A genetic algorithm for fuzzy order acceptance and scheduling problem
A genetic algorithm for fuzzy order acceptance and scheduling problem
In light of the imprecise and fuzzy nature of real production environments, theorder acceptance and scheduling (OAS) problem is associated with fuzzyprocessing times, fuzzy sequence dependent set up time and fuzzy due dates. Inthis study, a genetic algorithm (GA) which uses fuzzy ranking methods isproposed to solve the fuzzy OAS problem. The proposed algorithm is illustratedand analyzed using examples with different order sizes. As illustrative numericalexamples, fuzzy OAS problems with 10, 15, 20, 25, 30 and 100 orders areconsidered. The feasibility and effectiveness of the proposed method aredemonstrated. Due to the NP-hard nature of the problem, the developed GA hasgreat importance to obtain a solution even for big scale fuzzy OAS problem.Also, the proposed GA can be utilized easily by all practitioners via thedeveloped user interface.
___
- Slotnick, S.A. (2011). Order acceptance and
scheduling: a taxonomy and review. European
Journal of Operational Research, 212, 1-11.
- Bilgintürk, Z. (2007). Order acceptance and
scheduling decision in make to order systems.
Master’s Thesis. Koç University.
- Charnsirisakskul, K., Griffin, P.M., & Keskinocak,
P. (2004). Order selection and scheduling with lead
time flexibility. IIE Transactions, 36, 697–707.
- Charnsirisakskul, K., Griffin, P.M., & Keskinocak,
P. (2006). Pricing and scheduling decisions with
lead time flexibility. European Journal of
Operational Research, 171, 153–169.
- Slotnick, S., & Morton T.E. (2007). Order
acceptance with weighted tardiness. Computers and
Operations Research, 34, 3029–3042.
- Rom, W.O., & Slotnick, S.A. (2009). Order
acceptance using genetic algorithms. Computers
and Operations Research, 36, 1758–1767.
- Oguz, C., Salman, F.S., & Yalcin, Z.B. (2010).
Order acceptance and scheduling decisions in make-to-order systems. International Journal of
Production Economics, 125 (1), 200–211.
- Nobibon, F.T., & Leus, R.. (2011). Exact
algorithms for a generalization of the order
acceptance and scheduling problem in a single-
machine environment. Computers and Operations
Research, 38, 367–378.
- Cesaret, B., Oguz, C., & Salman F.S. (2012). A
tabu search algorithm for order acceptance and
scheduling. Computers and Operations Research,
39, 1197–1205.
- Lin, S.W., & Ying, K.C. (2013). Increasing the
total net revenue for single machine order
acceptance and scheduling problems using an
artificial bee colony algorithm. Journal of the
Operational Research Society, 64, 293-311.
- Chen, C., Yang, Z., Tan, Y., & He, R. (2014).
Diversity controlling genetic algorithm for order
acceptance and scheduling problem. Mathematical
Problems
in
Engineering
doi:
http://dx.doi.org/10.1155/2014/367152.
- Xie, X., & Wang, X. (2016). An enhanced ABC
algorithm for single machine order acceptance and
scheduling with class setups. Applied Soft
Comping, 44, 255-566.
- Zandieh, M., & Roumani, M. A biogeography-
based optimization algorithm for order acceptance
and scheduling. Journal of Industrial and
Production Engineering, 34(4), 312-321.
- Chaurasia, S.N., & Singh, A. (2017). Hybrid
evolutionary approached for the single machine
order acceptance and scheduling problem. Applied
Soft Comping, 52, 725-747.
- Wester, F.A.W., Wijngaard J., & Zijm W.H.M.
(1992). Order acceptance strategies in a production-
to-order environment with setup times and due-
dates. International Journal of Production
Research, 30, 1313–1326.
- De, P., Ghosh, J.B., & Wells, C.E. (1993). Job
selection and sequencing on a single machine in a
random environment. European Journal of
Operational Research, 70, 425–431.
- Stadje, W. (1995). Selecting jobs for scheduling on
a machine subject to failure. Discrete Applied
Mathematics, 63, 257–265.
- Rogers, P., & Nandi, A. (2007). Judicious order
acceptance and order release in make-to-order
manufacturing systems. Production Planning and
Control, 18 (7), 610–625.
- Kate, H.A. (1994). Towards a better understanding
of order acceptance. International Journal of
Production Economics, 37, 139–152.
- Carr, S., & Duenyas, I. (2000). Optimal admission
control and sequencing in a make-to-stock/ make-
to-order production system. Operations Research,
48 (5), 709–720.
- Koyuncu, E. (2017). A fuzzy mathematical model
for order acceptance and scheduling problem.
International
Journal
of
Mathematical
Computational, Physical, Electrical and Computer
Engineering, 11(4).
- Quelhadj, D., & Petrovic, S. (2009). A Survey of
dynamic scheduling in manufacturing systems.
Journal of Scheduling, 12(4), 417-431.
- Baykasoglu, A., & Gocken, T. (2012). A direct
solution approach to fuzzy mathematical programs
with fuzzy decision variables. Expert System with
Applications, 39, 1972-1978.
- Liou, T.S., & Wang, M.J. (1992). Ranking fuzzy
numbers with integral value. Fuzzy Sets and
Systems, 50, 247–255.
- Zhou, W., & Gong, P. (2004). Economic effects of
environmental concerns in forest management: an
analysis of the cost of achieving environmental
goals. Journal of Forest Economics, 10, 97-113.
- Liou, T.S., & Chen, C.W. (2006). Fuzzy decision
analysis for alternative selection using a fuzzy
annual worth criterion. The Engineering Economist,
51, 19-34.
- Goncalves, J.F., Mendes, J.M., & Resende, M.G.C.
(2008). A genetic algorithm for the resource
constrained multi-project scheduling problem.
European Journal of Operational Research, 189,
1171–1190.
- Nicoara, E.S. (2015). Applying genetic algorithm to
optimization problems in economics. Economics
Insights-Trends and Challenges, 4(3), 125-132.
- Bean, J.C. (1994). Genetic algorithms and random
keys for sequencing and optimization. ORSA
Journal on Computing, 6(2), 154-160.
- Wang, H.F., & Wu, K.Y. (2004). Hybrid genetic
algorithm for optimization problems with
permutation property. Computers and Operations
Research, 31(14), 2453–2471.