A matrix-based Bayesian approach for manufacturing resource allocation planning in supply chain management

Nowadays, the supply chain of manufacturing resources is typically a large complex network, whose management requires network-based resource allocation planning. This paper presents a novel matrix-based Bayesian approach for recommending the optimal resource allocation plan that has the largest probability as the optimal selection within the context specified by the user. A proposed matrix-based representation of the resource allocation plan provides supply chain modelling with a good basis to understand problem complexity, support computer reasoning, facilitate resource re-allocation, and add quantitative information. The proposed Bayesian approach produces the optimal, robust manufacturing resource allocation plan by solving a multi-criteria decision-making problem that addresses not only the ontology-based static manufacturing resource capabilities, but also the statistical nature of the manufacturing supply chain, i.e. probabilities of resource execution and resource interaction execution. A genetic algorithm is employed to solve the multi-criteria decision-making problem efficiently. We use a case study from manufacturing domain to demonstrate the applicability of the proposed approach to optimal manufacturing resource allocation planning.     

A CBFSA approach to resolve the distributed manufacturing process planning problem in a supply chain environment

In this competitive world cost and lead time reduction are of prime concern for manufacturing firms. To achieve this objective manufacturing entities are adopting several management philosophies such as Total Quality Management (TQM), just-in-time (JIT), and theory of constraints (TOC). The present paper addresses the advanced computer-aided process planning (ACAPP) problem in a distributed manufacturing supply chain environment and aims at cost and lead times reduction under several technological constraints. To deal with the complexity of the problem the constraint based fast simulated annealing (CBFSA) algorithm has been explored in this article. Extensive computations have been performed over the well-known benchmarks of advanced planning problems and the results obtained prove the superiority of the proposed algorithm over the prior approaches.     

A bidding decision model in multiagent supply chain planning

The multiagent environment for supply chain planning application is based on a framework unifying the internal behaviour of agents and coordination among agents. This system presents a formal view of coordination using Contract Net Protocol (CNP) that relies on the basic loop of agent behaviours: order receiving, order announcement, bid calculation, and order scheduling followed by order execution. Among these, bid calculation is most difficult. It needs to determine the quantity, cost and time in which a new order can be implemented. Fuzzy programming has made progress in mathematics since Bellman and Zadeh (1970) first studied decision-making in a fuzzy mathematics programming. Currently there are many valuable works in this field (Zimmermann 1983, 1985, Tanaka and Asai 1984) and fuzzy programming has become an effective tool to deal with the decision-making problems in fuzzy systems. Similarly, stochastic programming is a useful tool to treat decision-making problems in a stochastic system (Kolbin 1977, Kall and Wallace 1994). However, in many practical systems, fuzzy factors and random factors arise concurrently, and this problem has not received the attention it deserves. Therefore, there is a need to develop a new kind of optimization technique to make decisions in a fuzzy random system. In this paper, we build the Bid Calculation model, including a random parameter set, the set of product quantity that will be stored to inventory, a fuzzy parameter set, the Maximum Sales Rates (MSR) set, and we discuss an approach to solve the model, as well as present an implementation procedure with the GA method.     

A control engineering approach to the assessment of supply chain resilience

There is no consensus on the supply chain management definition of resilience. To aid in evaluating the dynamic behaviour of such systems we need to establish clearly elucidated performance criteria that encapsulate the attributes of resilience. A literature review establishes the latter as readiness, responsiveness and recovery. We also identify robustness as a necessary condition that would complement resilience. We find that the Integral of the Time Absolute Error (ITAE) is an appropriate control engineering measure of resilience when it is applied to inventory levels and shipment rates. We use the ITAE to evaluate an often used benchmark model of make-to-stock supply chains consisting of three decision parameters. We use both linear and nonlinear forms of the model in our evaluation. Our findings suggest that optimum solutions for resilience do not yield a system that is robust to uncertainties in lead-time. Hence supply chains will experience drastic changes in their resilience performance when lead-time changes.     

The point of purchase decision in a supply chain with value-added reselling

We explore a stylised decision model in order to better understand the trade-offs inherent in locating the point of purchase on a supply chain. In simple terms, a supply chain can be visualised as a series of value-adding production stages where the end product becomes increasingly differentiated as it travels downstream. The manufacturer is responsible for all processing up to the point of purchase, whereas the buyer (a value-added reseller) is responsible for further processing up to the end of product completion. Product demand becomes more and more uncertain with each downstream stage resulting in ever larger errors in matching demand with supply. Our paper proposes an approach and a model for finding the best location for the interface between the manufacturer and buyer so that the buyer’s expected total profit is maximised, and explores how product characteristics such as configurability and lead time sensitivity of demand might impact the results.     

Optimal manufacturing and delivery schedules in a supply chain system of deteriorating items

The market is changing from minute to minute nowadays. Increasing cooperation and pursuing of the optimal interest of the integrated supply chain system become more effective than acting alone in the face of competition. In this research, an integrated inventory policy between a single producer and a multi-buyer is developed. The model extends the research of Lin and Lin (2007 Lin, C and Lin, YS. 2007. A cooperative inventory policy with deteriorating items for a two-echelon model. European Journal of Operational Research, 178(1): 92–111. [Crossref], [Web of Science ®] , [Google Scholar], A cooperative inventory policy with deteriorating items for a two-echelon model. European Journal of Operational Research, 178 (1), 92–111) by changing the single-buyer system to the multi-buyer system. This problem is solved under the assumptions of equal replenishments and production cycles. The producer and buyers collaboratively intend to decrease their joint total cost of operation. We consider the deterioration of items at all levels of producer, buyer and in-transport, and the backorders at buyer's level only. The algorithm to find the optimal solution is given, and Matlab and Maple software are used. A numerical example is also presented.     

A network approach to cell formation in cellular manufacturing

Formation of machine cells often results in some intercellular movements between the cells. These movements cause lack of segregation among the cells. This is in conflict with the main objective of group technology which aims at independently operating cells. This paper presents a non-heuristic network approach to form manufacturing cells with minimum intercellular interactions. The machine-part matrix containing machining times is represented as a network which is subsequently partitioned by using a modified Gomory-Hu algorithm to find a minimum intercellular interaction. The modified algorithm improves the computational efficiency.     

A decomposition approach for the performance analysis of a serial multi-echelon supply chain

Over the last decade, supply chain coordination has attracted much attention in the context of industrial logistics systems, because of the role played in production support logistics. There is a growing interest in the analysis and measurement of performances, because performance analysis allows us to build supporting decision tools for the design, planning and management of the supply chain. This paper presents an analytical approach for performance analysis based on a decomposition principle. The approach is applied to a serial supply chain that consists of two levels of suppliers, a production plant and a distribution centre, and operates as a pull system. This configuration is typical for bulk materials such as grain. The method allows us to approximately calculate performance indicators (such as customer satisfaction and inventory levels) taking into account all the interactions between the different echelons of the supply chain. The accuracy of the approximations is confirmed by the results obtained via a simulation model. Numerical analysis allows us to outline interesting and non-intuitive behaviour of the performance indicators for different stages of the supply chain.     

A multi-criteria decision model to determine inspection intervals of condition monitoring based on delay time analysis

In periodic monitoring, the main problem is determining the inspection interval of condition monitoring. For this problem, the decision variable is represented by the time of next inspection of condition monitoring. There are several studies that deal with prescribing inspection intervals. But only a few of these allow the decision maker to observe simultaneously more than one aspect. This does not accord with the natural tendency of the decision maker who desires to see the decision problem from a broader perspective, by having different viewpoints or dimensions of choices. Therefore, the main objective of this paper is to propose a decision model, which can simultaneously determine inspection intervals for condition monitoring regarding the failure behavior of equipment to be inspected, features of maintainability and decision maker preferences about cost and downtime.     

A heuristic approach to the formulation of manufacturing strategy

In this paper an attempt is made to define and analyse the critical subsystems relationships within a manufacturing strategy. For this purpose the whole production function is described as a pressurized Venturi system in an effort to clarify complex input-output and control relationships within a typical batch manufacturing environment. The value of scheduling methods is also discussed with attention directed towards critical value added stages and throughput time in the manufacturing process.     

A machine learning approach to yield management in semiconductor manufacturing

Yield improvement is one of the most important topics in semiconductor manufacturing. Traditional statistical methods are no longer feasible nor efficient, if possible, in analysing the vast amounts of data in a modern semiconductor manufacturing process. For instance, a typical wafer fabrication process has more than 1000 process parameters to record on a single wafer and one manufacturing plant may produce tens of thousands wafers a day. Traditional approaches have limits in extracting the full benefits of the data. Therefore, the manufacturing data is poorly exploited even in the most sophisticated processes. Now it is widely accepted that machine learning techniques can provide powerful tools for continuous quality improvement in a large and complex process such as semiconductor manufacturing. In this work, memory based reasoning (MBR) and neural network (NN) learning are combined for yield improvement and an integrated framework is proposed for a yield management system based on hybrid machine learning techniques. In this hybrid system of NN and MBR, the feature weight set which is calculated from the trained neural network plays the core role in connecting both learning strategies and the explanation on prediction can be given by obtaining and presenting the most similar examples from the case base. The proposed system has advantages in typical semiconductor manufacturing problems such as scalability to large datasets, high dimensions and adaptability to dynamic situations.     

A hybrid approach to multi-scale modelling of cancer

In this paper, we review multi-scale models of solid tumour growth and discuss a middle-out framework that tracks individual cells. By focusing on the cellular dynamics of a healthy colorectal crypt and its invasion by mutant, cancerous cells, we compare a cell-centre, a cell-vertex and a continuum model of cell proliferation and movement. All models reproduce the basic features of a healthy crypt: cells proliferate near the crypt base, they migrate upwards and are sloughed off near the top. The models are used to establish conditions under which mutant cells are able to colonize the crypt either by top-down or by bottom-up invasion. While the continuum model is quicker and easier to implement, it can be difficult to relate system parameters to measurable biophysical quantities. Conversely, the greater detail inherent in the multi-scale models means that experimentally derived parameters can be incorporated and, therefore, these models offer greater scope for understanding normal and diseased crypts, for testing and identifying new therapeutic targets and for predicting their impacts.     

The power of coordinated decisions for short-life-cycle products in a manufacturing and distribution supply chain

This paper explores ways of conceptualizing and studying the power of coordination and strategic alliances within a manufacturing and distribution supply chain consisting of one manufacturer and one distributor. The supply chain operates to meet price-sensitive random demand of a product with a short product life cycle. We derive closed-form multi-attribute measures of performance for the supply chain in the presence of and in the absence of coordination of pricing and production/ordering decisions. The optimal coordinated pricing and production/ordering policies, as well as each party's optimal policies in the absence of coordination, are developed. Our results yield insights into basic managerial issues for this type of manufacturing and distribution supply chains.     

A decision model for evaluation of flexible manufacturing systems in the presence of both cardinal and ordinal factors

This paper presents a model for evaluating alternative Flexible Manufacturing Systems by considering both quantitative and qualitative factors. The evaluation process utilizes a recently proposed Data Envelopment Analysis (DEA) model, which incorporates both ordinal and cardinal measures, in conjunction with new extensions in DEA. The paper contributes to the body of knowledge in two important ways. First, it provides a new model based on pair-wise comparisons of specific alternatives, which significantly improves the discriminatory power of DEA. The second major contribution is to detail the model's utility in its application for evaluating strategic manufacturing technologies, specifically Flexible Manufacturing Systems. The consideration of both tangible and intangible factors, which is a primary goal in strategic evaluation of systems, is achieved in this methodology. The model is illustrated through an application on a previously reported data set. The analysis of results provide useful insights that include both seller's and buyer's perspectives of system evaluation.     

A decision support framework for global supply chain modelling: an assessment of the impact of demand,supply and lead-time uncertainties on performance

We developed a decision support framework for a global manufacturer of specialty chemicals to study the relative impact of demand, supply and lead-time uncertainties on cost and customer service performance. Our approach combines optimisation and simulation methodologies as follows: mathematical models provide optimal plans via a novel approach to the supply chain planning mechanism of the Company. Simulation models execute the supply chain plans so as to allow the examination of the outcomes under the various sources of uncertainty. The iterative use of optimisation and simulation methodologies allows the user the benefit of obtaining optimal solutions while revealing the impact of uncertainties on system performance. Our results indicate that demand uncertainty has the greatest negative impact on performance for the supply chain that we modelled in this study, emphasising the importance of effective forecasting. The relative importance of supply and lead-time uncertainties varies according to the performance measures. While our results are valid for the specific supply chain and the operating environment we modelled, our study emphasises the importance of the ability to model supply chains realistically to obtain valid and useful results.     

A systems approach to photolithography process optimization in an electronics manufacturing environment

There are many complex problems in the optimization of an electronics manufacturing environment, and it is the view of the authors that these problems should not be solved and optimized in isolation, but analysed in the framework of a system. A systems approach offers an overall approach for solving problems, and optimizing the whole of the system as well as discrete subsystems. The research introduced in this paper integrates several techniques, namely: Integrated computer aided manufacturing DEFinition (IDEF), and experimental design and response surface methods for the analysis, control and optimization of electronic manufacturing processes. Electronics manufacturing includes three major processes; Printed Circuit Board (PCB) manufacturing, semiconductor device manufacturing and electronics assembly. This paper describes a novel generic systematic methodology that has been used to create a model to optimize the photolithography process in PCB manufacture. For this, photolithography has been considered as a whole system made up of several sub-systems. This is shown in the process map for PCBs that focuses on photolithography and its subprocesses. A model of the manufacturing process is then given with the results of this being validated using an industrial study. Optimized settings for processing equipment are given resulting in an increase in process yield within industry.     

A due date-based approach to part type selection in flexible manufacturing systems

Nearly all of the literature on part type selection for flexible manufacturing systems has focused on maximizing throughput. We present an approach for part type selection in which meeting due dates is the primary objective. The approach is based on the idea of using information from the solution of an approximate, aggregate scheduling problem as the basis for determining release priorities. The need for the approximation and aggregation arises because the exact machine configuration (partitioning of identical machines into groups, and loading of tools) cannot be decided until the parts are selected. We develop and evaluate several different policies for setting release priorities in a context where the approximate, aggregate schedule is constructed using list scheduling (dispatching) rules. The results indicate that using information from such a schedule to set release priorities performs far better than using simpler procedures.     

A hybrid approach to the compliance testing of training shoes

There is, as yet, no totally rational method for testing training shoes. The situation is complicated by the coupling that exists between the physical characteristics of the shoes and the biomechanical response to them. This paper sets out to establish a basis for the physical aspects of testing, and shows how the biomechanical response may be integrated into a suitable model. The basic assessment of the compliance properties of training shoes was carried out by the measurement of load-deflection curves. The analysis of these curves was based on the McMahon/Green two mass, two spring model of limb-ground interaction, which defines the tangent modulus of the load-deflection curve as the relevant quality parameter. For a selection of 30 shoe types, the load-deflection curves were analysed to show how a systematic approach may be developed to understanding the variances in the shoe characteristics.
It has also been shown how these findings may in part explain why running shoes which have apparently widely differing load-deflection characteristics have been found to perform rather similarly insofar as the measured level of ground reaction is concerned.