Integrated management of hierarchical levels: towards a CAPE tool

The integration of decision-making procedures usually assigned to different hierarchical production systems requires the use of complex mathematical models and high computational efforts, in addition to the need of an extensive management of data and knowledge within the production systems. This work addresses this integration problem and proposes a comprehensive solution approach, as well as guidelines for Computer Aided Process Engineering (CAPE) tools managing the corresponding cyberinfrastructure. This study presents a methodology based on a domain ontology which is used as the connector between the introduced data, the different available formulations developed to solve the decision-making problem, and the necessary information to build the finally required problem instance. The methodology has demonstrated its capability to help exploiting different available decision-making problem formulations in complex cases, leading to new applications and/or extensions of these available formulations in a robust and flexible way.Peer ReviewedPostprint (author's final draft

A distributed multi-agent framework for shared resources scheduling

Nowadays, manufacturers have to share some of their resources with partners due to the competitive economic environment. The management of the availability periods of shared resources causes a problem because it is achieved by the scheduling systems which assume a local environment where all resources are on the same site. Therefore, distributed scheduling with shared resources is an important research topic in recent years. In this communication, we introduce the architecture and behavior of DSCEP framework (distributed, supervisor, customer, environment, and producer) under shared resources situation with disturbances. We are using a simple example of manufacturing system to illustrate the ability of DSCEP framework to solve the shared resources scheduling problem in complex systems

Communication and control in small batch part manufacturing

This paper reports on the development of a real-time control network as an integrated part of a shop floor control system for small batch part manufacturing. The shop floor control system is called the production control system (PCS). The PCS aims at an improved control of small batch part manufacturing systems, enabling both a more flexible use of resources and a decrease in the economical batch size. For this, the PCS integrates various control functions such as scheduling, dispatching, workstation control and monitoring, whilst being connected on-line to the production equipment on the shop floor. The PCS can be applied irrespective of the level of automation on the shop floor. The control network is an essential part of the PCS, as it provides a real-time connection between the different modules (computers) of the PCS, which are geographically distributed over the shop floor. An overview of the requirements of such a control network is given. The description of the design includes the services developed, the protocols used and the physical layout of the network. A prototype of the PCS, including the control network, has been installed and tested in a pilot plant. The control network has proven that it can supply a manufacturing environment, consisting of equipment from different vendors with different levels of automation, with a reliable, low cost, real-time communication facility

Planning and Scheduling of Business Processes in Run-Time: A Repair Planning Example

Over the last decade, the efficient and flexible management of business processes has become one of the most critical success aspects. Furthermore, there exists a growing interest in the application of Artificial Intelligence Planning and Scheduling techniques to automate the production and execution of models of organization. However, from our point of view, several connections between both disciplines remains to be exploited. The current work presents a proposal for modelling and enacting business processes that involve the selection and order of the activities to be executed (planning), besides the resource allocation (scheduling), considering the optimization of several functions and the reach of some objectives. The main novelty is that all decisions (even the activities selection) are taken in run-time considering the actual parameters of the execution, so the business process is managed in an efficient and flexible way. As an example, a complex and representative problem, the repair planning problem, is managed through the proposed approach.Ministerio de Ciencia e Innovación TIN2009-13714Junta de Andalucía P08-TIC-0409

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

Today’s Schedules for Today’s Workforce: Hourly Employees and Work-Life Fit

[Excerpt] This paper provides employers with the tools they need to match today’s workplace to today’s workforce. First, the paper describes “just-in-time” schedules – schedules which create instability and unpredictability for hourly workers. It then suggests several means by which employers can reduce such instability in the just-in-time sector, providing increased scheduling effectiveness. Next, the paper discusses scheduling rigidity and provides employers with a variety of means for easing this rigidity by creating a more flexible workplace. The ultimate goal of this paper is to create a win-win outcome for hourly workers and their employers by synchronizing today’s workplace with today’s workforce. We seek to accomplish this goal by helping employers understand the needs of hourly employees with family responsibilities and demonstrating that accommodating these needs does not result in increased labor costs. To the contrary, introducing flexible scheduling and/or predictable scheduling can in fact reduce labor costs

Distributed smart charging of electric vehicles for balancing wind energy

To meet worldwide goals of reducing CO2 footprint, electricity production increasingly is stemming from so-called renewable sources. To cater for their volatile behavior, so-called demand response algorithms are required. In this paper, we focus particularly on how charging electrical vehicles (EV) can be coordinated to maximize green energy consumption. We present a distributed algorithm that minimizes imbalance costs, and the disutility experienced by consumers. Our approach is very much practical, as it respects privacy, while still obtaining near-optimal solutions, by limiting the information exchanged: i.e. consumers do not share their preferences, deadlines, etc. Coordination is achieved through the exchange of virtual prices associated with energy consumption at certain times. We evaluate our approach in a case study comprising 100 electric vehicles over the course of 4 weeks, where renewable energy is supplied by a small scale wind turbine. Simulation results show that 68% of energy demand can be supplied by wind energy using our distributed algorithm, compared to 73% in a theoretical optimum scenario, and only 40% in an uncoordinated business-as-usual (BAU) scenario. Also, the increased usage of renewable energy sources, i.e. wind power, results in a 45% reduction of CO2 emissions, using our distributed algorithm