Modelos lineales mixtos para la programación de la producción con una sola etapa: estado del arte

En este documento se presenta una revisión bibliográfica y una posterior taxonomía para modelos de programación lineal entera mixta (PLEM) en planificación de la producción. En concreto, se analizan modelos de una sola etapa por su interés en diversos tipos de procesos productivos. Se han estudiado un total de 30 modelos que se clasifican en cuanto a los objetivos perseguidos, a su formulación, a su representación y, además, según qué características y restricciones han sido tenidas en cuenta. Como resultado, estos modelos se presentan de forma clara y concisa dando un marco de trabajo para futuros desarrollos

Medium-term optimization-based approach for the integration of production planning, scheduling and maintenance

A medium-term optimization-based approach is proposed for the integration of production planning, scheduling and maintenance. The problem presented in this work considers a multiproduct single-stage batch process plant with parallel units and limited resources. An MILP continuous-time formulation is developed based on the main ideas of travelling salesman problem and precedence-based constraints to deal with, sequence-dependent unit performance decay, flexible recovery operations, resource availability and product lifetime. Small scheduling examples have been solved and compared with adapted formulations from the literature, based on discrete-time and global-time events, demonstrating the effectiveness of the proposed solution approach. Additional planning and scheduling problems have been proposed by considering several time periods. Multi-period examples have been efficiently solved by the model showing the applicability of the solution approach for medium-size problems

MILP-based approaches for medium-term planning of single-stage continuous multiproduct plants with parallel units

Scheduling, Mixed-integer linear programming, Hybrid time representation, Traveling salesman problem, Parallel machines, Rolling horizon,

Optimisation approaches for supply chain planning and scheduling under demand uncertainty

This work presents efficient MILP-based approaches for the planning and scheduling of multiproduct multistage continuous plants with sequence-dependent changeovers in a supply chain network under demand uncertainty and price elasticity of demand. This problem considers multiproduct plants, where several products must be produced and delivered to supply the distribution centres (DCs), while DCs are in charge of storing and delivering these products to the final markets to be sold. A hybrid discrete/continuous model is proposed for this problem by using the ideas of the Travelling Salesman Problem (TSP) and global precedence representation. In order to deal with the uncertainty, we proposed a Hierarchical Model Predictive Control (HMPC) approach for this particular problem. Despite of its efficiency, the final solution reported still could be far from the global optimum. Due to this, Local Search (LS) algorithms are developed to improve the solution of HMPC by rescheduling successive products in the current schedule. The effectiveness of the proposed solution techniques is demonstrated by solving a large-scale instance and comparing the solution with the original MPC and a classic Cutting Plane approach adapted for this work

Supply chain management for the process industry

This thesis investigates some important problems in the supply chain management (SCM) for the process industry to fill the gap in the literature work, covering production planning and scheduling, production, distribution planning under uncertainty, multiobjective supply chain optimisation and water resources management in the water supply chain planning. To solve these problems, models and solution approaches are developed using mathematical programming, especially mixed-integer linear programming (MILP), techniques. First, the medium-term planning of continuous multiproduct plants with sequence-dependent changeovers is addressed. An MILP model is developed using Travelling Salesman Problem (TSP) classic formulation. A rolling horizon approach is also proposed for large instances. Compared with several literature models, the proposed models and approaches show significant computational advantage. Then, the short-term scheduling of batch multiproduct plants is considered. TSP-based formulation is adapted to model the sequence-dependent changeovers between product groups. An edible-oil deodoriser case study is investigated. Later, the proposed TSP-based formulation is incorporated into the supply chain planning with sequence-dependent changeovers and demand elasticity of price. Model predictive control (MPC) is applied to the production, distribution and inventory planning of supply chains under demand uncertainty. A multiobjective optimisation problem for the production, distribution and capacity planning of a global supply chain of agrochemicals is also addressed, considering cost, responsiveness and customer service level as objectives simultaneously. Both ε- constraint method and lexicographic minimax method are used to find the Pareto-optimal solutions Finally, the integrated water resources management in the water supply chain management is addressed, considering desalinated water, wastewater and reclaimed water, simultaneously. The optimal production, distribution and storage systems are determined by the proposed MILP model. Real cases of two Greek islands are studied