Modelling and simulation of dynamically integrated manufacturing systems

Fluctuations in demand patterns and products?? mixes, driven by continuous changes in customer requirements, are inducing significant changes on the operations of manufacturing organisations. How to respond to such changes rapidly and at minimum cost constitutes a major challenge for manufacturers. The DIMS project (Dynamically Integrated Manufacturing Systems) has developed an agent-based approach that enables manufacturing systems to be modelled using multi-agent systems such that optimal and timely responses to changes are generated from the interactions taking place within the multi-agents systems. This approach also incorporates a distributed discrete event simulation mechanism that enables ??what-if?? system configurations that have been generated through agent interactions to be evaluated dynamically for system restructure. This paper presents the approach with particular focus on the distributed simulation mechanism.     

Stochastic skill-based manpower allocation in a cellular manufacturing system

In this paper, stochastic skill-based manpower allocation problem is addressed, where operation times and customer demand are uncertain. A four-phased hierarchical methodology is developed. Egilmez and Süer's [1] stochastic general manpower allocation problem is extended such that each worker's individual performance is considered for a more accurate manpower allocation to manufacturing cells to maximize the production rate. The proposed methodology optimized the manpower levels, product-cell formations and individual worker assignment hierarchically with respect to a specified risk level. Three stochastic nonlinear mathematical models were developed to deal with manpower level determination, cell loading and individual worker assignment phases. In all models, processing times and demand were assumed to be normally distributed. Firstly, alternative configurations were generated. Secondly, IID sampling and statistical analysis were utilized to convert probabilistic demand into probabilistic capacity requirements. Thirdly, stochastic manpower allocation was performed and products were loaded to cells. In the final phase, individual worker assignments were performed. The proposed methodology was illustrated with an example problem drawn from a real manufacturing company. The hierarchical approach allows decision makers to perform manpower level determination, cell loading and individual worker assignment with respect to the desired risk level. The main contribution of this approach is that each worker's expected and standard deviation of processing time on each operation is considered individually to optimize the manpower assignment to cells and maximize the manufacturing system production rate within a hierarchical robust optimization approach.     

System dynamics of supply chain network organization structure

Information technology is providing manufacturers with additional flexibility with regard to their supply chain network choices. Our research studies supply chain network organization structures categorized by the organic and mechanistic management control structures. The structural impacts on cost and fill rate performance are studied in two-echelon and two-supply-chain network organization models under different market coordination conditions using system dynamic simulations. Our results show significant effects of demand and network structural factors, and their interactions, on these measures. As demand becomes dynamic, the cooperative interaction model, where supply chains cooperate to satisfy customer demand, is found to have better system performance than the competitive supply chain model. The analysis also suggests that increasing the responsiveness at the downstream plant is particularly important to the overall system performance improvement.     

A solution procedure for mixed-integer nonlinear programming formulation of supply chain planning with quantity discounts under demand uncertainty

Quantity discount policy is decision-making for trade-off prices between suppliers and manufacturers while production is changeable due to demand fluctuations in a real market. In this paper, quantity discount models which consider selection of contract suppliers, production quantity and inventory simultaneously are addressed. The supply chain planning problem with quantity discounts under demand uncertainty is formulated as a mixed-integer nonlinear programming problem (MINLP) with integral terms. We apply an outer-approximation method to solve MINLP problems. In order to improve the efficiency of the proposed method, the problem is reformulated as a stochastic model replacing the integral terms by using a normalisation technique. We present numerical examples to demonstrate the efficiency of the proposed method.     

Optimal decisions for a two-echelon supply chain with capacity and demand information

Due to the applications of Internet of Things and big data in the Industry 4.0 context, more information in and out of a smart factory can be collected and shared between manufacturers and retailers. In this study, we consider two types of information that can be available in a supply chain consisting of a manufacturer and a retailer in Industry 4.0: the capacity information for the later rush production and the demand information shared between the retailer and manufacturer. In the supply chain, the manufacturer provides two orders with maximum limits by using a capacitated normal production and two capacitated rush production modes. To study the effects of the information, we investigate the optimal decisions and profits for the supply chain with and without the capacity information and demand information sharing. In addition, we propose a coordination mechanism for the supply chain with both the capacity information and demand information sharing. The coordination mechanism does not only rely on cost parameters, but also on the capacity and demand information. The numerical examples show that the supply chain profit can be improved by as large as 16.76% in the coordinated system, compared with the original system without the capacity information and demand information sharing.     

Dynamically Integrated Manufacturing Systems (DIMS)—A Multiagent Approach

Manufacturing businesses in today's market are facing immense pressures to react rapidly to dynamic variations in demand distributions across products and changing product mixes. To cope with the pressures requires dynamically integrated manufacturing systems (DIMS) that can manage optimal fulfillment of customer orders while simultaneously considering alternative system structures to suit changing conditions. This paper presents a multiagent approach to DIMS, where production planning and control decisions are integrated with systems reconfiguration and restructure. A multiagent framework, referred to as a hierarchical autonomous agent network, is proposed to model complex manufacturing systems, their structures, and constraints. It allows the hierarchical structures of complex systems to be modeled while avoiding centralized control in classical hierarchical/hybrid frameworks. Subsystems interact heterarchically with product orders to carry out optimal planning and scheduling. An agent coordination algorithm, operating iteratively under the control of a genetic algorithm, is developed to enable optimal planning and control decisions for order fulfillment to be made through interactions between agents. This algorithm also allows the structural constraints of systems to be relaxed gradually during agent interaction, so that planning and control are first carried out under existing constraints, but when satisfactory solutions cannot be found, subsystems are allowed to regroup to form new configurations. Frequently used configurations are detected and evaluated for system restructure. The approach also enables Petri-net models of new system structures to be generated dynamically and the structures to be evaluated through agent-based discrete event simulation.     

Admission control and scheduling in simple series parallel networksusing fuzzy logic

Two outstanding problems of admission control and scheduling in networks with three and two workstations, respectively, are solved using fuzzy logic. Neither problem has been tackled up until now analytically, whereas the fuzzy approach provides computational solutions. In the first case, we have one workstation with two parallel ones. A reward is earned whenever the first stage accepts a customer and a holding cost is incurred by a customer in queue in the second stage. The class of customer to be next served by the first stage is dynamically selected so as to maximize an average benefit over an infinite horizon. In the second case, there are two parallel servers and three arrival processes generated by independent Poisson streams. Each server has its own queue and receives customers from its own arrival stream. A third arrival stream consists of customers with resource demand on both servers. Each customer pays a holding cost per unit time in the system. Again, the scheduling policy is specified which minimizes the average cost. The fuzzy models are new in this context and tackle computationally problems for which we have not analytical solutions     

Model‐centric software architecture reconstruction

Much progress has been achieved in defining methods, techniques, and tools for software architecture reconstruction (SAR). However, less progress has been achieved in constructing reasoning frameworks from existing systems that support organizations in architecture analysis and design decisions. These reasoning frameworks are necessary, for example, to assemble existing components and deploy them in new system configurations. We propose a model‐centric approach where this kind of reasoning is driven by the analysis of quality attribute scenarios. The scenarios and the related quality attribute models guide the SAR effort by focusing on the elicitation of model relevant artifacts. The approach further drives the model construction towards the analytical support of What If scenarios that explore responses stimulated by new requirements, such as new deployments of existing components. The paper provides two real‐world case studies. The first case study introduces the model‐centric reconstruction approach in the context of a large satellite tracking system. The second case study provides the construction of a time performance model for an existing embedded system in the automotive industry. The model allows us to perform cost‐efficient predictions of component assemblies in new customer configurations. Copyright © 2005 John Wiley & Sons, Ltd.     

Process-data-warehousing-based operator support system for complex production technologies

Process manufacturing is increasingly being driven by market forces, customer needs, and perceptions, resulting in more and more complex multiproduct manufacturing technologies. The increasing automation and tighter quality constraints related to these processes make the operator's job more and more difficult. This makes decision support systems (DSSs) for the operator more important than ever before. A traditional operator support system (OSS) focuses only on specific tasks that are performed. In the case of complex processes, the design of an integrated information system is extremely important. The proposed data-warehouse-based OSS makes possible linking complex and isolated production units based on the integration of the heterogenous information collected from the production units of a complex production process. The developed OSS is based on a data warehouse designed by following the proposed focus-on-process data-warehouse-design approach, which means stronger focus on the material and information flow through the entire enterprise. The resulting OSS follows the process through the organization instead of focusing separate tasks of the isolated process units. For human-computer interaction, front-end tools have been worked out, where exploratory data analysis and advanced multivariate statistical models are applied to extract the most informative features of the operation of the technology. The concept is illustrated by an industrial case study, where the OSS is designed for the monitoring and control of a high-density polyethylene (HDPE) plant.     

Empty container repositioning strategy in intermodal transport with demand switching

In this paper, we study the empty container inventory repositioning problem with customer demand switching in intermodal transport. The objective of this article is to solve the empty container repositioning problem by contract coordination theory, and to improve the coordination of empty container management and the profit of each participant. We consider an intermodal transport system composed of the rail firm and the liner firm. First, we have considered the situation of no cooperation between the dry port and the seaport, and established a model where there is only the customer demand switching without the occurrence of empty container repositioning. Next, we consider the cooperation between the dry port and the seaport in the decentralized model and the centralized model, and set up the empty container repositioning models from the seaport to the dry port respectively. We analyse the optimal inventory level for the dry port and the seaport under different models, and the effect of the repositioning price on the optimal inventory level. We then apply the contract coordination theory to the empty container inventory repositioning problem. We propose an inventory coordination strategy based on a revenue sharing contract and coordinate the intermodal transport system by choosing the appropriate contract parameters. The results of the study show that under the guidance of the seaport, the revenue sharing contract can achieve a win-win situation for the dry port and the seaport.     

An augmented Petri Net approach for error recovery in manufacturing systems control

The construction of error recovery Petri subnets and similar representations have received considerable attention in the literature. Previous work has presented a multi-agent system representing various levels of control in a reconfigurable architecture. Agents pertaining to production, mediation, and error recovery within such an architecture were considered. Our focus here is on the workstation level of a hierarchy where the workstation has the capability for recovery from physical errors. The implications of error recovery tasks from the perspective of control are also discussed. The approach is based on integrating Petri subnet models within a general Petri Net model for a manufacturing system environment. In essence, the error recovery plan consists of a trajectory (Petri subnet) having the detailed recovery steps that are then incorporated into the workstation control logic. The logic is based on a Timed Petri Net model of the total production system. The Petri subset models consist of a sequence of steps required to reinstate the system back to a normal state. Once generated, the recovery subnet is incorporated into the Petri Net model of the original expected (error-free) model. Petri Net augmentations pertaining to various issues are discussed in detail throughout the paper. Issues include the implication of generated error recovery trajectories in the production activities, linking of production activity Net and the error recovery subnet, potential deadlocks, the role of resources, and part handling.     

Proposing an architectural framework of a hybrid knowledge-based system for production rescheduling

An architectural configuration of a knowledge-based system for production rescheduling reported in this paper uncovers a number of points of interest to practitioners as well as researchers. The study shows that knowledge-based methods applied to production rescheduling are a valuable approach for manufacturers to manage production disturbances and deliver customer orders on time. Very often, developing an effective scheduling system whilst solving some problems requires an appropriate combination of a rigorous analysis of the production system state and the rules of thumb used by the human scheduler. In the actual performance of this hybrid system, an expert simulation system was used to produce new schedules that fit the real production environment.     

Impact of the shape of demand distribution in decision models for operations management

Decision support tools are increasingly used in operations where key decision inputs such as demand, quality, or costs are uncertain. Often such uncertainties are modeled with probability distributions, but very little attention is given to the shape of the distributions. For example, state-of-the-art planning systems have weak, if any, capabilities to account for the distribution shape. We consider demand uncertainties of different shapes and show that the shape can considerably change the optimal decision recommendations of decision models. Inspired by discussions with a leading consumer electronics manufacturer, we analyze how four plausible demand distributions affect three representative decision models that can be employed in support of inventory management, supply contract selection and capacity planning decisions. It is found, for example, that in supply contracts flexibility is much more appreciated if demand is negatively skewed, i.e., has downside potential, compared to positively skewed demand. We then analyze the value of distributional information in the light of these models to find out how the scope of improvement actions that aim to decrease demand uncertainty vary depending on the decision to be made. Based on the results, we present guidelines for effective utilization of probability distributions in decision models for operations management.     

Data-driven dynamic bottleneck detection in complex manufacturing systems

Production (throughput) bottlenecks are the critical stations defining and constraining the overall productivity of a system. Effective and timely identification of bottlenecks provide manufacturers essential decision input to allocate limited maintenance and financial resources for throughput improvement. However, identifying throughput bottleneck in industry is not a trivial task. Bottlenecks are usually non-static (shifting) among stations during production, which requires dynamic bottleneck detection methods. An effective methodology requires proper handling of real-time production data and integration of factory physics knowledge. Traditional data-driven bottleneck detection methods only focus on serial production lines, while most production lines are complex. With careful revision and examination, those methods can hardly meet practical industrial needs. Therefore, this paper proposes a systematic approach for bottleneck detection for complex manufacturing systems with non-serial configurations. It extends a well-recognized bottleneck detection algorithm, “the Turning Point Method”, to complex manufacturing systems by evaluating and proposing appropriate heuristic rules. Several common industrial scenarios are evaluated and addressed in this paper, including closed loop structures, parallel line structures, and rework loop structures. The proposed methodology is demonstrated with a one-year pilot study at an automotive powertrain assembly line with complex manufacturing layouts. The result has shown a successful implementation that greatly improved the bottleneck detection capabilities for this manufacturing system and led to an over 30% gain in Overall Equipment Effectiveness (OEE).     

全球仓库配送系统创建高效的物流

The heart of any modern manufacturing business is an efficient warehouse management and distribution service. Customers' expectations about product delivery times and availability have changed such that they demand fast service-at low cost and with low errors. This paper introduces a computerized system solution called Warehouse Administration Service System or WASS, which enables the manufacturers, especially those manufacturers who are doing global business, to control their product flow from production to the customer. WASS supports the receiving, storing, shipping and inventory management of goods in which manufacturers create an effective logistics and supply chain management. The paper also shows the successful case that SKF* uses the WASS in its global warehouse distribution network to service customers in the most efficient way and concept of green supply chain.     

Scheduling concurrent production over a finite planning horizon: polynomially solvable cases

The paper analyzes a manufacturing system made up of one workstation which is able to produce concurrently a number of product types with controllable production rates in response to time-dependent product demands. Given a finite planning horizon, the objective is to minimize production cost, which is incurred when the workstation is not idle and inventory and backlog costs, which are incurred when the meeting of demand results in inventory surpluses and shortages. With the aid of the maximum principle, optimal production regimes are derived and continuous-time scheduling is reduced to a combinatorial problem of sequencing and timing the regimes. The problem is proved to be polynomially solvable if demand does not exceed the capacity of the workstation or it is steadily pressing and the costs are “agreeable”.

Scope and purpose

Efficient utilization of modern flexible manufacturing systems is heavily dependent on proper scheduling of products throughout the available facilities. Scheduling of a workstation which produces concurrently a number of product types with controllable production rates in response to continuous, time-dependent demand is under consideration. Similar to the systems considered by many authors in recent years, a buffer with unlimited capacity is placed after the workstation for each product type. The objective is to minimize inventory storage, backlog and production costs over a finite planning horizon. Numerical approaches are commonly used to approximate the optimal solution for similar problems. The key contribution of this work is that the continuous-time scheduling problem is reduced to a combinatorial problem, exactly solvable in polynomial time if demand does not exceed the capacity of the workstation or the manufacturing system is organized such that the early production and storage of a product to reduce later backlogs are justified.     

Using Kano model to differentiate between future vehicle-driving services

Services are constantly changing with the introduction of new technologies, which affect the service systems of both conventional and autonomous driving. New theories and technologies are also key factors affecting the design and development trends of service models and solutions. Major automobile manufacturers aspire to provide customers with unique services and experiences, resulting in a growing demand for systematic approaches to characterize customer behaviors and scientific methods to accurately interpret data stored in databases. This study proposes a scientific engineering and operation framework for driving services that enables conventional automobile manufacturers to re-evaluate their service models and solutions as they expand into the domain of autonomous driving, integrating customized consumer interactions and mass production efficiency to develop new technologies, and subsequently applying these technologies to innovate their driving services, form service innovation guidelines, and accelerate the development of smart applications for the automobile industry. A Kano two-dimensional model of quality was employed. A Kano questionnaire was administered to analyze consumers' perceived satisfaction concerning different service quality elements; the elements were then ranked in the order of requiring improvement to determine the elements that are essential in conventional vehicles. Finally, suggestions were proposed for improving the service quality of driving products and evaluating driver satisfaction. A total of 56 valid questionnaires were collected from potential buyers of four-door sedans. The questionnaire evaluated respondents’ perceived value and satisfaction of 30 product elements categorized into two groups (specific functions and intangible value-added services) across eight major quality dimensions (basic safety functions, multimedia entertainment systems, information and communication systems, value-added systems, active matching, automatic service systems, hardware–software integration, and customer service and support). In addition, Kano quality categories were statistically analyzed to elucidate whether significant differences existed between groups. Using the Kano quality categories, 30 design elements were classified: 10 as “attractive,” 7 as “one-dimensional,” 3 as “must-be,” 4 as “indifferent,” and 6 as “reverse.” Enterprises can effectively reduce customer dissatisfaction and enhance customer satisfaction based on the quality category of the product and the product improvement order proposed in this study.Relevance to industryThis study determined that using the Kano quality categories, enterprises can effectively reduce customer dissatisfaction and enhance customer satisfaction based on the quality category of the product and the product improvement order proposed in this study.     

The integrated production–inventory–distribution–routing problem

The integration of production and distribution decisions presents a challenging problem for manufacturers trying to optimize their supply chain. At the planning level, the immediate goal is to coordinate production, inventory, and delivery to meet customer demand so that the corresponding costs are minimized. Achieving this goal provides the foundations for streamlining the logistics network and for integrating other operational and financial components of the system. In this paper, a model is presented that includes a single production facility, a set of customers with time varying demand, a finite planning horizon, and a fleet of vehicles for making the deliveries. Demand can be satisfied from either inventory held at the customer sites or from daily product distribution. In the most restrictive case, a vehicle routing problem must be solved for each time period. The decision to visit a customer on a particular day could be to restock inventory, meet that day’s demand or both. In a less restrictive case, the routing component of the model is replaced with an allocation component only. A procedure centering on reactive tabu search is developed for solving the full problem. After a solution is found, path relinking is applied to improve the results. A novel feature of the methodology is the use of an allocation model in the form of a mixed integer program to find good feasible solutions that serve as starting points for the tabu search. Lower bounds on the optimum are obtained by solving a modified version of the allocation model. Computational testing on a set of 90 benchmark instances with up to 200 customers and 20 time periods demonstrates the effectiveness of the approach. In all cases, improvements ranging from 10–20% were realized when compared to those obtained from an existing greedy randomized adaptive search procedure (GRASP). This often came at a three- to five-fold increase in runtime, however.     

Inventory management with log-normal demand per unit time

This paper examines optimal policies in a continuous review inventory management system when demand in each time period follows a log-normal distribution. In this scenario, the distribution for demand during the entire lead time period has no known form. The proposed procedure uses the Fenton-Wilkinson method to estimate the parameters for a single log-normal distribution that approximates the probability density function (PDF) for lead time demand, conditional on a specific lead time. Once these parameters are determined, a mixture of truncated exponentials (MTE) function that approximates the lead time demand distribution is constructed. The objective is to include the log-normal distribution in a robust decision support system where the PDF that best fits the historical period demand data is used to construct the lead time demand distribution. Experimental results indicate that when the log-normal distribution is the best fit, the model presented in this paper reduces expected inventory costs by improving optimal policies, as compared to other potential approximations.     

A real-time inventory decision system using Western Electric run rules and ARMA control chart

Inventory management is an important area of production control. In 1999, Pfohl et al. [Pfohl, H.-C., Cullmann, O., & Stölzle, W. (1999). Inventory management with statistical process control: Simulation and evaluation. Journal of Business Logistics, 20, 101–120] developed a real-time inventory decision support system by using the individual control charts for monitoring the inventory level (i.e., stock quantity) and the market demand, in which a series of decision rules are provided to help the inventory manager to determine the time and the quantity to order. In the present paper, a real-time inventory decision system is proposed by incorporating Western Electric run rules into the decision rules of the system. Since the data of demand sometimes present a pattern of time series (i.e., autocorrelation may exist in the data of demand), in the proposed decision system the ARMA control chart is employed to monitor the market demand and the individual control chart is used to monitor the inventory level. A simulation study is conducted to investigate the effects of demand pattern and autocorrelation on the proposed inventory decision system and to verify the effectiveness of the system. The index “service level” is selected as the key indicator for the system performance. Based on the results of the simulation study, it is shown that the performance of the proposed inventory decision system is quite consistent with service level always greater than 90% for various demand patterns.