Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Project scheduling under undertainty – survey and research potentials.

The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;

Scheduling and discrete event control of flexible manufacturing systems based on Petri nets

A flexible manufacturing system (FMS) is a computerized production system that can simultaneously manufacture multiple types of products using various resources such as robots and multi-purpose machines. The central problems associated with design of flexible manufacturing systems are related to process planning, scheduling, coordination control, and monitoring. Many methods exist for scheduling and control of flexible manufacturing systems, although very few methods have addressed the complexity of whole FMS operations. This thesis presents a Petri net based method for deadlock-free scheduling and discrete event control of flexible manufacturing systems. A significant advantage of Petri net based methods is their powerful modeling capability. Petri nets can explicitly and concisely model the concurrent and asynchronous activities, multi-layer resource sharing, routing flexibility, limited buffers and precedence constraints in FMSs. Petri nets can also provide an explicit way for considering deadlock situations in FMSs, and thus facilitate significantly the design of a deadlock-free scheduling and control system. The contributions of this work are multifold. First, it develops a methodology for discrete event controller synthesis for flexible manufacturing systems in a timed Petri net framework. The resulting Petri nets have the desired qualitative properties of liveness, boundedness (safeness), and reversibility, which imply freedom from deadlock, no capacity overflow, and cyclic behavior, respectively. This precludes the costly mathematical analysis for these properties and reduces on-line computation overhead to avoid deadlocks. The performance and sensitivity of resulting Petri nets, thus corresponding control systems, are evaluated. Second, it introduces a hybrid heuristic search algorithm based on Petri nets for deadlock-free scheduling of flexible manufacturing systems. The issues such as deadlock, routing flexibility, multiple lot size, limited buffer size and material handling (loading/unloading) are explored. Third, it proposes a way to employ fuzzy dispatching rules in a Petri net framework for multi-criterion scheduling. Finally, it shows the effectiveness of the developed methods through several manufacturing system examples compared with benchmark dispatching rules, integer programming and Lagrangian relaxation approaches

A Hybrid Method Based on Fuzzy AHP and VIKOR for the Discrete Time-Cost-Quality Trade-off Problem

Time, cost and quality are considered as the main components in managing each project. Previous researches have mainly focused on the time-cost trade-off problems. Recently quality is considered as the most important factor in project�s success, which is influenced by time acceleration that is the less time is spent the more success is gained. In time-cost-quality trade-off problems, each activity can be done in various execution modes and determination of these execution modes is seen as to minimize the project time and cost and maximize its quality .In this paper, three integer programming models are provided and one of the main objectives is optimized in each model by assigning the proper bound to other objectives. Following the non-dominated solutions obtained by solving models, and by means of hybrid approach of Fuzzy AHP strategy and VIKOR method regarded as multi-criteria decision making methods, the best possible alternative (from among non-dominated solutions) has been suggested.Fuzzy AHP method has been used to determine the importance rate of each objective. In this method linguistic variables were used which take us closer to reality. At the end, with applying these weights in VIKOR method, the best possible alternatives (among non-dominated solutions) were found. Using this hybrid approach can help managers, to a great extent, in selecting the appropriate solution so that maximum desirability is obtained due to the importance rate of the objective functions from the viewpoint of decision maker

SELECTION OF FORKLIFT UNIT FOR TRANSPORT HANDLING USING INTEGRATED MCDM UNDER NEUTROSOPHIC ENVIRONMENT

In material handling, warehousing, manufacturing and construction applications, forklifts are vital equipment, which are used to engage, lift and move palletized items. So, selection of the most appropriate forklift is an essential task for transportation of materials in warehouses for optimal use of the equipment. The present treatise introduces a well-known multi-criteria decision making (MCDM) technique, namely fully consistent method (FUCOM) under neutrosophic environment (NE) to model and solve the problem of selecting the best forklift for warehouse. In this regard, the linguistic assessments of the criteria have been represented in terms of single valued triangular neutrosophic numbers (SVTNNs). A novel triangular neutrosphic score function and ranking function are also proposed. To calculate criteria weights, a novel SVTN linear programming problem (SVTNLPP) has been developed. The alternatives have been ranked through multi-objective optimization on the basis of ratio analysis (MOORA). The robustness, consistency and reliability of the proposed integrated method have been checked through comparative and sensitivity analyses. This study makes a significant contribution by developing an original integrated model which provides warehousing system managers a quantifiable analysis, based on which they may make future decisions in order to improve the overall efficiency of the organization in transport handling

Multi-Criteria Flow-Shop Scheduling Optimization

A flow-shop is a type of manufacturing job shop where similar jobs follow a similar, linear sequence through the shop. Every day, flow-shops receive several different orders and it is up to the scheduler to plan the daily schedule. This schedule should be designed to prevent bottlenecks in the shop, to have on-time delivery of products, and satisfy several other requirements. Often, schedulers perform subjective scheduling and utilize simple heuristics or just intuition to schedule the jobs. With computer-based scheduling, schedulers can now create schedules and determine quantitatively what sorts of schedules work best. Currently, much of the computer-based schedules only try to optimize for one KPI such as Total Tardiness. This paper considers incorporating multiple-criteria into computer based scheduling so that schedulers can have more flexibility and develop schedules which optimize multiple-criteria; this paper specifically considers minimizing Total Tardiness and maximizing Throughput. Comparisons between single-criterion models and the multiple-criteria model are made and it is discovered the multiple-criteria model provides a great compromise in optimizing both KPIs. A user-friendly program is developed where schedulers of any flow-shop can utilize the software to compute schedules for cases up to 10 jobs and 10 machines

Energy Efficient Policies, Scheduling, and Design for Sustainable Manufacturing Systems

Climate mitigation, more stringent regulations, rising energy costs, and sustainable manufacturing are pushing researchers to focus on energy efficiency, energy flexibility, and implementation of renewable energy sources in manufacturing systems. This thesis aims to analyze the main works proposed regarding these hot topics, and to fill the gaps in the literature. First, a detailed literature review is proposed. Works regarding energy efficiency in different manufacturing levels, in the assembly line, energy saving policies, and the implementation of renewable energy sources are analyzed. Then, trying to fill the gaps in the literature, different topics are analyzed more in depth. In the single machine context, a mathematical model aiming to align the manufacturing power required to a renewable energy supply in order to obtain the maximum profit is developed. The model is applied to a single work center powered by the electric grid and by a photovoltaic system; afterwards, energy storage is also added to the power system. Analyzing the job shop context, switch off policies implementing workload approach and scheduling considering variable speed of the machines and power constraints are proposed. The direct and indirect workloads of the machines are considered to support the switch on/off decisions. A simulation model is developed to test the proposed policies compared to others presented in the literature. Regarding the job shop scheduling, a fixed and variable power constraints are considered, assuming the minimization of the makespan as the objective function. Studying the factory level, a mathematical model to design a flow line considering the possibility of using switch-off policies is developed. The design model for production lines includes a targeted imbalance among the workstations to allow for defined idle time. Finally, the main findings, results, and the future directions and challenges are presented

Stability measure for a generalized assembly line balancing problem

AbstractA generalized formulation for assembly line balancing problem (GALBP) is considered, where several workplaces are associated with each workstation. Thus, all tasks assigned to the same workstation have to be partitioned into blocks: each block regroups all tasks to be performed at the same workplace. The product items visit all workplaces sequentially, therefore, all blocks are proceeded in a sequential way. However, the tasks grouped into the same block are executed simultaneously. As a consequence, the execution of a block takes only the time of its longest task. This parallel execution modifies the manner to take into account the cycle time constraint. Precedence and exclusion constraints also exist for workstations and their workplaces. The objective is to assign all given tasks to workstations and workplaces while minimizing the line cost estimated as a weighted sum of the number of workstations and workplaces. The goal of this article is to propose a stability measure for feasible and optimal solutions of this problem with regard to possible variations of the processing time of certain tasks. A heuristic procedure providing a compromise between the objective function and the suggested stability measure is developed and evaluated on benchmark data sets

Advances in Methodology and Applications of Decision Support Systems

These Proceedings are composed of a selection of papers of the Workshop on Advances in Methodology and Applications of Decision Support Systems, organized by the System and Decision Sciences (SDS) Program of IIASA and the Japan Institute of Systems Research (JISR). The workshop was held at IIASA on August 20-22, 1990. The Methodology of Decision Analysis (MDA) Project of the SDS Program focuses on a system-analytical approach to decision support and is devoted to developing methodology, software and applications of decision support systems concentrated primarily around interactive systems for data analysis, interpretation and multiobjective decisionmaking, including uncertainty analysis and group decision making situations in both their cooperative and noncooperative aspects. The objectives of the research on decision support systems (DSS) performed in cooperation with the MDA Project are to: compare various approaches to decision support systems; advance theory and methodology of decision support; convert existing theories and methodologies into usable (simple to use, user-friendly and robust) tools that could easily be used in solving real-life problems. A principal characteristic of decision support systems is that they must be tuned to specific decision situations, to complex real-life characteristics of every application. Even if the theory and methodology of decision support is quite advanced, every application might provide impulses for further theoretical and methodological advances. Therefore the principle underlying this project is that theoretical and methodological research should be strongly connected to the implementation and applications of its results to sufficiently complicated, real-life examples. This approach results in obtaining really applicable working tools for decision support. The papers for this Proceedings have been selected according to the above summarized framework of the research activities. Therefore, the papers deal both with theoretical and methodological problems and with real-life applications