Josefa Mula

Mathematical programming models for supply chain production and transport planning

This paper presents a review of mathematical programming models for supply chain production and transport planning. The purpose of this review is to identify current and future research in this field and to propose a taxonomy framework based on the following elements: supply chain structure, decision level, modeling approach, purpose, shared information, limitations, novelty and application. The research objective is to provide readers with a starting point for mathematical modeling problems in supply chain production and transport planning aimed at production management researchers.

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.

A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment

This paper models supply chain (SC) uncertainties by fuzzy sets and develops a fuzzy linear programming model for tactical supply chain planning in a multi-echelon, multi-product, multi-level, multi-period supply chain network. In this approach, the demand, process and supply uncertainties are jointly considered. The aim is to centralize multi-node decisions simultaneously to achieve the best use of the available resources along the time horizon so that customer demands are met at a minimum cost. This proposal is tested by using data from a real automobile SC. The fuzzy model provides the decision maker (DM) with alternative decision plans with different degrees of satisfaction.

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.

A social network‐based organizational model for improving knowledge management in supply chains

Purpose – This paper seeks to provide a social network‐based model for improving knowledge management in multi‐level supply chains formed by small and medium‐sized enterprises (SMEs).Design/methodology/approach – This approach uses social network analysis techniques to propose and represent a knowledge network for supply chains. Empirical experience from an exploratory case study in the construction sector is also presented.Findings – This proposal improves the establishment of inter‐organizational relationships into networks to exchange knowledge among the companies along the supply chain and to create specific knowledge by promoting confidence and motivation.Originality/value – This proposed model is useful for academics and practitioners in supply chain management to gain a better understanding of knowledge management processes, particularly for supply chains formed by SMEs.

Fuzzy estimations and system dynamics for improving supply chains

In this paper, we evaluate the behavior of fuzzy estimations of demand instead of demand forecasts based on exponential smoothing in a two-stage, single-item, multi-period supply chain. A system dynamics model with fuzzy estimations of demand has been constructed for supply chain simulation. Fuzzy numbers are used to model fuzzy demand estimations. With a numerical example, we show that the bullwhip effect and the amplification of the inventory variance (NSamp) can be effectively reduced.

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...

A review of discrete-time optimization models for tactical production planning

This study presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production-planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, set-up times, parallel machines, overtime capacities and network-type multisite configuration stand out in terms of extensions; (4) the most widely used modelling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modelling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.