Large volume surveillance problem Robotic cell scheduling Computer processor scheduling Timetabling Crew scheduling Railway scheduling Air traffic control

6. The large volume surveillance problem is a complex decision problem characterized by the employment of mobile and fixed assets to a large geographic area in order to accomplish the maximum number of surveillance tasks. Example of surveillance problem: - fishing boat in distress - search of illegal immigrants - piracy situations 3. Problem Description

7. System constraints What is the ‘best’ and feasible resources assignment and task scheduling to achieve mission goals ? A set of heterogeneous and distributed resources + A set of surveillance tasks A set of heterogeneous and distributed resources + A set of surveillance tasks Problem 3.1. Research Problematic

8.  3.2. Motivations Project scheduling is an important task in project management. Many decision problems can be formulated as a scheduling and assignment problem. Scheduling area continue to provide opportunities for fruitful interaction between theory and practice. Moreover, task duration might depend on the resources selected for execution

9. 3.2. Motivations Surveillance Tasks Distributed resources There are few works related model the resource management for large volume surveillance as Multi-Objective and Multi-Mode Assignment and Scheduling problem.

10. 4. Literature Review Assignment and Scheduling problem Single mode Multi-mode Without preemption With preemption Multi-Objectif Mono-Objectif Nonrenewable resources Renewable resources

11. Multi-Mode Each task can be accomplished by one out of a set of different modes. executing time, cost and amount of resources depend on the adopted mode. Single Mode Each task has only one execution mode, this means that the duration and the requirements for resources are constant.

12. Multi-Objective We consider more than one objective to optimize. we search not only the best optimal solution but the pareto optimal solutions. x x x x x x x x x x x x x x obj 1 obj2

13. Single Objective We consider only one objective to optimize. The main and the most used objective in literature is the minimization of the makespan which represents the total duration of the project. obj min

14. Renewable resources A known amount of resources available with its full capacity during the planning horizon. Example: machines, equipments, manpower. Nonrenewable resources They are limited in amount and are not recoverable. Example: financial budget

15. Without Preemption A Task cannot be interrupted once it has been started. With Preemption A Task can be interrupted after each integer unit of its processing time.

16. … Resolution approaches e.g.:Sprecher et al. (1997) Heilmann (2003) Zhu et al. (2006) e.g.: Li et al. (2008) e.g.: Mendes et al. (2009) Lova et al. (2009) e.g.: Loukil et al.(2005) e.g.: Lee et Lee (2003) Ben Abdelaziz et al. (2007) Lo et al. (2008) e.g.: Belfares et al. (2007) Example of resolution approaches

17. . Genetic Algorithms have been implemented for providing high-quality solutions to a wide variety of challenging scheduling problems. In this work, we investigate the ability of a genetic algorithm to effectively solve the Assignment and Scheduling Problem.

18. Resource Assignment and Scheduling problem Multi-mode without Preemption Multi-Objectif Renewable resources 5. Proposed Model

19. Mode 1Mode 2

21.  Mathematical Formulation Min makespan Min Cost Max probability of sucess Objectives functions

22. System constraints

23. The Multi-objective and Multi-mode Assignment and Scheduling Problem NP-Hard Genetic Algorithms have been implemented for providing high-quality solutions to a wide variety of challenging scheduling problems. In this work, we investigate the ability of a genetic algorithm to effectively solve the Assignment and Scheduling Problem

24. 6. A Multi-criteria Genetic Approach Selection: elitism methodCrossover: random keyMutation Chromosome representation Each solution chromosome is made of 3n genes ( n: number of tasks) Genetic operators

25. Consists of retaining the best individuals from the current population into the next generation based on their fitness value. This selection method is called elitist or elitism. It forms a succesful selection strategy used to ensure that the best solutions are preserved in the next generation and allows to converge towards the pareto frontier. Selection Operator

26. Two individuals are randomly selected from the current population to act as parents. For each gene a random number between [0,1] is generated. If the generated number is smaller than a threshold value, the gene of the first parent is copied into the offstring chromosome. Otherwise, the gene of the second parent is used. The threshold value is an input data and is called Crossover Probability. Crossover Operator

27. Randomly applied to explore other areas in the solution space and avoid the convergence caused by selection and crossover operators. The probability of the mutation Mr is inversely propotional to the population size. After the crossover has occurred, an individual can be selected from the current population for mutation. It consists to switch the mode associated to the selected task i based on its neighborhood set Hi of the resources’ combination. Mutation Operator

28. Generate the initial population initialize the parameters At iteration g Select two chromosomes parents Apply crossover operator Evaluation of the new population (fitness) Activate/Deactivate mutation Stopping criterion Stop New population no yes Generate offstring chromosome The Algorithm

29. The experimental results

31. Cardinality of the approximation set Diversity of the approximation set Diversity of the pareto approximation front

33. Wilcoxon signed –rank test

34. As we address simultaneously assignment and scheduling problem While the proposed approach is effective for medium assignment and scheduling problem, The proposed model becomes computationally intractable for large sized problem when adding some realistic assumptions. Hence, we propose a rigorous bi-level decomposition model that reduces the computational effort of the problem We decompose the original problem into an upper and a lower level.

35. 7. The bi-level ASP Objectif  Minimize the makespan Constraints  Task  precedence constraints  time window  priority  localization Objectif  Minimize the total cost Constraints  Resources  Availability  fuel constraints/autonomy Upper level: Scheduling Lower level: Assignment Problem

36. InformLab simulator Distributed Dynamic Information Fusion (DIF) Distributed Dynamic Resource Management (DRM) GoalsSituationEvidence Decision 8. The Integration to InformLab

37. Cooperative Search need to be detected: ‘fish boat in distress’ Non-Cooperative Search attemps to avoid detection: ‘illegal immigrants’

38. Integration process Scheduler code Input data ModePlan Schedules C/C++ Data File JavaNativeInterface (JNI) Dynamic library InformLab Testbed Java Scheduler class PlansExtractor class ScheduleConverter class Scheduler Interface Schedules (Java object) ModePlan objects Proxy Schedules Editor Viewer XML files XML vignette

41. we proposed a new formulation for the resource allocation and tasks scheduling for large volume surveillance problem. A Multi-criteria GA it was developed to solve the problem formulation The approach was tested using InformLab Multi-agent simulator We will propose an alternative model based on the bi-level formulation 9. Conclusion