On-line planning and scheduling: an application to controlling modular printers

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge

On-line case-based policy learning for automated planning in probabilistic environments

Many robotic control architectures perform a continuous cycle of sensing, reasoning and acting, where that reasoning can be carried out in a reactive or deliberative form. Reactive methods are fast and provide the robot with high interaction and response capabilities. Deliberative reasoning is particularly suitable in robotic systems because it employs some form of forward projection (reasoning in depth about goals, pre-conditions, resources and timing constraints) and provides the robot reasonable responses in situations unforeseen by the designer. However, this reasoning, typically conducted using Artificial Intelligence techniques like Automated Planning (AP), is not effective for controlling autonomous agents which operate in complex and dynamic environments. Deliberative planning, although feasible in stable situations, takes too long in unexpected or changing situations which require re-planning. Therefore, planning cannot be done on-line in many complex robotic problems, where quick responses are frequently required. In this paper, we propose an alternative approach based on case-based policy learning which integrates deliberative reasoning through AP and reactive response time through reactive planning policies. The method is based on learning planning knowledge from actual experiences to obtain a case-based policy. The contribution of this paper is two fold. First, it is shown that the learned case-based policy produces reasonable and timely responses in complex environments. Second, it is also shown how one case-based policy that solves a particular problem can be reused to solve a similar but more complex problem in a transfer learning scope.This paper has been partially supported by the Spanish Ministerio de Econom a y Competitividad TIN2015-65686-C5-1-R and the European Union's Horizon 2020 Research and Innovation programme under Grant Agreement No. 730086 (ERGO)

Collaborative Planning on Cross-Border Service of Water Supply in Surakarta Urban Border Area, Indonesia

Recently, many planners apply collaborative planning theory in planning involving two or more parties, such as between government, private and community, including in the interregion cooperation. However, the theory has rarely been used to explain the interaction between regions in city border area, especially in the cases in Indonesia. This article discusses the case of cross-border service of water supply of the Local Water Company (PDAM-Perusahaan Daerah Air Minum) of Surakarta City into the urban border area of Sukoharjo Regency, based on collaborative planning theory. This article is written based on results of the research on the case using case study research method. The discussion concludes that the approach of collaborative planning theory used on the case is cooperative-accommodation approach. It is because PDAM of Surakarta City accommodate the cross-border region service as a reciprocal policy, as most of their water inputs come from their neighboring regions. In general, such an approach is in accordance with the need of the interacting regions, which one region needs supporting service to meet the need of their communities in water service, and another can fulfill the need based on its capacity. In this case, the concerned technical agencies, PDAM of every region interact each other directly in providing the service. The important thing, the interaction is in line with the prevailing cross-border region bureaucratic regulations and does not infringe the autonomy of every region

Flexibility-Driven Planning Of Flow-Based Mixed-Model Assembly Structures

Trends such as mass customization, changing customer preferences and resulting output fluctuations increasingly challenge the production industry. Mixed-model assembly lines are affected by the rising product variety, which ultimately leads to ascending cycle time spreads and efficiency losses. Matrix assembly addresses these challenges by decoupling workstations and dissolving cycle time constraints while maintaining the flow. Both matrix and line assembly are flow-based assembly structures characterized by assembly objects moving along the stations. In assembly system planning, competing assembly structures are developed and the one best meeting the use case's requirements is selected for realization. During assessing requirements and selecting the superior assembly structure, the systematic consideration of flexibility is often not ensured within the planning approach. Therefore, a preferred assembly structure may not have the flexibility required for a use case. The systematic and data-driven assessment of required and provided flexibility in assembly system planning is necessary. This paper presents an assessment model that matches a use case's requirements with the flexibility of flow-based assembly structures based on production program and process data. On the one hand, requirements are defined by flexibility criteria that evaluate representative product mixes and process time heterogeneity. On the other hand, provided flexibility of flow-based assembly structures is assessed in a level-based classification. A method for comparing the requirements and the classification's levels to prioritize assembly structures for application in a case is developed. The flexibility requirements and assembly structure of an exemplary use case are determined and discussed under the planning project's insights to evaluate the developed model. This work contributes to the objective and data-driven selection of assembly structures by utilizing use case-specific data available during the phase of structural planning to meet flexibility requirements and ensure their consideration along the assembly planning process