A Case Study of Decision Support System and Warehouse Management System Integration

2021 ◽  
pp. 111-138
Author(s):  
Alparslan Sari ◽  
Ismail Butun
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
System Integration ◽  
Management System ◽  
Process Model ◽  
Real Life ◽  
Warehouse Management ◽  
Warehouse Management System ◽  
Smart Things

A warehouse is an indispensable part of the logistics. A warehouse management system (WMS) is designed to improve efficiency in warehouses to increase their throughput and potential. The rise of IoT and its commercialization enabled ‘smart things' to be widely adopted by hobbyists and companies. Cheap sensors and smart devices triggered better automation opportunities. Many devices and sensors that are being deployed in the industry and warehousing are affected by this trend. A well-designed WMS is needed to connect devices and humans in a heterogenous warehouse environment. This chapter introduces a prototype of a WMS powered by a decision support system (DSS) based on real-life requirements. In order to have fast, reliable, and efficient decision making in warehousing, the importance of employing DSS in the WMS is emphasized. Warehouse-related IoT technology is briefly introduced, and its security considerations are discussed thoroughly. The main contribution of this chapter is to show how warehouse operations can be modeled in business process model notation and executed in a DSS.

scholarly journals Decision Support System for Green Real-Life Field Scheduling Problems

2017 ◽  
pp. 355-369
Author(s):  
Yizi Zhou ◽  
Anne Liret ◽  
Jiyin Liu ◽  
Emmanuel Ferreyra ◽  
Rupal Rana ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Real Life ◽  
Scheduling Problems

scholarly journals Intelligent decision support method in a typical situation

2021 ◽  
Vol 11 (1) ◽  
pp. 126-136
Author(s):  
V.V. Antonov ◽  
◽  
K.A. Konev
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Base ◽  
Business Process ◽  
Support System ◽  
System Integration ◽  
General Structure ◽  
Typical Situation ◽  
Ontological Model ◽  
Decision Support Method

The article discusses a decision support method using a knowledge base. The relevance of the study of issues related to decision-making in typical situations is shown. In order to increase the effectiveness of management activities, the is-sues of system integration of the regulatory framework, ontological model and knowledge base of the intelligent sub-system of decision support within the framework of the business process are considered. In support of the proposed method, a model has been formed for replenishing the knowledge base both at the structural and analytical levels, which demonstrates the connection between the most important elements of the system: the ontological model, the knowledge base and the normative subsystem. An example of using the proposed scheme is shown. To demonstrate the model of functioning of the decision support system, an algorithm for replenishing the knowledge base is proposed and described. As a conceptual basis for the formal description of the model, operations for working with knowledge are described in the set-theoretic aspect. The principles of adaptation of the ontological model as an information object for linking with the knowledge base are considered. The conceptual diagram of the general structure of the ontological model for making decisions within the framework of the business process as a set of interrelated concepts is proposed and demonstrated. An information model of a specialized database has been developed and presented, serving as a technical basis for building a knowledge base of a decision support system in a typical situation, its main structural el-ements, the principles of their interrelation and an approach to ensuring the consistency of its internal structure are de-scribed.

Bidder Selection in Public Procurement using a Fuzzy Decision Support System

Fuzzy Systems ◽  
2017 ◽  
pp. 1620-1642
Author(s):  
Vjekoslav Bobar ◽  
Ksenija Mandic ◽  
Milija Suknovic
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Public Procurement ◽  
Real Life ◽  
Case Scenario ◽  
Uncertain Variables ◽  
Public Money ◽  
Fuzzy Extent Analysis ◽  
The Cost

Bidder selection in public procurement is a decision making problem whose primary purpose is to achieve the cost effectiveness and efficiency in the expenditure of public money. This principle is also known as the principle of “value for money”. This selection is based on many alternatives and many quantitative and qualitative criteria where qualitative criteria are often expressed as linguistic uncertain variables. The theory of fuzzy sets is a tool suitable to model uncertainty when applied to a variety of problems in real life. However, many fuzzy methods require complex calculation and they are not appropriate for using in public procurement because they slow down this process. In this paper, in order to make a quick decision in public procurement, a Decision Support System based on the fuzzy extent analysis method is developed. In order to demonstrate the usefulness of this system, a real-life case scenario of public procurement is presented.

Bidder Selection in Public Procurement using a Fuzzy Decision Support System

2015 ◽  
Vol 7 (1) ◽  
pp. 31-49 ◽  
Author(s):  
Vjekoslav Bobar ◽  
Ksenija Mandic ◽  
Milija Suknovic
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Public Procurement ◽  
Real Life ◽  
Case Scenario ◽  
Uncertain Variables ◽  
Public Money ◽  
Fuzzy Extent Analysis ◽  
The Cost

Bidder selection in public procurement is a decision making problem whose primary purpose is to achieve the cost effectiveness and efficiency in the expenditure of public money. This principle is also known as the principle of “value for money”. This selection is based on many alternatives and many quantitative and qualitative criteria where qualitative criteria are often expressed as linguistic uncertain variables. The theory of fuzzy sets is a tool suitable to model uncertainty when applied to a variety of problems in real life. However, many fuzzy methods require complex calculation and they are not appropriate for using in public procurement because they slow down this process. In this paper, in order to make a quick decision in public procurement, a Decision Support System based on the fuzzy extent analysis method is developed. In order to demonstrate the usefulness of this system, a real-life case scenario of public procurement is presented.

Evaluation of a computational decision support system for molecularly targeted treatment planning by the clinical outcome data of the randomized trial SHIVA01.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 3642-3642
Author(s):  
Anna Dirner ◽  
Robert Doczi ◽  
Peter Filotas ◽  
Barbara Vodicska ◽  
Edit Varkondi ◽  
...  
Keyword(s):  
Decision Support ◽  
Targeted Therapy ◽  
Decision Support System ◽  
Support System ◽  
Clinical Decision Making ◽  
Real Life ◽  
Molecular Profile ◽  
Evidence Based ◽  
Precision Oncology ◽  
Molecular Profiles

3642 Background: Precision oncology requires the identification of individual molecular pathomechanisms to find optimal personalized treatment strategies for every cancer patient. Incorporation of complex molecular information into routine clinical practice remains a significant challenge due to the lack of a reproducible, standardized process of clinical decision making. Methods: To provide a standardized process for molecular interpretation, we develop a precision oncology decision support system, the Realtime Oncology Molecular Treatment Calculator (MTC). MTC is a rule-based medical knowledge engine that dynamically aggregates and ranks relevant scientific and clinical evidence using currently 26,000 evidence-based associations and reproducible algorithm scoring of drivers, molecular targets to match molecular alterations to efficient therapies. To validate this novel method and system, we used data of the SHIVA01 trial of molecularly targeted therapy (Lancet Oncol 2015 16:1324-34). Molecular profiles of participants were uploaded to MTC and aggregated evidence level (AEL) values of associated targeted treatments were calculated, including those used in the SHIVA01 trial. Results: The MTC output provided a prioritized list of drugs associated with the driver alterations in the patient molecular profile, where ranking is based on AEL values. Of 113 patients who received targeted therapy with available clinical best response data, disease control was experienced in 63 cases (PR: 5, SD: 58), while disease progression occurred in 50 cases. The average AEL score for the therapies applied was significantly higher in the responsive group than in the non-responsive group (1512 and 614, respectively (p = 0.049)). In 94 cases, drugs other than those used for therapy were ranked higher by the MTC. The average AEL difference between the top-ranked and the used drugs was in an inverse correlation with clinical response, i.e. smaller differences associated with a better outcome. Conclusions: Results indicate that the aggregation of evidence-based tumor-driver-target-drug associations using standardized mathematical algorithms of this computational tool is a promising novel approach to improve clinical decisions in precision oncology. Further validation based on the results of other targeted clinical trials and real-life data using more detailed molecular profiles is warranted to explore the full clinical potential of this novel medical solution.

scholarly journals A MULTIPLE CRITERIA DECISION SUPPORT SYSTEM FOR ANALYZING THE CORRELATION BETWEEN THE THICKNESS OF A THERMO-INSULATION LAYER AND ITS PAYBACK PERIOD OF THE EXTERNAL WALL

2015 ◽  
Vol 21 (6) ◽  
pp. 827-835 ◽  
Author(s):  
Donatas Aviža ◽  
Zenonas Turskis ◽  
Artūras Kaklauskas
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Management System ◽  
Energy Performance ◽  
Payback Period ◽  
Multiple Criteria ◽  
Insulation Layer ◽  
Model Base ◽  
External Wall

A case study provides the correlation analysis of the thickness of the thermo-insulation layer (expanded polystyrene – EPS70) of the typical details of the external wall and energy performance class in a modern newly constructed residential low-energy building (one and two-room apartment). The conducted analysis focused on what impact different geographical areas of building construction and different energy performance classes of the building may had on the thickness of the thermo-insulation layer. Lithuania was chosen as the object of this study. Calculations were carried out in seven towns, including Vilnius, Klaipeda, Kaunas, Siauliai, Panevezys, Utena and Dukstas. According to requirements for legal acts passed in the Republic of Lithuania and to Directive 2010/31/EU (2010), as a result, the necessary thickness of thermo-insulation layers (EPS70) and a payback period were calculated. A multiple criteria decision support system for analyzing the correlation between the thickness of the thermo-insulation layer and its payback period of the external wall (DSS-ACTILPW) consisting of a database, a database management system, a model-base, a model-base management system and a user interface was developed. Information on the performed analysis is important to building designers, energy consumption auditors and investment experts who make the final decisions on energy efficiency of buildings in the residential building sector.

scholarly journals SOM BASED DECISION SUPPORT IN FAILURE MANAGEMENT

2014 ◽  
pp. 124-130
Author(s):  
Miki Sirola ◽  
Golan Lampi ◽  
Jukka Parviainen
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Nuclear Power ◽  
Process Model ◽  
Primary Circuit ◽  
Self Organizing Map ◽  
Computerized Decision Support ◽  
Knowledge Based ◽  
Management Scenario

Computerized decision support system field covers many methodologies and application areas. In this paper Self-Organizing Map (SOM) and knowledge-based techniques are used in combination to reason problematic situations in failure management. A process model that consists of individual connected process components has been developed. A primary circuit of a boiling water nuclear power plant including two branches has been composed. A failure management scenario is thoroughly analyzed and solved with the SOM based decision support system. The structure and reasoning of the Computerized Decision Support System (CDSS) is also shortly discussed. The process model is demonstrated together with the CDSS and shown to be useful. The tool helps operators decision making with various visualizations, and by giving concrete recommendations for possible control actions or other acts.

Sign in / Sign up

Export Citation Format

Share Document