Possibility Approach o Newsboy Problem

The newsboy problem, also known as news-vendor or the single-period problem is a well-known inventory management problem. Interest in such a problem has increased over the past 40 years partially because the increased dominance of service industrial for which newsboy problem is very applicable in both retailing and service organization. Also, the reduction in product life cycles makes newsboy problem more relevant. Many extensions have been made in last decade, such as different objects and utility function, different supplier pricing policies, different new-vendor pricing policies [2][3][4]. However, almost all of extensions have been made in the probabilistic framework, that is, the uncertainty of demand and supply is characterized by the probability distribution, and the objective function is used to maximizing the expected profit or probability measure of achieving a target profit. There are still some problems left. The one is for life-cycle short products, such as fashion goods, season presents, there is no data to be used for statistical analysis to predict the coming demand. The other is newsboy problem is a typical one-shoot decision problem so that maximizing the expected profit or probability measure seems less meaningful. It seems that possibility theory-based method is another alternative to deal with such kind of decision problem. In this paper the plausible information of demand is dharacterized by the possibility distribution and the optimal order is determined according to the possibilistic decision criteria

A single period inventory model for incorporating two-ordering opportunities under imprecise demand information

The ordering strategy for a single period inventory model is the key to achieve success in the competitive business environment. This article considers demand in a form of fuzzy number and discusses the SPIM in which the retailer has the opportunity to reorder once during the period. The entire period/season is divided into two slots and the reorder is to be made during the mid-season after the early-season demand has been observed. The objective is to find the expected optimal order quantity together with profit maximization. We illustrate the implementation of the proposed model using a numerical example and explain that the explicit consideration of this reordering opportunity could lead us to better results in terms of profitability

Coordination of a Retail Supply Chain Distribution Flow

Retail supply chains are very sensitive by their nature and need to adapt to several situations with the aim to increase their reliability, flexibility and convenience. There are many factors affecting the effectiveness of a distribution flow, from perishability, capacities of storage areas, lead times, untimely deliveries and others. Because the latter heavily depend on the planned and realized distribution and not on the demand side perspective, we partially neglect them in the initial study. We focus only on the demand satisfaction, without considering any pricing policies, perishability factors, etc. Beside stochastic demand modelling we introduce the multi-objective optimization approach to cope with the minimization of transport and warehouse costs, minimization of over stock effects and the maximization of customers’ service level. Methodology used produces a set of solutions and its quality estimations in order to find the desired distribution plan that is near optimal. The paper further explains the integration of management decisions with respect to the obtained results of the modelling approach. The applicability of the model will be explained using a numerical example

Fuzzy Bi-level Decision-Making Techniques: A Survey

© 2016 the authors. Bi-level decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a bi-level hierarchy. A challenge in handling bi-level decision problems is that various uncertainties naturally appear in decision-making process. Significant efforts have been devoted that fuzzy set techniques can be used to effectively deal with uncertain issues in bi-level decision-making, known as fuzzy bi-level decision-making techniques, and researchers have successfully gained experience in this area. It is thus vital that an instructive review of current trends in this area should be conducted, not only of the theoretical research but also the practical developments. This paper systematically reviews up-to-date fuzzy bi-level decisionmaking techniques, including models, approaches, algorithms and systems. It also clusters related technique developments into four main categories: basic fuzzy bi-level decision-making, fuzzy bi-level decision-making with multiple optima, fuzzy random bi-level decision-making, and the applications of bi-level decision-making techniques in different domains. By providing state-of-the-art knowledge, this survey paper will directly support researchers and practitioners in their understanding of developments in theoretical research results and applications in relation to fuzzy bi-level decision-making techniques

An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection

Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts’ evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm

An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions

Every practice in supply chain management (SCM) requires decision making. However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions. In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM. Although there were review papers about either fuzzy methods or SCM, most of them did not use bibliometrics methods or did not consider fuzzy sets theory-based techniques comprehensively in SCM. In this paper, for the purpose of analyzing the advances of fuzzy techniques in SCM, we review 301 relevant papers from 1998 to 2020. By the analyses in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection, are found. Finally, we propose future directions regarding fuzzy techniques in SCM. It is hoped that this paper would be helpful for scholars and practitioners in the field of fuzzy decision making and SCM

Supply chain outsourcing risk using an integrated stochastic-fuzzy optimization approach.

a b s t r a c t A stochastic fuzzy multi-objective programming model is developed for supply chain outsourcing risk management in presence of both random uncertainty and fuzzy uncertainty. Utility theory is proposed to treat stochastic data and fuzzy set theory is used to handle fuzzy data. An algorithm is designed to solve the proposed integrated model. The new model is solved using the proposed algorithm for a three stage supply chain example. Computation suggests an analysis of risk averse and procurement behavior, which indicates that a more risk-averse customer prefers to order less under uncertainty and risk. Tradeoff game analysis yields supported points on the trade-off curve, which can help decision makers to identify proper weighting scheme where Pareto optimum is achieved to select preferred suppliers