A genetic algorithm for thepart family formation problem

Recently, there has been a lot of interest in group technology (GT) from researchers as well as from practitioners. This interest is explained by the fact that GT supports new manufacturing philosophies. One of the main issues in GT is the part family formation problem which is concerned with grouping similar products into same families. Many researchers have tackled this problem and many algorithms have been proposed for it. In this paper, we present a genetic technique-based heuristic for the quadratic integer programming model of the part family formation problem which was formulated by Kusiak et al. (1986). The heuristic is tested on several problems from the literature, and preliminary results are very promising.     

Development of an algorithm for the application of Group Technology in the design of Manufacturing Systems

The Group Technology theory identifies and groups similar parts into families and machines into cells, advantages being in the similarity of the parts made in each cell, and designing and manufacturing. The benefits are: simple material flow, cost reduction in material handling, reduction in work in progress, reduction in the cycle time and set-up time, increased manufacturing flexibility, increased quality, and increased job satisfaction. The objective of the work presented in this paper was to develop a Group Technology (GT) algorithm by means of cell analysis for the design of the productive system to batch production, e.g. water heater manufacturing, system manufacturing, lathes manufacturing, etc. The TGIP algorithm allows the definition of technical and economical parameters for the application of Group Technology, also taking into account the demand for the performance of the algoof heating gasrithm. The benefits of the algorithm developed are the focus on solving the case of multiple machines and the assignment of the parts to the groups related to the load of the machines; also it allows the definition of technical and economical parameters to differentiate the machines that do the same operation or even the consideration of the demand for a certain period of time.     

A simulation analysis of the impact of family configuration on virtual cellular manufacturing

Past research has shown that it is possible to simultaneously achieve the setup efficiencies of traditional cellular manufacturing systems and the routeing flexibility of a job shop by viewing cells not as permanent, physical structures, but as temporary, 'virtual' entities. This research demonstrates that the advantages of virtual manufacturing cells can be obtained over a range of part family configurations. In particular, virtual cellular manufacturing is robust to changes in the number and size of families being processed. Further, the research shows that the benefits can be obtained under setup conditions impartial to a family-oriented part environment.     

A New Approach to the Group Ranking Problem: Finding Consensus Ordered Segments from Users’ Preference Data

The group ranking problem involves constructing coherent aggregated results from users’ preference data. The goal of most group ranking problems is to generate an ordered list of all items that represents the user consensus. There are, however, two weaknesses to this approach. First, a complete list of ranked items is always output even when there is no consensus or only a slight consensus. Second, due to similarity of performance, in many practical situations, it is very difficult to differentiate whether one item is really better than another within a set. These weaknesses have motivated us to apply the clustering concept to the group ranking problem, to output an ordered list of segments containing a set of similarly preferred items, called consensus ordered segments. The advantages of our approach are that (i) the list of segments is based on the users’ consensuses, (ii) the items with similar preferences are grouped together in the same segment, and (iii) the relationships between items can be easily seen. An algorithm is developed to construct the consensus of the ordered segments from the users’ total ranking data. Finally, the experimental results indicate that the proposed method is computationally efficient, and can effectively identify consensus ordered segments.     

Multi-objective machine-component grouping in cellular manufacturing: A genetic algorithm

The cellular manufacturing system (CMS) is an important group technology (GT) application. The first step of CMS design is cell formation, generally known as machinecell formation (MCF) or machine-component (MCG). A genetic algorithm (GA) is a robust adaptive optimization method based on principles of natural evolution and is appropriate for the MCG problem, which is an NP complete complex problem. In this study, we propose a GA-based procedure to solve the MCG problem. More specifically, this study aims to minimize (1) total cost, which includes intercell and intracell part transportation costs and machines investment cots; (2) intracell machine loading imbalance; and (3) intercell machine loading imbalance under many realistic considerations. An illustrative example and comparisons demonstrate the effectiveness of this procedure. The proposed procedure is extremely adaptive, flexible, efficient and can be used to solve real MCG problems in factories by providing robust manufacturing cell formation in a short execution time.     

A robust approach to the share-of-choice product design problem

A critical issue when solving the share-of-choice product design problem is the reliability of the optimal solution in the presence of partworth uncertainty. Existing approaches use point estimates of an individual's partworth utilities as input to the product optimization stage, ignoring within-person variability in estimates. Post-optimality sensitivity analysis is occasionally performed to assess the degree to which a solution is negatively impacted by partworth uncertainty. We propose a robust optimization model that explicitly captures variation in partworth estimates during the optimization process. Using a large, commercial dataset, we benchmark our model's performance against its deterministic counterpart. We also present inferential theory to guide the selection of model parameters controlled by the analyst. Results reveal that the new approach produces robust solutions in the face of measurement error. Out-of-sample coverage for individuals drawn from the target population is significantly higher than corresponding solutions from published methods.     

A general modeling approach to online optimization with lookahead

A vast number of real world problems are coined by an information release over time and the related need for repetitive decision making over time. Optimization problems arising in this context are called online since decisions have to be made although not all data is known. Due to technological advances, algorithms may also resort to a limited preview (lookahead) on future events. We first embed the paradigm of online optimization with lookahead into the theory of optimization and develop a concise understanding of lookahead. We further find that the effect of lookahead can be decomposed into an informational and a processual component. Based on analogies to discrete event systems, we then formulate a generic modeling framework for online optimization with lookahead and derive a classification scheme which facilitates a thorough categorization of different lookahead concepts. After an assessment of performance measurement approaches with relevance to practical needs, we conduct a series of computational experiments which illustrate how the general concept of lookahead applies to specific instantiations and how a knowledge pool on lookahead effects in applications can be built up using the general classification scheme.     

Evaluating productive performance: A new approach based on the product-mix problem consistent with Data Envelopment Analysis

We propose a new approach to estimate technical coefficients from a set of Decision Making Units (DMUs) under the assumption that their production plans are set by process engineers through Linear Programming (LP) techniques. The idea behind this approach is that most manufacturing and agricultural firms routinely resort to LP-based modeling in their decision making processes in order to plan output production and, therefore, this particularity should be taken into account when estimating their technical efficiency. A usual model of LP for these sectors is the so-called product-mix problem, which we relate to a standard Data Envelopment Analysis (DEA) model in terms of the Directional Distance Function. In this paper, we finally show how to estimate the technical coefficients of a sample of Andalusian farms in Spain and how this information can be seen as a complement to the usual by-products associated with estimating technical efficiency by DEA.     

A Virtual Cellular Manufacturing Approach to Batch Production*

In this paper, Virtual Cellular Manufacturing (VCM), an alternative approach to implementing cellular manufacturing, is investigated. VCM combines the setup efficiency typically obtained by Group Technology (GT) cellular manufacturing (CM) systems with the routing flexibility of a job shop. Unlike traditional CM systems in which the shop is physically designed as a series of cells, family-based scheduling criteria are used to form logical cells within a shop using a process layout. The result is the formation of temporary, virtual cells as opposed to the more traditional, permanent, physical cells present in GT systems. Virtual cells allow the shop to be more responsive to changes in demand and workload patterns. Production using VCM is compared to production using traditional cellular and job shop approaches. Results indicate that VCM yields significantly better flow time and due date performance over a wide range of common operating conditions, as well as being more robust to demand variability.     

A low-cost approach to computer-integrated manufacturing in a small chemical unit

Many firms are considering CIM as a means of reducing costs and increasing quality. Sometimes, however, a major problem in this kind of project seems to be the high investments involved. In this paper a methodology for developing a low-cost CIM application is presented. The proposed methodology was successfully experimented in a small chemical unit of one of the leading producers of nylon in the world. The paper reports on the design and implementation of the computer-integrated manufacturing and the associated costs and benefits.     

A new fuzzy-c-means and assignment technique-based cell formation algorithm to perform part- type clusters and machine-type clusters separately

The incorporation of fuzziness in the cell formation problem by Chu and Hayya (1991 International Journal of Production Research, 29, 1475-1487) is a notable contribution, in which non-binary classification logic is used. However, despite this development, numerical illustrations performed in this research demonstrate that the Chu and Hayya approach can result in solutions with empty party-type cluster(s) and/or empty machine-type cluster(s). Further, it is noted that solutions based on the Chu and Hayya approach can contain non-empty part-type cluster(s) being assigned to empty machine-type cluster(s) and vice versa. Three strategies are offered in this research to overcome these inadequacies: the separate formation of part-type and machine-type clusters; modification of the stopping criterion; and the adoption of the assignment technique in the formation of the final manufacturing cell solutions. A new algorithm has resulted from these modifications and has been rigorously compared to the performance of the Chu and Hayya approach. In general, the new algorithm demonstrates superiorities in global efficiency and in generating feasible party-type clusters, machine-type clusters and manufacturing cells.     

Is leadership a part of me? A leader identity approach to understanding the motivation to lead

Drawing on social comparison and identity literature, we suggest that individuals' comparisons of themselves to their own standards of leadership relate to their leadership motivation. We propose and test a model of motivation to lead (MTL) based on two types of self-to-leader comparisons: self-to-exemplar and self-to-prototype comparisons with respect to affiliation. In our main study, using data from a sample of 180 executives, we apply structural equation models to test our predictions. We find that self-comparisons with concrete, influential leaders of the past or present (self-to-exemplar comparisons) relate positively to MTL. We also find that self-comparisons with more general representations of leaders (self-to-prototype comparisons in affiliation) relate to MTL. Whereas the effect of self-to-exemplar comparisons is mediated through individuals' leadership self-efficacy perceptions, the effect of self-to-prototype comparisons is not. We replicate these findings in three follow-up studies using different research designs. We derive implications for theory and practice.     

An integrated hybrid approach to the examination timetabling problem

This paper is derived from an interest in the development of automated approaches to tackle examination timetabling problems effectively. We propose a hybrid approach that incorporates effective heuristic operators within the great deluge algorithm. The latter was chosen because of consistently good performances being reported within the examination timetabling research domain. The additional heuristic procedures further enhance the overall effectiveness of this integrated hybrid approach. These procedures are drawn from methodologies that have appeared in the literature under term the “electromagnetic-like mechanism”. The aim is to move sample points towards a high quality solution while avoiding local optima by utilising a calculated force value. This value, which is calculated dynamically, is treated as a decay rate in determining the level within the great deluge algorithm. To evaluate the proposed algorithm, we carry out experimental work on two types of examination timetabling datasets. All the related results and analysis obtained illustrate that this hybrid approach is effective when compared with existing approaches in the literature.     

The stability of job and family involvement: Applying the multi-wave,multi-variable technique to longitudinal data

The main aim of the study was to examine the stability of job and family involvement, applying multi-wave, multi-variable modelling within the framework of LISREL models. The abbreviated job and family involvement scales based on Kanungo's (1979, 1982) motivational model were examined on the basis of 3-year longitudinal data. The study was carried out by means of questionnaires, which were filled in by Finnish employees (n = 109) on three occasions. The results show that both job and family involvement remained relatively stable during the follow-up period. Further, the scales showed reasonable unidimensionality across time. In addition, the item reliability of the scales was found to be adequate. Of the demographic characteristics studied (i.e. gender, age, socioeconomic status, presence of children at home, age of the youngest child) only gender had a significant effect on involvement: men were found to be more involved with their jobs than were women.     

Mapping the organization a methodological approach to organizational change initiation

facturers have realized that in order to survive in this market a new way of doing business is required. Initiating and planning changes is the focus of this paper, which describes a new methodology developed with the Israeli Military Industries. The methodology is designed to promote and initiate change in operations by defining a well-structured approach to Abstract. The defence industry is facing a shrinking market and tough competition. Most defence contractors and manu     

A solution approach to a synchronisation problem in a JIT production system

Delivering of orders on time, increasing productivity and reducing costs are all challenges that companies have to cope with on a regular basis. Making production lines compatible solves these problems and means a reduction in line stoppages and cycle time. In continuous production systems in which production is carried out in lots, the main ways to ensure an uninterrupted and smooth flow and have a high production rate, are line balancing and synchronising work stations. In this paper, a line stoppage and productivity problem at an automotive factory (Toyota Turkey plant, Sakarya city) is solved by root-cause analysis. Cycle time and in-process inventory inconsistency causes the problem between paint and assembly lines. Different solutions are researched and the most appropriate one is selected and implemented.     

A genetic algorithm-based decomposition approach to solve an integrated equipment-workforce-service planning problem

We develop a new genetic algorithm to solve an integrated Equipment-Workforce-Service Planning problem, which features extremely large scales and complex constraints. Compared with the canonical genetic algorithm, the new algorithm is innovative in four respects: (1) The new algorithm addresses epistasis of genes by decomposing the problem variables into evolutionary variables, which evolve with the genetic operators, and the optimization variables, which are derived by solving corresponding optimization problems. (2) The new algorithm introduces the concept of Capacity Threshold and calculates the Set of Efficient and Valid Equipment Assignments to preclude unpromising solution spaces, which allows the algorithm to search much narrowed but promising solution spaces in a more efficient way. (3) The new algorithm modifies the traditional genetic crossover and mutation operators to incorporate the gene dependency in the evolutionary procedure. (4) The new algorithm proposes a new genetic operator, self-evolution, to simulate the growth procedure of an individual in nature and use it for guided improvements of individuals. The new genetic algorithm design is proven very effective and robust in various numerical tests, compared to the integer programming algorithm and the canonical genetic algorithm. When the integer programming algorithm is unable to solve the large-scale problem instances or cannot provide good solutions in acceptable times, and the canonical genetic algorithm is incapable of handling the complex constraints of these instances, the new genetic algorithm obtains the optimal or close-to-optimal solutions within seconds for instances as large as 84 million integer variables and 82 thousand constraints.     

A Lagrangian relaxation approach for the multiple sequence alignment problem

We present a branch-and-bound (bb) algorithm for the multiple sequence alignment problem (MSA), one of the most important problems in computational biology. The upper bound at each bb node is based on a Lagrangian relaxation of an integer linear programming formulation for MSA. Dualizing certain inequalities, the Lagrangian subproblem becomes a pairwise alignment problem, which can be solved efficiently by a dynamic programming approach. Due to a reformulation w.r.t. additionally introduced variables prior to relaxation we improve the convergence rate dramatically while at the same time being able to solve the Lagrangian problem efficiently. Our experiments show that our implementation, although preliminary, outperforms all exact algorithms for the multiple sequence alignment problem. Furthermore, the quality of the alignments is among the best computed so far.     

The stability of job and family involvement: applying the multi-wave, multi-variable technique to longitudinal data

The main aim of the study was to examine the stability of job and family involvement, applying multi-wave, multi-variable modelling within the framework of LISREL models. The abbreviated job and family involvement scales based on Kanungo's (1979, 1982) motivational model were examined on the basis of 3-year longitudinal data. The study was carried out by means of questionnaires, which were filled in by Finnish employees (n = 109) on three occasions. The results show that both job and family involvement remained relatively stable during the follow-up period. Further, the scales showed reasonable unidimensionality across time. In addition, the item reliability of the scales was found to be adequate. Of the demographic characteristics studied (i.e. gender, age, socioeconomic status, presence of children at home, age of the youngest child) only gender had a significant effect on involvement: men were found to be more involved with their jobs than were women.