Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

A System Dynamics Approach for Modeling Return on Quality for ECS Industry

The Electronic Components and Systems industry (ECS) is characterized by long lead times and high market volatility. Besides fast technological development within this industry, cyclic market up- and downturns are influencing the semiconductor market. Therefore, adequate capacity and inventory management as well as continuous process improvements are important success factors for semiconductor companies to be competitive. In this study, the authors focus on a manufacturing excellence approach to increase front-end supply reliability and the availability of inventory within the customer order decoupling point. Here, development and manufacturing processes must be designed in a way that highest levels of product quality, flexibility, time and costs are reached. The purpose of this study is to explore the impact of return on quality in manufacturing systems. Therefore, multimethod simulation modelling including discrete-event and system dynamics simulation is applied

Event Monitoring System to Classify Unexpected Events for Production Planning

[EN] Production planning prepares companies to a future production scenario. The decision process followed to obtain the production plan considers real data and estimated data of this future scenario. However, these plans can be affected by unexpected events that alter the planned scenario and in consequence, the production planning. This is especially critical when the production planning is ongoing. Thus providing information about these events can be critical to reconsider the production planning. We herein propose an event monitoring system to identify events and to classify them into different impact levels. The information obtained from this system helps to build a risk matrix, which determines the significance of the risk from the impact level and the likelihood. A prototype has been built following this proposal.This research has been carried out in the framework of the project GV/2014/010 funded by the Generalitat Valenciana (Identificacion de la informacion proporcionada por los nuevos sistemas de deteccion accesibles mediante internet en el ambito de las "sensing enterprises" para la mejora de la toma de decisiones en la planificacion de la produccion).Boza, A.; Alarcón Valero, F.; Alemany Díaz, MDM.; Cuenca, L. (2017). Event Monitoring System to Classify Unexpected Events for Production Planning. Lecture Notes in Business Information Processing. 291:140-154. https://doi.org/10.1007/978-3-319-62386-3_7S140154291Barták, R.: On the boundary of planning and scheduling: a study (1999)Buzacott, J.A., Corsten, H., Gössinger, R., Schneider, H.M.: Production Planning and Control: Basics and Concepts. 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Syst. 45(1), 104–112 (2004)Boza, A., Alemany, M.M.E., Vicens, E., Cuenca, L.: Event management in decision-making processes with decision support systems. In: 5th International Conference on Computers Communications and Control (2014)Liao, S.-H.: Expert system methodologies and applications–a decade review from 1995 to 2004. Expert Syst. Appl. 28(1), 93–103 (2005)ISO: 73: 2009: Risk management vocabulary. International Organization for Standardization (2009)Chan, F.T.S., Au, K.C., Chan, P.L.Y.: A decision support system for production scheduling in an ion plating cell. Expert Syst. Appl. 30(4), 727–738 (2006)Weinstein, L., Chung, C.-H.: Integrating maintenance and production decisions in a hierarchical production planning environment. Comput. Oper. Res. 26(10–11), 1059–1074 (1999)Poon, T.C., Choy, K.L., Chan, F.T.S., Lau, H.C.W.: A real-time production operations decision support system for solving stochastic production material demand problems. Expert Syst. 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Auxiliary variables for Bayesian inference in multi-class queueing networks

Queueing networks describe complex stochastic systems of both theoretical and practical interest. They provide the means to assess alterations, diagnose poor performance and evaluate robustness across sets of interconnected resources. In the present paper, we focus on the underlying continuous-time Markov chains induced by these networks, and we present a flexible method for drawing parameter inference in multi-class Markovian cases with switching and different service disciplines. The approach is directed towards the inferential problem with missing data, where transition paths of individual tasks among the queues are often unknown. The paper introduces a slice sampling technique with mappings to the measurable space of task transitions between the service stations. This can address time and tractability issues in computational procedures, handle prior system knowledge and overcome common restrictions on service rates across existing inferential frameworks. Finally, the proposed algorithm is validated on synthetic data and applied to a real data set, obtained from a service delivery tasking tool implemented in two university hospitals

Exact and Approximate Analysis of Multi-Echelon, Multi-Indenture Spare Parts Systems with Commonality

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Flexible Manufacturing Systems: background examples and models

In this paper, we discuss recent innovations in manufacturing technology and their implications on the design and control of manufacturing systems. Recognizing the need to respond properly to rapidly changing market demands, we discuss several types of flexibility that can be incorporated in our production organisation to achieve this goal. We show how the concept of a Flexible Manufacturing System (FMS) naturally arises as an attempt to combine the advantages of traditional Job Shops and dedicated production lines.The main body of the paper is devoted to a classification of FMS problem areas and a review of models developed to understand and solve these problems. For each problem area, a number of important contributions in the literature is indicated. The reader, interested in the applications of Operations Research models but not familiar with the technical background of FMS’s, will find the descriptions of some essential FMS elements useful. Some final remarks and directions for future research conclude the paper.<br/