Rule-based optimization and multicriteria decision support for packaging a truck chassis

Trucks are highly individualized products where exchangeable parts are flexibly combined to suit different customer requirements, this leading to a great complexity in product development. Therefore, an optimization approach based on constraint programming is proposed for automatically packaging parts of a truck chassis by following packaging rules expressed as constraints. A multicriteria decision support system is developed where a database of truck layouts is computed, among which interactive navigation then can be performed. The work has been performed in cooperation with Volvo Group Trucks Technology (GTT), from which specific rules have been used. Several scenarios are described where the methods developed can be successfully applied and lead to less time-consuming manual work, fewer mistakes, and greater flexibility in configuring trucks. A numerical evaluation is also presented showing the efficiency and practical relevance of the methods, which are implemented in a software tool.     

An integrated decision support system for strategic supply chain optimisation in process industries: the case of a zinc company

We introduce a menu-driven user-friendly decision support system (DSS) for supply chain planning based on optimisation. The DSS is based on a multi-source (supplier), multi-destination (warehouse) network having multiple manufacturing facilities, with multiple materials and multiple storage areas. This integrated supply chain model performs multiple period planning. The use of this DSS requires little knowledge of management sciences tools. We discuss the need for an integrated approach towards supply chain modelling for the process industry. We present the integrated model in the form of a database structure. We validate the model with the real data of a zinc company and demonstrate the impact of optimisation in terms of percentage improvement. The result shows that it is possible to improve unit contribution to profit from 1.89 to 4.66%.     

Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.     

Towards building an intelligent decision support system for project management

Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).     

A decision support system for selection of net-shape primary manufacturing processes

Developments in the capabilities of the manufacturing processes increased the number of processes that can produce a part within the requirements determined by its design and market research. The increased number of processes and unfamiliarity of manufacturing engineers to many new manufacturing processes forces the researchers to develop systematic process selection tools instead of depending on the accumulated human expertise only. In this paper, a net-shape primary manufacturing process selection decision support system (DSS), which is named PROSEL (PROcess SELection), is developed. The developed selection programme eliminates the unsuitable processes step by step by checking a part’s material, annual production quantity, specified shape, thickness and presents the most economical process as the most appropriate net-shape primary process after a final cost analysis. The developed DSS is written in Visual Studio and tested with a great deal of real-life examples. It can be concluded from the tests that the programme provides the same or better primary manufacturing process selection decisions than the practical usage, and it is a very useful support tool for net-shape primary process selections.     

A B2B flexible pricing decision support system for managing the request for quotation process under e-commerce business environment

In the era of digitalisation, e-commerce retail sites have become decisive channels for reaching millions of potential customers worldwide. Digital marketing strategies are formulated by the marketing teams in order to increase the traffic on their e-commerce sites, thereby boosting the sales of the products. With the massive amount of data available from the cloud, which were conventionally made with a high degree of intuition based on decision makers’ knowledge and experience, can now be supported with the application of artificial intelligence techniques. This paper introduces a novel approach in applying the fuzzy association rule mining approach and the fuzzy logic technique, for discovering the factors influencing the pricing decision of products launched in e-commerce retail site, and in formulating flexible, dynamic pricing strategies for each product launched in an e-commerce site. A pricing decision support system for B2B e-commerce retail businesses, namely Smart-Quo, is developed and implemented in a Hong Kong-based B2B e-commerce retail company. A six-month pilot run reveals a significant improvement in terms of the efficiency and effectiveness in making pricing decisions on each product. The case study demonstrates the feasibility and potential benefits of applying artificial intelligence techniques in marketing management in today’s digital age.     

Mixed integer programming for strategic decision support in the slaughter by-product chain

A mixed integer linear programming model to support strategic decision making is presented, in order to find the optimal processing structure for a slaughter byproduct business. In an integrated approach environmental objectives (such as odour emission, water pollution and energy consumption), market outlets, quality requirements (both for product and process), costs of processing, transportation, environmental measures and governmental restrictions are modelled to find the optimal investment strategy for future ways of processing slaughter by-products. Emphasis has not been put on development of new models or techniques. The value of this contribution is how to find new cost-effective logistical solutions within a framework of environmental restrictions, quality and technological opportunities.     

The role of life cycle assessment as a decision support tool for packaging

Emerging environmental pressures over the last two decades have urged the development of sophisticated tools to evaluate the environmental burdens and impacts of products and processes. Life cycle assessment (LCA) was first used some 30 years ago and has now developed into a valuable tool for businesses to support their decision making in packaging development and application. This paper outlines the concept of LCA and how it can be applied to support strategic business development. The paper also describes the integration of the LCA concept in an existing Code of Practice, as well as future developments in furthering the integration with economical and social decision parameters. Copyright © 2000 John Wiley & Sons, Ltd.     

Intelligent decision support system for the adaptive control of a flexible manufacturing system with machine and tool flexibility

This paper describes an intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS). The controller is capable of classifying symptoms in developing the control policies on FMSs with flexibility in operation assignment and scheduling of multi-purpose machining centres which have different tools with their own efficiency. The proposed system is implemented by coupling of rule-based IDSS, simulation block and centralised simulation optimiser for elicitation of shop floor control knowledge. This posteriori adaptive controller uses a new bilateral mechanism in simulation optimiser block for offline training of IDSS based on multi-performance criteria simulation optimisation. The proposed intelligent controller receives online information of the FMS current state and trigger appropriate control rule within real-time simulation data exchange. Finally the FMS intelligent controller is validated by a benchmark test problem. Application of this adaptive controller showed that it could be an effective approach for real time control of various flexible manufacturing systems.     

Application of rough sets for environmental decision support in industry

Practical environmental decision-making in industry is a complex task that often entails a subtle interplay between alternatives and criteria. Quantitative tools are used to aid decision-makers to arrive at rational conclusions. However, conventional decision aids are often limited by the need to define a priori weights for the criteria being considered; identifying the correct weights to use is not a trivial task and has been the subject of considerable research. An alternative approach based on rough set methodology is described in this work. The procedure develops an empirical, rule-based model from example responses derived from an expert panel. The model can then be used for decision-making in cases resembling the example used previously. Rough set theory also provides numerical measures of the reliability of the rule-based model developed. The approach is illustrated with two case studies, the first involving comparison of alternative energy sources, and the second involving the ranking of pollution prevention strategies in manufacturing.     

Hesitant fuzzy integrated MCDM approach for quality function deployment: a case study in electric vehicle

Quality function deployment (QFD) is a product planning management instrument which has been used in a broad range of industries. However, the traditional QFD method has been criticised much for its deficiencies in acquiring experts’ opinions, weighting customer requirements (CRs) and ranking engineering characteristics (ECs). To overcome the limitations, an integrated analytical model is presented in this study for obtaining the importance ratings of ECs in QFD by integrating decision-making trial and evaluation laboratory (DEMATEL) technique and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method under hesitant fuzzy environment. In particular, the hesitant fuzzy DEMATEL is used to analyse the interrelationships among CRs and determine their weights, and the hesitant fuzzy VIKOR is utilised to prioritise ECs. Finally, the feasibility and practicality of the proposed method are verified by an example regarding the product development of electric vehicle.     

An integrated probabilistic risk analysis decision support methodology for systems with multiple state variables

Probabilistic risk analysis (PRA) methods have been proven to be valuable in risk and reliability analysis. However, a weak link seems to exist between methods for analysing risks and those for making rational decisions. The integrated decision support system (IDSS) methodology presented in this paper attempts to address this issue in a practical manner. In consists of three phases: a PRA phase, a risk sensitivity analysis (SA) phase and an optimisation phase, which are implemented through an integrated computer software system. In the risk analysis phase the problem is analysed by the Boolean representation method (BRM), a PRA method that can deal with systems with multiple state variables and feedback loops. In the second phase the results obtained from the BRM are utilised directly to perform importance and risk SA. In the third phase, the problem is formulated as a multiple objective decision making problem in the form of multiple objective reliability optimisation. An industrial example is included. The resultant solutions of a five objective reliability optimisation are presented, on the basis of which rational decision making can be explored.     

A decision model for the analysis of ergonomic investments

Costs associated with worker injuries can be high and some suggest investments in ergonomic solutions can lower those costs. However, many employers are still unsure if investing in various ergonomic solutions will generate benefits such as increased production and/or reduce workmen's compensation expenses. Estimating current losses and predicting possible gains provide most of the information needed to decide upon ergonomic investment. This paper presents a model employers can use to predict cost savings from ergonomic investments given certain input factors, including current losses, cost of implementation, and success of implementation.     

Selecting suppliers using a new fuzzy multiple criteria decision model: the fuzzy balancing and ranking method

In this paper, we present a fuzzy multiple criteria decision making (FMCDM) model known as fuzzy balancing and ranking. In contrast to other MCDM models, our proposed model does not require the weights of decision making criteria. First, we appraise the performance of alternatives against criteria via linguistic variables which are expressed as triangular fuzzy numbers. The foregoing model obtains the alternative rankings through a four-stage process. Second, an outranking matrix is derived indicating that the frequency with which one alternative is superior to all other alternatives based on each criterion. Third, the outranking matrix is triangularised to obtain an implicit pre-ordering or provisional order of alternatives. Fourth, the provisional order of alternatives is subjected to various screening and balancing operations that require sequential application of a balancing principle to the so-called advantages–disadvantages table that combines the criteria with the pair-wise comparisons of alternatives. Additionally, to demonstrate the procedural implementation of the proposed model and its effectiveness, we apply it on a case study regarding the problem of supplier selection.     

A model, heuristic procedure and decision support system for solving the movie shoot scheduling problem

Creating a movie shoot schedule is an important part of the movie production process. Even for a small movie project already 50 activities requiring 130 resources such as different actors, director, team, special effects and locations etc. have to be scheduled respecting complex constraints which may be imposed on single resources as well as on every activity. In this paper, we present the movie shoot scheduling problem and formulate a conceptual model. We present a metaheuristic approach for generating operational schedules, outline the modules of the decision support system Schedule This which we have developed and finally we shortly report practical experiences. Our experience from using the DSS in real movie shooting projects shows significant improvements with respect to faster and better scheduling as well as ad hoc re-scheduling.     

用于卫星通信数字调制分类的一种新颖的支持向量机模糊网络

为了使调制分类系统更加适合大范围信噪比(SNR)内的信号,将支持向量机(SVM)和模糊积分(fuzzy integral)结合起来提出了一种新颖的支持向量机模糊网络(SVMFN).SVMFN中采用了一种新的模糊密度定义,同时考虑了分类器的精确度和不确定性,提高了识别的可靠性.从信号中直接提取3个顽健性强且计算简单的特征值来识别11种数字调制方式(2ASK,4ASK,2FSK,4FSK,2PSK,4PSK,8PSK,16QAM,TFM,π/4QPSK和OQPSK).计算机仿真结果表明这种方案具有高度的准确性和可靠性,在SNR不低于0dB时,识别率在95%以上,适于工程应用.     

Meta-heuristic based decision support for portfolio optimization with a case study on tracking error minimization in passive portfolio management

In this paper we describe the concept and design of a meta-heuristic based decision support system generator (DSS-generator) for portfolio optimization. We report extensively on experience with the application of a specific DSS that has been customized for controlling and optimizing passively managed stock funds. Here, the constraints from the law on investment trust companies as well as several fund specific guidelines prohibit that the benchmark can be identically reproduced. For measuring the performance of the portfolio a tracking error model with data stemming from a factor model is applied. Our results show that the system provides proposals for the fund manager in acceptable time which are feasible with respect to the guidelines and excellent in quality.     

A decision model to determine the number of shuttles in a tier-to-tier SBS/RS

Shuttle-based storage and retrieval systems (SBS/RSs), which are designed to increase throughput capacity and flexibility, are a type of automated storage and retrieval system used for lightweight loads. SBS/RSs can increase throughput capacity by using multiple shuttles and elevators as storage and retrieval machines (SRMs). They can also facilitate improvements in flexibility since they are able to adjust the number of SRMs according to transaction demands. Thus, determining the number of shuttles is an important issue in tier-to-tier SBS/RSs. In this paper, a decision model to determine the number of shuttles is proposed. The model is based on the travel time model, and it considers parameters such as the physical configuration, velocity profile and the probability that the shuttle operates a dual command. Finally, the throughput capacity from the travel time model is compared with that from a simulation-based approach in order to verify the effects of the model. In addition, a critical discussion regarding the characteristics of the tier-to-tier system is provided.     

Development of a real-time monitoring system as a decision-support system for flood hazard mitigation

This study proposes a real-time monitoring system to integrate the supportive flood-related information for hazard mitigation purposes. Through integrating photography, communication, geometric information display and network technology, this system can provide the real-time monitoring images at the critical gauge stations, inundation-prone areas and important hydraulic facilities during floods. With the development of mobile monitoring modules, the maneuverability of users and facilities could be significantly improved. Users can easily employ mobile phones to receive or report real-time flood-related information at any place and time. Furthermore, the water-level image recognition method is developed and applied to the selected monitoring stations for acquiring early warning of flood events. In final, an application scenario is given to illustrate how this system can greatly improve the effectiveness and efficiency of emergency responses to flood hazard mitigation for the decision-makers.