Evelyn C. Brown

A hybrid grouping genetic algorithm for the cell formation problem

The machine-part cell formation problem consists of constructing a set of machine cells and their corresponding product families with the objective of minimizing the inter-cell movement of the products while maximizing machine utilization. This paper presents a hybrid grouping genetic algorithm for the cell formation problem that combines a local search with a standard grouping genetic algorithm to form machine-part cells. Computational results using the grouping efficacy measure for a set of cell formation problems from the literature are presented. The hybrid grouping genetic algorithm is shown to outperform the standard grouping genetic algorithm by exceeding the solution quality on all test problems and by reducing the variability among the solutions found. The algorithm developed performs well on all test problems, exceeding or matching the solution quality of the results presented in previous literature for most problems.

CF-GGA: A grouping genetic algorithm for the cell formation problem

In manufacturing, the machine-part cell formation (MPCF) problem addresses the issues surrounding the formation of part families based on the processing requirements of the components, and the identification of machine groups based on their ability to process specific part families. Past research has shown that one key aspect of attaining efficient groupings of parts and machines is the block-diagonalization of the given machine-part (MP) incidence matrix. This paper presents and tests a grouping genetic algorithm (GGA) for solving the MPCF problem and gauges the quality of the GGAs solutions using the measurements of efficiency (Chandrasekharan and Rajagopalan 1986a) and efficacy (Kumar and Chandrasekharan 1990). The GGA in this study, CF-GGA, a grouping genetic algorithm for the cell formation problem, performs very well when applied to a variety of problems from the literature. With a minimal number of parameters and a straightforward encoding, CF-GGA is able to match solutions with several highly complex algorithms and heuristics that were previously employed to solve these problems.

Evaluating performance advantages of grouping genetic algorithms

The genetic algorithm (GA) and a related procedure called the grouping genetic algorithm (GGA) are solution methodologies used to search for optimal solutions in constrained optimization problems. While the GA has been successfully applied to a range of problem types, the GGA was created specifically for problems involving the formation of groups. Falkenauer (JORBEL-Belg. J. Oper. Res. Stat. Comput. Sci. 33 (1992) 79), the originator of the GGA, and subsequent researchers have proposed reasons for expecting the GGA to perform more efficiently than the GA on grouping problems. Yet, there has been no research published to date which tests claims of GGA superiority. This paper describes empirical tests of the performance of GA and GGA in three domains which have substantial, practical importance, and which have been the subject of considerable academic research. Our purpose is not to determine which of these two approaches is better across an entire problem domain, but rather to begin to document practical differences between a standard off-the-shelf GA and a tailored GGA. Based on the level of solution quality desired, it may be the case that the additional time and resources required to design a tailored GGA may not be justified if the improvement in solution quality is only minor or non-existent.

A GROUPING GENETIC ALGORITHM FOR THE MULTIPLE TRAVELING SALESPERSON PROBLEM

The multiple traveling salesperson problem (MTSP) involves scheduling m > 1 salespersons to visit a set of n > m locations. Thus, the n locations must be divided into m groups and arranged so that each salesperson has an ordered set of cities to visit. The grouping genetic algorithm (GGA) is a type of genetic algorithm (GA) designed particularly for grouping problems. It has been successfully applied to a variety of grouping problems. This paper focuses on the application of a GGA to solve the MTSP. Our GGA introduces a new chromosome representation to indicate which salesperson is assigned to each tour and the ordering of the cities within each tour. We compare our method to standard GAs that employ either the one-chromosome or two-chromosome representation for MTSP. This research demonstrates that our GGA with its new chromosome representation is capable of solving a variety of MTSP problems from the literature and can outperform the traditional encodings of previously published GA methods.

Impact of the replacement heuristic in a grouping genetic algorithm

Abstract The grouping genetic algorithm (GGA), developed by Emmanuel Falkenauer, is a genetic algorithm whose encoding and operators are tailored to suit the special structure of grouping problems. In particular, the crossover operator for a GGA involves the development of heuristic procedures to restore group membership to any entities that may have been displaced by preceding actions of the operator. In this paper, we present evidence that the success of a GGA is heavily dependent on the replacement heuristic used as a part of the crossover operator. We demonstrate this by comparing the performance of a GGA that uses a naive replacement heuristic (GGA0) to a GGA that includes an intelligent replacement heuristic (GGACF). We evaluate both the naive and intelligent approaches by applying each of the two GGAs to a well-known grouping problem, the machine-part cell formation problem. The algorithms are tested on problems from the literature as well as randomly generated problems. Using two measures of effectiveness, grouping efficiency and grouping efficacy, our tests demonstrate that adding intelligence to the replacement heuristic enhances the performance of a GGA, particularly on the larger problems tested. Since the intelligence of the replacement heuristic is highly dependent on the particular grouping problem being solved, our research brings into question the robustness of the GGA. Scope and purpose Our research investigates the significance of the replacement heuristic used as a part of the crossover operator in a grouping genetic algorithm (GGA). We test two GGAs and compare their replacement heuristics using test problems from the well-known machine-part cell formation domain. The purpose of our research is three-fold. First, we compare and contrast the GGA with standard GA to improve understanding of how they differ in problem representation and operation. Second, we provide evidence that GGA is limited not only to problems where the objective is to form groups, but also to problems where it is practical to incorporate a substantial amount of problem-specific information. Third, we estimate the impact that the GGA replacement heuristic has on performance. Results indicate that GGA performs up to 40% worse when problem-specific knowledge is not incorporated into the replacement heuristic.

A grouping genetic algorithm for the microcell sectorization problem

The number of wireless users has steadily increased over the last decade, leading to the need for methods that efficiently use the limited bandwidth available. Reducing the size of the cells in a cellular network increases the rate of frequency reuse or channel reuse, thus increasing the network capacity. The drawback of this approach is increased costs associated with installation and coordination of the additional base stations. A code-division multiple-access network where the base stations are connected to the central station by fiber has been proposed to reduce the installation costs. To reduce the coordination costs and the number of handoffs, sectorization (grouping) of the cells is suggested. We propose a dynamic sectorization of the cells, depending on the current sectorization and the time-varying traffic. A grouping genetic algorithm is proposed to find a solution which minimizes costs. The computational results demonstrate the effectiveness of the algorithm across a wide range of problems. The GGA is shown to be a useful tool to efficiently allocate the limited number of channels available.

CPG EA : a grouping genetic algorithm for material cutting plan generation

Construction firms specializing in large commercial buildings often purchase large steel plates, cut them into pieces and then weld the pieces into H-beams and other construction components. We formalize the material ordering and cutting problem faced by this industry and propose a grouping genetic algorithm, called CPGEA, for efficiently controlling the relevant costs. We test the quality of CPGEA in various ways. Three sets of simulated problems with known optimal solutions are solved using CPGEA, and the gap between its solutions and optimal solutions is measured. The same problem sets are also solved with an expert system and a multi-start greedy heuristic. CPGEA solutions are found to be consistently lower cost than the competing methods. The difference in solution quality is most pronounced for difficult problems requiring multiple identical plates in the optimal solution. CPGEA is also tested using data from actual construction projects of a company faced with this problem. Since an optimal solution for the problems is not available, a lower bound is created. For the historical problems tested, the average percent difference between CPGEA solutions and the lower bound is 0.67%. To put this performance in context, the results of solving these problems with an expert system and using experienced engineers is also reported. Of these three methods, CPGEA achieves the best performance and the human experts the worst performance.

Grouping Genetic Algorithm for the Blockmodel Problem

Many areas of research examine the relationships between objects. A subset of these research areas focuses on methods for creating groups whose members are similar based on some specific attribute(s). The blockmodel problem has as its objective to group objects in order to obtain a small number of large groups of similar nodes. In this paper, a grouping genetic algorithm (GGA) is applied to the blockmodel problem. Testing on numerous examples from the literature indicates a GGA is an appropriate tool for solving this type of problem. Specifically, our GGA provides good solutions, even to large-size problems, in reasonable computational time.

Shift Detection Properties of Moving Centerline Control Chart Schemes

In statistical process monitoring, violating the assumption of independent data results in a control chart that exhibits increased false alarms and trends on both sides of the centerline. Autocorrelation requires modification to traditional control chart techniques. This paper explores the shift detection capability of the moving centerline exponentially weighted moving average (MCEWMA) chart and recommends enhancements for quicker detection of small process upsets.

Reducing Room Turnaround Time at a Regional Hospital

Room turnaround time is a vital measure of performance for a number of service industries. For hospitals, reducing the room turnaround time leads to increased revenues as well as increased patient satisfaction. If a room is ready sooner, a waiting patient is required to spend less time in the emergency department. This article explores one hospitals approach to reduce room turnaround time. Process-mapping techniques as well as heuristic approaches integrated into an existing bed-tracking system are examined. The article also explores the practical steps the hospital took to improve room turnaround time. Infection control is a requirement for any hospital; therefore, an examination of the current room-cleaning procedures is included to verify that the improved room turnaround time did not come at the expense of infection control. Using initial data from 2004 and current data from 2008, the magnitude of the reduction in room turnaround time is analyzed.