An integrated AHP–DEA methodology for bridge risk assessment

The traditional analytic hierarchy process (AHP) method can only compare a very limited number of decision alternatives, which is usually not more than 15. When there are hundreds or thousands of alternatives to be compared, the pairwise comparison manner provided by the traditional AHP is obviously infeasible. In this paper we propose an integrated AHP–DEA methodology to evaluate bridge risks of hundreds or thousands of bridge structures, based on which the maintenance priorities of the bridge structures can be decided. The proposed AHP–DEA methodology uses the AHP to determine the weights of criteria, linguistic terms such as High, Medium, Low and None to assess bridge risks under each criterion, the data envelopment analysis (DEA) method to determine the values of the linguistic terms, and the simple additive weighting (SAW) method to aggregate bridge risks under different criteria into an overall risk score for each bridge structure. The integrated AHP–DEA methodology is applicable to any number of decision alternatives and is illustrated with a numerical example.     

ABC inventory classification in the presence of both quantitative and qualitative criteria

Organizations classically employ the ABC analysis to have an efficient control on a large number of inventory items. The customary classification method considers just one criterion, i.e., the annual dollar usage to classify inventory items. Recently, several methods have been developed for ABC inventory classification, especially DEA-like models that account for other important criteria leading to more logical results in practice. However, these models assume that all criteria are of quantitative type and hence cannot handle the qualitative criteria which are not stated numerically but as linguistic terms. To alleviate this shortcoming, this paper proposes a modified version of an existent common weight DEA-like model by using of some concepts in the current imprecise DEA (IDEA) models and then applies it for ABC inventory classification in the case where there exist both quantitative and qualitative criteria. The merits of employing the modified model to solve the multi criteria inventory classification (MCIC) problem are discussed. A case example is also illustrated to demonstrate the applicability of the modified model in the context of MCIC problem as well as its superiority over existing approaches.     

An integrated eigenvector–DEA–TOPSIS methodology for portfolio risk evaluation in the FOREX spot market

The foreign exchange market (FOREX) is the largest financial market in the world, with a volume of over $2 trillion daily. Decision making about buying and selling the existing products in this market depends on several effective factors which cause the high risk in it and make it a sensitive job. So in this paper a new method which is extracted from the multiple decision making methods named eigenvector–DEA–TOPSIS methodology is presented to evaluate the risk of the number of related portfolios to this market. The eigenvector technique is used to determine the weights of criteria and some linguistic terms are applied for assessing portfolio risks under each criterion, then in order to determine the value of linguistic terms we use the data envelopment analysis (DEA) method. Finally we use TOPSIS method for aggregating portfolio risks under different criteria into an overall risk score for each portfolio and ranking the portfolios according to their risks. The integrated eigenvector–DEA–TOPSIS methodology is applicable to any number of decision alternatives and is illustrated with a numerical example.     

Peer-estimation for multiple criteria ABC inventory classification

Inventory classification is an effective way to manage a large number of items. As a basic methodology, ABC analysis is widely used for classification. The traditional ABC classification is based on only a single criterion. However, it is generally recognized that multiple criteria should be considered in practice. A peer-estimation approach is proposed in this paper for multi-criteria inventory classification (MCIC). The proposed approach determines two common sets of criteria weights and aggregates the resulting two performance scores in the most favorable and least favorable senses for each item without any subjectivity. Comparisons of the proposed approach with some previous methods are illustrated based on a classical MCIC problem. It is shown that our proposed approach can provide a more reasonable and comprehensive performance index for MCIC.     

A new approach of inventory classification based on loss profit

Modern production planning and inventory control has been developed in order to treat more practical and more complicated circumstances, such as researching supply chain instead of single stock point; multi-items with correlation instead of single item and so on. In this paper, how to classify inventory items which are correlated each other is discussed by using the concept of ‘cross-selling effect’. In history, the ABC classification is usually used for inventory items aggregation because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. A fundamental principle in ABC classification is that ranking all inventory items with respect to a notion of profit based on historical transactions. The difficulty is that the profit of one item not only comes from its own sales, but also from its influence on the sales of other items or reverse, i.e., the ‘cross-selling effect’. We had previously developed a classification approach for inventory items by using the association rules to deal with the ‘cross-selling effect’ and found that a very different classification can be obtained when comparing with traditional ABC classification. However, the ‘cross-selling effect’ may be considered in different ways. In this paper, a new consideration of inventory classification based on loss rule is presented. The lost profit of item/itemset with ‘cross-selling effect’ is discussed and defined as criterion for evaluating of importance of item, based on which new algorithms on classifying inventory items, also on discovering maximum profit item selection, are presented. A simple example is used to explain the new algorithm, and large amount of empirical experiments, both on real database collected from Japanese convenient store and on downloaded benchmark database, are implemented to evaluate the performances on effectiveness and utility. The results show that the proposed approach in this paper can gain a well insight into the cross-selling effect among items and is applicable for large-sized transaction database.     

Multi-criteria ABC analysis using artificial-intelligence-based classification techniques

ABC analysis is a popular and effective method used to classify inventory items into specific categories that can be managed and controlled separately. Conventional ABC analysis classifies inventory items three categories: A, B, or C based on annual dollar usage of an inventory item. Multi-criteria inventory classification has been proposed by a number of researchers in order to take other important criteria into consideration. These researchers have compared artificial-intelligence (AI)-based classification techniques with traditional multiple discriminant analysis (MDA). Examples of these AI-based techniques include support vector machines (SVMs), backpropagation networks (BPNs), and the k-nearest neighbor (k-NN) algorithm. To test the effectiveness of these techniques, classification results based on four benchmark techniques are compared. The results show that AI-based techniques demonstrate superior accuracy to MDA. Statistical analysis reveals that SVM enables more accurate classification than other AI-based techniques. This finding suggests the possibility of implementing AI-based techniques for multi-criteria ABC analysis in enterprise resource planning (ERP) systems.     

Controlling inventory by combining ABC analysis and fuzzy classification

The objective of inventory management is to make decisions regarding the appropriate level of inventory. In practice, all inventories cannot be controlled with equal attention. The most widespread used inventory system is the ABC classification system, but the limitation of the ABC control system is that only one criterion is considered.The purpose of this paper is to propose a new inventory control approach called ABC–fuzzy classification (ABC–FC), which can handle variables with either nominal or non-nominal attribute, incorporate manager’s experience, judgment into inventory classification, and can be implemented easily.Our ABC–FC approach is implemented based on the data of the Keelung Port. The results of our study show that 59 items are identified as very important group, 69 items as important group, and the remaining 64 items as unimportant group. By comparing the results of ABC–FC with the original data, we find that our ABC–FC analysis shows a high accuracy of classification. Some concluding remarks and suggestions for inventory control are also provided.     

Cross-evaluation-based weighted linear optimization for multi-criteria ABC inventory classification

Multi-criteria ABC inventory classification (MCIC), which aims to classify inventory items by considering more than one criterion, is one of the most widely employed techniques for inventory control. This paper suggests a cross-evaluation-based weighted linear optimization (CE-WLO) model for MCIC that incorporates a cross-efficiency evaluation method into a weighted linear optimization model for finer classification (or ranking) of inventory items. The present study demonstrated the inventory-management-cost effectiveness and advantages of the proposed model using a simulation technique to conduct a comparative experiment with the previous, related investigations. We established that the proposed model enables more accurate classification of inventory items and better inventory management cost effectiveness for MCIC, specifically by mitigating the adverse effect of flexibility in the choice of weights and yielding a unique ordering of inventory items.     

A supplier-selection model with classification and joint replenishment of inventory items

Since inventory costs are closely related to suppliers, many models in the literature have selected the suppliers and also allocated orders, simultaneously. Such models usually consider either a single inventory item or multiple inventory items which have independent holding and ordering costs. However, in practice, ordering multiple items from the same supplier leads to a reduction in ordering costs. This paper presents a model in capacity-constrained supplier-selection and order-allocation problem, which considers the joint replenishment of inventory items with a direct grouping approach. In such supplier-selection problems, the following items are considered: a fixed major ordering cost to each supplier, which is independent from the items in the order; a minor ordering cost for each item ordered to each supplier; and the inventory holding and purchasing costs. To solve the developed NP-hard problem, a simulated annealing algorithm was proposed and then compared to a modified genetic algorithm of the literature. The numerical example represented that the number of groups and selected suppliers were reduced when the major ordering cost increased in comparison to other costs. There were also more savings when the number of groups was determined by the model in comparison to predetermined number of groups or no grouping scenarios.     

An integrated production-inventory-marketing model for deteriorating items

Generally, inventory control policies for deteriorating items are very sensitive to different marketing policies especially in chemical, food and pharmaceutical industries. Realizing the importance of such inventory policies in practice, an integrated production-inventory-marketing model is developed for determining the economic production quantity (EPQ) and economic order quantity (EOQ) for raw materials in a multi-stage production system. This model considers the effect of different marketing policies such as the price per unit product and the advertisement frequency on the demand of a perishable item. A search method is employed to determine the values of EPQ and EOQ which would result in the maximum total net profit.     

Multi-criteria inventory classification with reference items

The ABC method is a well-known approach to classify inventory items into ordered categories, such as A, B and C. As emphasized in the literature, it is reasonable to evaluate the inventory classification problem in the multi-criteria context. From this point of view, it corresponds to a sorting problem where categories are ordered. Here, one important issue is that the weights of the criteria and categorization preferences can change from industry to industry. This requires the analysis of the problem in a specific framework where the decision maker (expert)’s preferences are considered. In this study, the preferences of the decision maker are incorporated into the decision making process in terms of reference items into each class. We apply two utility functions based sorting methods to the problem. We perform an experiment and compare results with other algorithms from the literature.     

An integrated supplier selection methodology incorporating QFD and DEA with imprecise data

Supplier evaluation and selection is an important group decision making problem that involves not only quantitative criteria but also qualitative factors incorporating vagueness and imprecision. This paper proposes a novel fuzzy multi-criteria group decision making framework for supplier selection integrating quality function deployment (QFD) and data envelopment analysis (DEA). The proposed methodology allows for considering the impacts of inner dependence among supplier assessment criteria through constructing a house of quality (HOQ). The lower and upper bounds of the weights of supplier assessment criteria are identified by adopting fuzzy weighted average (FWA) method that enables the fusion of imprecise and subjective information expressed as linguistic variables. An imprecise DEA methodology is implemented for supplier selection, which employs the weights of supplier assessment criteria computed by FWA utilizing the data from the HOQ and the supplier ratings with respect to supplier assessment criteria. The application of the proposed framework is demonstrated through a case study in a private hospital in Istanbul.     

A tabu search procedure for coordinating production,inventory and distribution routing problems

This paper addresses the problem of optimally coordinating a production‐distribution system over a multi‐period finite horizon, where a facility production produces several items that are distributed to a set of customers by a fleet of homogeneous vehicles. The demand for each item at each customer is known over the horizon. The production planning determines how much to produce of each item in every period, while the distribution planning defines when customers should be visited, the amount of each item that should be delivered to customers and the vehicle routes. The objective is to minimize the sum of production and inventory costs at the facility, inventory costs at the customers and distribution costs. We also consider a related problem of inventory routing, where a supplier receives or produces known quantities of items in each period and has to solve the distribution problem. We propose a tabu search procedure for solving such problems, and this approach is compared with vendor managed policies proposed in the literature, in which the facility knows the inventory levels of the customers and determines the replenishment policies.     

ABC inventory classification with multiple-criteria using weighted linear optimization

Inventory classification using ABC analysis is one of the most widely employed techniques in organizations. The need to consider multiple criteria for inventory classification has been stressed in the literature. A simple classification scheme is proposed in this paper using weighed linear optimization. The methodology is illustrated using an example.     

Optimal ABC inventory classification using interval programming

Inventory classification is one of the most important activities in inventory management, whereby inventories are classified into three or more classes. Several inventory classifications have been proposed in the literature, almost all of which have two main shortcomings in common. That is, the previous methods mainly rely on an expert opinion to derive the importance of the classification criteria which results in subjective classification, and they need precise item parameters before implementing the classification. While the problem has been predominantly considered as a multi-criteria, we examine the problem from a different perspective, proposing a novel optimisation model for ABC inventory classification in the form of an interval programming problem. The proposed interval programming model has two important features compared to the existing methods: it provides optimal results instead of an expert-based classification and it does not require precise values of item parameters, which are not almost always available before classification. Finally, by illustrating the proposed classification model in the form of numerical example, conclusion and suggestions for future works are presented.     

A simulation model for evaluating centralized vs. distributed inventory systems

This paper investigates an inventory problem encountered by the retail industry where a single organization operates through several customer outlets. Each outlet stocks several items for which a random demand reduces the level of inventory. The demand is dependent on the location of the outlet and varies from one location to another. Each item is replenished from one or more exogenous suppliers who have a positive delivery lead time. The ordering cost has both fixed and linear components. The items also have linear holding and shortage costs charged at the end of the review period. Inventories of the items are reviewed periodically and ordering decisions taken.     

Centralized and decentralized inventory policies for a single-vendor two-buyer system with permissible delay in payments

In today's business transactions, vendors usually offer their buyers a delay period in payment. This strategy has benefits to the vendor since it attracts new buyers who consider the delay period as a type of price reduction. In addition, permissible delay in payments also is advantageous for the buyers since they do not have to pay the vendor immediately after they receive the items. In contrast, the buyers can delay the payment until the end of the allowed period and during the credit period they can earn interest on the accumulated revenues. However, if the payment is not settled by the end of the credit period, a higher interest is charged. Under this scenario, an inventory model consisting of a single vendor which supplies an item to two different buyers is analyzed. First, we address the problem assuming that buyers and vendor are willing to cooperate and the integrated model is derived in terms of single-cycle policies. Next, we analyze a decentralized model where the buyers and the vendor make decisions independently. A numerical example is solved to illustrate both strategies. We carry out a computational study to compare integrated and decentralized policies. A sensitivity analysis is also performed to examine the effects of each parameter on both total costs. According to the computational results and the statistical analysis, in most scenarios the integrated policies outperform the decentralized strategies.     

A multi-class closed queueing maintenance network model with a parts inventory system

The system we address is a maintenance network of repairable items where a set of bases is supported by a centrally located repair depot and a consumable replacement parts inventory system. If an item fails, a replacement part must be obtained at the parts inventory system before the failed item enters the repair depot. The ordering policy for the parts is the (S,Q) inventory policy. An approximation method for this system is developed to obtain performance measures such as steady-state probabilities of the number of items at each site and the expected backorders at the parts inventory system. The proposed system is modelled as a multi-class closed queueing network with a synchronization station and analyzed using a product-form approximation method. Particularly, the product-form approximation method is adapted so that the computational effort on estimating the parameters of the equivalent multi-class network is minimized. In analyzing a sub-network, a recursive method is used to solve balance equations by exploiting the special structure of the Markov chain. Numerical tests show that the approximation method provides fairly good estimation of the performance measures of interests.     

A fuzzy DEA and knapsack formulation integrated model for project selection

Project selection has become crucial in the fields of science and engineering. This paper discusses the specific problem of selecting a portfolio of projects that achieves an organization's objectives without exceeding limited capital resources, especially when each project possesses vague input and output data in the selection. In this paper, a data envelopment analysis (DEA), knapsack formulation and fuzzy set theory integrated model is proposed to deal with the problem, and the model is demonstrated via a case study problem in engineering-procurement-construction (EPC) industry. Moreover, this paper applies three constraint handling techniques, which are factor-free penalty function based, to transform a constrained optimization problem into an unconstrained problem, and for the first time adopts the artificial bee colony (ABC) algorithm to search the solutions. The performances of these three constraint handling techniques with respect to the ABC algorithm are compared for the first time in this paper.     

Application of fuzzy goal programming approach to multi-objective linear fractional inventory model

In this paper, we propose a model and solution approach for a multi-item inventory problem without shortages. The proposed model is formulated as a fractional multi-objective optimisation problem along with three constraints: budget constraint, space constraint and budgetary constraint on ordering cost of each item. The proposed inventory model becomes a multiple criteria decision-making (MCDM) problem in fuzzy environment. This model is solved by multi-objective fuzzy goal programming (MOFGP) approach. A numerical example is given to illustrate the proposed model.