Anisoara Calinescu

An information‐theoretic methodology for measuring the operational complexity of supplier‐customer systems

In a dynamic environment such as the supply chain, even basic supplier‐customer systems with structurally simple information and material flow formations have a tendency to exhibit operational complexity. The operational complexity of supplier‐customer systems is primarily characterised by the uncertainty of the system. As the operational complexity of a system increases there is an associated increase in the amount of information required to monitor and manage that system. Based on this understanding, a novel information‐theoretic entropy‐based methodology for measuring and analysing the operational complexity of supplier‐customer systems has been developed. This paper makes contributions in the theoretical, conceptual and practical developments of the methodology. The methodology can quantitatively detect and prioritise operational complexity hotspots. At the interface, the framework can identify and quantify the transfer of operational complexity. Within the internal manufacturing system, the framework...

Dealing with Uncertainty in Manufacturing: The Impact on Scheduling

Today’s manufacturing environment is characterised by ever shorter product life cycles, greater product variety and quicker response to customers’ demands. To satisfy these requirements, manufacturers introduce greater flexibility to their process, which may lead to higher levels of complexity and uncertainty. Planning and scheduling are affected by these factors. A rigid plan is no longer valid to satisfy all the constraints and changes in the production. Our research, carried out in a leading UK manufacturer, has provided us with an understanding of the realities of the shop-floor and the effects that uncertainty has over the scheduling function. Our work has revealed the need of a new approach for solving the scheduling problem. This perception of scheduling will have to take into account not only the manufacturing variables but also external elements such as organisational features, human factors and information flow.

MODELLING AND SIMULATION OF A REAL COMPLEX PROCESS-BASED MANUFACTURING SYSTEM

This paper describes the approach adopted in a case-study involving the modelling and simulation of a process-based manufacturing system. The results presented are part of an on-going project on assessing manufacturing complexity and its effect on schedule reliability in manufacturing. We emphasise the importance of a thorough knowledge and understanding of the modelled system and briefly present the methods used for achieving them. The model we designed takes into account the interactions between the information flow, decision-making and the production process. The issues involved in creating the model and its current status are presented. The trade-offs between the model complexity and its accuracy and validity are also investigated.

Towards a Secure Two-Stage Supply Chain Network: A Transportation-Cost Approach

The robustness, resilience and security of supply chain transportation is an active research topic, as it directly determines the overall supply chain resilience and security. In this paper, we propose a theoretical model for the transportation problem within a two-stage supply chain network with security constraints called the Secure Supply Chain Network (SSCN). The SSCN contains a manufacturer, directly connected to several distribution centres DC, which are directly connected to one or more customers C. Each direct link between any two elements of Secure Supply Chain Network is allocated a transportation cost. Within the proposed model, the manufacturer produces a single product type; each distribution centre has a fixed capacity and a security rank. The overall objective of the Secure Supply Chain Network is 100 % customer satisfaction whilst fully satisfying the security constraints and minimizing the overall transportation costs. A heuristic solving technique is proposed and discussed.

Assessing Decision-Making and Process Complexity in a Manufacturer Through Simulation

Abstract This paper describes how modelling and simulation are used in a case study which assesses the complexity of a manufacturing system and its effect on planning and scheduling reliability in order to identify directions for improvement. The approach chosen, the real system and the structure of the model are briefly presented. A direct relationship between production cost and the sources of complexity in the analysed facility is proposed as a means to better control the process. The model is currently being validated using real data and feedback from the people in the shop-floor and planning and finance departments.

Revisiting Agent-Based Models of Algorithmic Trading Strategies

Algorithmic trading (AT) strategies aim at executing large orders discretely, in order to minimize the order’s impact, whilst also hiding the traders’ intentions. The contribution of this paper is twofold. First we presented a method for identifying the most suitable market simulation type, based on the specific market model to be investigated. Then we proposed an extended model of the Bayesian execution strategy. We implemented and assessed this model using our tool AlTraSimBa (ALgorithmic TRAding SIMulation BAcktesting) against the standard Bayesian execution strategy and naive execution strategies, for momentum, random and noise markets, as well as against historical data. Our results suggest that: (i) momentum market is the most suitable model for testing AT strategies, since it quickly fills the Limit Order book and produces results comparable to those of a liquid stock; (ii) the priors estimation method proposed in this paper \(-\) within the Bayesian adaptive agent model \(-\) can be advantageous in relatively stable markets, when trading patterns in consecutive days are strongly correlated, and (iii) there exists a trade-off between the frequency of decision making and more complex decision criteria, on one side, and the negative outcome of lost trading on the agents’ side due to them not participating actively in the market for some of the execution steps.

A Model for the Control of Complexity in Manufacturing Systems

Abstract Information variety and uncertainty are the key elements of complexity in manufacturing systems. An information-theoretic model based on entropy is proposed here. The model shows the interaction between the scheduling function and the production function, which provides a realistic framework for the control of complexity in manufacturing systems. The usefulness of the model can be summarised as supporting the understanding, monitoring and decision-making when controlling complexity. Some results of a case study are presented to prove the applicability of the model. Furthermore, the model has provided a basis for the investigation on related topics.

Adaptive-Structure Discrete Event Dynamic Systems

Abstract This paper proposes the dynamic adaptive configuration of the structure of complex Discrete Event Dynamic Systems depending on particular loading conditions. The main purpose of this new approach is to assure an optimum trade-off between the efficient utilization of resources and the production cost. The system behaviour for a particular task/system structure is simulated, and, based on the loading knowledge thus obtained and on the followed purpose, the system is reconfigured. Although simple, this solution is successfully applicable whenever identical resources are used by more autonomous systems, the adaptive auto-configuration increasing their efficiency.