The scheduling problem for flexible manufacturing systems (FMSs) has been attempted in this paper using the ant colony optimization (ACO) technique. Since the operation of a job in FMSs can be performed on more than one machine, the scheduling of the FMS is considered as a computationally hard problem. Ant algorithms are based on the foraging behaviour of real ants. The article deals with the ant algorithm with certain modifications that make it suitable for application to the required problem. The proposed solution procedure applies a graph-based representation technique with nodes and arcs representing operation and transfer from one stage of processing to the other. Individual ants move from the initial node to the final node through all nodes desired to be visited. The solution of the algorithm is a collective outcome of the solution found by all the ants. The pheromone trail is updated after all the ants have found out their respective solutions. Various features like stagnation avoidance and prevention from quick convergence have been incorporated in the proposed algorithm so that the near-optimal solution is obtained for the FMS scheduling problem, which is considered as a non-polynomial (NP)-hard problem. The algorithm stabilizes to the solution in considerably lesser computational effort. Extensive computational experiments have been carried out to study the influence of various parameters on the system performance.
In today's competitive market, manufacturers need to quickly adapt to the changing demands of the customers. Reconfigurable manufacturing system (RMS) is a cost-effective system that can easily absorb frequent changes in product demands. In this article such a system is modelled using expert enhanced coloured fuzzy Petri net (EECFPN), which considers the demands of customers as a fuzzy parameter and vividly captures the reconfigurability aspect of RMS. A fuzzy control strategy (FCS) is proposed to deal with the information delays occurring during information transfer or decision implementation. After intensive computational experimentation, it has been found that FCS outperforms the alternative priority (AP) heuristic and it is considered an effective measure to deal with situations where considerable information delay is involved.
Product platforms have been effectively used by many successful companies for product family design. Technological advancements and changes in customer needs pose problems for robustly designing product platforms over a given planning horizon. To date, most product platform formation approaches are directed by structural (subassemblies and components) considerations and are seldom undertaken at the conceptual design stage. We argue that product platform design should commence at the conceptual design stage rather than the detailed design stage. It is noteworthy that physical structures are the end results of designs already frozen at higher level of functional abstraction. Hence, tackling the platform formation problem should start much before structures are materialized. We propose that the product platform formation approach should be considered at two different stages: (i) conceptual design stage; and (ii) detailed design stage. In reference to the Function -Behavior -Structure model proposed by Gero and Kannengiesser (Gero, J.S. and Kannengiesser, U., Function-behavior-structure: A model for social situated agents. Workshop on design would refer to the design of products at function and technology stage, whereas detailed design would refer to the design of products at the structure stage. This paper discusses a method to form product platforms at the Function-Technology stage which can be correspondingly mapped to the structural stages. Thus, forming product platforms at a higher level of abstraction would enable a better understanding of the complications met at structural level. The FT approach uses Function Technology Ant Colony Optimization (FTACO) method to determine product platform configuration(s). We demonstrate the proposed approach using the example of a computer mouse product family.
Product platform concepts are often deployed to achieve product variety and hence effective product customization. One of the popular methods to achieve product variety is to scale one or more design variables called the scaling variable(s). This necessitates efficient methods for identifying the values for scaling variables. This paper presents a graph-based optimization method called Platform Ant Colony Optimization (PACO) for identifying the values of the scaling variable(s) for platform formation. In PACO, the overall decision is a function of the cumulative decisions of simple computing agents called the ‘ants.’ The method employs an autocatalytic mechanism using a probabilistic search to improve the solution iteratively. We use a universal electric motor example cited in the literature to test the efficiency of the proposed method. Simulation results on the example problem indicate that the PACO method produces promising results.
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