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Research on production scheduling under uncertainty has recently received much attention. This paper presents a novel decomposition-based approach (DBA) to flexible flow shop (FFS) scheduling under stochastic setup times. In comparison with traditional methods using a single approach, the proposed DBA combines and takes advantage of two different approaches, namely the Genetic Algorithm (GA) and the...
In this paper, an ordinal optimization based approach is proposed to solve for a good enough schedule that minimizes expected sum of storage expenses and tardiness penalties of stochastic classical job shop scheduling problem using limited computation time. The proposed approach consists of exploration and exploitation stage. The exploration stage uses a genetic algorithm to select a good candidate...
For a container terminal system, efficient berth and quay crane (QC) schedules have great impact on the improvement of both operation efficiency and customer satisfaction. In this paper we address both berth and quay crane scheduling problems in a simultaneous way, with uncertainty of container handling time. The berth is of discrete type and vessels arrive dynamically with different service priorities...
This paper deals with job shop scheduling with stochastic processing time in normal distribution. The extended Giffler-Thompson procedure in the stochastic context is first presented and some operations on the stochastic processing time are defined. A new permutation-based representation method is then proposed, in which the substring related to each machine is a permutation. The conflict among the...
A class of order optimization (OO) with optimal computing budgets allocation (OCBA) based genetic algorithm (GA) is designed to perform local search in the framework of nested partitions method (NP). The local searching algorithm borrows from the idea of OO to ensure the quality of the design found with a reduction in computation effort and applies the evolutionary searching mechanism and learning...
Based on the analysis of the stochastic earliness and tardiness parallel machine scheduling, a Quantum Genetic Scheduling Algorithm (QGSA) is presented. In the QGSA, the Q-bit based representation in discrete 0-1 hyperspace is employed, which is then converted into decimal scheduling code and quantum gate is used to update the current generation, meanwhile catastrophe operator is added to avoid premature...
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