# A Multi-Criteria Assessment of Manufacturing Cell Performance Using the AHP Method
## 2 Literature Background  
The literature background conducts a review regarding the above-mentioned optimization criteria or factors influencing decision making in cellular manufacturing design. Line balancing is considered an effective tool to optimize layout design and reduce product cycle times. The objectives of line balancing techniques in flow shop scheduling problems are usually focused on the reduction and/or redesign of workstations in order to minimize production costs, work in process in order to reduce storage space and bound capital, and minimization of makespan and flow times [24; 25]. In general, line balancing techniques are divided into deterministic types, where all input parameters are known and not changed over time, and probabilistic types, which deal with parameter uncertainties [26]. An innovative stochastic line balancing method was proposed by [PERSON] et al. [27]. Their algorithm was developed for the purpose of achieving optimized total production costs and to allow a good level of line balancing. Among the other important deterministic line balancing techniques can be mentioned the largest candidate rule [28], the Kilbridge and Wester Column method [29], and the ranked positional weight method proposed by [PERSON] and [PERSON] [30]. The latter method was developed to minimize idle times and the number of workstations. The specific type of these optimization tasks consists of large-scale line balancing problems that deal with uncertainty in line balancing. [PERSON] and [PERSON] [31] proposed two optimization models that belong to these problems, by which it is possible to generate exact solutions of cellular manufacturing designs. Another optimization technique based on the identification of equilibrium between line efficiency and equipment cost was proposed by [PERSON] et al. [32]. It is also useful to note that their method supports decisions at the early design stage of production lines. As it is impossible to involve all pertinent works on the topic, comprehensive literature studies focused on the comparative evaluation of line balancing techniques overcome this drawback. Such review studies can be found, e.g., in [33; 34; 35]. Operational and structural complexity is another relevant factor affecting the efficiency of CM systems. According to [PERSON] [36], the main reason for the investigation of manufacturing system complexity is to comprehend and control the behavior of such systems in order to make them more productive and predictive. [PERSON] and [PERSON] [37] argue that by measuring system complexity, managers can better identify problems in manufacturing systems that hinder production flow. Therefore, the determination of quantitative metrics of manufacturing system complexity, either static or dynamic, is one of the crucial elements in this effort [38]. There are several different approaches to the utilization of complex concepts in this application domain, but it has not yet been possible to find closed-form equations able to describe the dynamic behavior of manufacturing systems [39]. For this reason, available operational complexity measures reflect only selected facets of such systems. Manufacturing system complexity is often divided into structural and operational types. The second type of complexity measure, which is of interest in this study, is based on measuring uncertainties involved in manufacturing systems. This type of complexity is further divided into time independent and time dependent [40]. [PERSON] [41] analyzed the relationship between cellular manufacturing system complexity and utility in order to show that increasing complexity can be beneficial for manufacturers until it reaches a critical value. Beyond this critical value, the situation becomes the opposite. The mentioned system complexity consequences on design and managerial practice were originally introduced by [PERSON] [42] and are widely respected in many research communities.