Application of Artificial Intelligence (AI) methods for designing and analysis of Reconfigurable Cellular Manufacturing System (RCMS)

This work focuses on the design and control of a novel hybrId manufacturing system: Reconfigurable Cellular Manufacturing System (RCMS) by using Artificial Intelligence (AI) approach. It is hybrid as it combines the advantages of Cellular Manufacturing System (CMS) and Reconfigurable Manufacturing System (RMS). In addition to inheriting desirable properties from CMS and RMS, RCMS provides additional benefits including flexibility and the ability to respond to changing products, product mix and market conditions during its useful life, avoiding premature obsolescence of the manufacturing system. The emphasis of this research is the formation of Reconfigurable Manufacturing Cell (RMC) which is the dynamic and logical clustering of some manufacturing resources, driven by specific customer orders, aiming at optimally fulfilling customers' orders along with other RMCs in the RCMS

Variant-oriented Planning Models for Parts/Products Grouping, Sequencing and Operations

This research aims at developing novel methods for utilizing the commonality between part/product variants to make modern manufacturing systems more flexible, adaptable, and agile for dealing with less volume per variant and minimizing total changes in the setup between variants. Four models are developed for use in four important domains of manufacturing systems: production sequencing, product family formation, production flow, and products operations sequences retrieval. In all these domains, capitalizing on commonality between the part/product variants has a pivotal role. For production sequencing; a new policy based on setup similarity between product variants is proposed and its results are compared with a developed mathematical model in a permutation flow shop. The results show the proposed algorithm is capable of finding solutions in less than 0.02 seconds with an average error of 1.2%. For product family formation; a novel operation flow based similarity coefficient is developed for variants having networked structures and integrated with two other similarity coefficients, operation and volume similarity, to provide a more comprehensive similarity coefficient. Grouping variants based on the proposed integrated similarity coefficient improves changeover time and utilization of the system. A sequencing method, as a secondary application of this approach, is also developed. For production flow; a new mixed integer programing (MIP) model is developed to assign operations of a family of product variants to candidate machines and also to select the best place for each machine among the candidate locations. The final sequence of performing operations for each variant having networked structures is also determined. The objective is to minimize the total backtracking distance leading to an improvement in total throughput of the system (7.79% in the case study of three engine blocks). For operations sequences retrieval; two mathematical models and an algorithm are developed to construct a master operation sequence from the information of the existing variants belonging to a family of parts/products. This master operation sequence is used to develop the operation sequences for new variants which are sufficiently similar to existing variants. Using the proposed algorithm decreases time of developing the operations sequences of new variants to the seconds

dynamic design and management of reconfigurable manufacturing systems

Abstract This research proposes an approach to design and to manage Cellular Reconfigurable Manufacturing Systems (CRMSs) from a multi-product and multi-period perspective. The production environment consists of multiple cells of machines equipped with Reconfigurable Machine Tools (RMTs) made of basic and auxiliary custom modules to perform specific tasks. The approach acts into two steps; the former is the machine cell design phase, assigning machines to cells, the latter is the cell loading phase, assigning modules to each machine and cell. The goal is to guarantee the economic sustainability of the manufacturing system by exploring how to best balance the part flow among machines already equipped with the required modules and the effort to install the necessary modules on the machine on which the part is located

Group Scheduling in a Cellular Manufacturing Shop to Minimise Total Tardiness and nT: a Comparative Genetic Algorithm and Mathematical Modelling Approach

In this paper, family and job scheduling in a cellular manufacturing shop is addressed where jobs have individual due dates. The objectives are to minimise total tardiness and the number of tardy jobs. Family splitting among cells is allowed but job splitting is not. Two optimisation methods are employed in order to solve this problem, namely mathematical modelling (MM) and genetic algorithm (GA). The results showed that GA found the optimal solution for most of the problems with high frequency. Furthermore, the proposed GA is efficient compared to the MM especially for larger problems in terms of execution times. Other critical aspects of the problem such as family preemption only, impact of family splitting on common due date scenarios and dual objective scenarios are also solved. In short, the proposed comparative approach provides critical insights for the group scheduling problem in a cellular manufacturing shop with distinctive cases

Cell Production System Design: A Literature Review

Purpose In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design. Methodology: To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously. Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified. Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed

Design Methodologies Towards a Sustainable Manufacturing Enterprise

Sustainability is increasingly becoming a crucial concern in many aspects of life. Even though, there is a relatively growing interest from both academic researchers and practitioners in various design aspects of sustainability, one can see that design issues of sustainable manufacturing systems have not received adequate attention. Through an extensive literature review on design for sustainability and sustainability issues, it is observed that, attaining sustainability in manufacturing needs a huge amount of effort and needs to take into consideration many aspects from different perspectives. These include considering the sustainability in both the closed loop supply chain (CLSC) and the manufacturing system levels simultaneously, considering Cellular Manufacturing Systems (CMSs), considering reconfigurability for the production systems, considering Hybrid Manufacturing-Remanufacturing Systems as well as considering the recovery options such as recycling and remanufacturing. This research presents a simultaneous investigation of Reconfigurable Cellular Manufacturing Systems and Hybrid Manufacturing-Remanufacturing Systems (HMRSs), and proposes an integrated approach in design optimization, analysis, and process planning aspects as an attempt to address to a large number of design issues for Sustainable Manufacturing Systems, while the options of remanufacturing, recycling, and disposing are introduced. Four mathematical model have been developed. Third part cellular remanufacturing systems design are considered within the first model, which is initially formulated as a mixed integer non-linear program that incorporates multi-period production planning, dynamic system reconfiguration, and workforce management with deterministic production requirements. It consists the costs of machines maintenance and overhead, relocation costs for machines installation and removal, part holding cost, workers’ costs of salary, hiring, and firing, part intercellular movement cost, machine procurement cost, internal production cost, machine operating cost, the cost of acquiring the returned products, setup cost for disassembly operations, disassembly cost, the inventory cost of the returned products, parts disposal cost. Linearization procedures are proposed to convert it into a linearized mixed integer programming formulation. This linearized mixed integer program is solved using an exact solution (ES) procedure through the simplex-based branch and cut procedure of CPLEX software. The second model considered the design of cellular hybrid manufacturing-remanufacturing system, where manufacturing new products using an outsourced parts and remanufacturing using returned products are performed in the same facility by using shared resources. The overall objective of the model is to minimize the total cost of the three main categories of costs; 1) Machine cost: maintenance and overhead costs, relocation costs of installation and removal of machines, machine procurement costs, and machine operating costs, 2) Costs associated with manufacturing and remanufacturing: production costs for both new and remanufactured components, holding cost for new components, holding cost for remanufactured components, setup cost for new components, setup cost for remanufactured components, 3) Costs associated with returned products for remanufacturing: cost of acquiring the returned products, setup cost for disassembly operations, disassembly cost, and inventory cost of the returned products. Computational results and sensitivity analysis for an important design features are also reported. The third model addresses the same attributes as the second one but an important extension is the introduction of recycling (for the end-of-life parts) and disposing of the parts with no further use. In addition, the new parts production in the third model are totally depends on the recycled parts coming from the recycling center, wherein the second model it depends on the raw material purchasing from outsourcing. As the third model is the most comprehensive one, which considers a closed loop supply chain starts from a cellular hybrid manufacturing-remanufacturing system and ends with the customer zone, through the introducing of different centers like, collection, disassembly, and recycling centers, and in order to have one more step toward the design of sustainable closed loop supply chain, the fourth model are formulated. The fourth model is designed to minimize the carbon foot prints and the total cost which contains the opening costs for different centers and the transportation costs between these centers Keywords: Sustainability, Sustainable manufacturing system, cellular manufacturing systems design, Reconfigurable manufacturing system, mixed integer programming, Hybrid manufacturing-remanufacturing system, Closed loop supply chain, Reverse logistics, Carbon footprints, Facility location

Dynamic Facility Layout for Cellular and Reconfigurable Manufacturing using Dynamic Programming and Multi-Objective Metaheuristics

The facility layout problem is one of the most classical yet influential problems in the planning of production systems. A well-designed layout minimizes the material handling costs (MHC), personnel flow distances, work in process, and improves the performance of these systems in terms of operating costs and time. Because of this importance, facility layout has a rich literature in industrial engineering and operations research. Facility layout problems (FLPs) are generally concerned with positioning a set of facilities to satisfy some criteria or objectives under certain constraints. Traditional FLPs try to put facilities with the high material flow as close as possible to minimize the MHC. In static facility layout problems (SFLP), the product demands and mixes are considered deterministic parameters with constant values. The material flow between facilities is fixed over the planning horizon. However, in today’s market, manufacturing systems are constantly facing changes in product demands and mixes. These changes make it necessary to change the layout from one period to the other to be adapted to the changes. Consequently, there is a need for dynamic approaches of FLP that aim to generate layouts with high adaptation concerning changes in product demand and mix. This thesis focuses on studying the layout problems, with an emphasis on the changing environment of manufacturing systems. Despite the fact that designing layouts within the dynamic environment context is more realistic, the SFLP is observed to have been remained worthy to be analyzed. Hence, a math-heuristic approach is developed to solve an SFLP. To this aim, first, the facilities are grouped into many possible vertical clusters, second, the best combination of the generated clusters to be in the final layout are selected by solving a linear programming model, and finally, the selected clusters are sequenced within the shop floor. Although the presented math-heuristic approach is effective in solving SFLP, applying approaches to cope with the changing manufacturing environment is required. One of the most well-known approaches to deal with the changing manufacturing environment is the dynamic facility layout problem (DFLP). DFLP suits reconfigurable manufacturing systems since their machinery and material handling devices are reconfigurable to encounter the new necessities for the variations of product mix and demand. In DFLP, the planning horizon is divided into some periods. The goal is to find a layout for each period to minimize the total MHC for all periods and the total rearrangement costs between the periods. Dynamic programming (DP) has been known as one of the effective methods to optimize DFLP. In the DP method, all the possible layouts for every single period are generated and given to DP as its state-space. However, by increasing the number of facilities, it is impossible to give all the possible layouts to DP and only a restricted number of layouts should be fed to DP. This leads to ignoring some layouts and losing the optimality; to deal with this difficulty, an improved DP approach is proposed. It uses a hybrid metaheuristic algorithm to select the initial layouts for DP that lead to the best solution of DP for DFLP. The proposed approach includes two phases. In the first phase, a large set of layouts are generated through a heuristic method. In the second phase, a genetic algorithm (GA) is applied to search for the best subset of layouts to be given to DP. DP, improved by starting with the most promising initial layouts, is applied to find the multi-period layout. Finally, a tabu search algorithm is utilized for further improvement of the solution obtained by improved DP. Computational experiments show that improved DP provides more efficient solutions than DP approaches in the literature. The improved DP can efficiently solve DFLP and find the best layout for each period considering both material handling and layout rearrangement costs. However, rearrangement costs may include some unpredictable costs concerning interruption in production or moving of facilities. Therefore, in some cases, managerial decisions tend to avoid any rearrangements. To this aim, a semi-robust approach is developed to optimize an FLP in a cellular manufacturing system (CMS). In this approach, the pick-up/drop-off (P/D) points of the cells are changed to adapt the layout with changes in product demand and mix. This approach suits more a cellular flexible manufacturing system or a conventional system. A multi-objective nonlinear mixed-integer programming model is proposed to simultaneously search for the optimum number of cells, optimum allocation of facilities to cells, optimum intra- and inter-cellular layout design, and the optimum locations of the P/D points of the cells in each period. A modified non-dominated sorting genetic algorithm (MNSGA-II) enhanced by an improved non-dominated sorting strategy and a modified dynamic crowding distance procedure is used to find Pareto-optimal solutions. The computational experiments are carried out to show the effectiveness of the proposed MNSGA-II against other popular metaheuristic algorithms

The development of methods for the design and evolution of reconfigurable cellular manufacturing systems.

Doctor of Philosophy in Engineering (Mechanical Engineering)The concept of reconfigurable manufacturing is presently being researched due to the need for production systems that are able to economically respond to changes in markets and the rapid introduction of new products. Cellular Manufacturing Systems (CMS) are a central concept in just-in-time and lean manufacturing. Although CMS are able to provide a strategic operating advantage, machine cell clusters do not remain optimal over an extended period of time. The concept of a Dynamic CMS (DCMS) has received attention in recent years; a DCMS is a system where the layout of machines change in order to improve the responsiveness of CMS to changing production requirements. A deficiency in existing DCMS methods is that reconfiguration plans are generated without the consideration of an initial design of the factory floor space for future change. This research distinguishes Reconfigurable CMS (RCMS) from DCMS, as a system that is designed at the outset for changes to system layout and cell configurations. The concept of a Factory Configuration Template (FCT) is proposed in this research; the FCT is a design of the factory floor space to ensure the feasible implementation of reconfiguration plans generated by mathematical models. A nine step method for FCT design is presented that uses a Simultaneous Fuzzy Clustering Heuristic to develop manufacturing cells and part families. A Tabu Search algorithm was develop to generate the optimal arrangement of machine sites in cells. Three multi-period machine assignment models were developed that determine reconfiguration plans based on changing product demand and the introduction of new products. The models that were developed included two integer linear programs that determine the distribution of machine resources among cells over multiple periods. A quadratic zero-one programming model was developed that distributes machines among available sites in cells to promote unidirectional part flow. The results show that RCMS is able to provide a more economical solution than traditional CMS with the added advantage of improved part flow in the system

SIMAID: a rapid development methodology for the design of acyclic, bufferless, multi-process and mixed model agile production facilities for spaceframe vehicles

The facility layout problem (FL) is a non-linear, NP-complete problem whose complexity is derived from the vast solution space generated by multiple variables and interdependent factors. For reconfigurable, agile facilities the problem is compounded by parallelism (simultaneity of operations) and scheduling issues. Previous work has either concentrated on conventional (linear or branched) facility layout design, or has not considered the issues of agile, reconfigurable facilities and scheduling. This work is the first comprehensive methodology incorporating the design and scheduling of parallel cellular facilities for the purpose of easy and rapid reconfiguration in the increasingly demanding world of agile manufacturing. A novel three-stage algorithm is described for the design of acyclic (asynchronous), bufferless, parallel, multi-process and mixed-model production facilities for spaceframe-based vehicles. Data input begins with vehicle part processing and volume requirements from multiple models and includes time, budget and space constraints. The algorithm consists of a powerful combination of a guided cell formation stage, iterative solution improvement searches and design stage scheduling. The improvement iterations utilise a modified (rules-based) Tabu search applied to a constant-flow group technology, while the design stage scheduling is done by the use of genetic algorithms. The objective-based solution optimisation direction is not random but guided, based on measurement criteria from simulation. The end product is the selection and graphic presentation of the best solution out of a database of feasible ones. The case is presented in the form of an executable program and three real world industrial examples are included. The results provide evidence that good solutions can be found to this new type and size of heavily constrained problem within a reasonable amount of time

Research study of state-of-the-art algorithms for flexible job-shop scheduling problem

The paper discusses various approaches used to solve flexible job-shop scheduling problem concentrating on formulations proposed in the last ten years. It mainly refers to the applied metaheuristic techniques which have been exploited in this research area. A comparison of presented approaches is attempted, some concluding insights are highlighted. Finally future research directions are suggested