CHANGE-READY MPC SYSTEMS AND PROGRESSIVE MODELING: VISION, PRINCIPLES, AND APPLICATIONS

The last couple of decades have witnessed a level of fast-paced development of new ideas, products, manufacturing technologies, manufacturing practices, customer expectations, knowledge transition, and civilization movements, as it has never before. In today\u27s manufacturing world, change became an intrinsic characteristic that is addressed everywhere. How to deal with change, how to manage it, how to bind to it, how to steer it, and how to create a value out of it, were the key drivers that brought this research to existence. Change-Ready Manufacturing Planning and Control (CMPC) systems are presented as the first answer. CMPC characteristics, change drivers, and some principles of Component-Based Software Engineering (CBSE) are interwoven to present a blueprint of a new framework and mind-set in the manufacturing planning and control field, CMPC systems. In order to step further and make the internals of CMPC systems/components change-ready, an enabling modeling approach was needed. Progressive Modeling (PM), a forward-looking multi-disciplinary modeling approach, is developed in order to modernize the modeling process of today\u27s complex industrial problems and create pragmatic solutions for them. It is designed to be pragmatic, highly sophisticated, and revolves around many seminal principles that either innovated or imported from many disciplines: Systems Analysis and Design, Software Engineering, Advanced Optimization Algorisms, Business Concepts, Manufacturing Strategies, Operations Management, and others. Problems are systemized, analyzed, componentized; their logic and their solution approaches are redefined to make them progressive (ready to change, adapt, and develop further). Many innovations have been developed in order to enrich the modeling process and make it a well-assorted toolkit able to address today\u27s tougher, larger, and more complex industrial problems. PM brings so many novel gadgets in its toolbox: function templates, advanced notation, cascaded mathematical models, mathematical statements, society of decision structures, couplers--just to name a few. In this research, PM has been applied to three different applications: a couple of variants of Aggregate Production Planning (APP) Problem and the novel Reconfiguration and Operations Planning (ROP) problem. The latest is pioneering in both the Reconfigurable Manufacturing and the Operations Management fields. All the developed models, algorithms, and results reveal that the new analytical and computational power gained by PM development and demonstrate its ability to create a new generation of unmatched large scale and scope system problems and their integrated solutions. PM has the potential to be instrumental toolkit in the development of Reconfigurable Manufacturing Systems. In terms of other potential applications domain, PM is about to spark a new paradigm in addressing large-scale system problems of many engineering and scientific fields in a highly pragmatic way without losing the scientific rigor

Parallel Differential Evolution approach for Cloud workflow placements under simultaneous optimization of multiple objectives

International audienceThe recent rapid expansion of Cloud computing facilities triggers an attendant challenge to facility providers and users for methods for optimal placement of workflows on distributed resources, under the often-contradictory impulses of minimizing makespan, energy consumption, and other metrics. Evolutionary Optimization techniques that from theoretical principles are guaranteed to provide globally optimum solutions, are among the most powerful tools to achieve such optimal placements. Multi-Objective Evolutionary algorithms by design work upon contradictory objectives, gradually evolving across generations towards a converged Pareto front representing optimal decision variables – in this case the mapping of tasks to resources on clusters. However the computation time taken by such algorithms for convergence makes them prohibitive for real time placements because of the adverse impact on makespan. This work describes parallelization, on the same cluster, of a Multi-Objective Differential Evolution method (NSDE-2) for optimization of workflow placement, and the attendant speedups that bring the implicit accuracy of the method into the realm of practical utility. Experimental validation is performed on a real-life testbed using diverse Cloud traces. The solutions under different scheduling policies demonstrate significant reduction in energy consumption with some improvement in makespan

Heuristic Approach for a Combined Transfer Line Balancing and Buffer Allocation Problem Considering Uncertain Demand

Featured Application This research was initiated by an industrial project. The problem was the design and configuration of machining lines for engine blocks. The proposed approach was validated using four real cases provided by the industrial partners of the project. The proposed approach could easily be applied to the design and configuration of any machining line for the production of a single complex mechanical component. In this paper, we refer to a real case study of an industrial partner recently committed to its project on the design of a multi-unit and multi-product manufacturing system. Although the considered problem refers to an actual complex manufacturing system, it can be theoretically classified as a union of two key problems that need to be solved during the transfer line design stage: the transfer line balancing problem (TLBP) and the buffer allocation problem (BAP). As two closely related problems, TLBP and BAP usually have similar optimizing directions and share the same purpose: finding a balance between the performance of the transfer line system as well as minimizing investment costs. These problems are usually solved sequentially, but this leads to solutions close to a local optimum in the solution space and not to the global optimum of the overall problem. This paper presents a multi-objective optimization for concurrently solving transfer line balancing and buffer allocation problems. The new approach is based on a combination of evolutionary and heuristic-based algorithms and takes into account the uncertainty of market demand. To validate the proposed approach, an industrial case study in a multi-unit manufacturing system producing multiple products (four engine blocks) is discussed

An optimization approach for predictive-reactive job shop scheduling of reconfigurable manufacturing systems

The manufacturing industry is now moving forward rapidly towards reconfigurability and reliability to meet the hard-topredict global business market, especially job-shop production. However, even if there is a properly planned schedule for production, and there is also a technique for scheduling in Reconfigurable Manufacturing System (RMS) but job-shop production will always come out with errors and disruption due to complex and uncertainty happening during the production process, hence fail to fulfil the due-date requirements. This study proposes a generic control strategy for piloting the implementation of a complex scheduling challenge in an RMS. This study is aimed to formulate an optimization-based algorithm with a simulation tool to reduce the throughput time of complex RMS, which can comply with complex product allocations and flexible routings of the system. The predictive-reactive strategy was investigated, in which Genetic Algorithm (GA) and dispatching rules were used for predictive scheduling and reactivity controls. The results showed that the proposed optimization-based algorithm had successfully reduced the throughput time of the system. In this case, the effectiveness and reliability of RMS are increased by combining the simulation with the optimization algorithm

Energy Sustainability in Changeable Manufacturing Systems

University of Minnesota M.S.E.M. thesis. October 2017. Major: Engineering Management. Advisor: Tarek AlGeddawy. 1 computer file (PDF); vi, 76 pages.In a dynamic production environment, not only the product portfolio and demands are varying throughout a multi-period horizon, but also the economic aspects of the environment, such as energy pricing, change with time. The thesis of this work states that energy price fluctuation has a considerable optimizable effect on manufacturing system structural and operational decisions. This work progressively presents three novel linear mathematical models to optimize that effect. In the first step, a novel basic linear mixed integer mathematical model is proposed to maximize the sustainability of changeable manufacturing systems (MSCM) on the operational level. The model focuses on three factors, which are the change pattern in energy prices throughout the day, the transportation cost of jobs between machines, and the setup cost of each machine, which is dependent on the job sequence. The model output is a system configuration plan, indicating arrangement of machines in the system, and the sequence of jobs, which need to be produced on one day. It is solved by CPLEX solver in GAMS software for nine different problem sizes. The new LMI model finds the optimum configuration plan and job sequence in a reasonable time, which illustrates the efficiency and practicality of the proposed model. In the second step, a new linear mathematical model is presented to maximize the sustainability of changeable manufacturing systems on the structural level (MSSCM) by selecting the layout reconfiguration and material handling system in each period. It is solved by CPLEX solver in GAMS software to analyze influence of energy pricing and demand fluctuation on system convertibility and scalability, which can affect layout configuration selection. In the last step, a novel mixed integer linear mathematical model (MILTEC) is presented to maximize the sustainability of RMS on both the structural and operational levels. The system configuration planning in each period of time consists of machines layout and task scheduling which are the most interrelated decisions on the system level. The novel aspect of the presented model is the consideration of energy sustainability concurrently with system configuration and task scheduling decisions in a changing manufacturing environment. The model objective is to minimize total costs of energy consumption, system reconfiguration throughout the planning horizon, and part transportation between machines, which all depend on fluctuations in energy pricing and demand during different periods of time. Several case studies are solved by GAMS Software using the branch-and-bound technique to illustrate the performance of the presented model and analyze its sensitivity to the volatility of energy pricing and demand and their effect on system changeability. An efficient genetic algorithm (GA) has been developed to solve the proposed model in larger scale due to its NP-hardness (non-deterministic polynomial-time hardness). The results are compared to GAMS to validate the developed GA. It shows that the proposed GA finds near-optimal solutions in 70% shorter time than GAMS on average. Different examples are also solved resulting in negligible differences between solutions in several runs of each example to verify the efficiency of the proposed GA

Knowledge-based process planning for economical re-scheduling in production control

Nowadays, high flexibility and responsiveness towards capacity adjustments are key to successful production planning and control in manufacturing. Moreover, many companies – especially job shops – have to deal with short-term re-scheduling. This article presents an approach for knowledge-based process planning to enable an economic evaluation of re-scheduling in the manufacturing system. For that purpose, the manufacturing costs for each workpiece are calculated based on determined parameter sets and process time under consideration of potential capacity adjustments. The knowledge-based process planning is necessary to derive reliable process times for re-scheduling and cost calculating. Hence, a pre-study is carried out to define flexible machine learning algorithms for knowledge-based process planning. © 2019 The Authors. Published by Elsevier Ltd