Galina Merkuryeva

Advanced river flood monitoring, modelling and forecasting

Abstract The paper presents the state-of-the-art in flood forecasting and simulation applied to a river flood analysis and risk prediction. Different water flow forecasting and river simulation models and systems are analysed. An advanced river flood monitoring, modelling and forecasting approach is introduced. It extends the traditional approach based on modelling river physical processes by integration of different types of models and technologies such as input data clustering and filtering, digital maps of a relief and river terrain, data crowdsourcing, heterogeneous data processing, hydrological models for time scale modelling water flows and geo-simulation, inundation visualisation and duly warning on flooding. A case study on river flow forecasting and simulation for river flood risk analysis and management is given.

Simulation-based planning and optimization in multi-echelon supply chains

In this paper we present a methodology and simulation environment for solving multi-echelon supply chain planning and optimization problems for industries with batch and semi-batch processes. The introduced methodology is aimed to analyze efficiency of a specific planning policy over the product life cycle within the entire supply chain for automated switching from a non-cyclic to cyclic and to optimize the cyclic planning policy for products at the maturity phase. For optimization of a multi-echelon cyclic schedule, the simulation optimization algorithm developed is based on integration of the multi-objective genetic algorithm (GA) and response surface-based local search to improve GA solutions. The comparative analysis of planning policies is based on estimation of the difference between mean values of their total costs by using the Paired-t confidence interval method and evaluation of an additional cost of the cyclic schedule. The simulation environment allows one to describe input data to build the supply chain network and store it in an external file, computing effective planning policies, automatically generating and running a network simulation model, generating production rules for switching from one planning policy to another and optimizing parameters of a multi-echelon cyclic schedule. Finally, a business case is described that illustrates the practical application of the presented methodology.

Vehicle Schedule Simulation with AnyLogic

This paper discusses simulation of the vehicle schedule with time windows. The simulation model is developed in AnyLogic simulation environment. A short overview of AnyLogic structure and scope is provided. A detailed description of a conceptual model with focus on input and output data is given. The structure of the simulation model and operation of its main blocks are described. Each vehicle is modelled as a separate object used to construct the overall schedule for all vehicles. The simulation model is used as a decision support tool for an analyst, which allows estimating efficiency of vehicle schedules with time windows generated by a standard software or/and modified by a planner.

Simulation-Based Approach for Comparison of (s, Q) and (R, S) Replenishment Policies Utilization Efficiency in Multi-echelon Supply Chains

Cyclic and non-cyclic planning methods are traditionally used in the multi-echelon supply chains. However, the utilization rules and efficiency of these methods so far have been investigated partially. The aim of the paper is to evaluate utilization efficiency of two types of inventory policies, i.e. so called (s, Q) and (R, S) ones, in multi-echelon supply chain. In this paper the simulation-based approach for estimation of two observed inventory policies within multi-echelon supply chain is presented. The comparison is based on costs analysis of alternatives. The influence of different parameters i.e. coefficient of demand variation (CODVAR), capacity utilization and number of echelons, is described. The numerical examples are given for three - echelon linear supply chain.

Supply Chain Simulation in the ECLIPS Project

ECLIPS is a European research project addressing the state-of-the-art in supply chain management. It is aimed at minimization of total inventories through the whole supply chain, taking into account a product life- cycle, from its introduction into market, through a maturity phase, and finally to an end-of life phase. In order to achieve this goal, simulation is used intensively in the ECLIPS project. From one hand, it supports supply chain management processes (e.g., optimization and decision making), thus providing conditions for minimization of inventories. From another hand, simulation provides a platform for testing algorithms and tools, being developed within the project. The paper discusses different aspects of using simulation in the ECLIPS project.

Knowledge Based Simulation Systems - A Review

The paper presents a review in the field of knowledge based simulation systems (KBSS). Development and application of such systems allow the user to perform simulation studies without special training in programming languages, simulation theory, and other skills related to simulation project realization. KBSS architecture, programming paradigms and development tools are considered. Problem areas and characteristics of the main applications of KBSS are described. More than 50 examples of research and commercial systems are given in the tables. Quality assurance techniques which may be used in KBSS are considered. They support tools for model reduction and parallel processing techniques. The perspectives for KBSS development are discussed. Among them are support of both on-line digital control, simulation modelling and data base management, development of parallel and distributed systems, fuzzy simulation environment development, combination of the expert system capabilities with potential for adaptation by artificial neural nets. Bibliography includes 168 references.

Multi-objective stochastic simulation-based optimisation applied to supply chain planning

Abstract The paper discusses the optimisation of complex management processes, which allows the reduction of investment costs by setting the optimal balance between product demand and supply. The systematisation of existing methods and algorithms that are used to optimise complex processes by linking stochastic discrete-event simulation and multi-objective optimisation is given. The two-phase optimisation method is developed based on hybrid combination of compromise programming, evolutionary computation and response surface-based methods. Approbation of the proposed method is performed on the multi-echelon supply chain planning problem that is widely distributed in industry and its solution plays a vital role in increasing the competitiveness of a company. Three scenarios are implemented to optimise supply chain tactical planning processes at the chemical manufacturing company based on using different optimisation methods and software. The numerical results prove the competitive advantages of the develope...

Business simulation game for teaching multi-echelon supply chain management

This paper presents a new business game which helps to understand concepts developed in the ECLIPS project of the European Comission. The game provides an insight into different aspects of supply chain management, i.e., general supply chain mechanisms, non-cyclic and cyclic inventory replenishment policies. This allows for people who have no deep notion in this area to better understand project concepts and evaluate their efficiency in practice. Demonstrating concepts in a playful way is considered as more powerful and effective than purely explaining the underlying theory. The paper describes game rules, playing process and provides results of game test sessions.

Integrated planning and scheduling built on cluster analysis and simulation optimisation

This paper proposes integrated solutions for product delivery planning and scheduling in distribution centres. Cluster analysis, computer simulation and metaheuristic optimisation are applied to improve planning decisions at tactical and operational levels. An integrated approach to product delivery planning and scheduling built on integration of these technologies is described. A cluster analysis of product demand data of stores is used to identify typical dynamic demand patterns and associated product delivery tactical plans. Customer regional clusters are built through multi-objective optimisation. Metaheuristic optimisation techniques are applied to define optimal parameters of product transportation and delivery schedules. Here, vehicle scheduling is performed for the routed solution. Integrated solutions are illustrated and adjusted to a specific business case.

Simulation-Based fitness landscape analysis and optimisation for vehicle scheduling problem

The paper presents simulation optimisation methodology and tools for the vehicle scheduling problem (VSP) with time windows. The optimisation problem statement is given. The fitness landscape analysis is used to evaluate the hardness of the problem. The tool for fitness landscape analysis is build up. To evaluate fitness of solutions the vehicle schedule simulation model in AnyLogic 6 is developed, and Java applications generate landscape path solutions and analyse their fitness series. A genetic algorithm is applied for simulation-based vehicle schedule optimisation. The results of the experimental study are described.