Multiple objective decision making approach to cell formation: A goal programming model

A Reactive Population Approach on the Dolphin Echolocation Algorithm for Solving Cell Manufacturing Systems

In this paper, we integrate the autonomous search paradigm on a swarm intelligence algorithm in order to incorporate the auto-adjust capability on parameter values during the run. We propose an independent procedure that begins to work when it detects a stagnation in a local optimum, and it can be applied to any population-based algorithms. For that, we employ the autonomous search technique which allows solvers to automatically re-configure its solving parameters for enhancing the process when poor performances are detected. This feature is dramatically crucial when swarm intelligence methods are developed and tested. Finding the best parameter values that generate the best results is known as an optimization problem itself. For that, we evaluate the behavior of the population size to autonomously be adapted and controlled during the solving time according to the requirements of the problem. The proposal is testing on the dolphin echolocation algorithm which is a recent swarm intelligence algorithm based on the dolphin feature to navigate underwater and identify prey. As an optimization problem to solve, we test a machine-part cell formation problem which is a widely used technique for improving production flexibility, efficiency, and cost reduction in the manufacturing industry decomposing a manufacturing plant in a set of clusters called cells. The goal is to design a cell layout in such a way that the need for moving parts from one cell to another is minimized. Using statistical non-parametric tests, we demonstrate that the proposed approach efficiently solves 160 well-known cell manufacturing instances outperforming the classic optimization algorithm as well as other approaches reported in the literature, while keeping excellent robustness levels.

Solving the Manufacturing Cell Design Problem through an Autonomous Water Cycle Algorithm

Metaheuristics are multi-purpose problem solvers devoted to particularly tackle large instances of complex optimization problems. However, in spite of the relevance of metaheuristics in the optimization world, their proper design and implementation to reach optimal solutions is not a simple task. Metaheuristics require an initial parameter configuration, which is dramatically relevant for the efficient exploration and exploitation of the search space, and therefore to the effective finding of high-quality solutions. In this paper, the authors propose a variation of the water cycle inspired metaheuristic capable of automatically adjusting its parameter by using the autonomous search paradigm. The goal of our proposal is to explore and to exploit promising regions of the search space to rapidly converge to optimal solutions. To validate the proposal, we tested 160 instances of the manufacturing cell design problem, which is a relevant problem for the industry, whose objective is to minimize the number of movements and exchanges of parts between organizational elements called cells. As a result of the experimental analysis, the authors checked that the proposal performs similarly to the default approach, but without being specifically configured for solving the problem.

Capability based formulation and solution of multiple objective cell formation problems using simulated annealing

An integer multiple objective non‐linear mathematical programming formulation is developed for simultaneously forming part/machine cells. In the proposed model, generic capability units which are termed as resource elements are used to define the processing requirements of parts and processing capabilities of machine tools. Machine capabilities are not generally taken into account in the previous cell formation procedures. Explicit consideration of unique and overlapping machine capabilities can result in better manufacturing cell designs with higher utilisation levels and less machine duplication. The proposed cell formation model has distinguishing features. Several important cell formation objectives, such as minimisation of part dissimilarity (based on production requirements and processing sequences of parts) in formed cells, minimisation of cell load imbalance, and minimisation of extra capacity requirements for cell formation, are considered. In order to solve the mathematical programming model, a simulated annealing algorithm is developed. Cooperative game theoretic approach is applied for evaluating multiple objectives.