The use of intelligent feedback for work order release in an uncertain manufacturing system

A turbulent manufacturing environment where uncertainty is inevitable does not allow for the availability of the required materials and resources when they are needed. This paper studies the implications of demand surges, lead-time variations and resources breakdown on the ability of a manufacturing system to achieve its delivery target. Simulation modelling was used to represent a stochastic manufacturing system, which is disturbed by these uncertainties. Manufacturing systems each with and without intelligent feedback were modelled. An intelligent feedback is represented via a set of algorithm, which reanalyse and self-organise the new status of the order in the presence of the uncertainties and update the relevant attributes before the order is released. Four types of intelligence were examined: (1) lead-time allowance, (2) capacity allowance, (3) safety stock allowance and (4) batching flexibility. Experiments results from each system were compared. It was found that the manufacturing system with intelligent feedback has a higher ability to achieve its delivery target by proactively tackling the variations caused by uncertainties. This study also found that the reliability of a work order in the presence of uncertainty could be improved by using an appropriate type of intelligence, which is dependent upon how and when the order was released. It was concluded from this research that intelligent feedback could help manufacturing enterprises proactively readjust the release of work orders that will be affected by uncertainties in order to improve the reliability and delivery of work orders.     

A review of function blocks for process planning and control of manufacturing equipment

Manufacturing in a job-shop environment is often characterized by a large variety of products in small batch sizes, requiring real-time monitoring for dynamic distributed decision making, and adaptive control capabilities that are able to handle, in a responsive way, different kinds of uncertainty, such as changes in demand and variations in production capability and functionality. In many manufacturing systems, traditional methods, based on offline processing performed in advance, are used. These methods are not up to the standard of handling uncertainty, in the dynamically changing environment of these manufacturing systems. Using real-time manufacturing intelligence and information to perform at a maximum level, with a minimum of unscheduled downtime, would be a more effective approach to handling the negative performance impacts of uncertainty. The objective of our research is to develop methodologies for distributed, adaptive and dynamic process planning as well as machine monitoring and control for machining and assembly operations, using event-driven function blocks. The implementation of this technology is expected to increase productivity, as well as flexibility and responsiveness in a job-shop environment. This paper, in particular, presents the current status in this field and a comprehensive overview of our research work on function block-enabled process planning and execution control of manufacturing equipment.     

The performance of materials management in multi-level product structures with demand uncertainty and back-ordering

This paper investigates the impact of planned backordering in the context of demand uncertainty for a multi-level product structure environment operating under a rolling horizon. To this end, the performance of lot sizing algorithms incorporating back-orders is evaluated for such an environment by means of simulation modeling. Given the presence of demand uncertainty, the results suggest that backordering algorithms can sometimes improve performance.     

CPS-enabled and knowledge-aided demand response strategy for sustainable manufacturing

The utilization of advanced industrial informatics, such as industrial internet of things and cyber-physical system (CPS), provides enhanced situation awareness and resource controllability, which are essential for flexible real-time production scheduling and control (SC). Regardless of the belief that applying these advanced technologies under electricity demand response can help alleviate electricity demand–supply mismatches and eventually improve manufacturing sustainability, significant barriers have to be overcome first. Particularly, most existing real-time SC strategies remain limited to short-term scheduling and are unsuitable for finding the optimal schedule under demand response scheme, where a long-term production scheduling is often required to determine the energy consumption shift from peak to off-peak hours. Moreover, SC strategies ensuring the desired production throughput under dynamic electricity pricing and uncertainties in manufacturing environment are largely lacking. In this research, a knowledge-aided real-time demand response strategy for CPS-enabled manufacturing systems is proposed to address the above challenges. A knowledge-aided analytical model is first applied to generate a long-term production schedule to aid the real-time control under demand response. In addition, a real-time optimization model is developed to reduce electricity costs for CPS-enabled manufacturing systems under uncertainties. The effectiveness of the proposed strategy is validated through the case study on a steel powder manufacturing system. The results indicate the exceptional performance of the proposed strategy as compared to other real-time SC strategies, leading to a reduction of electricity cost up to 35.6% without sacrificing the production throughput.     

Semiconductor supply planning by considering transit options to take advantage of pre-productions and order cancellations

One of the objectives of supply planning is to find when and how many productions have to be started to minimize total cost. We aim to find the optimum. Base data like the length of transit time are important parameters to plan for the optimum start of production. In this research, we considered two kinds of transit options: normal transit and emergency transit, and we distinguished between planned and executed transit. The decision regarding which transit option to choose for the execution is trivial because emergency is only used when it is needed since normal transit is more cost efficient. However, for planning purpose, it is more difficult to decide which transit option should be used since the uncertainty in demand and supply between plan and execution can allow to plan an emergency transit but to execute the delivery with normal transit, which is a huge benefit in the competitive capital intensive semiconductor industry. If we planned an emergency, we can save inventory and production cost as we can delay the start of production. In contrast, we need pay additional transit cost in case that emergency transit is actually executed. Many characteristics of the semiconductor industry, which might be the front runner for many other industries, are considered in this model such as demand uncertainty, supply uncertainty and economic volatility. In our numerical experiments, we could gain the optimum, depending on each economic situation. Furthermore, we conducted sensitivity analysis of the effect of demand and supply uncertainties on total cost.     

Supply planning under uncertainties in MRP environments: A state of the art

Inventory control in a supply chain is crucial for companies desiring to satisfy their customers demands as well as controlling costs. This paper examines specifically supply planning under uncertainties in MRP environments. Models from literature that deal with random demand or lead time uncertainties are described and commented. Promising research areas emerge from this survey. It appears that lead time uncertainty has been ignored in the past, in spite of their significant importance. In particular, an interesting topic concerns assembly systems with uncertain lead times, for which the main difficulty comes from the inter-dependence of components inventories. Another promising issue, which is also presented, relates to supply planning under simultaneously demand and lead time uncertainties, which is certainly of great interest for both the academic and industrial communities.     

Robust manufacturing system design using multi objective genetic algorithms,Petri nets and Bayesian uncertainty representation

Decisions involving robust manufacturing system configuration design are often costly and involve long term allocation of resources. These decisions typically remain fixed for future planning horizons and failure to design a robust manufacturing system configuration can lead to high production and inventory costs, and lost sales costs. The designers need to find optimal design configurations by evaluating multiple decision variables (such as makespan and WIP) and considering different forms of manufacturing uncertainties (such as uncertainties in processing times and product demand). This paper presents a novel approach using multi objective genetic algorithms (GA), Petri nets and Bayesian model averaging (BMA) for robust design of manufacturing systems. The proposed approach is demonstrated on a manufacturing system configuration design problem to find optimal number of machines in different manufacturing cells for a manufacturing system producing multiple products. The objective function aims at minimizing makespan, mean WIP and number of machines, while considering uncertainties in processing times, equipment failure and repairs, and product demand. The integrated multi objective GA and Petri net based modeling framework coupled with Bayesian methods of uncertainty representation provides a single tool to design, analyze and simulate candidate models while considering distribution model and parameter uncertainties.     

A continuous review production–inventory system in fuzzy random environment: Minmax distribution free procedure

In many manufacturing systems, the production process may take some time to start the initial phase due to various reasons such as delay in installation of machines, short supply of raw materials, unavailability of workers, etc. Thus, the organization should plan accordingly so that the manufacturing process can start at the desired time. In an economic production quantity (EPQ) model, lead-time plays a significant role in ensuring that the manufacturing process starts on time. As we know, when both lead-time and demand rate are deterministic and constant, then demand during the lead-time is constant, and is referred to as zero lead-time. Moreover, when either or both of them are random variables, then lead-time demand (LTD) is a random variable. In such a case, a crucial question is: “when should the order be placed?” On the other hand, the distributional information on demand may not always be available or there may be many distribution functions in the practice, which have same mean and variance, but their frequencies are different. In this study, we develop an EPQ model in stochastic framework, wherein the distribution function of demand is unknown, but the mean and variance are known. The inventory level is continuously reviewed, and an order is placed when it reaches the reorder level. The real-life business situations are so sophisticated and floating in nature that the consideration of ‘impreciseness’ along with ‘statistical variability’ in demand parameter is more preferable. To be a part of this contingency, we further extend the model in the fuzzy random environment by considering demand rate as a fuzzy random variable (FRV). Furthermore, we mathematically analyze the cost function and propose a heuristic procedure to find the global optimum. Numerical examples with sensitivity analysis are also provided for illustration purpose.     

Optimization of Order Fulfillment in Distribution Network Problems

This paper focuses on optimization of order due date fulfillment reliability in multi-echelon distribution network problems with uncertainties present in the production lead time, transportation lead time, and due date of orders. Reliability regarding order due date fulfillment is critical in customer service, and customer retention. However, this reliability can be seriously influenced by supply chain uncertainties, which may induce tardiness in various stages throughout the supply chain. Supply chain uncertainty is inevitable, since most input values are predicted from historical data, and unexpected events may happen. Hence, a multi-criterion genetic integrative optimization methodology is developed for solving this problem. The proposed algorithm integrates genetic algorithms with analytic hierarchy process to enable multi-criterion optimization, and probabilistic analysis to capture uncertainties. The optimization involves determination of demand allocations in the network, transportation modes between facilities, and production scheduling in manufacturing plants. A hypothetical three-echelon distribution network is studied, and the computation results demonstrated the reliability of the proposed algorithms. Received: October 2004 / Accepted: September 2005     

An integrated supply chain model for new products with imprecise production and supply under scenario dependent fuzzy random demand

In the present day business scenario, instant changes in market demand, different source of materials and manufacturing technologies force many companies to change their supply chain planning in order to tackle the real-world uncertainty. The purpose of this paper is to develop a multi-objective two-stage stochastic programming supply chain model that incorporates imprecise production rate and supplier capacity under scenario dependent fuzzy random demand associated with new product supply chains. The objectives are to maximise the supply chain profit, achieve desired service level and minimise financial risk. The proposed model allows simultaneous determination of optimum supply chain design, procurement and production quantities across the different plants, and trade-offs between inventory and transportation modes for both inbound and outbound logistics. Analogous to chance constraints, we have used the possibility measure to quantify the demand uncertainties and the model is solved using fuzzy linear programming approach. An illustration is presented to demonstrate the effectiveness of the proposed model. Sensitivity analysis is performed for maximisation of the supply chain profit with respect to different confidence level of service, risk and possibility measure. It is found that when one considers the service level and risk as robustness measure the variability in profit reduces.     

Quantification of model-form and predictive uncertainty for multi-physics simulation

Traditional uncertainty quantification in multi-physics design problems involves the propagation of parametric uncertainties in input variables such as structural or aerodynamic properties through a single, or series of models constructed to represent the given physical scenario. These models are inherently imprecise, and thus introduce additional sources of error to the design problem. In addition, there often exists multiple models to represent the given situation, and complete confidence in selecting the most accurate model among the model set considered is beyond the capability of the user. Thus, quantification of the errors introduced by this modeling process is a necessary step in the complete quantification of the uncertainties in multi-physics design problems. In this work, a modeling uncertainty quantification framework was developed to quantify to quantify both the model-form and predictive uncertainty in a design problem through the use of existing methods as well as newly developed modifications to existing methods in the literature. The applicability of this framework to a problem involving full-scale simulation was then demonstrated using the AGARD 445.6 Weakened Wing and three different aeroelastic simulation packages to quantify the flutter conditions of the wing.     

Finite horizon planning for a production system with permitted shortage and fixed-interval deliveries

Raw material ordering policy and the manufacturing batch size for frequent deliveries of finished goods for a finite horizon plays a significant role in managing the supply chain logistics economically. This research develops an ordering policy for raw materials and determines an economic batch size for a product in a manufacturing system that supplies finished products to customers for a finite planning horizon. Fixed quantities of finished products are delivered to customers frequently at a fixed interval of time. In this model, an optimal multi-ordering policy for procurement of raw materials and production cycle time for a two-stage production and supply system is developed to minimize the total cost incurred due to raw materials and finished goods inventories. The problem is then extended to compensate for the lost sales of finished products. A closed-form solution to the problem is obtained for the minimal total cost. A lower bound on the optimal solution is also developed for problem with lost sale. It is shown that the solution and the lower bound are consistently tight.     

A simulation study comparing GT vs. job shop manufacturing systems

Two discrete simulation models were developed, one representing a typical job shop (JS) manufacturing system (process layout) and the other representing group technology (GT) manufacturing system (cellular layout). Hypothetical data generated by FORTRAN program and MICRO-CRAFT layout package were used to validate the two models. Simulations were performed using SLAM II. The two layouts were compared with one another under controlled experimental conditions using various combinations of four operating variables: batch size, demand rate, ratio of setup time to process time, and transporter speed. Six indicators of systems performance were used in the study. These were: average time in system, products completed, jobs in process, average machine utilization, average queue length, and average waiting time.

The study found that for environment as set for the models, GT system outperformed JS system as expected. However, using large batch size (more than 75) in Experiment 1 (changing batch size) allowed JS system to perform as well as GT system. Certain specific changes in batch size also allowed to establish optimum results in both systems. No individual change of demand rate (Experiment 2), ratio of setup time to process time (Experiment 3), or transporter speed (Experiment 4) allowed JS system to outperform GT system.     

Approximation algorithms for minimizing the total weighted number of late jobs with late deliveries in two-level supply chains

We study a supply chain scheduling problem in which n jobs have to be scheduled on a single machine and delivered to m customers in batches. Each job has a due date, a processing time and a lateness penalty (weight). To save batch-delivery costs, several jobs for the same customer can be delivered together in a batch, including late jobs. The completion time of each job in the same batch coincides with the batch completion time. A batch setup time has to be added before processing the first job in each batch. The objective is to find a schedule which minimizes the sum of the weighted number of late jobs and the delivery costs. We present a pseudo-polynomial algorithm for a restricted case, where late jobs are delivered separately, and show that it becomes polynomial for the special cases when jobs have equal weights and equal delivery costs or equal processing times and equal setup times. We convert the algorithm into an FPTAS and prove that the solution produced by it is near-optimal for the original general problem by performing a parametric analysis of its performance ratio.     

制造业分形供应链的适应与协调

由蛋白质分形结构的启示,提出了供应链分形代理的内部结构、分形信息组件和协调单元的作用以及制造业分形供应链的嵌套模式,分析了分形供应链在成本和协同性方面的特点．针对供应链运作环境的不确定性,研究了制造业分形供应链动态适应的方法和协调系统的原理,并以汽车制造业为背景对分形供应链管理进行了探索．     

Mini–max regret strategy for robust capacity expansion decisions in semiconductor manufacturing

Semiconductor manufacturing is capital intensive and the capacity utilization significantly affects the capital effectiveness and profitability of semiconductor manufacturing companies. Semiconductor companies have to rely on various demand forecasts of the products fabricated by different technologies as a basis to determine capital investments for capacity expansion plans that require long-lead time. Moving into the consumer electronics era, semiconductor products have increasingly shortened product life cycles with demand fluctuation. However, given the involved uncertainties and long lead-time for capacity expansion, the capacity expansion decisions of semiconductor manufacturing companies are under both the risks of capacity shortage and surplus along with the time. This study aims to propose a mini?Cmax regret strategy for capacity planning under demand uncertainty to improve capacity utilization and capital effectiveness in semiconductor manufacturing. This model considers each of possible outcomes of the multi-period demand forecasts from sales and marketing departments to generate robust capacity expansion plan to minimize the maximum regrets of capacity oversupply and shortage. An empirical study was conducted in a leading semiconductor company in Taiwan for the validation. The results have shown robustness and practical viability of the proposed mini?Cmax strategy for capacity expansion planning under demand uncertainties.     

A hierarchical framework for visualising and simulating supply chains in virtual environments

This paper presents research into applying virtual environment (VE) technology to supply chain management (SCM). Our research work has employed virtual manufacturing environments to represent supply chain nodes to simulate processes and activities in supply chain management. This will enable those who are involved in these processes and activities to gain an intuitive understanding of them, so as to design robust supply chains and make correct decisions at the right time. A framework system and its hierarchical structure for visualising and simulating supply chains in virtual environments are reported and detailed in this paper.     

Agent-based modeling and simulation of an autonomic manufacturing execution system

Production management systems must constantly deal with unplanned disruptive events and disturbances such as arrivals of rush orders, raw material shortage/delays or equipment breakdowns along with a multitude of interactions in the supply chain which constantly demand on-line task rescheduling and order execution control. For responsiveness and agility at the shop-floor, a distributed design for manufacturing execution systems is proposed based on autonomic units that fill the gap between production planning and shop-floor control. An interaction mechanism designed around the concept of order and resource agents implementing the monitor-analyze-plan-execution loop is described. Generative simulation modeling of an autonomic manufacturing execution system (@MES) is proposed in order to evaluate emerging behaviors and macroscopic dynamics in a multiproduct batch plant. Results obtained for an industrial case study using a simulation model of the proposed @MES are presented. The usefulness of agent-based modeling and simulation as a tool for distributed MESs design and to verify performance, stability and disturbance rejection capability of an interaction mechanism is highlighted.     

Regional Air Quality Conformity in Transportation Networks with Stochastic Dependencies: A Theoretical Copula-Based Model

The air quality levels in various regions around the world remain a large public concern. Transportation is known to be a major contributor to reduced air quality levels. Until now, the modeling of the regional impact of transportation on air quality has been based on the assumption of determinism. On the other hand, it is well recognized that transportation systems are subject to both demand and supply uncertainties. In this paper, we relax the assumption of determinism and allow for capacity and link flow uncertainty. We introduce a probability measure – coined the conformity probability – to capture the full probabilistic behavior of vehicular emissions. Moreover, stochastic dependencies are modeled using copulas, generalizing other commonly used dependence modeling techniques in the transportation network modeling arena. In a case study we demonstrate that such a generalization is critical as the ranking of capacity expansion projects to improve air quality is shown to be dependent on the hypothesized dependence structure. Finally, we present some preliminary results that suggest that capacity uncertainty is more detrimental to the environment (i.e. leads to lower conformity probabilities) than demand uncertainty.     

Agent-based web service for the design of a dynamic coordination mechanism in supply networks

The theory of network coordination presents an effective approach to improve the business processes within supply networks. The automation of the negotiation process among buyers and suppliers has become an important policy in the transactional networks. This leads to assessing the roles of both quantifiable and non-quantifiable parameters in coordination mechanisms with the aim of achieving higher performance. Here, we develop an e-based supply chain multi-agent model for the design of mass-customized on-line services. The model addresses the bullwhip effect in multi-stage supply chain and also clarifies the evaluation of inventory policies in various supply and demand uncertainties. To illustrate the feasibility of the approach, we implement a prototype system and evaluate its performance by simulation using Colored Petri Nets (CPNs). The validation results reveal the model efficiency in providing a more realistic optimization process that takes the dynamic information flow in uncertainty environments into consideration.