Project scheduling with modular project completion on a bottleneck resource.

In this paper, we model a research-and-development project as consisting of several modules, with each module containing one or more activities. We examine how to schedule the activities of such a project in order to maximize the expected profit when the activities have a probability of failure and when an activity’s failure can cause its module and thereby the overall project to fail. A module succeeds when at least one of its constituent activities is successfully executed. All activities are scheduled on a scarce resource that is modeled as a single machine. We describe various policy classes, establish the relationship between the classes, develop exact algorithms to optimize over two different classes (one dynamic program and one branch-and-bound algorithm), and examine the computational performance of the algorithms on two randomly generated instance sets.Scheduling; Uncertainty; Research and development; Activity failures; Modular precedence network;

Exact and heuristic approaches to detect failures in failed k-out-of-n systems

This paper considers a k-out-of-n system that has just failed. There is an associated cost of testing each component. In addition, we have apriori information regarding the probabilities that a certain set of components is the reason for the failure. The goal is to identify the subset of components that have caused the failure with the minimum expected cost. In this work, we provide exact and approximate policies that detects components’ states in a failed k-out-of-n system. We propose two integer programming (IP) formulations, two novel Markov decision process (MDP) based approaches, and two heuristic algorithms. We show the limitations of exact algorithms and effectiveness of proposed heuristic approaches on a set of randomly generated test instances. Despite longer CPU times, IP formulations are flexible in incorporating further restrictions such as test precedence relationships, if need be. Numerical results illustrate that dynamic programming for the proposed MDP model is the most effective exact method, solving up to 12 components within one hour. The heuristic algorithms’ performances are presented against exact approaches for small to medium sized instances and against a lower bound for larger instances

Applying MILP/Heuristic algorithms to automated job-shop scheduling problems in aircraft-part manufacturing

This work presents efficient algorithms based on Mixed-Integer Linear Programming (MILP) and heuristic strategies for complex job-shop scheduling problems raised in Automated Manufacturing Systems. The aim of this work is to find alternative a solution approach of production and transportation operations in a multi-product multi-stage production system that can be used to solve industrial-scale problems with a reasonable computational effort. The MILP model developed must take into account; heterogeneous recipes, single unit per stage, possible recycle flows, sequence-dependent free transferring times and load transfer movements in a single automated material-handling device. In addition, heuristic-based strategies are proposed to iteratively find and improve the solutions generated over time. These approaches were tested in different real-world problems arising in the surface-treatment process of metal components in the aircraft manufacturing industry.Fil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina. Universidad Nacional del Nordeste; ArgentinaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina. Universidad Nacional del Nordeste; ArgentinaFil: García Sanchez, Alvaro. Universidad Politecnica de Madrid; EspañaFil: Ortega Mier, Miguel. Universidad Politecnica de Madrid; Españ

Applying MILP-based algorithms to automated job-shop scheduling problems in aircraft-part manufacturing

This work presents efficient algorithms based on Mixed-Integer Linear Programming (MILP) for complex job-shop scheduling problems raised in Automated Manufacturing Systems. The aim of this work is to find alternative solution approaches of production and transportation operations in a multi-product multistage production process that can be used to solve industrial-scale problems with reasonable computational effort. The MILP model developed must take into account; dissimilar recipes, single unit per production stage, re-entrant flows, sequence- dependent free transferring times and load transfer movements in a single automated material-handling device. In addition, logical-based strategies are proposed to iteratively find and improve the solutions generated over time. These approaches were tested in different real-world problems appeared in the surfacetreatment process of metal components in aircraft manufacturing industry.Sociedad Argentina de Informática e Investigación Operativ

Applying MILP-based algorithms to automated job-shop scheduling problems in aircraft-part manufacturing

This work presents efficient algorithms based on Mixed-Integer Linear Programming (MILP) for complex job-shop scheduling problems raised in Automated Manufacturing Systems. The aim of this work is to find alternative solution approaches of production and transportation operations in a multi-product multistage production process that can be used to solve industrial-scale problems with reasonable computational effort. The MILP model developed must take into account; dissimilar recipes, single unit per production stage, re-entrant flows, sequence- dependent free transferring times and load transfer movements in a single automated material-handling device. In addition, logical-based strategies are proposed to iteratively find and improve the solutions generated over time. These approaches were tested in different real-world problems appeared in the surfacetreatment process of metal components in aircraft manufacturing industry.Sociedad Argentina de Informática e Investigación Operativ

Applying MILP-based algorithms to automated job-shop scheduling problems in aircraft-part manufacturing

This work presents efficient algorithms based on Mixed-Integer Linear Programming (MILP) for complex job-shop scheduling problems raised in Automated Manufacturing Systems. The aim of this work is to find alternative solution approaches of production and transportation operations in a multi-product multistage production process that can be used to solve industrial-scale problems with reasonable computational effort. The MILP model developed must take into account; dissimilar recipes, single unit per production stage, re-entrant flows, sequence- dependent free transferring times and load transfer movements in a single automated material-handling device. In addition, logical-based strategies are proposed to iteratively find and improve the solutions generated over time. These approaches were tested in different real-world problems appeared in the surfacetreatment process of metal components in aircraft manufacturing industry.Sociedad Argentina de Informática e Investigación Operativ

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table