Decomposability and scalability in space-based observatory scheduling

In this paper, we discuss issues of problem and model decomposition within the HSTS scheduling framework. HSTS was developed and originally applied in the context of the Hubble Space Telescope (HST) scheduling problem, motivated by the limitations of the current solution and, more generally, the insufficiency of classical planning and scheduling approaches in this problem context. We first summarize the salient architectural characteristics of HSTS and their relationship to previous scheduling and AI planning research. Then, we describe some key problem decomposition techniques supported by HSTS and underlying our integrated planning and scheduling approach, and we discuss the leverage they provide in solving space-based observatory scheduling problems

Models and algorithms for Integration of Vehicle and Crew Scheduling

This paper deals with models, relaxations and algorithms for an integrated approach to vehicle and crew scheduling. We discuss potential benefits of integration and provide an overview of the literature, which considers mainly partial integration. Our approach is new in the sense that we can tackle integrated vehicle and crew scheduling problems of practical size.We propose new mathematical formulations for integrated vehicle and crew scheduling problems and we discuss corresponding Langrangian relaxations and Lagrangian heuristics. To solve the Lagrangian relaxations, we use column generation applied to set partitioning type of models. The paper is concluded with a computational study using real life data, which shows the applicability of the proposed techniques to practical problems. Furthermore, we also address the effectiveness of integration in different situations.Lagrangian relaxation;column generation;crew scheduling;integrated planning;vehicle scheduling

Integrated payload and mission planning, phase 3. Volume 1: Integrated payload and mission planning process evaluation

The integrated payload and mission planning process for STS payloads was defined, and discrete tasks which evaluate performance and support initial implementation of this process were conducted. The scope of activity was limited to NASA and NASA-related payload missions only. The integrated payload and mission planning process was defined in detail, including all related interfaces and scheduling requirements. Related to the payload mission planning process, a methodology for assessing early Spacelab mission manager assignment schedules was defined

STAN4 : a hybrid planning strategy based on subproblem abstraction

Planning domains often feature subproblems such as route planning and resource handling. Using static domain analysis techniques, we have been able to identify certain commonly occurring subproblems within planning domains, making it possible to abstract these subproblems from the overall goals of the planner and deploy specialized technology to handle them in a way integrated with the broader planning activities. Using two such subsolvers our hybrid planner, stan4, participated successfully in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS'00) planning competition

The APT/ERE planning and scheduling manifesto

The Entropy Reduction Engine, ERE project, is focusing on the construction of integrated planning and scheduling systems. Specifically, the project is studying the problem of integrating planning and scheduling in the context of the closed loop plan use. The results of this research are particularly relevant when there is some element of dynamism in the environment, and thus some chance that a previously formed plan will fail. After a preliminary study of the APT management and control problem, it was felt that it presents an excellent opportunity to show some of the ERE Project's technical results. Of course, the alignment between technology and problem is not perfect, so planning and scheduling for APTs presents some new and difficult challenges as well

Automated multigravity assist trajectory planning with a modified ant colony algorithm

The paper presents an approach to transcribe a multigravity assist trajectory design problem into an integrated planning and scheduling problem. A modified Ant Colony Optimization (ACO) algorithm is then used to generate optimal plans corresponding to optimal sequences of gravity assists and deep space manoeuvers to reach a given destination. The modified Ant Colony Algorithm is based on a hybridization between standard ACO paradigms and a tabu-based heuristic. The scheduling algorithm is integrated into the trajectory model to provide a fast time-allocation of the events along the trajectory. The approach demonstrated to be very effective on a number of real trajectory design problems

Energy-aware integrated process planning and scheduling for job shops

Process planning that is based on environmental consciousness and energy-efficient scheduling currently plays a critical role in sustainable manufacturing processes. Despite their interrelationship, these two topics have often been considered to be independent of each other. It therefore would be beneficial to integrate process planning and scheduling for an integrated energy-efficient optimisation of product design and manufacturing in a sustainable manufacturing system. This article proposes an energy-aware mathematical model for job shops that integrates process planning and scheduling. First, a mixed integrated programming model with performance indicators such as energy consumption and scheduling makespan is established to describe a multi-objective optimisation problem. Because the problem is strongly non-deterministic polynomial-time hard (NP-hard), a modified genetic algorithm is adopted to explore the optimal solution (Pareto solution) between energy consumption and makespan. Finally, case studies of energy-aware integrated process planning and scheduling are performed, and the proposed algorithm is compared with other methods. The approach is shown to generate interesting results and can be used to improve the energy efficiency of sustainable manufacturing processes at the process planning and scheduling levels

Integrated Production Planning and Scheduling Optimization

Este trabalho propõe um método de solução iterativa para abordar a integração do planeamento táctico (dimensionamento de lotes) e operacional (sequenciamento) numa produção industrial com setups dependentes da sequencia. Este método quebra o problema da integração em dois. No primeiro sub-problema do planeamento táctico, o plano de produção é optimizado sem ter em conta setups necessários. O sequenciamento dos produtos é depois definido usando estratégias de pesquisa local que irão conceber regras para complementarem o primeiro sub-problema. De seguida, o planeamento táctico é repetido, considerando as novas regras definidas anteriormente. O algoritmo continua iterativamente até que as funções objectivo dos dois níveis convirjam. De modo a analisar resultados obtidos, dois experimentos computacionais são propostos. O primeiro para comparar o método iterativo com outros métodos de solução encontrados na literatura para problemas similares, nomeadamente meta-heuristicas e modelos MIP. Por fim, a investigação foi focada num caso de uma indústria de nutrição animal, onde o setup de produção é dependente da sequência e normalmente não-triangular, podendo produtos evitarem limpeza se produzidos entre outros dois que de outro modo necessitariam de setup. O propósito do segundo experimento é avaliar os eventuais ganhos a uma abordagem hierárquica usualmente usada nesta indústria.This work proposes an iterative solution method to address the integration of the tactical (lot-sizing) and operational (scheduling) levels in production planning with sequence dependent setups. This method breaks the integrated lot-sizing and scheduling problem into two. In the first sub-problem, at the tactical level, the production plan is optimized with production setups disregarded. The production scheduling solution is then defined using local search strategies that will also construct rules for the tactical level. After that, the tactical level is optimized again, considering the rules defined from the operational level. The algorithm continues iteratively until objective functions from both levels converge. In order to analyse results, two computational experiments are proposed. The first is performed to compare the solution method proposed with mixed-integer programming models and meta-heuristics from the literature. Then the research will focus on an animal-feed industry case, in which production setup is sequence dependent and usually presents non-triangular setups, so products can avoid cleaning setups if produced between two products that otherwise would require a setup. The purpose of the second experiment is to evaluate the potential gains to a hierarchical approach usually used in this industry

Integrated Optimization of Production Planning and Scheduling in Mixed Model Assembly Line

AbstractIn order to solve the separation in the traditional serial production planning and scheduling in mixed model assembly line, the integrated optimization complete model of production planning and scheduling based on multiple objectives and constraints was constructed. Since the integrated optimization complete model is difficult to solve, the heuristic approach was adopt, and the modified discrete particle swarm optimization(MDPSO) was presented to solve the model. The experiments verifies the presented model and algorithm can realize the simultaneously optimization of production planning and scheduling in mixed model assembly line and contribute to performance improvement and the application scope expand of the new intelligent optimization