A qualitative perspective to deriving weights from pairwise comparison matrices

Deriving weights from pairwise comparison matrices (PCM) is a highly researched topic. The analytic hierarchy process (AHP) traditionally uses the eigenvector method for the purpose. Numerous other methods have also been suggested. A distinctive feature of all these methods is that they associate a quantitative meaning to the judgemental information given by the decision-maker. In contrast, the verbal scale used in AHP to capture judgements does not associate such a quantitative meaning. Though this issue of treating judgements qualitatively is recognized in the extant literature on multi-criteria decision making, unfortunately, there is no research effort so far in the AHP literature. Deriving motivation from the application of data envelopment analysis (DEA) for deriving weights, it is proposed in this paper that DEA models developed to deal with a mix of qualitative and quantitative factors can be used to derive weights from PCMs by treating judgements as qualitative factors. The qualitative DEA model is discussed and illustrated in this paper.     

Pricing decisions in a multi-criteria setting for product recovery facilities

Unpredictability in the arrival time and quantity of discarded products at product recovery facilities (PRFs) and varying demand for recovered components contribute to the volatility in their inventory levels. Achieving profit under such capricious inventory levels and stringent environmental legislations remains a challenge to many PRFs. This paper presents a multi-criteria decision model to determine a pricing policy that can simultaneously address two issues: stabilize inventory fluctuations and boost profits. The model considers that PRFs passively accepts discarded products as well as acquires them proactively if necessary. Under a multi-criteria setting, the current work determines prices of reusable and recyclable components to maximize revenue and minimize product recovery costs. A genetic algorithm is employed to solve the multi-criteria decision making problem. Sensitivity analysis is performed to investigate the effect of sorting yield, disassembly yield, and reusable component yield on the profits, prices, inventory levels, and disposal quantities.     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

A Sensitivity Analysis Approach for Some Deterministic Multi-Criteria Decision-Making Methods*

Often, data in multi-criteria decision making (MCDM) problems are imprecise and changeable. Therefore, an important step in many applications of MCDM is to perform a sensitivity analysis on the input data. This paper presents a methodology for performing a sensitivity analysis on the weights on the decision criteria and the performance values of the alternatives expressed in terms of the decision criteria. The proposed methodology is demonstrated on three widely used decision methods. These methods are the weighted sum model (WSM), the weighted product model (WPM), and the analytic hierarchy process (AHP). This paper formalizes a number of important issues on sensitivity analysis and derives some critical theoretical results. Also, a number of illustrative examples and computational experiments further illustrate the application of the proposed methodology.     

随机需求直接发运的运输与库存整合优化研究

分销系统的运输与库存整合优化是供应链管理的重要研究课题.本文分析了随机需求两级分销系统的三种配送策略,并具体给出了基于整车直接发运策略的运输决策与库存控制整合优化数学模型,可得到最优的两级最优库存水平和最优的运输频率.最后对给出的算例,利用MATLAB7的规划求解功能容易获得了问题的最优解.     

Multi-criteria Decision-Making for Greenways: The Case of Trabzon,Turkey

Greenways are multi-objective planning tools for sustainable development that take several criteria into account. In order to resolve the conflicts among objectives, greenway planning requires advanced strategies for decision-making processes and techniques. To aid in this, multi-criteria decision-making (MCDM) systems have significant capacity to analytically prioritize and select alternatives. This research develops a strategy for defining, prioritizing and selecting greenway alternatives with the support of Analytical Hierarchy Process (AHP) based on an empirical study in the urban region of Trabzon (Turkey). The study finds that AHP has promising capacity to analytically prioritize alternatives and rationally select the best alternative.     

An application of the AHP in vendor selection of a telecommunications system

Vendor selection of a telecommunications system is an important problem to a telecommunications company as the telecommunications system is a long-term investment for the company and the success of telecommunications services is directly affected by the vendor selection decision. Furthermore, the vendor selection of a telecommunications system is a complex multi-person, multi-criteria decision problem. The group decision-making process can be improved by a systematic and logical approach to assess priorities based on the inputs of several people from different functional areas within the company. The analytic hierarchy process (AHP) can be very useful in involving several decision-makers with different conflicting objectives to arrive at a consensus decision. In this paper, an AHP-based model is formulated and applied to a real case study to examine its feasibility in selecting a vendor for a telecommunications system. The use of the proposed model indicates that it can be applied to improve the group decision making in selecting a vendor that satisfies customer specifications. Also, it is found that the decision process is systematic and that using the proposed AHP model can reduce the time taken to select a vendor.     

A classification approach based on the outranking model for multiple criteria ABC analysis

The multiple criteria ABC analysis is widely used in inventory management, and it can help organizations to assign inventory items into different classes with respect to several evaluation criteria. Many approaches have been proposed in the literature for addressing such a problem. However, most of these approaches are fully compensatory in multiple criteria aggregation. This means that an item scoring badly on one or more key criteria could be placed in good classes because these bad performances could be compensated by other criteria. Thus, it is necessary to consider the non-compensation in the multiple criteria ABC analysis. To the best of our knowledge, the ABC classification problem with non-compensation among criteria has not been studied sufficiently. We thus propose a new classification approach based on the outranking model to cope with such a problem in this paper. However, the relational nature of the outranking model makes the search for the optimal classification solution a complex combinatorial optimization problem. It is very time-consuming to solve such a problem using mathematical programming techniques when the inventory size is large. Therefore, we combine the clustering analysis and the simulated annealing algorithm to search for the optimal classification. The clustering analysis groups similar inventory items together and builds up the hierarchy of clusters of items. The simulated annealing algorithm searches for the optimal classification on different levels of the hierarchy. The proposed approach is illustrated by a practical example from a Chinese manufacturer. Furthermore, we validate the performance of the approach through experimental investigation on a large set of artificially generated data at the end of the paper.     

基于库存价值改变和随机需求的经济订货批量模型

大多数库存研究的重点都集中在各种复杂的限制条件和模型的变换上.没有考虑到库存本身可能发生贬值或增值的情况,而实际中库存本身常常会发生价值变化.针对以往库存模型中没有考虑库存价值变化的问题,提出了在需求为随机连续分布、库存价值发生变化情况下的单周期经济订货批量模型,给出了最优订货策略.模型中以先进先出为假设条件,基于报童模型的思想,以订货量为决策变量、期望收益为目标函数,结合随机需求的分布情况得到最优订货量和最大期望收益,并给出了相应的数学证明.通过算例对模型进行说明,并对影响最优订货批量和最大期望收益的各个参数进行敏感性分析.     

Optimizing inventory and marketing policy for non-instantaneous deteriorating items with generalized type deterioration and holding cost rates

This paper considers an inventory system with non-instantaneous deteriorating item in which demand rate is a function of advertisement of an item and selling price. This paper aids the retailer in maximizing the total profit by determining optimal inventory and marketing parameters. In contrast to previous inventory models, an arbitrary holding cost rate and arbitrary deterioration rate have been incorporated to provide general framework to the model. First, a mathematical model is formulated and then some useful theoretical results have been framed to characterize the optimal solutions. The necessary and sufficient conditions for the existence and uniqueness of the optimal solutions are also derived. An algorithm is designed to find the optimum solutions of the proposed model. Numerical examples are included to illustrate the algorithmic procedure and the effects of key parameters are studied to analyze the behavior of the model.     

Understanding multi-methodology: Evaluating the perceived impact of mixing methods for group budgetary decisions

This paper reports on the design and implementation of a multi-methodology intervention intended to support a budget prioritisation decision by a multi-organisational group tasked with tackling the problem of teenage pregnancy in an English borough. The intervention approach involved the combined use of cognitive/causal mapping and a multi-criteria decision analysis method to develop and prioritise a number of projects aimed at alleviating issues associated with teenage pregnancy. The paper describes and discusses the process of developing and applying the intervention approach, and provides an evaluation of its perceived impact by the client group. Drawing on the rich seam of data gathered over the course of our work with the multi-organisational group, we explore the varying degrees of impact that the mapping and multi-criteria evaluation methods achieved during and after the intervention. Analysis of the intervention data suggests that both methods were perceived to facilitate a different way of thinking and learning. In addition, we find that the mapping and multi-criteria evaluation methods successfully addressed participants' democratic and rational concerns. However, despite these reported positive effects, the full recommendations of the intervention were not implemented. An attempt is made to explain this outcome in terms of both the multi-organisational context within which the intervention took place and the nature of the group task. Directions for further research are then proposed.     

An arborescent inventory model in a supply chain system

This study develops an arborescent (tree-like) inventory model with a constant demand rate. By considering the integration of the producer, distributors and retailers, a mathematical model and an economic ordering policy are developed. It can be shown that the integrated approach results in an impressive cost-reduction compared with an independent decision by the partial party. A numerical example of an arborescent inventory system is attached to demonstrate the economic ordering policy with the help of mathematical software - Maple V.     

Inventory control of spares in a transport corporation — A case study

This paper reports on a study carried out in a transport organisation to find the optimal inventory of recoverable spares in a two-level inventory system consisting of a central depot and a set of 20 depots all over the territory of the organisation, with repair facilities. The problems of the present system are diagnosed and it is observed that the upper echelon's inability to meet the lower echelon's demand has resulted in an ever-increasing shortage. A mathematical model is developed to suit the system structure and a set of optimal inventory policies for recoverable spares at both the echelons is obtained for the case studied.     

PRODUCTION-DISTRIBUTION PLANNING IN A LARGE SCALE COMMODITY PROCESSING NETWORK

This paper describes a methodology for production-distribution planning in a large scale commodity processing network. Based on earlier research efforts dealing with single-commodity and multi-commodity distribution system modeling and on production planning for a single-plant commodity processing facility, a mathematical programming methodology is developed for a multiplant soybean processing network. Application of the model leads to the specification of a production plan for a multi-period time horizon, while at the same time indicating the quantities of soybean meal and soybean oil to be supplied to various customers in various locales. Both sets of decisions are made under the general criterion of maximizing the net income produced by the soybean processing complex, subject to various production, inventory, capacity, supply and demand constraints. Test results from application of the model are presented and discussed.     

Improving spare parts inventory control at a repair shop

We study spare parts inventory control for an aircraft component repair shop. Inspection of a defective component reveals which spare parts are needed to repair it, and in what quantity. Spare part shortages delay repairs, while aircraft operators demand short component repair times. Current spare parts inventory optimization methods cannot guarantee the performance on the component level, which is desired by the operators. To address this shortfall, our model incorporates operator requirements as time-window fill rate requirements for the repair turnaround times for each component type. In alignment with typical repair shop policies, spare parts are allocated on a first come first served basis to repairs, and their inventory is controlled using (s, S) policies. Our solution approach applies column generation in an integer programming formulation. A novel method is developed to solve the related pricing problem. Paired with efficient rounding procedures, the approach solves real-life instances of the problem, consisting of thousands of spare parts and components, in minutes.A case study at a repair shop reveals how data may be obtained in order to implement the approach as an automated method for decision support. We show that the implementation ensures that inventory decisions are aligned with performance targets.     

Multiechelon (R,S) Inventory Model*

Most studies in multiechelon inventory systems have concentrated on understanding the specific aspects of a system's behavior. The problem of optimal policy computation has largely been ignored. In this paper, we investigate a two-echelon inventory system experiencing stochastic demand and a pull system of inventory allocation. Both echelons use an order-up-to-level type control policy. A mathematical model is developed to determine the optimal order level at all echelons and validated through simulation. Two simple algorithms to locate the optimum solution are presented. The use of graphical tools in optimal policy calculation is also discussed.     

AHP决策模型在怒江流域水电能源开发评价中的应用研究

本文以AHP决策理论为基础,针对水能资源开发对整个流域生态环境影响较大的特点,建立了一种新的标度矩阵和量化决策模型。该模型基于流域梯级开发环境影响因子的变化特性,提出量化AHP决策模型方法,以怒江流域水电能源开发方案为例,进行评估决策。在充分考虑对整个流域生态环境影响的前提下,对开发方案进行评价与决策,实证研究表明所建立的量化AHP决策模型效果良好。     

Modeling and solution of practical management problems through netforms

This paper shows some useful schemes to graphically depict netform models of practical inventory problems. We begin with a simple inventory problem and then progress to more comprehensive representations of an inventory system by network and netform model techniques. Some text books mention that very basic inventory problems may be modeled as networks. But they usually offer very little instruction in the actual development of a network inventory model, and invariably conclude that network models can't accommodate the complications of the vast majority of realworld inventory problems. Further, they completely fail to convey the modeling power available to more general netform (network-related formulation) techniques. As text books catch up to the recent innovations now being applied in practical settings, this deficiency will change. Our purpose is to introduce the fundamental ideas and to provide an understanding of the possibilities inherent in these model innovations. We begin from first principles, so that no prior familiarity with network model representations is necessary to follow the exposition. Because of a heavy reliance on pictorial illustrations (another of the unique advantages of networks and netforms), we have also avoided the use of complex mathematical notation, and develop the key concepts by informal discussion.     

A demand classification scheme for spare part inventory model subject to stochastic demand and lead time

In this study, we aim to develop a demand classification methodology for classifying and controlling inventory spare parts subject to stochastic demand and lead time. Using real data, the developed models were tested and their performances were evaluated and compared. The results show that the Laplace model provided superior performance in terms of service level, fill rate (FR) and inventory cost. Compared with the current system based on normal distribution, the proposed Laplace model yielded significant savings and good results in terms of the service level and the FR. The Laplace and Gamma optimisation models resulted in savings of 82 and 81%, respectively.     

Concepts,Theory, and Techniques DISTRIBUTED COMPUTER SYSTEM DESIGN: A MULTICRITERIA DECISION-MAKING METHODOLOGY*

The design of distributed computer systems (DCSs) requires compromise among several conflicting objectives. For instance, high system availability conflicts with low cost which in turn conflicts with quick response time. This paper presents an approach, based on multi-criteria decision-making techniques, to arrive at a good design in this multiobjective environment. An interactive procedure is developed to support the decision making of system designers. Starting from an initial solution, the procedure presents a sequence of non-dominated vectors to designers, allowing them to explore systematically alternative possibilities on the path to a final design. The model user has control over trade-offs among different design objectives. This paper focuses on the details of the mathematical model used to provide decision support. Accordingly, a formulation of DCS design as a multicriteria decision problem is developed. The exchange search heuristic used to generate nondominated solutions also is presented. We argue that multicriteria models provide a more realistic formulation of the DCS design problem than the single-criterion models used widely in the literature. While obtaining a clear definition of design objectives (single or multiple) is an important activity, by explicitly acknowledging the trade-offs among multiple objectives in the design process, our methodology is more likely to produce a better overall design than methods addressing a single criterion in isolation.