Facility layout problems deal with layout of facilities or departments in a shop floor. This article studies unequal-area static facility layout problems in order to minimize the sum of the material handling costs and unequalarea dynamic facility layout problems so as to minimize the sum of the material handling costs and rearrangement costs. Unequal-area static and dynamic facility layout problems are NP-hard. Therefore, a modified particle swarm optimization was suggested to solve them where the departments have fixed shapes and areas throughout the time horizon. The modified particle swarm optimization was tested using the available problem instances chosen from the literature. The proposed algorithm applied two local search methods and the department swapping method to improve the quality of solutions and to prevent local optima for dynamic and static problems. It also utilized the period swapping method to improve the solutions for dynamic problems. The results showed that the proposed algorithm has created encouraging layouts in comparison with other approaches.

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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.

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.