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This paper considers the problem of production scheduling in a real-life manufacturing plant belonging to the apparel industry. The problem is modeled as a two-stage flexible flow shop, with minimization of both makespan and number of tardy jobs. For this new scheduling problem, we propose a bi-objective evolutionary algorithm. A set of experiments is performed using real data from the company's data-base. Experimental results show the relevance of the proposed procedure in terms of performance metrics, as opposed to the current manual scheduling procedure as well as against available commercial schedulers. The procedure was actually implemented as the scheduler procedure at the company. Such results also illustrate the improvement in key performance metrics of the production line. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of vehicle location or positioning for material handling in automated manufacturing systems. The problem is inspired by a real-life application in semiconductor manufacturing. The main difference between this paper and previous works is the consideration of the unified nature of the transport system in which vehicles can travel from one machine to another without passing through intermediate storage. Since a vehicle fleet size is limited, it is necessary to implement intelligent strategies to satisfy transportation requests. Previous works have focused on hierarchical mathematical programming and simulation approaches. This paper studies the tactical decision level, in which, based on static demand estimates, the objective is to determine the best distribution of vehicles within the factory so as to minimize the time required to service a transport request. A greedy heuristic procedure is proposed. Experimental tests have been conducted using previous data from the literature based on a real-life automated factory. An improvement in both production metrics and computation time has been obtained. [ABSTRACT FROM AUTHOR]