Alan R. McKendall

Simulated annealing heuristics for the dynamic facility layout problem

In todays economy, manufacturing plants must be able to operate efficiently and respond quickly to changes in product mix and demand. Therefore, this paper considers the problem of arranging and rearranging (when there are changes between the flows of materials between departments) manufacturing facilities such that the sum of the material handling and rearrangement costs is minimized. This problem is known as the dynamic facility layout problem (DFLP). In this paper, two simulated annealing (SA) heuristics are developed for the DFLP. The first SA heuristic (SA I) is a direct adaptation of SA to the DFLP. The second SA heuristic (SA II) is the same as SA I with a look-ahead/look-back strategy added. To test the performance of the heuristics, a data set taken from the literature is used in the analysis. The results obtained show that the proposed heuristics are very effective for the dynamic facility layout problem.

Hybrid ant systems for the dynamic facility layout problem

Todays consumer market demands that manufacturers must be competitive. This requires the efficient operation of manufacturing plants and their ability to quickly respond to changes in product mix and demand. In addition, studies show that material-handling cost make up between 20 and 50 percent of the total operating cost. Therefore, this paper considers the problem of arranging and rearranging, when there are changes in product mix and demand, manufacturing facilities such that the sum of material handling and rearrangement costs is minimized. This problem is called the dynamic facility layout problem (DFLP). In this paper, hybrid ant systems (HASs) are developed to solve the DFLP. To test the performance of the meta-heuristics, two data sets taken from the literature are used in the analysis. The results show that the HASs are efficient techniques for solving the DFLP. More importantly, HASs found new best solutions for more than one-half of all the test problems.

New Tabu search heuristics for the dynamic facility layout problem

A manufacturing facility is a dynamic system that constantly evolves due to changes such as changes in product demands, product designs, or replacement of production equipment. As a result, the dynamic facility layout problem (DFLP) considers these changes and is defined as the problem of assigning departments to locations during a multi-period planning horizon such that the sum of the material handling and re-arrangement costs is minimised. In this paper, three tabu search (TS) heuristics are presented for this problem. The first heuristic is a simple TS heuristic. The second heuristic adds diversification and intensification strategies to the first, and the third heuristic is a probabilistic TS heuristic. To test the performances of the heuristics, two sets of test problems from the literature are used in the analysis. The results show that the second heuristic out-performs the other proposed heuristics and the heuristics available in the literature.

Facility layout of irregular-shaped departments using a nested approach

The facility layout problem is a very difficult and widely studied optimization problem. As a result, many facility layout models and techniques have been developed. However, the literature does not fully consider or control irregular-shaped departments. In this paper, the nested facility layout problem is defined whereby irregular-shaped departments (i.e. L-shaped, O-shaped or U-shaped) can be generated and controlled. This is a unique problem that can be used to efficiently layout workstations, storage areas and other departments within departments, while arranging the departments with respect to an objective. The objective considered here is to minimize material handling cost. We present a formulation and solution technique for the nested facility layout problem. The formulation consists of a modification of Montreuils mixed-integer problem (MIP) to consider nesting departments. Finally, for illustrative purposes, several example problems are solved using the solution technique presented. The nested f...

Metaheuristics for the Integrated Machine Allocation and Layout Problem

The Integrated Machine Allocation and Layout Problem (IMALP) is the problem of assigning a set of machines (including machine replicas) to locations while assigning product flows to machines such that Material Handling Cost is minimised. A new mathematical formulation, a Tabu Search (TS) heuristic, and a Memetic Algorithm (MA) are presented for the IMALP. The algorithms were evaluated using a set of test problems available in the literature. TS and the MA obtained equal or better solutions for the dataset than previous techniques presented in the literature. More specifically, TS obtained better solutions in 47% of the instances, and MA improved the best known solution in 52.4% of the cases. As a result, MA out-performed TS with respect to solution quality and computation time.

The generalised machine layout problem

The Generalised MAchine Layout Problem (GMALP) is a generalisation of the integrated machine and layout problem, which is an extension of the machine layout problem. More specifically, the GMALP is the designing of a facility layout by defining the product mix, selecting the number of machines to be used, assigning these machines to the plant floor, and assigning products to machines such that total profit is maximised. Moreover, the GMALP integrates the quadratic assignment problem with a multicommodity flow problem. Therefore, the GMALP is a computationally intractable problem. Consequently, a mixed-integer nonlinear programming model was developed and used to solve small problem instances. Also, two simple construction algorithms and a tabu search (TS) heuristic were developed for solving large GMALP instances in acceptable computation times. In addition, a test dataset was used to evaluate the performances of the TS heuristic using the different construction algorithms. The results show that the TS heuristic perform slightly better with the second construction algorithm.

Simulated annealing heuristics for managing resources during planned outages at electric power plants

This paper presents a mathematical model and simulated annealing heuristics for assigning activities to workspaces and resources (e.g., equipment, parts, and toolboxes) to work/storage spaces during planned outages at electric power plants. These assignments are made such that the distance resources (toolboxes) travel throughout the duration of the outage is minimized. This problem is defined as the dynamic space allocation problem. To test the performance of the proposed techniques, a data set is generated and used in the analysis. The results show that the simulated annealing heuristics perform well with respect to solution quality and computational time.

Software engineering technology watch

Predicting the evolution of software engineering technology is, at best, a dubious proposition; it is fast paced and determined by an array of factors, many of them outside the software engineering arena. The authors discuss their first ventures in this domain and preliminary conclusions. The goal of watching software engineering trends means to determine what information we must gather and maintain to gain a comprehensive view of the discipline and its evolution. This information must be sufficiently rich to support discipline-wide assessments and trend-specific analysis. The authors identified a number of software engineering-specific and technology-related indicators, which they divided into seven categories which are presented.

A tabu search heuristic for a generalized quadratic assignment problem

The generalized quadratic assignment problem (GQAP) is the task of assigning a set of facilities to a set of locations such that the sum of the assignment and transportation costs is minimized. The facilities may have different space requirements, and the locations may have varying space capacities. Also, multiple facilities may be assigned to each location such that space capacity is not exceeded. In this paper, an application of the GQAP is presented for assigning a set of machines to a set of locations on the plant floor. Construction algorithms and a simple tabu search heuristic are developed for the GQAP. A set of test problems available in the literature was used to evaluate the performances of the TS heuristic using different construction algorithms. The results show that the simple TS heuristic is effective for solving the GQAP.

Solution techniques for a crane sequencing problem

In the areas of power plant maintenance, shipyard and warehouse management, resources (items) assigned to locations need to be relocated. It is essential to develop efficient techniques for relocating items to new locations using a crane such that the sum of the cost of moving the items and the cost of loading/unloading the items is minimised. This problem is defined as the crane sequencing problem (CSP). Since the CSP determines the routes for a crane to relocate items, it is closely related to some variants of the travelling salesman problem. However, the CSP considers the capacities of locations and intermediate drops (i.e. preemptions) during a multiple period planning horizon. In this article, a mathematical model and hybrid ant systems are developed for the CSP. Computational experiments were conducted to evaluate the performances of the proposed techniques, and results show that the proposed heuristics are effective.