arXiv subjects

Mohammed M. S. El-Kholany

Publications and source records attributed to Mohammed M. S. El-Kholany.

4 recordsLinked to original sources

arXiv2025

Decomposition Strategies and Multi-shot ASP Solving for Job-shop Scheduling

The Job-shop Scheduling Problem (JSP) is a well-known and challenging combinatorial optimization problem in which tasks sharing a machine are to be arranged in a sequence such that encompassing jobs can be completed as early as possible. In this paper, we investigate problem decomposition into time windows whose operations can be successively scheduled and optimized by means of multi-shot Answer Set Programming (ASP) solving. From a computational perspective, decomposition aims to split highly complex scheduling tasks into better manageable subproblems with a balanced number of operations such that good-quality or even optimal partial solutions can be reliably found in a small fraction of runtime. We devise and investigate a variety of decomposition strategies in terms of the number and size of time windows as well as heuristics for choosing their operations. Moreover, we incorporate time window overlapping and compression techniques into the iterative scheduling process to counteract optimization limitations due to the restriction to window-wise partial schedules. Our experiments on different JSP benchmark sets show that successive optimization by multi-shot ASP solving leads to substantially better schedules within tight runtime limits than single-shot optimization on the full problem. In particular, we find that decomposing initial solutions obtained with proficient heuristic methods into time windows leads to improved solution quality.

cs.AI↗

arXiv2023

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.

cs.AI↗

arXiv2022

Decomposition Strategies for Solving Scheduling Problems in Industrial Applications

This article presents an overview of a research study of a crucial optimization problem in the Computer Science/Operations research field: The Job-shop Scheduling Problem (JSP). The JSP is a challenging task in which a set of operations must be processed using a set of scarce machines to optimize a particular objective. The main purpose of the JSP is to determine the execution order of the processes assigned to each machine to optimize an objective. Our main interest in this study is to investigate developing decomposition strategies using logic programming to solve the JSP. We split our goal into two main phases. The first phase is to apply the decomposition approach and evaluate the proposed model by solving a set of known benchmark instances. The second phase is to apply the successful decomposition methods obtained from the first phase to solve a scheduling problem in the real-life application. In the current state, we finished the first phase and started the second one aiming to have a model that can provide a schedule of a factory for a short-time period.

cs.LO↗

arXiv2021

Solving a Multi-resource Partial-ordering Flexible Variant of the Job-shop Scheduling Problem with Hybrid ASP

Many complex activities of production cycles, such as quality control or fault analysis, require highly experienced specialists to perform various operations on (semi)finished products using different tools. In practical scenarios, the selection of a next operation is complicated, since each expert has only a local view on the total set of operations to be performed. As a result, decisions made by the specialists are suboptimal and might cause significant costs. In this paper, we consider a Multi-resource Partial-ordering Flexible Job-shop Scheduling (MPF-JSS) problem where partially-ordered sequences of operations must be scheduled on multiple required resources, such as tools and specialists. The resources are flexible and can perform one or more operations depending on their properties. The problem is modeled using Answer Set Programming (ASP) in which the time assignments are efficiently done using Difference Logic. Moreover, we suggest two multi-shot solving strategies aiming at the identification of the time bounds allowing for a solution of the schedule optimization problem. Experiments conducted on a set of instances extracted from a medium-sized semiconductor fault analysis lab indicate that our approach can find schedules for 87 out of 91 considered real-world instances.

cs.AI↗