Florin Gheorghe Filip

DECISION SUPPORT AND CONTROL FOR LARGE-SCALE COMPLEX SYSTEMS

Abstract The technical and social systems of the present day are ever more complex and complicated objects. Their models are characterized by large numbers of state and control variables, time delays, and different time constants. Also they show constraints in their information infrastructure and risk sensitivity aspects. Such systems are called large –scale complex systems (LSS). Hierarchical approach has been for several decades one of the most utilized methodologies for controlling large-scale systems. When human intervention is necessary decision support systems (DSS) can represent a solution. A DSS is an adaptive and evolving information system meant to implement several of the functions of a human support team that would otherwise be needed to help the decision-maker to overcome his/her limits and constraints he/she may face when approaching decision problems that count in the organization. This paper aims at reviewing several aspects concerning LSS control and the utilization and technology of DSS. Particular emphasis is put on real-time DSS and multiparticipant (group) DSS. Several advanced solutions such as mixed knowledge systems, that combine numerical methods with AI-based tools, and the prospects of using Ambient Intelligence concepts in DSS construction are described.

From plant and logistics control to multi-enterprise collaboration

Current and emerging manufacturing and logistics systems are posing new challenges and opportunities for the automation and control community. This milestone report describes the main problems, such as management of complexity, scalability, increasing costs, coordination, market-based resource allocation, and more. Recent accomplishments and trends are discussed: Control and automation techniques, manufacturing plant automation, collaborative control through integration and networking, and control methods applied to extended enterprises and large-scale critical infrastructure. Finally, forecasts are presented for the next generation manufacturing system; e-work; integration, coordination and collaboration; networked, distributed decision support (NDSS); and active middleware.

Large-Scale Complex Systems

Large-scale complex systems (LSS) have traditionally been characterized by large numbers of variables, structure of interconnected subsystems, and other features that complicate the control models such as nonlinearities, time delays, and uncertainties. The decomposition of LSS into smaller, more manageable subsystems allowed for implementing effective decentralization and coordination mechanisms. The last decade revealed new characteristic features of LSS such as the networked structure, enhanced geographical distribution and increased cooperation of subsystems, evolutionary development, and higher risk sensitivity. This chapter aims to present a balanced review of several traditional well-established methods and new approaches together with typical applications. First the hierarchical systems approach is described and the transition from coordinated control to collaborative schemes is highlighted. Three subclasses of methods that are widely utilized in LSS – decentralized control, simulation-based, and artificial-intelligence-based schemes – are then reviewed. Several basic aspects of decision support systems (DSS) that are meant to enable effective cooperation between man and machine and among the humans in charge with LSS management and control are briefly exposed. The chapter concludes by presenting several technology trends in LSS.

Sustainability decision support system based on collaborative control theory

Abstract Industries nowadays have more insight into corporate environmental, social and economic sustainability than ever before. Sustainability issues in various industries are all about choices – finding ways to be more strategic and reducing waste and energy, while benefiting the bottom line. Because of the complexity of sustainability decisions and strategies, these insights call for applying advanced control techniques. In this article we review the concepts of sustainability and its practical issues, specifically focusing on the issues of supply networks. Sustainability issues are usually complex because beyond their inherent challenges, there are conflicts among stakeholders within organizations and between organizations; hence, more useful methods are required for effective solutions. We consider Decision Support Systems (DSSs) to help optimize solutions related with sustainability issues, and review their concepts and usefulness based on previous work. We also suggest strategies applying the Collaborative Control Theory (CCT) principles to augment DSS by a new Sustainability – Decision Support Protocol (S-DSP) in order to overcome certain weaknesses. To model and illustrate the benefit of S-DSP as a control protocol, two practical supply delivery/production problems are analyzed. The results highlight how better collaborative solutions can be achieved to maximize the sustainability of supply networks. It is envisioned that sustainability decision support by such cyber-supported collaboration protocols will contribute to overcome the emerging challenges of sustainability planning and control.

Job shop scheduling optimization in real-time production control

Abstract An optimization algorithm using simulation as an evaluation procedure of the criterion to obtain an optimal schedule of jobs within a workshop is presented. The algorithm, which is recommended for middle-size problems, is embedded in a dedicated minicomputer-based practical system meant for real time production control in a discrete part manufacturing system environment.

Towards Intelligent Real-Time Decision Support Systems For Industrial Milieu

Decision support systems (DSS) are human-centered information systems meant to help managers placed on different authority levels to make more efficient and effective decisions for problems evincing an imperfect structure. These systems are very suitable information tools to apply to various management and control problems that are complex and complicated at the same time. Several issues concerning the modern trends to build anthropocentric systems are reviewed. Then the paper surveys several widely accepted concepts in the field of decision support systems and some specific aspects concerning real-time applications. Several artificial intelligence methods and their applicability to decision-making processes are reviewed next. The possible combination of artificial intelligence technologies with traditional numerical models within advanced decision support systems is discussed and an example is given.

Multilevel optimization algorithms in computer aided production control in process industry

Abstract The use of optimization techniques in production control is discussed. Two optimization problems in relation to typical process industry complexes are formulated. A modified version of Tamuras algorithm is reviewed. The promising performance of the computerised algorithms is illustrated by numerical results. DISPATCHER, a practical operative decision support system, is described.

DSS in numbers

AbstractDecision Support Systems (DSS) have evolved in time, influenced by technological and organizational developments. The scientists’ interest for DSS has increased over the years and the use of this type of systems has spread in all domains of activity. The paper presents the results of a literature search, made in September 2013, regarding the Decision Support Systems evolution as it is reflected in three scientific databases (ScienceDirect, IEEE Xplore Digital Library and ACM Digital Library). The aim of this research is to present the evolution in time of the published DSS research materials and to test the usefulness and relevance, for the topic searched, of the information provided by the above mentioned databases.

A method for dealing with multi-objective optimization problem of disassembly processes

Disassembly of manufactured goods induces both disassembly costs and revenues from the parts saved by the process. Thus, a good trade-off has to be found that depends, both on the depth of the disassembly and on the sequence of operations. This optimization problem depends upon the structure of the disassembly system: if it is made of a single workstation, the costs depend mainly upon the process duration. If the system is a line, the costs depend mainly upon the line balancing, when it is highly manual. In this paper, the authors consider the line structure and propose an algorithm which allow one to find a disassembly sequence that optimizes a very simple function integrating the income from the parts, the material produced by the process and the cycle time of the disassembly line. An example is given to illustrate the proposed algorithm.

Real Time Balancing of Complex Disassembly Lines

The objective of the Disassembly Line Balancing Problem (DLBP) is to use the resources of the disassembly line as efficiently as possible while meeting the demand. This issue is hard to attempt due to the inherent uncertainties that occur during the process. Starting from real industrial examples, this article presents a simple-to-apply method to accomplish the balancing of complex disassembly lines in real time. The basic idea of this method is to use mixed integer quadratic programming and branch and cut algorithm on the disassembly precedence graph. Results of simulations for disassembling two industrial products are presented