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This paper studies simultaneous scheduling of production and material transfer that arises in the semiconductor photolithography area. In particular, the right reticle and right job both need to be present to process the job. Jobs are transferred by a material handling system that employees a fleet of vehicles. Reticles serving as an auxiliary resource are also transferred from one place to another by a different set of vehicles. This intricate scheduling challenge, encompassing jobs, reticles, machines, and two distinct sets of vehicles, is explored here for the first time. The paper introduces a multi-stage methodology that involves relaxation, a constructive heuristic, constraint programming, and a warm-start approach to address this complex problem. [ABSTRACT FROM AUTHOR]

This paper studies a simultaneous scheduling of production and material transfer in a job shop environment. The simultaneous scheduling approach has been recently adopted by warehouse operations, wherein transbots pick up jobs and deliver to pick-machines for processing that requires a simultaneous scheduling of jobs, transbots, and machines. However, both a large proportion of literature and real-world scheduling systems consider only one side of the problem. In our study, machines and transbot are both considered as constraining resources. The contributions of this paper are twofold. First, we propose a novel application of constraint programming for the job shop scheduling problem (JSP) with transbots, significantly outperforming all other benchmark approaches in the literature and proving optimality of the well-known benchmark instances, for the first time. Second, we propose a medium-scale benchmark instance. [ABSTRACT FROM AUTHOR]

The dial-a-ride problem with time windows is studied in this article. First, a new efficient mixed integer programming formulation is proposed by merging some of drop-off nodes into paired pick-up nodes when a size of the order forbids ridesharing, so a vehicle must visit the paired drop-off nodes directly after the pick-up, thus reducing the number of decision variables by 40% on benchmark instances. Secondly, three different constraint programming formulations are proposed for the first time, generating efficient schedules within a few seconds. Thirdly, a real-time rescheduling perspective is further studied, since the ridesharing becomes dynamic when a schedule must be provided instantly. In particular, the warm-start search method, in which a precalculated schedule is continuously revised as new orders come in, is proposed. [ABSTRACT FROM AUTHOR]