A Human‐Oriented Simulation Approach for Labor Assignment Flexibility in Changeover Processes of Manufacturing Cells

Skilled operators are the most decisive key factors in manufacturing cells. An optimal assignment of operators is crucial for flexibility and productivity. Although there are many publications dealing with labor assignment problems, different forms of human cooperation on the shop floor and decentralized decision making, which are the main factors for system flexibility, are seldom concerned in existing models. In this article, a human‐oriented methodology to analyze, simulate, and evaluate labor assignment flexibility in changeover processes in manufacturing cells is introduced, which is characterized by an agent‐based approach. First, the problem architecture is presented along with the concepts of labor flexibility. Then, different types of human behavior in the changeover process are modeled. Furthermore, a human–machine interaction model is developed to integrate the human agent models into a generalized discrete event dynamic system (DEDS) process model. In this way, work process dynamics and cooperative behavior can be explicitly modeled and simulated. Third, the model is verified on the basis of a motorcycle engine manufacturing cell, and simulation experiments with different labor assignment schemes are designed and conducted. The simulation results show that assignment strategies incorporating different skill levels and cooperation styles have a significant impact on system performance. The agent‐based approach in conjunction with the human–machine interaction model can be used to analyze and solve a large class of assignment problems in flexible manufacturing systems, especially when human cooperation and collaboration are key factors shaping overall system performance.     

A Case-Based Micro Interactive Genetic Algorithm (CBMIGA) for interactive learning and search: Methodology and application to groundwater monitoring design

Interactive optimization algorithms use real–time interaction to include decision maker preferences based on the subjective quality of evolving solutions. In water resources management problems where numerous qualitative criteria exist, use of such interactive optimization methods can facilitate in the search for comprehensive and meaningful solutions for the decision maker. The decision makers using such a system are, however, likely to go through their own learning process as they view new solutions and gain knowledge about the design space. This leads to temporal changes (nonstationarity) in their preferences that can impair the performance of interactive optimization algorithms. This paper proposes a new interactive optimization algorithm – Case-Based Micro Interactive Genetic Algorithm – that uses a case-based memory and case-based reasoning to manage the effects of nonstationarity in decision maker’s preferences within the search process without impairing the performance of the search algorithm. This paper focuses on exploring the advantages of such an approach within the domain of groundwater monitoring design, though it is applicable to many other problems. The methodology is tested under non-stationary preference conditions using simulated and real human decision makers, and it is also compared with a non-interactive genetic algorithm and a previous version of the interactive genetic algorithm.     

Sustainability assessment using fuzzy-inference technique (SAFT): A methodology toward green products

Green products are increasingly becoming the center of attention for policy and decision makers worldwide not only because of environmental and eco-systems crisis but also to satisfy the current competitiveness in the markets. With this aim, it is highly attractive to count with mathematical tools that allow assessing the sustainability of the products. In this regard, fuzzy techniques have been broadly used in different studies due to uncertainty and vagueness associated with sustainability problems. However, these studies are mostly based on fuzzy rules generation which is time consuming and also can lead to redundancy and inaccuracy. In this study, we introduced a fuzzy-inference system to evaluate product/process sustainability (SAFT). The proposed method does not require generation of rules which simplifies the procedure and makes it more precise. Furthermore, fuzzy analytic hierarchy process accompanied by Shannon's entropy formula was employed to determine the relative importance of each element in the hierarchy. The methodology SAFT was compared with fuzzy rule-base technique and impressively pretty the same results were obtained. The method introduced in this paper was built as a user interface platform which can be used as a fuzzy expert system to facilitate the sustainability assessment of products/processes in different manufacturing industries.     

Fuzzy-based sustainability evaluation method for manufacturing SMEs using balanced scorecard framework

Sustainability has become a necessity, partly due to the threats created by traditional manufacturing practices, and due to regulations imposed by stakeholders. Performance evaluation is an important component of sustainability initiatives in manufacturing organizations. This study proposes a sustainability evaluation method for manufacturing SMEs using integrated fuzzy analytical hierarchal process (FAHP) and fuzzy inference system (FIS) approach. The performance indicators are identified from literature considering the characteristics of SMEs. Balanced scorecard framework is used to categorize the indicators among its four aspects. The linguistic variables are used to collect the opinions of decision makers about the performance ratings and importance of the aspects and corresponding indicators. The FAHP method is applied to determine the relative weights of measures and indicators. The performance ratings of the organization with respect to indicators and relative weights of indicators are combined to obtain the weighted performance ratings. The weighted performance ratings are considered as inputs to FIS. The hierarchal FIS is applied to derive the overall sustainability performance. Using a case study of manufacturing SME, the sustainability score of the organization was elicited in accordance with this procedure. Consequently, a sensitivity analysis of the proposed method reveals the most important basic indicators affecting overall sustainability, identifying areas which decision makers should place special attention. This method can also assist managers of larger enterprises to assess the effectiveness of their sustainability strategies, especially when dealing with suppliers from the SMEs.     

A case-based reasoning approach for building a decision model

A methodology based on case-based reasoning is proposed to build a topological-level influence diagram. It is then applied to a project proposal review process. The formulation of decision problems requires much time and effort, and the resulting model, such as an influence diagram, is applicable only to one specific problem. However, some prior knowledge from the experience in modeling influence diagrams can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem-solving experience to solve new problems.
In this paper, we suggest case-based decision class analysis (CB-DCA), a methodology based on case-based reasoning, to build an influence diagram. CB-DCA is composed of a case retrieval procedure and an adaptation procedure. Two measures are suggested for the retrieval procedure, one a fitting ratio and the other a garbage ratio. The adaptation procedure is based on decision-analytic knowledge and decision participants' domain-specific knowledge. Our proposed methodology has been applied to an environmental review process in which decision-makers need decision models to decide whether a project proposal is accepted or not. Experimental results show that our methodology for decision class analysis provides decision-makers with robust knowledge-based support.     

A decision support system for automotive product planning and competitive market analysis

A decision support system (DSS) for automotive product marketing, design and manufacturing in China is presented in this paper. The DSS is developed as a tool to support product planning, competitive market analysis, supply chain analysis and subsequent manufacturing systems planning and deployment. The system consists of a number of automotive related databases which provide information about manufacturers' performance in each market segment as well as production information of all existing market players in the Chinese auto industry. Product planning, one of the key modules of the DSS prototype, is highlighted in this paper. It supports decision makers in determining suitable strategies for market entry by analyzing existing competitors' status, growth estimation of each market segment, and competitive market analysis for new vehicle products. A case study for new market entry is included here to demonstrate the feasibility and effectiveness of the proposed methodology.     

A hybrid procedure for extracting rules of production performance in the automobile parts industry

In the manufacturing section, due to limitations of specific resources (e.g., time, people, and equipment), key determinants such as process capacity, human resources supply, and equipment availability may be in uncertain or out-of-control environments, followed by decreasing production performance. Traditionally, earlier studies of related issues of production performance usually used statistical methods for handling these problems. However, these methods become more complex when relationships in the input/output dataset are nonlinear. Furthermore, statistical techniques rely on the restrictive assumption on linear separability, multivariate normality and independence of the predictive variables; unfortunately, many of the common models of production performance violate these assumptions. To remedy these existing shortcomings, the study proposes a hybrid procedure that focuses on the opinions of experts, discretization of decision attributes, and application of well-known artificial intelligent (AI) approaches, such as decision trees (DT), artificial neural networks (ANN), and DT+ANN techniques, for objectively classifying production performance to solve real-world problems that are faced by the automobile parts industry. Two practically collected datasets are employed to verify the proposed hybrid procedure. The experimental results reveal that the proposed hybrid procedure is a good alternative to classify production performance from an intelligent manufacturing perspective objectively. Moreover, the output that is created by the DT C4.5 algorithm is a set of comprehensible and meaningful rules applied readily in knowledge-based performance-evaluating systems for manufacturing managers and HR division managers. The study findings and implications are of value to academicians and practitioners.     

Decision making on the factory floor: An integrated approach to process planning and scheduling

Computers make accesible large amounts of information to the different levels of manufacturing organizations. However, this information can be of limited use if adequate decision making methodology is not applied. Very often, decisions made on the factory floor have a substantial impact on the performance of the entire manufacturing system. Process planning and scheduling are two activities that influence significantly these decisions. The common aspect of these activities is the assignment of various factory resources to the production tasks. The method presented in this paper seeks to use this commonality to integrate process planning and scheduling.     

Development of a decision support system for machining center selection

The machining centers are key resources for manufacturing companies in their dealing with their fierce competitive market environments. However, although selecting the most appropriate machining center is a very important decision for manufacturing companies, the availability of wide-range of types and models makes the selection process a complex and difficult task. In this study, a decision support system (DSS), namely MACSEL, is developed to help the decision makers in their machining center selection decisions. Several issues and applicability of the MACSEL is illustrated with case problems in the paper.Within the developed DSS, to select the feasible set of machining centers fifteen questions are placed in the elimination (pre-selection) module. The developed DSS uses fuzzy analytical hierarchy process (FAHP) or fuzzy technique for order preference by similarity to ideal solution (FTOPSIS), which are extended versions of multi-criteria decision making approaches, to rank the feasible machining centers. In the DSS, FAHP is used if a detailed pair-wise weighting of the hierarchically structured criteria is wanted. On the other hand, when a simpler separate weighting of each criterion is be considered as enough, FTOPSIS is used.     

Consensus reaching for group decision making with multi-granular unbalanced linguistic information: A bounded confidence and minimum adjustment-based approach

In group decision making problems, there exist the situations that decision makers may use unbalanced linguistic term sets that are not uniformly and symmetrically distributed to provide their linguistic assessments over alternatives. Moreover, due to the difference in knowledge and culture backgrounds, it is also possible that multi-granular linguistic term sets may also be used by decision makers. How to manage multi-granular unbalanced linguistic information in consensus-based group decision making has becoming an important topic in linguistic decision making. In this paper, we first revise Herrera’s unbalanced linguistic term sets and propose a simplified linguistic computational model to fuse multi-granular unbalanced linguistic terms. Afterwards, for multi-criteria group decision making problems with multi-granular unbalanced linguistic information, we develop two optimization models to generate adjustment advice for decision makers who have to change his/her opinions in consensus reaching process, which consider both the bounded confidence levels and minimum adjustment of decision makers’ linguistic assessments. Moreover, an algorithm is further proposed to help decision makers reach consensus in group decision making. Eventually, an application example for ERP system supplier selection and some simulation results are presented to illustrate and justify the consensus reaching algorithm.     

An integrated approach to machine selection problem using fuzzy SMART-fuzzy weighted axiomatic design

In respond to new market requirements and competitive positioning of manufacturing companies selecting optimal machines that are consistent with manufacturing goals is of crucial importance. As it involves multiple conflicting criteria and inherent ambiguity and vagueness, election of a suitable machine can be regarded as a fuzzy multi-criteria decision making problem. In this study, for the first time in the literature, an integrated approach consisting of fuzzy simple multiattribute rating technique (SMART) approach and fuzzy weighted axiomatic design (FWAD) approach is proposed to determining the optimal continuous fluid bed tea dryer for a privately owned tea plant operating in Turkey. The weights of the evaluation criteria are calculated via fuzzy SMART and then FWAD is utilized to rank competing machine alternatives in terms of their overall performance. In the FWAD application phase, five experts have determined functional requirements (FRs) and have rated alternatives. Therefore, individual fuzzy opinions were required to be aggregated in order to set up a group consensus. A group decision analysis, referred to as the least squares distance method is used to aggregating the ratings of FRs and alternatives. It is concluded that the proposed hybrid methodology is a robust decision support tool for ranking machine alternatives under fuzzy environment and furthermore, it can be exploited for other fuzzy decision making problems, as well.     

Sustainable supplier selection: A ranking model based on fuzzy inference system

In these days, considering the growth of knowledge about sustainability in enterprise, the sustainable supplier selection would be the central component in the management of a sustainable supply chain. In this paper the sustainable supplier selection criteria and sub-criteria are determined and based on those criteria and sub-criteria a methodology is proposed onto evaluation and ranking of a given set of suppliers. In the evaluation process, decision makers’ opinions on the importance of deciding the criteria and sub-criteria, in addition to their preference of the suppliers’ performance with respect to sub-criteria are considered in linguistic terms. To handle the subjectivity of decision makers’ assessments, fuzzy logic has been applied and a new ranking method on the basis of fuzzy inference system (FIS) is proposed for supplier selection problem. Finally, an illustrative example is utilized to show the feasibility of the proposed method.     

A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM

Over recent years, the manufacturing industry has seen constant growth and change. From one side, it has been affected by the fourth industrial revolution (Industry 4.0). From the other side, it has had to enhance its ability to meet higher customer expectations, such as producing more customized products in a shorter time. In the contemporary competitive market of manufacturing, quality is a criterion of primary importance for winning market share. Quality improvement must be coupled with a concern for high performance. One of the most promising concepts for quality control and improvement is called zero defect manufacturing (ZDM), which utilizes the benefits of Industry 4.0 technologies. ZDM imposes the rule that any event in the production process should have a counter-action to mitigate it. In light of this, the current research developed a methodology the manufacturer can use to correctly select or design appropriate ZDM strategies and equipment to implement at each manufacturing stage. This methodology consists of several steps. The first step is to conduct several simulations using a dynamic scheduling tool with specific data sets to develop a digital twin (DT). The data sets are created using the Taguchi design of experiments methodology. The DT model is created for use in predicting the results of the developed scheduling tool without actually using said tool. Using the DT, multiple ZDM parameter-combination sets can be created and plugged into the model. This process generates ZDM performance maps that show the effect of each ZDM strategy at each manufacturing stage under different control parameters. These maps are intended to provide information for comparing different ZDM-oriented equipment to help manufacturers reach a final decision on correct and efficient ZDM implementation or to assist in the design phase of a ZDM strategy implementation.     

Data-Enabled Modeling and Analysis of Multistage Manufacturing Systems with Quality Rework Loops

In a multistage serial production line, multiple inspection stations and repair processes are typically involved to ensure high product quality. Quality rework is the activity to repair or repeat the work on the defect parts during manufacturing processes. The rework process after each inspection can add cost and cycle time to the normal process and impose negative impact on the throughput. This paper studies real-time performance of multistage serial manufacturing systems with quality rework loops and machine random failures. A production line with multiple quality rework loops is first unify by segmenting it into a set of serially connected quality rework loops. An event-based data-enabled mathematical model is developed to evaluate real-time production rate of each machine for such a system. In addition, the system properties are analyzed and permanent production loss due to quality rework loops and random machine failures are identified respectively. The permanent production loss attribution to each disruption event and machine can be used as real-time performance indicators to diagnose production system inefficiency. The mathematical model and system performance identification methodology are studied analytically and validated through numerical case studies.     

A case-based method for service-oriented value chain and sustainable network design

PurposeThe purpose of this research is to present a case-based analytic method for a service-oriented value chain and a sustainable network design considering customer, environmental and social values. Enterprises can enhance competitive advantage by providing more values to all stakeholders in the network.Design/methodology/approachOur model employs a stylized database to identify successful cases of value chain application under similar company marketing conditions, illustrating potential value chains and sustainable networks as references. This work first identifies economic benefits, environmental friendliness and social contribution values based on prior studies. Next, a search engine which is developed based on the rough set theory will search and map similarities to find similar or parallel cases in the database. Finally, a visualized network mapping will be automatically generated to possible value chains.FindingsThis study applies a case-based methodology to assist enterprises in developing a service-oriented value chain design. For decision makers, this can reduce survey time and inspire innovative works based on previous successful experience. Besides, successful ideas from prior cases can be reused. In addition to customer values, this methodology incorporates environment and social values that may encourage a company to build their value chain in a more comprehensive and sustainable manner.Research implicationsThis is a pilot study which attempts to utilize computer-aided methodology to assist in service or value-related design. The pertinent existing solutions can be filtered from an array of cases to engage the advantages from both product-oriented and service-oriented companies. Finally, the visualized display of value network is formed to illustrate the results.Practical implicationsA customized service-oriented value chains which incorporates environment and social values can be designed according to different conditions. Also, this system engages the advantages from both product-oriented and service-oriented companies to build a more comprehensive value network. Apart from this, the system can be utilized as a benchmarking tool, and it could remind the decision makers to consider potential value from a more multifaceted perspective.Originality/valueThis is the first paper that applied a computer-aided method to design service-oriented value chains. This work also can serve as a decision support and benchmarking system because decision makers can develop different value networks according to various emphasized values. Finally, the visualized display of value network can improve the communication among stakeholders.     

A Semiautomatic Experience-Based Tool for Solving Product Innovation Problem

ABSTRACT

In this paper, we present the idea of Smart Innovation Engineering (SIE) System and its implementation methodology. The SIE system is semiautomatic system that helps in carrying the process of product innovation. It collects the experiential knowledge from the formal decisional events. This experiential knowledge is collected from the group of similar products having some common functions and features. The SIE system behaves like a group of experts in its domain as it collects, captures, and stores the experiential knowledge from similar products as well as reuses this experiential knowledge that ultimately enhances the innovation process of manufactured goods. Moreover, with SIE in hand, entrepreneurs and manufacturing organizations will be able to take proper, enhanced decisions and most importantly at appropriate time. The system gains expertise each time a decision is taken and stored in the form of set of experience that can be used in future for similar queries. Implementation of the SIE system using Set of Experience Knowledge Structure and Decisional DNA for case study suggests that the SIE system is capable of capturing and reusing the innovation-related experiences of the manufactured products. The case study confirmed that the SIE system can be beneficial for entrepreneurs and manufacturing organizations for efficient decision making in the product innovation process.     

A neuro-genetic approach to design and planning of a manufacturing cell

In this paper, we propose a neuro-genetic decision support system coupled with simulation to design a job shop manufacturing system by achieving predetermined values of targeted performance measures such as flow time, number of tardy jobs, total tardiness and machine utilization at each work center. When a manufacturing system is designed, the management has to make decisions on the availability of resources or capacity, in our setting, the number of identical machines in each work station and the dispatching rule to be utilized in the shop floor to achieve performance values desired. Four different priority rules are used as Earliest due date (EDD), Shortest Processing Time (SPT), Critical ratio (CR) and First Come First Serve (FCFS). In reaching the final decision, design alternatives obtained from the proposed system are evaluated in terms of performance measures. An illustrative example is provided to explain the procedure.     

Flexible decision modeling of reverse logistics system: A value adding MCDM approach for alternative selection

The most favorable reverse manufacturing alternative arriving to collection centers has always been a key strategic consideration of any product recovery system. The nature of these decisions usually is considered to be multidimensional, interdisciplinary, complex, and unstructured due to lack of certainty in environment and information regarding time, quantity and quality of returns, etc. Fuzzy decision methodology provides an alternative framework to handle these reverse logistics system (RLS) complexities and to determine the decision strategies for best alternative selection for reprocessing. Designing a decision-making model for the same requires quantitative and qualitative evaluation based on criteria such as cost/time, legislative factors, environmental impact, quality, market, etc. Performance must be considered on the basis of these criteria to determine a suitable reverse manufacturing option depending on the expert opinion in this domain. In this paper, we propose a multiple criteria decision-making (MCDM) model based on fuzzy-set theory. The proposed model can help in designing effective and efficient flexible return policy depending on the various criteria. Further, companies can use this analysis as a strategic decision-making tool to develop fresh reprocessing facilities or efficiently use the already exiting facility. Finally, an example has been illustrated to highlight the procedural implementation of the proposed model. Further, this paper also makes an attempt to bring fuzzy-based flexible MCDM and reverse logistics together as a well-suited group decision support tool for alternative selections.