Raul Poler

Fuzzy optimization for supply chain planning under supply, demand and process uncertainties

In todays global marketplace, individual firms do not compete as independent entities rather as an integral part of a supply chain. This paper proposes a fuzzy mathematical programming model for supply chain planning which considers supply, demand and process uncertainties. The model has been formulated as a fuzzy mixed-integer linear programming model where data are ill-known and modelled by triangular fuzzy numbers. The fuzzy model provides the decision maker with alternative decision plans for different degrees of satisfaction. This proposal is tested by using data from a real automobile supply chain.

MRP with flexible constraints: A fuzzy mathematical programming approach

In manufacturing environments with complex product structures and multiple production stages, material requirements planning (MRP) systems are the most commonly used for production planning and material supply decision making. However, in practice diverse difficulties arise, such as uncertainty in market demand, resources with limited available capacities, uncertainty in capacity data or uncertain costs. Likewise, classical procedures of resolution applied in MRP environments do not optimize production decisions. This paper provides a new linear programming model for medium term production planning in a capacity constrained MRP, multi-product, multi-level and multi-period manufacturing environment. Then, this model is transformed into three fuzzy models with flexibility in the objective function, in the market demand and in the available capacity of resources. The main goal is to determine the master production schedule, the MRP for each raw component in each period, stock levels, delayed demand, and capacity usage levels over a given planning horizon in such a way as to hedge against uncertainty. Finally, the model is tested using real data from an automobile seat manufacturer.

Material Requirement Planning with fuzzy constraints and fuzzy coefficients

We propose a new fuzzy mathematical programming model for production planning under uncertainty in an industrial environment. This model considers fuzzy constraints related to the total costs, the market demand and the available capacity of the productive resources and fuzzy coefficients for the costs due to the backlog of demand and for the required capacity. The main goal is to determine the master production schedule of each product, the MRP (Material Requirement Planning) for each raw component in each period, stock levels, demand backlog, and capacity usage levels over a given planning horizon. Finally, the proposed model is tested by using data from an automobile seat assembler and compared with other fuzzy mathematical programming approaches. The experiment shows that the proposed model has not got a better behaviour than more simple fuzzy models, but the advantage is that both types of uncertainties, fuzziness and lack of knowledge or epistemic uncertainty can be considered in a model with fuzzy constraints and fuzzy coefficients.

Non-Linear Programming

This chapter begins by introducing non-linear programming. Next, it proposes the formulation of a series of non-linear programming problems with their corresponding solutions. Specifically, multi-modal and multi-variable problems with inequality constraints are modelled. The solution is done by applying the Kuhn-Tucker conditions. It sets out different non-linear programming problems with their solutions in relation to Industrial Organisation Engineering and the management setting.

Collaborative forecasting in networked manufacturing enterprises

Purpose – This paper seeks to propose an overall model of collaborative forecasting for networked manufacturing enterprises.Design/methodology/approach – Contributions by several authors to collaborative forecasting have been analysed from different viewpoints. A collaborative‐forecasting model for networked manufacturing enterprises has been proposed and validated by means of a simulation study.Findings – This model significantly reduces the inventory levels of the whole network and improves customer service.Research limitations/implications – Simulation experiments were done with the enterprise network herein described. Future research will include the simulation of more complex enterprise network scenarios with different characteristics.Practical implications – The model can be implemented node‐to‐node, since not all the companies in the network have to participate, thus facilitating implementation and propagation throughout the network.Originality/value – The paper proposes a new structured planning a...

Capacity and material requirement planning modelling by comparing deterministic and fuzzy models

A model for the capacity and material requirement planning problem with uncertainty in a multi-product, multi-level and multi-period manufacturing environment is proposed. An optimization model is formulated which takes into account the uncertainty that exists in both the market demand and capacity data, and the uncertain costs for backlog. This work uses the concept of possibilistic programming by comparing trapezoidal fuzzy numbers. Such an approach makes it possible to model the ambiguity in market demand, capacity data, cost information, etc. that could be present in production planning systems. The main goal is to determine the master production schedule, stock levels, backlog, and capacity usage levels over a given planning horizon in such a way as to hedge against the uncertainty. Finally, the fuzzy model and the deterministic model adopted as the basis of this work are compared using real data from an automobile seat manufacturer. The paper concludes that fuzzy numbers could improve the solution of production planning problems.

Towards a sustainable interoperability in networked enterprise information systems

Sustaining interoperability in enterprise networks is the next research challenge.Not understanding the impact of a single system change may cause network failures.Pervasive information models and EA can support dynamic interoperability enablers.Combined use of model-driven and knowledge-based approaches can improve NG-EIS.We present and discuss the sustainable interoperability research framework. In a turbulent world, global competition and the uncertainty of markets have led organizations and technology to evolve exponentially, surpassing the most imaginary scenarios predicted at the beginning of the digital manufacturing era, in the 1980s. Business paradigms have changed from a standalone vision into complex and collaborative ecosystems where enterprises break down organizational barriers to improve synergies with others and become more competitive. In this context, paired with networking and enterprise integration, enterprise information systems (EIS) interoperability gained utmost importance, ensuring an increasing productivity and efficiency thanks to a promise of more automated information exchange in networked enterprises scenarios. However, EIS are also becoming more dynamic. Interfaces that are valid today are outdated tomorrow, thus static interoperability enablers and communication software services are no longer the solution for the future. This paper is focused on the challenge of sustaining networked EIS interoperability, and takes up input from solid research initiatives in the areas of knowledge management and model driven development, to propose and discuss several research strategies and technological trends towards next EIS generation.

A conceptual model for the production and transport planning process: An application to the automobile sector

This paper provides a conceptual foundation for understanding the production and transport planning process in the automobile sector context. To achieve this, a reference conceptual modelling methodology has been applied. Firstly, the main concepts related to the conceptual models are addressed. Then, a methodology for the identification and analysis of the inputs, outputs, processes and sub-processes of production and transport planning processes is used. Finally, a conceptual model that describes the main flows - products, information and decisions related to the production and transport planning process - in the automobile sector is developed.

Performance measurement system for business processes

All companies need to know at what level and to what extent they are complying with their objectives. We present a system called PMS-BP (performance measurement system for business processes) which has been designed to define indicators and evaluate company performance. The system is used to measure performance through the integrated management of business processes and to define indicators or parameters at all levels of the company. It also detects the linkages that exist between parameters at different levels and associates them with the companys objectives and strategies through critical success factors. PMS-BP has been built around three components: a methodology, an architecture and a performance measurement structure. The interconnection between the different components makes it easy to constantly trace the relationships between the different performance measurement elements at different levels (enterprise, business entity, processes, etc.). Processes are analysed by means of different types of graphs which make it simple to read and interpret the entire context of the company. We describe a real-life case in which the PMS-BP was applied to an SME in the metal/mechanics sector.

Supply chain planning under uncertainty: a fuzzy linear programming approach

A new fuzzy mathematical programming model for supply chain planning under supply, process and demand uncertainty is proposed in this paper. A tactical supply chain planning problem has been formulated as a fuzzy mixed integer linear programming model where data are ill-known and modelled by triangular fuzzy numbers in the setting of possibility theory. The fuzzy model provides alternative decision plans to the decision maker (DM) for different grades of possibility. Finally, the proposed model is tested by using data from a real automobile supply chain.