TY - GEN
T1 - Multi-objective flexible job shop scheduling with uncertain processing time and machine available constraint based on hybrid optimization approach
AU - Jing, Tian
AU - Tomohiro, Murata
PY - 2010
Y1 - 2010
N2 - Scheduling for the flexible job shop is very important in both fields of production management and combinatorial optimization. However, when we attempt to formulate job shop scheduling problems which closely describe and represent the real world problem, various factors involved are often imprecisely or ambiguously known to the analyst. Also, in the real situations, machines are not continuously available due to preventive maintenance activity. Besides, in the production system, there are many users and machines in various priorities. Often, user satisfactions, machine stability and schedule itself are in conflict. By considering the imprecise or fuzzy nature of the data in real world problem, job shop scheduling with uncertain processing time and machine available constraint is introduced. Under the fuzzy circumstance, we attempt to make a feasible solution for flexible job shop scheduling problem (FJSSP) based on a hybrid optimization approach with multi-objective which not only to minimize the makespan, but also maximize the user satisfaction and machine stability. In the experiment, we make the comparisons with some other algorithms, such as genetic algorithm, to demonstrate the hybrid approach is much better on efficient search and stability with three benchmark examples. Sensitivity analysis is conducted to study the impact, in term of total objective, when the proportion of machine stability is varied.
AB - Scheduling for the flexible job shop is very important in both fields of production management and combinatorial optimization. However, when we attempt to formulate job shop scheduling problems which closely describe and represent the real world problem, various factors involved are often imprecisely or ambiguously known to the analyst. Also, in the real situations, machines are not continuously available due to preventive maintenance activity. Besides, in the production system, there are many users and machines in various priorities. Often, user satisfactions, machine stability and schedule itself are in conflict. By considering the imprecise or fuzzy nature of the data in real world problem, job shop scheduling with uncertain processing time and machine available constraint is introduced. Under the fuzzy circumstance, we attempt to make a feasible solution for flexible job shop scheduling problem (FJSSP) based on a hybrid optimization approach with multi-objective which not only to minimize the makespan, but also maximize the user satisfaction and machine stability. In the experiment, we make the comparisons with some other algorithms, such as genetic algorithm, to demonstrate the hybrid approach is much better on efficient search and stability with three benchmark examples. Sensitivity analysis is conducted to study the impact, in term of total objective, when the proportion of machine stability is varied.
KW - Ant colony optimization & tabu search
KW - Flexible job shop scheduling
KW - Fuzzy processing time
KW - Machine available constraints
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=78149421027&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78149421027&partnerID=8YFLogxK
U2 - 10.1109/ICAL.2010.5585352
DO - 10.1109/ICAL.2010.5585352
M3 - Conference contribution
AN - SCOPUS:78149421027
SN - 9781424483754
T3 - 2010 IEEE International Conference on Automation and Logistics, ICAL 2010
SP - 581
EP - 586
BT - 2010 IEEE International Conference on Automation and Logistics, ICAL 2010
T2 - 2010 IEEE International Conference on Automation and Logistics, ICAL 2010
Y2 - 16 August 2010 through 20 August 2010
ER -