The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimisation problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronisation, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel bi-objective meta-heuristic approach for robust scheduling. The proposed algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete. © 2007 IEEE.
A bi-objective evolutionary approach to robust scheduling / Surico, Michele; Kaymak, Uzay; Naso, David; Dekker, Rommert. - (2007). (Intervento presentato al convegno 2007 IEEE International Conference on Fuzzy Systems, FUZZY tenutosi a London, UK nel July 23-26, 2007) [10.1109/FUZZY.2007.4295611].
A bi-objective evolutionary approach to robust scheduling
NASO, David;
2007-01-01
Abstract
The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimisation problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronisation, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel bi-objective meta-heuristic approach for robust scheduling. The proposed algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete. © 2007 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
