An artificial immune system algorithm for the resource availability cost problem

In this paper, an artificial immune system (AIS) algorithm for the resource availability cost problem (RACP) is presented, in which the total cost of the (unlimited) renewable resources required to complete the project by a pre-specified project deadline should be minimized. The AIS algorithm makes use of mechanisms inspired by the vertebrate immune system and includes different algorithmic components, such as a new fitness function, a probability function for the composition of the capacity lists, and a K-means density function in order to avoid premature convergence. All components are explained in detail and computational results for the RACP are presented.

[1]  Erik Demeulemeester,et al.  Resource-constrained project scheduling: A survey of recent developments , 1998, Comput. Oper. Res..

[2]  Vinícius Amaral Armentano,et al.  Scatter search for project scheduling with resource availability cost , 2006, Eur. J. Oper. Res..

[3]  Roman Słowiński,et al.  Advances in project scheduling , 1989 .

[4]  Peter Ross,et al.  Producing robust schedules via an artificial immune system , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[5]  A. A. Mastor,et al.  An Experimental Investigation and Comparative Evaluation of Production Line Balancing Techniques , 1970 .

[6]  Mario Vanhoucke,et al.  An evaluation of the adequacy of project network generators with systematically sampled networks , 2008, Eur. J. Oper. Res..

[7]  J. C. Tay,et al.  Applying the Clonal Selection Principle to Find Flexible Job-Shop Schedules , 2005, ICARIS.

[8]  Rema Padman,et al.  An integrated survey of deterministic project scheduling , 2001 .

[9]  S. Selcuk Erenguc,et al.  Project Scheduling Problems: A Survey , 1993 .

[10]  Shahram Shadrokh,et al.  A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty , 2007, Eur. J. Oper. Res..

[11]  Jan Karel Lenstra,et al.  Scheduling subject to resource constraints: classification and complexity , 1983, Discret. Appl. Math..

[12]  Jean-Yves Le Boudec,et al.  An Artificial Immune System for Misbehavior Detection in Mobile Ad-Hoc Networks with Virtual Thymus, Clustering, Danger Signal and Memory Detectors , 2004, Int. J. Unconv. Comput..

[13]  Gündüz Ulusoy,et al.  A survey on the resource-constrained project scheduling problem , 1995 .

[14]  Sönke Hartmann,et al.  A survey of variants and extensions of the resource-constrained project scheduling problem , 2010, Eur. J. Oper. Res..

[15]  Rainer Kolisch,et al.  Characterization and generation of a general class of resource-constrained project scheduling problems , 1995 .

[16]  Willy Herroelen,et al.  On the use of the complexity index as a measure of complexity in activity networks , 1996 .

[17]  Manoj Kumar Tiwari,et al.  Artificial immune system based approach for solving resource constraint project scheduling problem , 2007 .

[18]  Rainer Kolisch,et al.  Experimental investigation of heuristics for resource-constrained project scheduling: An update , 2006, Eur. J. Oper. Res..

[19]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[20]  Ramón Alvarez-Valdés Olaguíbel,et al.  Chapter 5 – HEURISTIC ALGORITHMS FOR RESOURCE-CONSTRAINED PROJECT SCHEDULING: A REVIEW AND AN EMPIRICAL ANALYSIS , 1989 .

[21]  Alper Döyen,et al.  A new approach to solve hybrid flow shop scheduling problems by artificial immune system , 2004, Future Gener. Comput. Syst..

[22]  Sávio B. Rodrigues,et al.  An exact algorithm for minimizing resource availability costs in project scheduling , 2010, Eur. J. Oper. Res..

[23]  D. S. Kim,et al.  A new heuristic for the multi-mode resource investment problem , 2005, J. Oper. Res. Soc..

[24]  E. W. Davis,et al.  Multiple Resource–Constrained Scheduling Using Branch and Bound , 1978 .

[25]  James H. Patterson,et al.  Project scheduling: The effects of problem structure on heuristic performance , 1976 .

[26]  Carlos A. Coello Coello,et al.  Use of an Artificial Immune System for Job Shop Scheduling , 2003, ICARIS.

[27]  Erik Demeulemeester,et al.  A classification scheme for project scheduling , 1999 .

[28]  Robert J Willis,et al.  An iterative scheduling technique for resource-constrained project scheduling , 1992 .

[29]  Mario Vanhoucke,et al.  An Invasive Weed Optimization Algorithm for the Resource Availability Cost Problem , 2011 .

[30]  Erik Demeulemeester,et al.  Minimizing resource availability costs in time-limited project networks , 1995 .

[31]  Dale F. Cooper,et al.  Heuristics for Scheduling Resource-Constrained Projects: An Experimental Investigation , 1976 .

[32]  Alf Kimms,et al.  Optimization guided lower and upper bounds for the resource investment problem , 2001, J. Oper. Res. Soc..

[33]  Mario Vanhoucke,et al.  An Artificial Immune System for the Multi-Mode Resource-Constrained Project Scheduling Problem , 2009, EvoCOP.

[34]  Rolf H. Möhring,et al.  Resource-constrained project scheduling: Notation, classification, models, and methods , 1999, Eur. J. Oper. Res..

[35]  Francisco Ballestín,et al.  Justification and RCPSP: A technique that pays , 2005, Eur. J. Oper. Res..

[36]  M. Chandrasekaran,et al.  Solving job shop scheduling problems using artificial immune system , 2006 .

[37]  Erik Demeulemeester,et al.  A branch-and-bound procedure for the multiple resource-constrained project scheduling problem , 1992 .

[38]  Rolf H. Möhring,et al.  Minimizing Costs of Resource Requirements in Project Networks Subject to a Fixed Completion Time , 1984, Oper. Res..

[39]  Shahram Shadrokh,et al.  Solving the resource availability cost problem in project scheduling by path relinking and genetic algorithm , 2008, Appl. Math. Comput..