Swarm Intelligence (SI) for Decision Support of Operations Management Methods and Applications

Transcription

1 Swarm Intelligence (SI) for Decision Support of Operations Management Methods and Applications Dr. Yi Wang The University of Manchester, Department of engineering and physics, School of materials Oxford Road, Manchester, M13 9PL, UK web-page: Prof. Kesheng Wang Norwegian University of Science and Technology S. P. Andersensveien 5, 7014 Trondheim, Norway web-page: Prof. Lilan Liu Shanghai University Yanchang Road , Shanghai, China web-page: 1

2 Content Background Swarm intelligence Manufacturing grid Case study 2

3 Background Collaborative project Complex product Complex network Plan resource allocation Swarm intelligence 3

4 Swarm intelligence The complexity and sophistication of Self-Organization is carried out with no clear leader What we learn about social insects can be applied to the field of Decision support Model how social insects collectively perform tasks Use this model as a basis upon which artificial variations can be developed Model parameters can be tuned within a biologically relevant range or by adding non-biological factors to the model

5 Swarm intelligence Ant Colony Optimization (ACO),Particle Swarm Optimization (PSO), Bees Colony Algorithms (BCO) and Stochastic Diffusion Search 5 (SDS)

6 Bird flocking of PSO ( p ( t) x ( t)) i i x ( t) i ( p x ( t)) g i + v ( t 1) i + x ( t 1) i v ( t) i v ( t + 1) = ω v ( t) + c r p x ( t) + c r p x ( t) ( ) ( ) id id 1 1 id id 2 2 gd id x ( t + 1) = x ( t) + v ( t + 1) id id id

7 Grid Manufacturing Grid: interconnected mesh of operations and services Consumer essentially sees a single supply route for his needs Collaboration towards common and different business goals Bring manufacturing resources together sometimes these are distributed physically Focus on network efficiency and efficiency of stand alone operations 7

8 Grid manaufacturing The (dynamic) harnessing of significant, disparate manufacturing capabilities and resources in order to satisfy one or more business requirements Publish Publish Task sks dispat tcher Resou urce Request Request Control Response Response Manage gement Resou urce Allocation Resou urce Vario ious Customers Application layer Resource 8

9 Why establish a Manufacturing Grid? Establish or Evolve, or Design or Develop? Innovation challenges: enable greater levels of customer response and customisation, especially, unpredictable customer demands cope with faster and faster technology development cope with complexity of system Efficiency challenges: maximise the use of existing manufacturing supply chain resources, and dispersed expertise and professional services exploit existing developments in IT infrastructure, virtual enterprise management, collaborative design etc Competition challenges: complementary specialist network and quick adaptation

10 Case Study

11 Process of PSO-based resource allocation Update all particle with Eq. (8) Start the iteration and Eq. (9) 10 particles be randomly initialized Modify location of particles out of bounds Calculate the gbest and pbest Modify speed of particles over the restrictions Move on iteration no End? yes Get the result of optimization 11

12 Manufacturing grid evolution model A new task 1. Generating parameters 2. Confirming resources requirements Generating one candidate group for each required resource respectively according to the following Looping until the N candidate groups are generated Probability 1-p : Adding new resource type Probability p : Choosing one from existing types with a preference A new resource type with only one resource Existing resources meet the needs? N Y Adding a new resource to this type Probability 1-q : Selecting in existing resources Probability q : Adding new resources 1. Probability 0.6: resource is provided by an existed factory 2. Probability 0.4: resource is provided by a new factory Adding all resources meeting the requirements to the candidate group Forming a candidate group N N candidates groups have been selected? Y PSO-based Resource Allocation Model Moving to the next loop N End? 12 Y Statistics and Analysis

13 The economic objectives for resource allocation Minimizing average processing cost P is the processing cost of resource R( j= 1, L, n) to accomplish sub-tasks T i ( i 1,, n) R j j s = L. Minimizing average logistics cost Which, P t is the logistics cost caused by the distance of each two resources j R ( x y; x, y 1,, n.) xy < = L to accomplish sub-task T i ( i 1,, n) s = L.

14 PSO-based multi-objective Resource allocation Task Economics objective System Robustness Objective Evaluation Function

15 Factory collaboration network structure 1000 tasks (460 factories) 3000 tasks (1264 factories) 5000 tasks (1720 factories) 15

16 Conclusion Swarm intelligence has the ability to collectively solve very complex problems A view of SI applications in OM with a specific focus on optimization problems for better decision support. The future research should focus of the development of perception-based modelling, and self-organized production, schedule and control. 16

17 Thank you!!!