2017 International Conference on Advanced Systems and Electric Technologies (IC_ASET)
TOPSIS Based Multi-Criteria Reconfiguration of Manufacturing Systems Considering Operational and Ergonomic Indicators Amina Bougrine
Saber Darmoul
Sonia Hajri-Gabouj
Université de Carthage Laboratoire d’Informatique des Systèmes Industriels, LISI Institut National des Sciences Appliquées et de Technologie, INSAT, B.P. 676, Centre Urbain Nord, 1080, Tunis, Tunisia
Industrial Engineering Department College of Engineering King Saud University P.O. Box 800, 11421 Riyadh, Saudi Arabia
Université de Carthage Laboratoire d’Informatique des Systèmes Industriels, LISI Institut National des Sciences Appliquées et de Technologie, INSAT, B.P. 676, Centre Urbain Nord, 1080, Tunis, Tunisia
[email protected]
[email protected]
[email protected]
Abstract—In reconfigurable manufacturing systems (RMS), the evaluation of configurations is usually based exclusively on technical performance indicators for an efficient and effective manufacturing. Ergonomics and human factors related aspects are rarely considered in the evaluation of reconfiguration opportunities, which leads to less realistic and pragmatic reconfiguration decisions. This article suggests both technical and ergonomic indicators to achieve a more realistic evaluation of reconfiguration decisions. A case study is introduced, and the Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS) is used to achieve multicriteria evaluation and selection of an alternative configuration when reconfiguring an RMS. We particularly compare reconfiguration decisions with and without ergonomic indicators, and therefore establish the worthiness of considering both aspects simultaneously. Keywords—Human factor; ergonomics; reconfigurable manufacturing systes;TOPSIS.
reconfiguration;
1. INTRODUCTION Nowadays, manufacturing systems are subject to intense competition and to hard requirements of quality and performance. They must be reactive to change, not only in terms of disturbances and risks (such as fluctuation of demand and/or unavailability/unreliability of resources)[1], but alsoin terms of mass customization[2], [3]. Being able to adapt production capacity, functionality, and/or organization to change is a requirement for durability. Reconfigurable manufacturing has appeared as a paradigm that provides advanced hardware, software and decision-making technologies to increase manufacturing systems’ flexibility, resilience and responsiveness to change [4], [5]. In Reconfigurable Manufacturing Systems (RMS),system capacity, functionality, and/or organization can be adapted based on several types of decisions, degrees of flexibility and technological enablers. For example, Mehrabi et al. [6] describe reconfigurable aspects related to machines, processes, layout, software and control. These aspects involve (without being limited to) adding, removing and/or relocating production resources, such as machines and workstations; resizing and/or relocating storage buffers; changing product routings, and updating required fixtures and tools. In highly automated industries, where products have high added value and are produced in large or medium series, reconfiguration relies on industrial automation technologies,
978-1-5090-6634-6/17/$31.00 ©2017 IEEE
such as robotics and automated guided vehicles, to achieve the above-mentioned tasks. Such industries include the automotive industry (e.g. assembly of car engines and gearboxes) [7], [8], the electronics and telecommunication industries (e.g. phones, printed circuit boards and computers) [9], [10], and the aerospace industry (e.g. assembly of airplane wings) [11], [12]. However, in industries producing low added value products, in relatively small series, and involving manual operations, such as the clothing/garment industry, the furniture industry and some electro-mechanical industries (e.g. automotive part suppliers), reconfiguration is often implemented using manual operations, usually achieved by human operators [4]. Such manual operations require physical efforts and mental loads that can affect the operator health, safety and/or performance. By handling important loads or adopting wrong postures, health and safety issues, such as musculoskeletal troubles, can appear [13], [14]. These issues can lead to stress, fatigue, and increased absenteeism rates due to sick leaves. For example [15](http://www.eumusc.net/index.cfm), musculoskeletal problems resulted in 36% of loss in productivity in the Netherlands, and in 23.7% of loss of working days due to sick leaves in Germany. If not taken into account in the reconfiguration process, they can negatively influence the expected performance of reconfiguration opportunities due to a decrease in operator cognitive abilities and higher error rates. Unfortunately, as it will be discussed in more details in our related works section, the evaluation of configurations is usually based exclusively on technical performance indicators for an efficient and effective manufacturing. Ergonomics and human factors related aspects are rarely considered in the evaluation of reconfiguration opportunities, which leads to less realistic and pragmatic reconfiguration decisions.Therefore, we suggest considering an ergonomic evaluation of reconfiguration opportunities during the selection of different alternatives in order to achieve a better compromise between operator safety and system performance. 2. RELATED WORKS Multi-criteria decision-making techniques have been widely used to evaluate reconfiguration opportunities in reconfigurable manufacturing. Reconfiguration related features, as well as operational features,are consideredwhen
329
2017 International Conference on Advanced Systems and Electric Technologies (IC_ASET)
selecting a configuration among a set of available alternatives. Alternatives are usually outranked based on additive weighting formulas, like in the Analytic Hierarchy Process (AHP) [13], [16]–[20], the Fuzzy AHP [21], ELECTRE [22], PROMETHEE [23]; or optimization techniques [24], [25].
selection process.The hierarchical multi-criteria decision framework of the study is shown in Figure 1.
Some authors addressed the design stage of a manufacturing system, and considered strategic indicators (e.g. scalability, convertibility, reliability and maintainability) [5], [16], or indicators related to reconfigurability, cost, and quality [18]. Such methods assume that all different production scenarios and technological evolution are known in advance at the time when a system is designed. Unfortunately, such an assumption is often unrealistic and can yield significantly poorer results than expected on the medium or long terms, and when the system is run. In production phase, performance criteria include operational indicators (throughput, product blocked time, product earliness, product lateness and machine utilization) [17], [26], and indicators related to manufacturing reconfigurability, cost, quality and performance[21], [27]. Indicators related to inventory and operators were considered in [19]. Operator related indicators evaluate the operator skills acquired by training services. These skills help in coping with configuration changes and are considered as a key factor for a successful reconfiguration of the manufacturing system. However, these indicators are not taken into account during the evaluation of the move from one configuration to another (evaluation of the reconfiguration decision). In these works, relying on the assumption that reconfiguration tasks (like moving machines, changing tools, resizing/relocating storage areas, etc.) are negligible or do not have an impact on operational indicators is often far from real operating conditions in workshops. Such tasks are generally very costly, time consuming and vary from one alternative to another. With this respect, strategic, operational and human factors related indicators were suggested in [13], [20]. With respect to the Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS), only [28] coupled simulated annealing with TOPSIS to solve the problem of process plan generation in RMS. To the best of the authors’ knowledge, TOPSIS has been considered only in [29]for multi-criteria configuration selection during RMS operation, but without considering ergonomic indicators. This article suggests both technical and ergonomic indicators to achieve a more realistic evaluation of reconfiguration decisions. A case study is introduced, and TOPSIS is used to achieve multi-criteria evaluation and selection of an alternative configuration when reconfiguring an RMS. We particularly compare reconfiguration decisions with and without ergonomic indicators, and therefore establish the worthiness of considering both aspects simultaneously. 3. MULTICRITERIA DECISION FRAMEWORK In this work, TOPSIS is considered for a multi-criteria evaluation of a set of available configurations, for the outranking of these available configurations, and for the selection of the top ranked configuration to reconfigure a manufacturing system at production execution stage. In addition to operational indicators, ergonomic indicators are developed and taken into account in the evaluation and
Fig.1. TOPSIS hierarchical decision-making framework.
4. ERGONOMIC INDICATORS FOR RECONFIGURATION During the reconfiguration task, several risk factorsmay cause musculoskeletal troubles. For example, handling heavy loads affect the spine resulting in back problems.Wrongbody posture causes neck, shoulders, elbow, hand, and/orwrist troubles. Thus, the evaluation of these risks requires on-site workplace observation and/or posture software simulation to examine work conditions, and to analyze the movements/motions required to achieve the reconfiguration tasks. To quantify these ergonomic aspects while reconfiguring the system, we used the Key Indicator Method (KIM) developed by the Federal Institution of Occupational Health and Safety[30]. KIM considers different factors, such as time/duration of the task, load, posture, working conditions and gender. Therefore, different tasks can be analyzed to move from one configuration to another, such as moving resources, relocating machines or workstations or resizing storage buffers. For each task, elementary operations are defined according to KIM to evaluate the musculoskeletal trouble indicator (MST). For example, to resize or relocate a storage buffer, specific movements are realized, such as twisting or bending to handle the load, holding the load, bending and putting the load in the right position. The following subsections provide a detailed description of the indicators used in the evaluation of the reconfiguration activity. A. Load The load rating score L scales weights handled by operators based on the gender of the operator and the weight of the load (cf. Table 1). TABLE 1: DETERMINATION OF LOAD RATING SCORES Effective load X for men
L
X
