Solving unequal area static facility layout problems by using a modified genetic algorithm

Finding locations or positions of machines, cells, facilities, or departments in a workspace is categorized as a facility design problem. This article has investigated unequalarea facility layout problems in order to minimize the sum of the material handling costs. In addition, the areas and shapes of departments were fixed throughout the time horizon and during the iteration of an algorithm. Exact methods could not solve facility layout problems within a reasonable computational time when the number of departments increases due to their complexity. Therefore, a modified genetic algorithm was suggested to solve the problems. The proposed method was tested by using some problem instances chosen from the literature. Local search method and swapping method were used in order to improve the quality of layouts. In accordance with the results, the proposed modified genetic algorithm has created encouraging layouts in contrast to other methods

Solving unequal-area dynamic facility layout problems based on slicing tree representation and simulated annealing

Facility design problems relate to the location or design of facilities or departments in a given area. This study deal with unequal-area dynamic facility layout problems in order to minimize the sum of the shifting costs and the sum of the material handling costs. The slicing tree structure has not been applied to these problems so far. In this paper, unequal-area dynamic facility layout problems based on the slicing tree representation are investigated. Due to their complexity, they could not be solved within a reasonable computational time by exact methods when the number of departments increases. Hence, a simulated annealing approach is suggested for solving them. The simulated annealing approach is tested with some problem instances taken from the literature. According to the results, this algorithm generates better solutions in comparison with other methods

Unequal-area stochastic facility layout problems: solutions using improved covariance matrix adaptation evolution strategy, particle swarm optimisation, and genetic algorithm

Determining the locations of departments or machines in a shop floor is classified as a facility layout problem. This article studies unequal-area stochastic facility layout problems where the shapes of departments are fixed during the iteration of an algorithm and the product demands are stochastic with a known variance and expected value. These problems are non-deterministic polynomial-time hard and very complex, thus meta-heuristic algorithms and evolution strategies are needed to solve them. In this paper, an improved covariance matrix adaptation evolution strategy (CMA ES) was developed and its results were compared with those of two improved meta-heuristic algorithms (i.e. improved particle swarm optimisation [PSO] and genetic algorithm [GA]). In the three proposed algorithms, the swapping method and two local search techniques which altered the positions of departments were used to avoid local optima and to improve the quality of solutions for the problems. A real case and two problem instances were introduced to test the proposed algorithms. The results showed that the proposed CMA ES has found better layouts in contrast to the proposed PSO and GA

Solving an industrial shop scheduling problem using genetic algorithm

Spool fabrication shop is an intermediate phase in the piping process for construction projects. The delivery of pipe spools at the right time in order to be installed in the site is very important. Therefore, effective scheduling and controlling of the fabrication shop has a direct effect on the productivity and successfulness of the whole construction projects. In this paper, a genetic algorithm (GA) is developed to create an active schedule for the operational level of pipe spool fabrication. In the proposed algorithm, an enhanced solution coding is used to suitably represent a schedule for the fabrication shop. The initial population is generated randomly in the initialization stage and precedence preserving order-based crossover (POX) and uniform crossover are used appropriately. In addition, different mutation operators are used. The proposed algorithm is applied with the collected data that consist of operations processing time from an industrial fabrication shop. The results showed that by using GA for scheduling the fabrication processes, the productivity of the spool fabrication shop has increased by 88 percent