Hybrid ASP-based multi-objective scheduling of semiconductor manufacturing processes (Extended version)

Modern semiconductor manufacturing involves intricate production processes consisting of hundreds of operations, which can take several months from lot release to completion. The high-tech machines used in these processes are diverse, operate on individual wafers, lots, or batches in multiple stages, and necessitate product-specific setups and specialized maintenance procedures. This situation is different from traditional job-shop scheduling scenarios, which have less complex production processes and machines, and mainly focus on solving highly combinatorial but abstract scheduling problems. In this work, we address the scheduling of realistic semiconductor manufacturing processes by modeling their specific requirements using hybrid Answer Set Programming with difference logic, incorporating flexible machine processing, setup, batching and maintenance operations. Unlike existing methods that schedule semiconductor manufacturing processes locally with greedy heuristics or by independently optimizing specific machine group allocations, we examine the potentials of large-scale scheduling subject to multiple optimization objectives.Comment: 17 pages, 1 figure, 4 listings, 1 table; a short version of this paper is presented at the 18th European Conference on Logics in Artificial Intelligence (JELIA 2023

Dynamic adjustment of dispatching rule parameters in flow shops with sequence dependent setup times

Decentralized scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence dependent setup times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs

Overview on: sequencing in mixed model flowshop production line with static and dynamic context

In the present work a literature overview was given on solution techniques considering basic as well as more advanced and consequently more complex arrangements of mixed model flowshops. We first analyzed the occurrence of setup time/cost; existing solution techniques are mainly focused on permutation sequences. Thereafter we discussed objectives resulting in the introduction of variety of methods allowing resequencing of jobs within the line. The possibility of resequencing within the line ranges from 1) offline or intermittent buffers, 2) parallel stations, namely flexible, hybrid or compound flowshops, 3) merging and splitting of parallel lines, 4) re-entrant flowshops, to 5) change job attributes without physically interchanging the position. In continuation the differences in the consideration of static and dynamic demand was studied. Also intermittent setups are possible, depending on the horizon and including the possibility of resequencing, four problem cases were highlighted: static, semi dynamic, nearly dynamic and dynamic case. Finally a general overview was given on existing solution methods, including exact and approximation methods. The approximation methods are furthermore divided in two cases, know as heuristics and methaheuristic

An Iterated Greedy Algorithm for a Parallel Machine Scheduling Problem with Re-entrant and Group Processing Features

This research paper addresses a novel parallel machine scheduling problem with re-entrant and group processing features, specifically motivated by the hot milling process in the modern steel manufacturing industry. The objective is to minimize the makespan. As no existing literature exists on this problem, the paper begins by analyzing the key characteristics of the problem. Subsequently, a mixed integer linear programming model is formulated. To tackle the problem, an improved iterated greedy algorithm (IGA) is proposed. The IGA incorporates a problem-specific heuristic to construct the initial solution. Additionally, it incorporates an effective destruction and reconstruction procedure. Furthermore, an acceptance rule is developed to prevent the IGA from getting stuck in local optima. The proposed approach is evaluated through computational experiments. The results demonstrate that the proposed IGA outperforms three state-of-the-art meta-heuristics, highlighting its high effectiveness. Overall, this research contributes to the understanding and solution of the parallel machine scheduling problem with re-entrant and group processing features in the context of the hot milling process. The proposed algorithm provides insights for practical applications in the steel manufacturing industry

Modeling and Performance Analysis of Manufacturing Systems Using Max-Plus Algebra

In response to increased competition, manufacturing systems are becoming more complex in order to provide the flexibility and responsiveness required by the market. The increased complexity requires decision support tools that can provide insight into the effect of system changes on performance in an efficient and timely manner. Max-Plus algebra is a mathematical tool that can model manufacturing systems in linear equations similar to state-space equations used to model physical systems. These equations can be used in providing insight into the performance of systems that would otherwise require numerous time consuming simulations. This research tackles two challenges that currently hinder the applicability of the use of max-plus algebra in industry. The first problem is the difficulty of deriving the max-plus equations that model complex manufacturing systems. That challenge was overcome through developing a method for automatically generating the max-plus equations for manufacturing systems and presenting them in a form that allows analyzing and comparing any number of possible line configurations in an efficient manner; as well as giving insights into the effects of changing system parameters such as the effects of adding buffers to the system or changing buffers sizes on various system performance measures. The developed equations can also be used in the operation phase to analyze possible line improvements and line reconfigurations due to product changes. The second challenge is the absence of max-plus models for special types of manufacturing systems. For this, max-plus models were developed for the first time for modeling mixed model assembly lines (MMALs) and re-entrant manufacturing systems. The developed methods and tools are applied to case studies of actual manufacturing systems to demonstrate the effectiveness of the developed tools in providing important insight and analysis of manufacturing systems performance. While not covering all types of manufacturing systems, the models presented in this thesis represent a wide variety of systems that are structurally different and thus prove that max-plus algebra is a practical tool that can be used by engineers and managers in modeling and decision support both in the design and operation phases of manufacturing systems

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