Genetic programming for order acceptance and scheduling

This paper focuses on order acceptance and scheduling (OAS) problem, where both acceptance and sequencing decisions have to be handled simultaneously. Because of its complexity, designing effective heuristics or meta-heuristics for OAS is challenging. This paper will investigate how genetic programming (GP) can be used to deal with OAS. The goal of this paper is to develop new GP frameworks to evolve high-performance scheduling rules/heuristics for OAS. The new frameworks are developed based on two key aspects: (1) separating acceptance and sequencing decisions, and (2) enhancing the quality of scheduling rules by embedding heuristic search mechanisms. The experimental results show that separating decisions is not trivial and can easily lead to overfitting issues. Meanwhile, embedding heuristic ideas into the scheduling rules can help search for better solutions for OAS.

[1]  Cheng Wu,et al.  Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[2]  F. Sibel Salman,et al.  Order acceptance and scheduling decisions in make-to-order systems , 2010 .

[3]  Whm Henk Zijm,et al.  Order acceptance strategies in a production-to-order environment with setup times and due-dates , 1992 .

[4]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[5]  T.C.E. Cheng,et al.  A modified artificial bee colony algorithm for order acceptance in two-machine flow shops , 2013 .

[6]  Peter Norvig,et al.  Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp , 1991 .

[7]  Purushothaman Damodaran,et al.  A branch and price solution approach for order acceptance and capacity planning in make-to-order operations , 2011, Eur. J. Oper. Res..

[8]  Mark Johnston,et al.  A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem , 2013, IEEE Transactions on Evolutionary Computation.

[9]  Mark Johnston,et al.  Learning Reusable Initial Solutions for Multi-objective Order Acceptance and Scheduling Problems with Genetic Programming , 2013, EuroGP.

[10]  Graham Kendall,et al.  Exploring Hyper-heuristic Methodologies with Genetic Programming , 2009 .

[11]  Jay B. Ghosh,et al.  Job selection in a heavily loaded shop , 1997, Comput. Oper. Res..

[12]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[13]  Christopher D. Geiger,et al.  Learning effective dispatching rules for batch processor scheduling , 2008 .

[14]  Ceyda Oguz,et al.  A tabu search algorithm for order acceptance and scheduling , 2012, Comput. Oper. Res..

[15]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[16]  Michael Pinedo,et al.  A heuristic to minimize the total weighted tardiness with sequence-dependent setups , 1997 .

[17]  Albert Jones,et al.  Survey of Job Shop Scheduling Techniques , 1999 .

[18]  Susan A. Slotnick,et al.  Order acceptance and scheduling: A taxonomy and review , 2011, Eur. J. Oper. Res..

[19]  Michel Gendreau,et al.  Hyper-heuristics: a survey of the state of the art , 2013, J. Oper. Res. Soc..

[20]  Domagoj Jakobovic,et al.  Dynamic Scheduling with Genetic Programming , 2006, EuroGP.

[21]  Robin O. Roundy,et al.  Capacity-driven acceptance of customer orders for a multi-stage batch manufacturing system: models and algorithms , 2005 .

[22]  Reha Uzsoy,et al.  Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach , 2006, J. Sched..

[23]  Susan A. Slotnick,et al.  Order acceptance with weighted tardiness , 2007, Comput. Oper. Res..

[24]  Ali M. S. Zalzala,et al.  Investigating the use of genetic programming for a classic one-machine scheduling problem , 2001 .

[25]  Mengshi Lu,et al.  Integrated order selection and production scheduling under MTO strategy , 2011 .

[26]  Walter O. Rom,et al.  Order acceptance using genetic algorithms , 2009, Comput. Oper. Res..

[27]  Mark Johnston,et al.  Learning iterative dispatching rules for job shop scheduling with genetic programming , 2013, The International Journal of Advanced Manufacturing Technology.