Serial-batch scheduling – the special case of laser-cutting machines

The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning

A common framework and taxonomy for multicriteria scheduling problems with Interfering and competing Jobs: Multi-agent scheduling problems

Most classical scheduling research assumes that the objectives sought are common to all jobs to be scheduled. However, many real-life applications can be modeled by considering different sets of jobs, each one with its own objective(s), and an increasing number of papers addressing these problems has appeared over the last few years. Since so far the area lacks a uni ed view, the studied problems have received different names (such as interfering jobs, multi-agent scheduling, mixed-criteria, etc), some authors do not seem to be aware of important contributions in related problems, and solution procedures are often developed without taking into account existing ones. Therefore, the topic is in need of a common framework that allows for a systematic recollection of existing contributions, as well as a clear de nition of the main research avenues. In this paper we review multicriteria scheduling problems involving two or more sets of jobs and propose an uni ed framework providing a common de nition, name and notation for these problems. Moreover, we systematically review and classify the existing contributions in terms of the complexity of the problems and the proposed solution procedures, discuss the main advances, and point out future research lines in the topic

Scheduling Algorithms: Challenges Towards Smart Manufacturing

Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms\u27 characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A single-machine scheduling problem with multiple unavailability constraints: A mathematical model and an enhanced variable neighborhood search approach

AbstractThis research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS) is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective

Optimization of job shop scheduling with material handling by automated guided vehicle

Job Shop Scheduling with Material Handling has attracted increasing attention in both industry and academia, especially with the inception of Industry 4.0 and smart manufacturing. A smart manufacturing system calls for efficient and effective production planning. On a typical modern shop floor, jobs of various types follow certain processing routes through machines or work centers, and automated guided vehicles (AGVs) are utilized to handle the jobs. In this research, the optimization of a shop floor with AGV is carried out, and we also consider the planning scenario under variable processing time of jobs. The goal is to minimize the shop floor production makespan or other specific criteria correlated with makespan, by scheduling the operations of job processing and routing the AGVs. This dissertation includes three research studies that will constitute my doctoral work. In the first study, we discuss a simplified case in which the scheduling problem is reformulated into a vehicle dispatching (assignment) problem. A few AGV dispatching strategies are proposed based on the deterministic optimization of network assignment problems. The AGV dispatching strategies take future transportation requests into consideration and optimally configure transportation resources such that material handling can be more efficient than those adopting classic AGV assignment rules in which only the current request is considered. The strategies are demonstrated and validated with a case study based on a shop floor in literature and compared to classic AGV assignment rules. The results show that AGV dispatching with adoption of the proposed strategy has better performance on some specific criterions like minimizing job waiting time. In the second study, an efficient heuristic algorithm for classic Job Shop Scheduling with Material Handling is proposed. Typically, the job shop scheduling problem and material handling problem are studied separately due to the complexity of both problems. However, considering these two types of decisions in the same model offers benefits since the decisions are related to each other. In this research, we aim to study the scheduling of job operations together with the AGV routing/scheduling, and a formulation as well as solution techniques are proposed. The proposed heuristic algorithm starts from an optimal job shop scheduling solution without limiting the size of AGV fleet, and iteratively reduces the number of available vehicles until the fleet size is equal to the original requirements. The computational experiments suggest that compared to existing solution techniques in literature, the proposed algorithm can achieve comparable solution quality on makespan with much higher computational efficiency. In the third study, we take the variability of processing time into consideration in optimizing job shop scheduling with material handling. Variability caused by random effects and deterioration is discussed, and a series of models are developed to accommodate random and deteriorating processing time respectively. With random processing time, the model is formulated as a Stochastic Programming Job Shop Scheduling with Material Handling model, and with deteriorating processing time the model can be nonlinear under specific deteriorating functions. Based on a widely adopted dataset in existing literature, the stochastic programming model were solved with Pyomo, and models with deterioration were linearized and solved with CPLEX. By considering variable processing time, the JSSMH models can better adapt to real production scenarios