2018
DOI: 10.1021/acs.iecr.8b03140
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Optimization Framework for the Simultaneous Batching and Scheduling of Multisite Production Environments

Abstract: In recent years, many companies have shifted from single-site production to more complex manufacturing environments involving multiple multiproduct facilities. At the operational level, the scheduling posed on each individual facility does not lead to efficient use of resources and consequently an optimal production management over the whole production system cannot be achieved. In this context, a multisite production scheduling framework is fundamental to respond appropriately to the market requirements. In t… Show more

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(1 citation statement)
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“…Naderi and Azab [5] used a mixed-integer linear programming model to solve the distributed job shop scheduling problem and proposed a new simulated annealing algorithm using advanced mobile operators. Ackermann, Fumero, and Montagn [6] proposed a mixed-integer linear programming (MILP) model for the simultaneous batching and scheduling of multi-site multi-product batch plants with non-identical parallel units. Asl, Solimanpur, and Shankar [7] studied the multi-model assembly line balancing problem, in which several products were produced in separate batches on a single assembly line, and proposed a multi-objective mixed-integer linear programming model.…”
Section: Introductionmentioning
confidence: 99%
“…Naderi and Azab [5] used a mixed-integer linear programming model to solve the distributed job shop scheduling problem and proposed a new simulated annealing algorithm using advanced mobile operators. Ackermann, Fumero, and Montagn [6] proposed a mixed-integer linear programming (MILP) model for the simultaneous batching and scheduling of multi-site multi-product batch plants with non-identical parallel units. Asl, Solimanpur, and Shankar [7] studied the multi-model assembly line balancing problem, in which several products were produced in separate batches on a single assembly line, and proposed a multi-objective mixed-integer linear programming model.…”
Section: Introductionmentioning
confidence: 99%