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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]

Deploying unmanned aerial vehicles, also known as drones, for final-mile delivery in logistics operations has inspired this research. One conceivable scenario is to use the drone to transfer jobs in a robotic mobile fulfillment center. Each job might be characterized by origin, destination, time window, and precedence relation. In particular, a take-away conveyor is continuously pushing forward shipping cartons, wherein drones must pick up jobs and drop them off to the cartons within the time window since the cartons would be closed and labeled at the end of the conveyor. Furthermore, each job contains two subtransfers for drone: flight from drone’s current location to pickup location and flight to dropoff location. Two exact approaches are proposed—a mixed integer programming and a constraint programming—and tested for a real-time perspective. Note to Practitioners—Inspired by Amazon’s significant expansion of its army of warehouse robots and the adaptation of drones for final-mile small parcel delivery, this article has mixed those two emerging technologies and proposed a drone-powered material transfer system in a warehouse, wherein jobs would be picked up by drone, transferred, and dropped off directly to shipping cartons. The most encouraging finding is that the proposed method impressively proved the optimality of 144 jobs with 10 drones that requires 28.8 transfers per drone on average, within 1 s. [ABSTRACT FROM AUTHOR]

A dual resource constrained (DRC) scheduling problem arises at the photolithography area in semiconductor manufacturing wherein reticles are required as an auxiliary resource. Reticles are transferred from one place to another. While this shared resource gives the manufacturer a flexibility, it certainly gives rise to a complex DRC scheduling problem surrounded by jobs, machines, and reticles, restricted by sequence-dependent setup-time and location-dependent reticle transfer-time. This study proposes a constraint programming (CP) approach and investigates methods for improving our initial CP using a hybrid method and variable ordering heuristic. Experiments indicate the variable ordering heuristic brings a significant improvement over a pure CP. Note to Practitioners—There has been yet no successful exact approach that addresses litho scheduling problem, covering jobs, machines, and reticles. This study demonstrates CP is able to find an optimal solution for most of realistic industry-size test problems. In a future smart semiconductor factory, seamlessly orchestrating machines, reticles, and jobs would not be the end state. The next level is to integrate material handling system to this already overcrowded production scheduling problem. The specialized keywords of CP, as demonstrated in this study, will enable practitioners to develop a concise code of integrating vehicles, jobs, machines, and reticles. [ABSTRACT FROM PUBLISHER]

We study a parallel batch-scheduling problem that involves the constraints of different job release times, non-identical job sizes, and incompatible job families, is addressed. Mixed integer programming and constraint programming (CP) models are proposed and tested on a set of common problem instances from a paper in the literature. Then, we compare the performance of the models with that of a variable neighborhood search (VNS) heuristic from the same paper. Computational results show that CP outperforms VNS with respect to solution quality and run time by 3.4%–6.8% and 47%–91%, respectively. When compared to optimal solutions, the results demonstrate CP is capable of generating a near optimal solution in a short amount of time. [ABSTRACT FROM AUTHOR]

The introduction of real-time dispatching (RTD) to semiconductor manufacturing has greatly improved productivities for the last two decades. Recently, researchers have revealed that data available and complexity of the algorithms to the dispatcher are limited—in effect the dispatcher has tunnel vision, dutifully rank ordering the lots in the queue, but oblivious to what is happening around it, which leads to the poor results compared to other modern optimization-based approaches. However, the author admits that most semiconductor manufacturing companies are still using RTD not only as a last-minute loading-check, but also as a scheduling logic-hub. Here comes a business need of improving the brainpower of RTD by using the optimization technologies. In this paper, we propose an integration of RTD with linear programming technique in photolithography area by using two-stage modeling. The transportation model at the first stage finds the least-cost means of assigning jobs to reticles and machines. Then, the sequencing module provides an expository sequencing decision by inheriting the same dispatching business rules. The experimental study demonstrates the proposed method can reduce the count of reticle delivery by 40.5% in a low-mix fab instances. [ABSTRACT FROM AUTHOR]