Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing

Enhancing productivity, reducing inaccuracy and avoiding time waste at changeover are considered major drivers in manufacturing system design. One of the emerging paradigms concerned with these characteristics is reconfigurable manufacturing systems (RMSs). The high responsiveness and performance efficiencies of RMS make it a convenient manufacturing paradigm for and flexible enabler of mass customization. The RMS offers customized flexibility and a variety of alternatives as features thanks to its reconfigurable machine tool (RMT). These machines represent a major component of RMS and are based on an adjustable, modular and reconfigurable structure. Hence, the system modularity is of great importance. This paper outlines a multi-objective approach to optimize the RMS design. Three objectives are considered: the maximization of the system modularity, the minimization of the system completion time and the minimization of the system cost. We developed a modularity-based multi-objective approach that uses an adapted version of the “Archived Multi-Objective Simulated Annealing” (AMOSA) method to solve the optimization problem by selecting from a set of candidate machines the most suitable ones. Implemented, the decision maker can use a multi-objective decision making tool based on the well-known “Technique for Order of Preference by Similarity to Ideal Solution” (TOPSIS) to choose the best solution in the Pareto front according to his preferences. We demonstrated the applicability of the proposed approach through an illustrative example and an analysis of the obtained numerical results.

Modularity assessment in reconfigurable manufacturing system (RMS) design: an Archived Multi-Objective Simulated Annealing-based approach

https://hal.archives-ouvertes.fr/hal-01575876/document

A bi-objective model in sustainable dynamic cell formation problem with skill-based worker assignment

In today’s competitive environment, customer demand has become increasingly diversified and customised. In order to stay competitive, the manufacturing companies need to adopt models that are able to rapidly adapt to market changes. The reconfigurable manufacturing system emerges from the scientific literature as a relevant manufacturing paradigm to meet changes and uncertainties in a dynamic manufacturing environment. This research proposes a cellular manufacturing system (CMS) with reconfigurable machines to handle the turbulent market conditions. The approach proposed concerns three mathematical models to support the design, reconfiguration and scheduling of the manufacturing system. The three linked models are developed in LINGO® package. The main optimisation model concerns the design of the CMS, considering product demand not deterministic but described by several discrete probability scenarios. Then, an optimisation sub-model has been developed to manage the reconfiguration at fixed periods. An all...

Design and reconfiguration models for dynamic cellular manufacturing to handle market changes

Cell formation in a cellular manufacturing system using simulation integrated hybrid genetic algorithm

This paper addresses the dynamic cell formation problem (DCF). In dynamic environment, the product demand and mix changes in each period of a multiperiod planning horizon. It causes need of reconfiguration of cells to respond to the product demand and mix change in each period. This paper proposes a mixed-integer nonlinear programming model to design the dynamic cellular manufacturing systems (DCMSs) under dynamic environment. The proposed model, to the best of the author’s knowledge, is the most comprehensive model to date with more integrated approach to the DCMSs. The proposed DCMS model integrates concurrently the important manufacturing attributes in existing models in a single model such as machine breakdown effect in terms of machine repair cost effect and production time loss cost effect to incorporate reliability modeling; production planning in terms of part inventory holding, part internal production cost, and part outsourcing; process batch size; transfer batch size for intracell travel; transfer batch size for intercell travel; lot splitting; alternative process plan, and routing and sequence of operation; multiple copies of identical copies; machine capacity, cutting tooling requirements, work load balancing, and machine in different cells constraint; machine in same cell constraint; and machine procurements and multiple period dynamic cell reconfiguration. Further, the objective of the proposed model is to minimize the sum of various costs such as intracell movement costs; intercell movement costs and machine procurement costs; setup cost; cutting tool consumption costs; machine operation costs; production planning-related costs such as internal part production cost, part holding costs, and subcontracting costs; system reconfiguration costs; and machine breakdown repair cost, production time loss cost due to machine breakdown, machine maintenance overheads, etc. ,in an integrated manner. Nonlinear terms of objective functions are transformed into linear terms to make mixed-integer linear programming model. The proposed model has been demonstrated with several problems, and results have been presented accordingly.

http://apem-journal.org/Archives/2010/APEM5-3_151-162.pdf

Lokesh Kumar Saxena

Papers

Dynamic cellular manufacturing systems design—a comprehensive model