Inventory Policy Proposal for Al-Qur’an Using Fuzzy Economic Production Quantity with Finite Production Rate Method at PT XYZ

Pada dasarnya, dalam model inventory produksi biasanya digunakan model inventory Economic Production Quantity (EPQ). Meskipun seringkali data demand dibuat tetap yang mana tidak mungkin di implementasikan di dunia nyata. Jadi untuk membuat model lebih realistik, kita haru membuat kuantitas demand menjadi variable fuzzy demand. Tujuan dari penelitian ini adalah untuk menghitung skema dari EPQ model dalam keadaan fuzzy sense untuk membuat total persediaan menjadi lebih rendah dari kondisi eksisting. Inventory policy, economic production quantity, fuzzy method, membership function, signed distance method, weighted average metho

Order Pattern Prediction Using Artificial Intelligence In An Inventory System Design

Achieving smmooth production is one of the major concern by the manufacturing industry. In order to have smooth production, waste must be avoided. Furthermore, the cost of investment in production can be high with contribution of Wasted activities especially high inventory management cost. Economic Order Quantity (EOQ) has been applied in inventory management in order to determine economic lot size. However, EOQ has limitation due to uncertain situation. Thus, the aim of this study to reduce cost investment in inventory. This study has three objectives, (1) to investigate ordering pattern ordering pattern which is affected the inventory, (2) to propose order pattern in inventory using ANFIS and (3) to evaluate proposed order pattern with cost investment. The study was conducted based on case study at the furniture company. The historical data of demand and supply was provided for 52 weeks. Firstly, the inventory level was investigated with the historical data based on stochastic EOQ model. From the investigation, shortage occurred because order does not make for a long time. Hence, the total cost of inventory was high. Then, investigated order pattern using Fuzzy Inference System and shortage still occurred. Thus, manual prediction order pattern was developed which to ensure the inventory just below reorder point. This purposed to ensure that every week order was took placed and shortage was avoided. Adaptive Neuro Fuzzy Inference System was used in order to find the parameters in forecasting the order quantity. The result showed that the proposed order pattern can avoid shortage and every week the inventory is below reorder point. Every week order is take place. Also, the total cost is reduced since no shortage occurs

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

New perspectives on realism, tractability, and complexity in economics

Fuzzy logic and genetic algorithms are used to rework more realistic (and more complex) models of competitive markets. The resulting equilibria are significantly different from the ones predicted from the usual static analysis; the methodology solves the Walrasian problem of how markets can reach equilibrium, starting with firms trading at disparate prices. The modified equilibria found in these complex market models involve some mutual self-restraint on the part of the agents involved, relative to economically rational behaviour. Research (using similar techniques) into the evolution of collaborative behaviours in economics, and of altruism generally, is summarized; and the joint significance of these two bodies of work for public policy is reviewed. The possible extension of the fuzzy/ genetic methodology to other technical aspects of economics (including international trade theory, and development) is also discussed, as are the limitations to the usefulness of any type of theory in political domains. For the latter purpose, a more differentiated concept of rationality, appropriate to ill-structured choices, is developed. The philosophical case for laissez-faire policies is considered briefly; and the prospects for change in the way we ‘do economics’ are analysed

Comparative Evaluation of the Performance of Spans of Control Designs in Grain Supply Chains

A fuzzy multi-objective linear programming model is used to analyze the performances of three spans of control designs that are observed in the U.S grain industry. Performance of the grain supply chain increases with amount of control and compromise.Crop Production/Industries,

Analysis of Grain Supply Chain Performance Based on Relative Impact of Channel Coordinator's Objectives on Firm Level Objectives

A fuzzy multi-objective programming model is used to analyze the optimal decisions in a multi-objective grain supply chain in which the firm-level firm goals are conflicting with the channel coordinator's goals. The relative impact of the channel coordinator's goals on performance of the supply chain is determined through a linear weighting method. The study finds that prioritizing the channel coordinator's goals enhances the overall performance of the system.Industrial Organization,

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF