A review of applications of genetic algorithms in operations management
Introduction
Operations management (OM) is the activity of managing the resources that create and deliver services and products (Slack et al., 2013). All organizations need operations to produce some mix of services and products. In any organization, three core functions are the marketing function, product/service development function and operations function. The marketing function is responsible for communicating the services and products to its markets to generate customer requests; the product/service development function is responsible for coming up with new or modified services and products to generate future customers’ requests; the operations function is responsible for creating and delivering services and products based on customer requests. In practice, however, there is not a clear division among these three functions while there exist other support functions enabling the core functions. From a broad perspective, OM has to interact with all other functions of an organization and thus comprises all activities necessary for the day-to-day fulfilment of customer requests. Many decisions in OM can be characterized as complex optimization problems that require a heuristic search method. Therefore, OM is an active research area benefited from the use of artificial intelligence (AI) techniques such as genetic algorithms (GAs) in the search for fast and good solutions to many practical problems. While GAs seem to be promising tools for solving OM related problems, a systematic review of applications of GAs in OM can help researchers to identify potential research areas and future directions.
The aim of the paper is to explore OM using GAs as useful tools, either as a standalone technique or integrated with other suitable techniques. However, for ease of exposition, some developments of GAs in the review are not included. First, though GAs are included in the evolutionary computation (EC) field, other EC methods or Darwinian approaches are not included in this paper. This is because these approaches deserve a separate review. For a detailed literature review of bio-inspired computing algorithms, readers are referred to a recent review by Kar (2016). Second, in line with the focus of this paper, the theoretical developments of the algorithms are not explored. There are many variants of GAs such as variable-length GAs and fuzzy GAs. Details of the modification of the algorithms are not discussed here. Instead, this paper focuses on the applications.
Given the growing number of GA applications, a number of papers surveying its applications have been published regularly. Coello (2000) provided a review of GA-based multi-objective optimization techniques. The discussion was at the technical side, and thus the applications of OM were not covered. Aguilar-Rivera et al. (2015) reviewed the GA applications in finance. In addition to GAs, they also reviewed other methods based on Darwinian evolution such as genetic programming, learning classifier systems, multi-objective evolutionary algorithms, co-evolutionary optimization schemes and competent evolutionary algorithms. In view of the fact that there are various focused areas in OM, some researchers reviewed applications of data mining and AI in particular areas, such as job-shop scheduling (Cheng et al., 1996), supplier evaluation and selection (Ho et al., 2010), and quality improvement (Köksal et al., 2011). Ho et al. (2010) concluded that GA applications in supplier evaluation and selection were limited while Köksal et al. (2011) identified that GAs were frequently used for parameter optimization for quality improvement. There are a number of reviews in OM areas and different researchers classified them in different ways. Rao and Lingaraj (1988) classified OM applications into strategically and tactically oriented applications. They identified eleven areas: aggregate planning, forecasting, location decisions, scheduling, capacity planning, layout, process and product design, quality control, job design, inventory control, and maintenance and reliability. Aytug et al. (2003) classified OM areas into six categories: production control (e.g. loading and scheduling), facility layout design, line balancing, production planning, supply chain management and design, and other. Jayaraman and Srivastava (1996) identified nineteen areas under two board categories of strategic and operational decisions. Chaudhry and Luo (2005) adopted Jayaraman and Srivastava’s (1996) classification scheme but added environment as an additional area to be considered under strategic decisions. Based on Chaudhry and Luo (2005) and Wong and Lai (2011) classified OM into eighteen areas for reviewing the applications of fuzzy set theory in production and OM. Kobbacy and Vadera (2011) reviewed AI in OM categorized into four areas: design, scheduling, process planning and control, and quality, maintenance and fault diagnosis. Subramanian and Ramanathan (2012) categorized OM into five broad themes: operations strategy, process and product design, planning and scheduling resources, project management, and managing the supply chain. The classification of OM areas by researchers is summarized in Table 1. To the best of our knowledge, there has not been a study that has reviewed GA applications in specific areas of OM, with the exception of a review by Chaudhry and Luo (2005), who presented a review of GA-related research published from 1990 to 2001. They did not discuss GA applications in specific OM areas individually in detail, but provided a journal-wise appearance of GA-related papers. This is considered a significant gap in the literature and this paper aims to provide an up-to-date review of GA applications in specific OM areas to fill the gap.
The rest of the paper is organized as follows. Section 2 describes the framework of the classification of GA applications in this paper. Section 3 presents a detailed review of GA applications in OM. Section 4 discusses the observations from the review and provides suggestions for future work. Section 5 concludes the paper.
Section snippets
A framework for classification
Based on Table 1, the applications of GAs in OM in this paper are categorized into three broad themes, namely (i) process and product design, (ii) operations planning and control, and (iii) operations improvement.
(i) Process and product design is a theme comprising design activities for satisfying customers’ requirements through shaping or configuring products, services and processes. Activities in this theme determine the resources required for the creation of services and products. At the
Process and product design
Key decision areas addressed by the researchers in the areas of process and product design include facility layout design, supply network design, job design and work, and forecasting. Table 3 provides information about some important papers in various decision areas related to process and product design.
Observations
Fig. 1 shows the distribution of GA studies in the nine OM areas. Scheduling is the most popular area where GAs have been vigorously applied, followed by inventory control. The third most popular areas is supply network design. The three least popular areas are forecasting, maintenance, and risk management. Fig. 2 shows the distribution of reviewed GA studies in the three OM themes. It can be seen that the majority of the reviewed work focused on operations planning and control. This is not
Conclusion
This paper made an attempt to review the applications of GAs in OM in the last decade. The reviewed literature from 2007 to 2017 was categorized into three themes, namely process and product design, operations planning and control, and operations improvement. The three themes contain nine different decision areas in total, which are facility layout design, supply network design, job design and work, forecasting, capacity planning, inventory control, scheduling, maintenance, and risk management.
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