Simulation-based Decision Support Research Articles

A simulation based Decision Support System for logistics management

This paper deals with designing and developing a Decision Support System (DSS) that will be able to manage the flow of goods and the business transactions between a port and a dry port. An integrated DSS architecture is proposed and specified and the main components are designed on the basis of simulation and optimization modules. In order to show the use and implementation of the DSS, this work tests and analyzes the case of the area of the Trieste port and manages the export flows of freights between a dry port and a seaport. An integrated approach is designed mainly at tactical and operational decision level exploiting simulation and optimization approaches and especially metaheuristic approaches.

A simulation-based decision support system for a multi-echelon inventory problem with service level constraints

In this paper, we present a simulation-based decision support system for solving the multi-echelon constrained inventory problem. The goal is to determine the optimal setting of stocking levels to minimize the total inventory investment costs while satisfying the expected response time targets for each field depot. We derive new decision support algorithms to be applied in different scenarios, including small-sample and large-sample cases. The first case requires that the set of alternative solutions is known at the beginning of the experiment, and the number of evaluated solutions may depend on the simulation budget (i.e., the time available to solve the problem). In the second case, the alternative solutions are generated sequentially during the searching process, and we may terminate the algorithm when the specified sampling budget is exhausted. Empirical studies are conducted to compare the performance of the proposed algorithms with other conventional optimization approaches.

Dynamic specialization for symbiotic simulation-based operational decision support using the evolutionary computing modelling language (ECML)

Simulation-based what-if analysis is an important tool that can be used to support operational decision making. Solutions to operational decision problems have to be found in a limited period of time. As a result of these time constraints, not all candidate solutions can be evaluated by means of simulation. Essentially, this problem solving process is an optimization process. Specialization of the optimization method can help to reduce the computing budget needed to find an appropriate solution. However, since operational conditions are constantly changing, the exact problem is not known at design time. Therefore, the optimization method has to be specialized during runtime. This can be done by dynamically incorporating situation-dependent problem-specific knowledge into the optimization process. We introduce a method based on an evolutionary algorithm, which supports dynamic specialization. Furthermore, we demonstrate the effectiveness of this method in the context of a semiconductor manufacturing application.

A simulation‐based apheresis platelet inventory management model

The primary goals of apheresis platelet (AP) inventory management are to meet demand and minimize waste. AP inventory management is complicated by unpredictable demand for a product with a shelf life of only a few days and by hospital requests for APs that match the ABO types of patients identically. A simulation-based decision support system was developed to assist blood centers in reducing waste while meeting the demand for ABO-identical APs. The proposed model is validated using data from a blood center located in a major city in the southeast region of the United States. Based on data provided by the blood center, the proposed simulation model is able to suggest appropriate collection strategies that can reduce AP waste from 12%-14% to 6%-7% and decrease the unmet demand for ABO-identical APs from 25% to 21%. The proposed simulation-based decision-making model is able to mimic the complexity of the AP inventory management system while reducing waste and predicting the need for ABO-identical APs.

A simulation-based decision support system to prevent and predict strain situations in emergency department systems

The management of patient flow, especially the flow resulting from health crises in emergency departments (ED), is one of the most important problems managed by ED managers. To handle this influx of patients, emergency departments require significant human and material resources, but these are limited. Under these conditions, the medical and paramedical staff are often confronted with strain situations. To deal with these situations, emergency departments have no choice but to adapt. The main purpose of this article is to develop a simulation-based decision support system (DSS) to prevent and predict strain situations in an ED in order to improve their management by the hospital system. A discrete-event simulation model was constructed in order to visualize the strain situations, examine the relationship between the strain situations and propose corrective actions. A simulation experiment is presented with the results, identifying several important aspects of the strain situations and corrective actions in ED systems. The results have proven the importance of anticipation and management of strain situations in emergency departments.

Simulation Based Decision Support Systems: An Industrial End-user Based Requirement Gathering Process

Modern manufacturing systems are growing in scale and complexity and increasingly require integrated predictive modelling and optimization methodologies to support routine decision making processes. The engineering of a free open source simulation modelling platform is the basis of the DREAM project, a Framework Programme 7 initiative co-funded by the European Commission. This paper reports on the capture of requirements for DREAM from four industrial end-users. These industrial end-users’ requirements and accompanying pilot cases drive the research, technology and development work in the project. The users vary in size from micro-SME to multi-national and global organizations and by industry; from semi-conductor manufacturing, medical device design and manufacture to industrial machine manufacture and pre- process/prototype manufacturers. To capture requirements from the organizations a methodology was developed which is reported on in the paper. Its outcomes provide the basis for the subsequent open-source software development. In particular, the work assessed the various partner needs in the pre-coding, simulation application development, simulation application use and data-analytics areas which cover the end-to-end usage of a simulation based decision support system

A Study to Evaluate the Effectiveness of Simulation based Decision Support System in ERP Implementation in SMEs

Abstract ERP system implementation is a challenging process and small medium enterprises (SMEs) face considerable challenges in implementing ERP system due to their limited resources and IT infrastructure. Still, due to their benefits, ERP systems are becoming an integral part of SMEs. This study evaluates the role simulation based modelling can play in assisting SMEs in ERP implementation. The key informants representing diverse backgrounds are interviewed to collected data. The findings of the research show that Key participants supported the idea of incorporating simulation based model during the implementation process since a simulation based approach make more sense since it will allow the implementation team to observe the implementation process and the role played by factors which are essential for the success of the implementation. Also, simulation model can also be useful in developing and analyzing different implementation strategies, predict efforts and resources needed for ERP implementation, which in turn can facilitate decision makers in adopting a ERP system or not.

302 Simulation-based decision support for lean practitioners

302 Simulation-based decision support for lean practitioners

High Performance Computations for Decision Support in Critical Situations: Introduction to the Third Workshop on Urgent Computing

This paper is the preface to the Third Workshop on Urgent Computing.The Urgent Computing workshops have been traditionally embedded in frame of International Conference of Computational Science (ICCS) since 2012. They are aimed to develop a dialogue on the present and future ofresearch and applications associated with the large-scale computations for decision support in critical situations. The key workshop topics in 2014 are: methods and principles of urgent computing, middleware, platforms and infrastructures, simulation-based decision support for complex systems control, interactive visualization and virtual reality for decision support in emergency situations, domain-area applications to emergency situations, including natural and man-made disasters, e.g.transportation problems, epidemics, criminal acts, etc.

Simulation-based Decision Support Tools for Demand Disaggregation in a Supply Chain Characterised by High Routing Flexibility

Routing flexibility is a key process feature in supply chains characterised by complex hierarchy at different manufacturing levels. When routing alternatives exist, controlling the supply chain to ensure uniformity and quality of the production outcome becomes a significant challenge, also from a demand planning perspective. In this paper, the demand planning problem at a supply chain operating in the semiconductor industry is investigated. Special attention is paid to mid-term demand planning when production orders are not yet finalised and aggregated demand forecast is considered. Within this planning frame, the demand planners face the difficult task of disaggregating aggregated demand into finer granularity products in order to generate provisional production plans that will be used to foresee potential capacity adjustment requirements. The demand disaggregation process entails routing decisions that also incorporate restrictions occasionally imparted by final customers on eligible routes for a specific product type. Historical demand patterns and routing constraints currently constitute the main decision drivers in the demand disaggregation process; likewise, the only objective accounted for is the timely satisfaction of customers’ orders. However, disregarding capacity constraints and ignoring incoming future demand characterised by more stringent routing requirements leads to uncapacitated production plans which might cause significant lateness in the orders’ production. In this study, simulation-based solution approaches able to facilitate the demand planners in the complex task of disaggregating demand forecast are developed. It is shown how either analytical simulation algorithms or discrete event simulation can be used to quantify the lateness deriving from the allocation of different demand profiles and predict the impact of future demand on the optimal disaggregation logic. For analysis purposes, the supply chain will be modelled as a serial-parallel multistage manufacturing system for which the facilities operating in parallel at each stage are similar from a process viewpoint but present different capacity.

A simulation-based decision support system for industrial field service network planning

Technical field services for industrial machinery and equipment have become increasingly important for original equipment manufacturers. To deliver services to their customers, companies have to build up new core competencies and infrastructure, a challenge due to the high complexity and dynamics of this business. To assist companies in the strategic design of their network and the planning of resources for delivering industrial field services, we present a model-driven decision support system that uses discrete event simulation to support decision makers in various aspects of strategic design and tactical planning. The benefits of the decision support system include the creation of a generic framework that makes it possible to create simulation models of different field service networks for multiple purposes. Specifically, the system can be used to support various tactical planning and strategic design decisions while keeping investments low in terms of time consumption and money spending. In addition, the paper closes an identified gap involving a lack of decision support for the management of field service networks. An application of the decision support system in an exemplary case is used to illustrate potential applications and benefits.

Management of biological and chemical incidents: simulation-based decision support

Decision making processes during biological or chemical incidents represent a challenging and demanding issue. This task constitutes of several complex activities and important decisions. In case of unintentional accidents in agricultural plants or within related industries, these decisions have to be made by personnel who are usually not primarily trained for such situations. Therefore any available support tool, which can increase the probability of successful management of these incidents, should be employed. The main aim is to minimize the consequences, ensure the quality of products and protect people and animals and other crucial assets. From the technological perspective, various approaches or principles have been already applied and intended for computer-based support of biological or chemical incidents management. Nevertheless, the multi-agent technologies can be also effectively utilized. As an example, this paper presents model applicable for the management of biological or chemical incidents created in the multi-agent NetLogo environment. The main contribution to the scientific field includes the characterization of specifics related to the discussed type of decision-making process. Moreover, within the paper the description of the simulation model is provided, parameterization is explained, and areas for further research are outlined.

A Comprehensive Study of Application of Decision Support System in Agriculture in Indian Context

Agriculture is the mainstay in Indian Economy and only sustainable agriculture is likely to provide the long-term benefits required to achieve development and scarcity improvement. The research and advancements in agriculture have made available huge amounts of data in different areas of agriculture. It is a great challenge to extract knowledge from data and this has led to methods and techniques such as decision support system that can bridge the knowledge gap. The effectiveness of decision making in agriculture domain can be improved by integrating geospatial information and advanced information technology techniques. This paper reviews and summarizes the application of simulation based decision support system, advisory decision support system in different agriculture practices in Indian context.

A simulation-based decision support system for workforce management in call centers

Workforce management is critical in call centers, where thousands of calls are handled by hundreds of agents every day. In a call center, where the call arrival rates tend to fluctuate during the day, the agent allocation plans are required to be planned flexibly and the number of operating call center agents ought to be updated whenever needed, in order to keep the customer satisfaction level over a predefined level. Workforce plans are usually generated by the use of queuing models that are based on Erlang C calculations. However, they have assumptions that oversimplify the real system and jeopardize the validation of the model. At this point, the simulation models, which do not have such restrictive assumptions, are effective in calculating the required number of agents for each time period and measuring the performance of a given shift schedule. In this study, a simulation-based decision support system (DSS) is developed that runs on real-time data for one of the largest call centers in Turkey. The graphical user interfaces (GUIs) are designed in accordance with the man–machine interaction consideration to increase the usability, functionality and effectiveness of the DSS. It is shown that the combination of the advantages of simulation with a flexible and user-friendly DSS environment provides more effective and efficient workforce planning and performance reporting in call centers.

Research and Application of Simulation-Based Decision-Support

To find a way to support decision making on basis of simulation, this paper researches the technology of simulation and decision support, and proposes a decision-support method based on simulation, illustrates the actual administrative demand of university, establishes a simulation model, builds the architecture of simulation-based decision-support system, discusses the key technology in the implementation. This module, by summarizing the results of testing and forming the advice of supporting, can well achieve the combination of simulation and decision-support, and thus getting a better result.

Simulation-based framework to improve patient experience in an emergency department

The global economic crisis has a significant impact on healthcare resource provision worldwide. The management of limited healthcare resources is further challenged by the high level of uncertainty in demand, which can lead to unbalanced utilization of the available resources and a potential deterioration of patient satisfaction in terms of longer waiting times and perceived reduced quality of services. Therefore, healthcare managers require timely and accurate tools to optimize resource utility in a complex and ever-changing patient care process. An interactive simulation-based decision support framework is presented in this paper for healthcare process improvement. Complexity and different levels of variability within the process are incorporated into the process modeling phase, followed by developing a simulation model to examine the impact of potential alternatives. As a performance management tool, balanced scorecard (BSC) is incorporated within the framework to support continual and sustainable improvement by using strategic-linked performance measures and actions. These actions are evaluated by the simulation model developed, whilst the trade-off between objectives, though somewhat conflicting, is analysed by a preference model. The preference model is designed in an interactive and iterative process considering decision makers preferences regarding the selected key performance indicators (KPIs). A detailed implementation of the framework is demonstrated on an emergency department (ED) of an adult teaching hospital in north Dublin, Ireland. The results show that the unblocking of ED outflows by in-patient bed management is more effective than increasing only the ED physical capacity or the ED workforce.

Simulation Based Optimization for World Line Card Production System

Simulation based decision support system is one of the commonly used tool to examine complex production systems. The simulation approach provides process modules which can be adjusted with certain parameters by using data relatively easily obtainable in production process. World Line Card production system simulation is developed to evaluate the optimality of existing production line via using discrete event simulation model with variaty of alternative proposals. The current production system is analysed by a simulation model emphasizing the bottlenecks and the poorly utilized production line. Our analysis identified some improvements and efficient solutions for the existing system.

Evaluation of carsharing network’s growth strategies through discrete event simulation

Carsharing organizations are nowadays faced with the emergence of new markets due to the growing popularity of their services. To keep up with the growing demand, they have to constantly adapt their network and balance their stations’ capacities by implementing new strategies. These strategies involve creation of new carsharing stations, increasing the capacity of stations, merging or demerging carsharing stations etc. Currently, the decision makers rely on an intuitive strategy selection process which often results in inadequate decisions being made representing an immediate loss in resources, time and market penetration. This paper presents a discrete event simulation based decision support tool that assists the decision makers in selecting best network growth strategies to implement for meeting adequately the demand growth while maximizing the members’ satisfaction level and minimizing the number of vehicles used. Our discrete event simulation model allows modeling the activities at any given set of carsharing stations, regardless of their number and capacities. A benchmarking comparison of different potential strategies is done. An application of the proposed model on a region of Communauto’s Montréal (Québec, Canada) carsharing network is provided.

Agent based decision support in the supply chain context

PurposeThe purpose of this paper is to investigate the benefits and the barriers of agent based decision support (ABDS) systems in the supply chain context.Design/methodology/approachTwo ABDS systems have been developed and evaluated. The first system concerns a manufacturing supply chain while the second concerns a service supply chain. The systems are based on actual case companies.FindingsThis research shows that the benefits of ABDS systems in the supply chain context include the possibility to increase versatility of system architecture, to improve supply chain visibility, to conduct experiments and what‐if analyses, to improve the understanding of the real system, and the possibility to improve communication within and between organizations in the supply chain. The barriers of ABDS systems in the supply chain context include the difficulty to access data from partners in the supply chain, the difficulty to access data on a higher level of granularity, and the difficulty to retrieve data from other information systems.Research limitations/implicationsThe research is explorative in nature therefore empirical data from similar and other research settings should be gathered to reinforce the validity of the findings.Practical implicationsThis research provides knowledge and insights on how ABDS systems may be developed and used in the supply chain context and demonstrates its main benefits and barriers.Originality/valueThis research expands the current research of benefits of ABDS systems to the supply chain domain and also addresses the barriers of ABDS systems to a larger extent than previous research. Comparisons to other simulation based decision support systems are also given.

A cognitive systems framework to inform delivery of analytic support for farmers’ intuitive management under seasonal climatic variability

The idea of the decision support system (DSS) for farmers remains an enigma. Clever technology to bridge the gap between agricultural science and farming practice still seems appropriate. Many more of the conditions for success appear to exist today than ever before. Yet the DSS has yet to significantly colonise farm decision making practice. This paper comes late in a long program of research conducted to see if, and under what conditions, computer simulation of farming scenarios, on which a DSS generally depends, can be valued by farmers. The research approach used an unconventional prototypic information system (IS), comprising local measurements, models, and facilitated discussions that evolved in an action research program. The aim has been to elucidate the means by which successful simulation-based decision support intervention can take place and why it usually does not. This required a significant expansion of the researchers’ concept of the farm as a system to include the farmer’s internal system of practical knowing and learning. This paper reports on a cognitive framework model with transactions at interfaces with both the production system and the analytical IS. Its coarse structure is the classical perception–action cycle influenced by goals and outcome feedback. In the highly uncertain production environment of Australian dryland farming, personal judgement plays a significant mediating role between perception and action, and theory of a continuum between the judgement modes of intuition and analysis adds to framework structure. Further structure comes from the theoretical distinctions between holistic and arbitrary intuition, and between causal and probabilistic analysis. Analytic interventions influence: (a) awareness of current situation conditions and (b) expectations of future conditions and action outcomes, and these serve as primary cognitive resources for evaluation of possible actions in planning and decision making. A theory that matches our research experience in bridging the gap between analytic intervention and intuitive practice posits that virtual situations simulated with analytic models and outputs represented graphically can facilitate vicarious experiential learning. This dovetails with theory concerning the education of intuition. The paper concludes by applying criteria from the field of cognitive engineering to test whether the framework presents a concept of mind that is workable for informing practical model-based research and development aimed at supporting farmers’ judgments and decisions.