Abstract
The production planning problem of flexible manufacturing system (FMS) concerns with decisions that have to be made before an FMS begins to produce parts according to a given production plan during an upcoming planning horizon. The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made. Such problems are not only combinatorial optimization problems, but also happen to be non-deterministic polynomial-time-hard, making it difficult to obtain satisfactory solutions using traditional optimization techniques. In this paper, an attempt has been made to address the machine loading problem with objectives of minimization of system unbalance and maximization of throughput simultaneously while satisfying the system constraints related to available machining time and tool slot designing and using a meta-hybrid heuristic technique based on genetic algorithm and particle swarm optimization. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as throughput and system utilization.
Highlights
In recent years, competitive market conditions coerce manufacturing firms to enhance response times and flexibility in all operations
Since the objective of this paper is to propose an efficient evolutionary search heuristic to solve problems pertaining to job selection and machine loading in random flexible manufacturing system (FMS) to optimize the system imbalance and throughput simultaneously, only the relevant literature are reviewed
The proposed HAO algorithm for the FMS loading problem is coded in Visual C++ and implemented in a Pentium IV PC
Summary
Competitive market conditions coerce manufacturing firms to enhance response times and flexibility in all operations. Flexible manufacturing systems (FMSs) have been proved to respond this challenge positively because of their ability to produce a variety of parts using the same system in the shortest possible lead time. According to Stecke (1983), FMS is characterized as an integrated, computer-controlled, complex arrangement of automated material handling devices and computer numerically controlled (CNC) machine tools that can simultaneously process medium-sized volumes of a variety of part types. The highly integrated FMS offers the opportunity to combine the efficiency of transfer line and the flexibility of a job shop to best suit the batch production of mid-volume and mid-variety of products. Flexibility has a cost, and the capital investment sustained by firms to acquire such systems is generally constraints with the objective of meeting certain performance measures. The problem is combinatorial in nature and happens to be non-deterministic polynomialtime (NP)-hard
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