Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm

[EN] Machine scheduling problems arise in many production processes, and are something that needs to be consider when optimizing the supply chain. Among them, flowshop scheduling problems happen when a number of jobs have to be sequentially processed by a number of machines. This paper addressees, for the first time, the Permutation Flowshop Scheduling problem with additional Resources during Setups (PFSR-S). In this problem, in addition to the standard permutation flowshop constraints, each machine requires a setup between the processing of two consecutive jobs. A number of additional and scarce resources, e.g. operators, are needed to carry out each setup. Two Mixed Integer Linear Programming formulations and an exact algorithm are proposed to solve the PFSR-S. Due to its complexity, these approaches can only solve instances of small size to optimality. Therefore, a GRASP metaheuristic is also proposed which provides solutions for much larger instances. All the methods designed for the PFSR-S in this paper are computationally tested over a benchmark of instances adapted from the literature. The results obtained show that the GRASP metaheuristic finds good quality solutions in short computational times.Juan C. Yepes-Borrero acknowledges financial support by Colfuturo under program Credito-Beca grant number 201503877 and from ElInstituto Colombiano de Credito Educativo y Estudios Tecnicos en el Exterior - ICETEX under program Pasaporte a la ciencia - Doctor-ado, Foco-reto pais 4.2.3, grant number 3568118. This research hasbeen partially supported by the Agencia Estatal de Investigacion (AEI)and the European Regional Development's fund (ERDF): PID2020-114594GB-C21; Regional Government of Andalusia: projects FEDER-US-1256951, AT 21_00032, and P18-FR-1422; Fundacion BBVA: project Netmeet Data (Ayudas Fundacion BBVA a equipos de investigacioncientifica 2019). The authors are partially supported by Agencia Valenciana de la Innovacion (AVI) under the project ireves (innovacionen vehiculos de emergencia sanitaria): una herramienta inteligente dedecision'' (No. INNACC/2021/26) partially financed with FEDER funds(interested readers can visit http://ireves.upv.es), and by the Spanish Ministry of Science and Innovation under the project OPRES-RealisticOptimization in Problems in Public Health'' (No. PID2021-124975OB-I00), partially financed with FEDER funds. Part of the authors aresupported by the Faculty of Business Administration and Managementat Universitat Politecnica de ValenciaYepes-Borrero, JC.; Perea, F.; Villa Juliá, MF.; Vallada Regalado, E. (2023). Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm. Computers & Operations Research. 154. https://doi.org/10.1016/j.cor.2023.10619215

Iterative repair for scheduling and rescheduling

An iterative repair search method is described called constraint based simulated annealing. Simulated annealing is a hill climbing search technique capable of escaping local minima. The utility of the constraint based framework is shown by comparing search performance with and without the constraint framework on a suite of randomly generated problems. Results are also shown of applying the technique to the NASA Space Shuttle ground processing problem. These experiments show that the search methods scales to complex, real world problems and reflects interesting anytime behavior

Rescheduling with iterative repair

This paper presents a new approach to rescheduling called constraint-based iterative repair. This approach gives our system the ability to satisfy domain constraints, address optimization concerns, minimize perturbation to the original schedule, produce modified schedules, quickly, and exhibits 'anytime' behavior. The system begins with an initial, flawed schedule and then iteratively repairs constraint violations until a conflict-free schedule is produced. In an empirical demonstration, we vary the importance of minimizing perturbation and report how fast the system is able to resolve conflicts in a given time bound. We also show the anytime characteristics of the system. These experiments were performed within the domain of Space Shuttle ground processing

Non-Reshuffle-Based Approach for Rescheduling of Flexible Manufacturing System

Scheduling and rescheduling play vital roles in ensuring the effectiveness of the production control in flexible manufacturing system (FMS).  The failure of these systems may interrupt the efficiency of the production activities and thus may lessen the profit to be gained by the company. The FMS scheduling problem is considered as dynamic as new orders may get in every day. The new orders need to be immediately desegregated with the existing production schedule by preserving the efficiency and stability of the existing schedule. This research applies the non-reshuffle-based genetic match-up algorithms which admit new orders by manipulating available machine idle times to address rescheduling problem in a FMS that practises the pull strategy. The idea of the match-up approach is to update only a part of the initial schedule and genetic algorithms used to optimise the solution within the rescheduling horizon in such a way in order to preserve the efficiency and stability of the shop floor. The proposed methodology has been tested using different rescheduling parameters. The experiments show that the rescheduling method improves efficiency and stability of the new schedule

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

Analysing the impact of rescheduling time in hybrid manufacturing control

Hybrid manufacturing control architectures merge the benefits of hierarchical and heterarchical approaches. Disturbances can be handled at upper or lower decision levels, depending on the type of disturbance, its impact and the time the control system has to react. This paper focuses particularly on a disturbance handling mechanism at upper decision levels using a rescheduling manufacturing method. Such rescheduling is more complex that the offline scheduling since the control system must take into account the current system status, obtain a satisfactory performance under the new conditions, and also come up with a new schedule in a restricted amount of time. Then, this paper proposes a simple and generic rescheduling method which, based on the satisfying principle, analyses the trade-off between the rescheduling time and the performance achieved after a perturbation. The proposed approach is validated on a simulation model of a realistic assembly cell and results demonstrate that adaptation of the rescheduling time might be beneficial in terms of overall performance and reactivity.info:eu-repo/semantics/publishedVersio

A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem

The 0-1 knapsack problem is a well-known combinatorial optimisation problem. Approximation algorithms have been designed for solving it and they return provably good solutions within polynomial time. On the other hand, genetic algorithms are well suited for solving the knapsack problem and they find reasonably good solutions quickly. A naturally arising question is whether genetic algorithms are able to find solutions as good as approximation algorithms do. This paper presents a novel multi-objective optimisation genetic algorithm for solving the 0-1 knapsack problem. Experiment results show that the new algorithm outperforms its rivals, the greedy algorithm, mixed strategy genetic algorithm, and greedy algorithm + mixed strategy genetic algorithm