Fuzzy logic approach to aggregate production planning and an application

İşletmelerde karşılaşılan temel karar verme problemlerinden biri Bütünleşik Üretim Plânlaması (BÜP)'dır. BÜP, orta dönemli plânlama kararlarının alınmasında işgücü düzeyinin, stok düzeyinin, normal ve fazla mesai üretim miktarlarının, ertelenen sipariş miktarlarının ve taşeron gereksiniminin bir bütün olarak değerlendirilmesini ve dengelenmesini amaçlamaktadır. Ancak, değişen çevre koşulları altında talepler, mevcut kaynaklar, kapasiteler ve ilgili üretim maliyetleri gibi parametreler çoğunlukla belirsizdir. Bu nedenle, BÜP problemlerinde verilerin deterministik değil de stokastik veya bulanık olarak alınması gerekmektedir. Bu çalışmada, gerçek hayatın özelliklerini yansıtabilen, belirsizliklerini göz ardı etmeyen ve karar verici ile çözüm süreci boyunca etkileşerek onun da karar sürecine katılımını sağlayan çok amaçlı, çok ürünlü ve çok dönemli bulanık bir BÜP problemi dikkate alınmıştır. Problemin çözümü için bir Etkileşimli Olabilirlikçi Doğrusal Programlama (EODP) modeli önerilmiştir. Önerilen modelin gerçek hayatta uygulanabilirliğini göstermek için Denizli ilinde faaliyet gösteren bir tekstil işletmesinin konfeksiyon bölümü ele alınarak bu bölümün bütünleşik üretim plânı hazırlanmıştır. Modelin etkileşimli yapısı, ele alınan sistemle ilgili olarak karar vericinin daha iyi çözümlere ulaşmayı öğrenebildiği bir öğrenme süreci ve kendi tercihlerine dayanan etkin bir çözüm sağlamıştır. Dolayısıyla, gerçek hayatta karşılaşılan ve belirsizlikler içeren BÜP problemlerinin çözümünde, bulanık mantığın gerçeğe, insanın düşünce ve karar verme mekanizmasına daha yakın sonuçlar verdiği yapılan bu uygulama ile ortaya konmuştur.One of the main decision making problems in firms is Aggregate Production Planning (APP). APP aims at evaluating and balancing the work force level, inventory level, regular and overtime production quantities, backordering levels and subcontract requirement as a whole in the process of taking planning decisions over an intermediate time horizon. However, under the changing environmental conditions, parameters such as demands, available resources, capacities and related production costs are often uncertain. Therefore, the data in APP problems should be taken as stochastic or fuzzy rather than deterministic. In this study, a multi-objective, multi-product and multi-period fuzzy APP problem that is able to reflect real-world features and which does not ignore its uncertainties and ensures decision makers? participation in decision making process by interacting with them during the solution process, has been considered. In order to solve this problem, an Interactive Possibilistic Linear Programming (i-PLP) model has been proposed. By examining the confection department of a textile company operating in Denizli, an aggregate production plan for this department has been prepared in order to demonstrate the applicability of the proposed model in real life. Interactive structure of the model has provided a learning process about the system that the decision makers can learn to achieve better solutions and an efficient solution according to their own preferences. Therefore, with this application it has been revealed that the fuzzy logic provides results closer to reality, human thought and decision-making mechanism for solving APP problems encountered in real life and including uncertainties

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

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

A framework for the selection of the right nuclear power plant

Civil nuclear reactors are used for the production of electrical energy. In the nuclear industry vendors propose several nuclear reactor designs with a size from 35–45 MWe up to 1600–1700 MWe. The choice of the right design is a multidimensional problem since a utility has to include not only financial factors as levelised cost of electricity (LCOE) and internal rate of return (IRR), but also the so called “external factors” like the required spinning reserve, the impact on local industry and the social acceptability. Therefore it is necessary to balance advantages and disadvantages of each design during the entire life cycle of the plant, usually 40–60 years. In the scientific literature there are several techniques for solving this multidimensional problem. Unfortunately it does not seem possible to apply these methodologies as they are, since the problem is too complex and it is difficult to provide consistent and trustworthy expert judgments. This paper fills the gap, proposing a two-step framework to choosing the best nuclear reactor at the pre-feasibility study phase. The paper shows in detail how to use the methodology, comparing the choice of a small-medium reactor (SMR) with a large reactor (LR), characterised, according to the International Atomic Energy Agency (2006), by an electrical output respectively lower and higher than 700 MWe

Improving the quality of the industrial enterprise management based on the network-centric approach

The article examines the network-centric approach to the industrial enterprise management to improve the ef ciency and effectiveness in the implementation of production plans and maximize responsiveness to customers. A network-centric management means the decentralized enterprise group management. A group means a set of enterprise divisions, which should solve by joint efforts a certain case that occurs in the production process. The network-centric management involves more delegation of authority to the lower elements of the enterprise’s organizational structure. The industrial enterprise is considered as a large complex system (production system) functioning and controlled amidst various types of uncertainty: information support uncertainty and goal uncertainty or multicriteria uncertainty. The information support uncertainty occurs because the complex system functioning always takes place in the context of incomplete and fuzzy information. Goal uncertainty or multicriteria uncertainty caused by a great number of goalsestablished for the production system. The network-centric management task de nition by the production system is formulated. The authors offer a mathematical model for optimal planning of consumers’ orders production with the participation of the main enterprise divisions. The methods of formalization of various types of uncertainty in production planning tasks are considered on the basis of the application of the fuzzy sets theory. An enterprise command center is offered as an effective tool for making management decisions by divisions. The article demonstrates that decentralized group management methods can improve the ef ciency and effectiveness of the implementation of production plans through the self-organization mechanisms of enterprise divisions.The work has been prepared with the financial support from the Russian Ministry of Education and Science (Contract No. 02.G25.31.0068 of 23.05.2013 as part of the measure to implement Decision of the Russian Government No. 218)

A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning

Real world combinatorial optimization problems such as scheduling are typically too complex to solve with exact methods. Additionally, the problems often have to observe vaguely specified constraints of different importance, the available data may be uncertain, and compromises between antagonistic criteria may be necessary. We present a combination of approximate reasoning based constraints and iterative optimization based heuristics that help to model and solve such problems in a framework of C++ software libraries called StarFLIP++. While initially developed to schedule continuous caster units in steel plants, we present in this paper results from reusing the library components in a shift scheduling system for the workforce of an industrial production plant.Comment: 33 pages, 9 figures; for a project overview see http://www.dbai.tuwien.ac.at/proj/StarFLIP

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

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (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 and future research directions to better exploit the decision support capabilities of MOO are proposed