Leandro Magatão

A combined CLP-MILP approach for scheduling commodities in a pipeline

This paper addresses the problem of developing an optimization model to aid the operational scheduling in a real-world pipeline scenario. The pipeline connects refinery and harbor, conveying different types of commodities (gasoline, diesel, kerosene, etc.). An optimization model was developed to determine pipeline scheduling with improved efficiency. This model combines constraint logic programming (CLP) and mixed integer linear programming (MILP) in a CLP-MILP approach. The proposed model uses decomposition strategies, continuous time representation, intervals that indicate time constraints (time windows), and a series of operational issues, such as the seasonal and hourly cost of electric energy (on-peak demand hours). Real cases were solved in a matter of seconds. The computational results have demonstrated that the model is able to define new operational points to the pipeline, providing significant cost savings. Indeed the CLP-MILP model is an efficient tool to aid operational decision-making within this real-world pipeline scenario.

Traveling worker assembly line (re)balancing problem: Model, reduction techniques, and real case studies

The assembly line balancing problem arises from equally dividing the workload among all workstations. Several solution methods explore different variants of the problem, but no model includes all characteristics real assembly lines might contain. This paper presents a mixed integer linear programming model that solves the Traveling Worker Assembly Line Balancing Problem (TWALBP). In this problem, the tasks’ balancing along with the assignment of workers to one or more workstations is determined for a given layout. The assignment flexibility is solved with a traveling salesman problem formulation integrated in the balancing model. Adapted standard datasets and three real case scenarios are used as benchmark sets. These scenarios present particularities such as human and robotic workers, assignment restrictions, zoning constraints, automatic and common tasks. The model successfully determines the tasks’ assignments and the routing of every worker for a layout aware optimization of assembly lines. Better quality balancing solutions were achieved allowing workers to perform tasks at multiple stations, showing a trade-off between assignment flexibility and movement time.

An integrated framework for operational scheduling of a real-world pipeline network

Abstract This paper addresses the problem of developing an optimisation structure to aid the operational decision-making of scheduling activities in a real-world pipeline network. During the scheduling horizon, many batches are pumped from (or passing through) different nodes (refineries, harbours or distribution terminals). Pipelines are shared resources operated and monitored 365 days a year, 24 hours per day. Scheduling details must be given, including pumping sequence in each node, volume of batches, tankage constraints, timing issues, while respecting a series of operational constraints. The balance between demand requirements and production campaigns, while satisfying inventory management issues and pipeline pumping procedures, is a difficult task. Based on key elements of scheduling, a decomposition approach is proposed using an implementation suitable for model increase. Operational insights have been derived from the obtained solutions, which are given in a reduced computational time for oil industrial-size scenarios.

Enhancing supply chain decisions using constraint programming: a case study

A new approach is proposed to tackle integrated decision making associated to supply chains. This procedure enables reliable decisions concerning the set of order demands along a supply chain. This is accomplished by means of supply chain scheduling simulations, based on the use of Constraint Programming. The definition of time windows for all tasks poses as an indication that no infeasibility was found during supply chain analysis. Scheduling of orders along the supply chain is treated as a constraint satisfaction problem. It suffices to identify any feasible schedule to state that simulated decisions are acceptable. The main contribution of this work is the integration of Constraint Programming concepts within a decision-support system to support supply chain decisions.

Balancing a robotic spot welding manufacturing line: An industrial case study

Resistance spot welding is an important procedure used in the automobile manufacturing industry. After stamping, sheets of metal are mostly welded together to build the car’s body. Many of the industrial robots found in assembly lines are spot welders. Process characteristics include accessibility limitations, the need to move between welding points and (when there are multiple robots per station) potential interferences between robots. The combination of such characteristics is not present in classical models for the assembly line balancing problem. In this paper, the balancing problem of robotic spot welding manufacturing lines is presented and modeled based on a real-world car factory on the outskirts of Curitiba, Brazil. The studied line has 42 robots that perform over 700 welding points on the later stages of the body-shop. The model was developed with Mixed Integer Linear Programming (MILP) techniques, validated with empirical data, and solved with a universal solver. The optimized balancing achieved a cycle time reduction of up to 6.6% compared to the as-is configurations. The total movement time was observed not to be necessarily minimized during the optimization process, implying that trade-offs exist between movement times and the number of welding points performed.

Genetic algorithm for type-2 assembly line balancing

The assembly line balancing problem (ALBP) consists in finding the best assignment of tasks between several workstations. An evenly distribution reduces idle time and therefore results in more efficient production systems. Although several models have been proposed for ALBP, real lines present restrictions that usually violate simplifications assumptions. This paper presents a hybrid genetic algorithm to solve balancing problems with assignment restrictions. Heuristics are dynamically used in the encoding process to reduce search space and to focus the search on promising areas. The hybrid GA is able to obtain solutions close to the optimal (0.79% in average) for the most used dataset in the literature. The presented GA can incorporate equipment or zoning restrictions that might be present in real assembly lines.

Using a MILP model for battery bank operation in the “White tariff” Brazilian context

In this paper, the optimal operation of a battery bank in the presence of a Photovoltaic System (PV System) is approached. The battery bank charge and discharge must be controlled to reduce the consumed electrical energy cost. Different tariffs are considered according to the day hour. The optimization model was formulated and solved by mixed integer linear programming - MILP - with Big-M formulation. The problem context, assumed model hypothesis, objective function, constraints, and results are discussed. This work aims to contribute for the insertion of micro distributed generation into the Brazilian energy matrix.

A MILP Planning Model for a Real-world Multiproduct Pipeline Network

Abstract This work proposes a generic MILP model for planning activities of a multiproduct, multi-pipeline system. This is applied to a real-world pipeline network. The network includes 30 bidirectional multiproduct pipelines associated with 14 areas: 4 refineries, 2 harbours, 6 depots, and 2 final clients. The pipelines can be shared by 34 oil derivatives and the products can be sent from an origin area to various destination areas, as can be received in a destination area from different origin areas. The necessary requirements on the supply and inventory management of the producers and consumers areas are taken into account. As final results, the model defines the products and the volumes to be transported in order to attain storage goals, while respecting consumers demands. Optimal solutions for various scenarios are obtained with a high level of performance.

A Mathematical Programming Approach to Optimize the Scheduling of Tanks in Oil Refineries

This paper presents a Mathematical Programming approach to optimize the scheduling of tanks in oil refineries. The considered problem is part of oil industry supply chain, where decisions are taken in strategic, tactical, and operational levels. The scheduling of tanks is part of the operational level and plays a fundamental role in integrating refinery production and transport operations. In particular, within the real-world studied scenario, a pipeline network is used to transport the derivatives, and the previously determined scheduling of pipelines is used as an input for the proposed model. Thus, the operational constraints and temporal issues from pipelines, which influence the transfer and storage refinery operations, are taken as assumptions within the proposed scheduling model for tanks. The proposed model is structured by a Mixed Integer Linear Programming (MILP) formulation with a continuous time representation. The obtained results are evaluated by three main aspects: the respect of time-windows imposed by pipeline operations; by the storage profiles in tanks; and, by computational tests. The obtained results indicate the models ability to propose a scheduling for refinery tanks that integrates the inherited scheduling of pipelines.

Um modelo híbrido (CLP-MILP) para scheduling de operações em polidutos

This work is motivated by the need of optimization in the pipeline-oil distribution scenario. The considered problem involves the short-term scheduling of activities in a specific pipeline. The pipeline is 93.5 km in length, and it connects refinery and harbor tankfarms, conveying different types of commodities (gasoline, diesel, kerosene, etc). An optimization model was developed to determine the pipeline scheduling with improved efficiency. Such model combines Constraint Logic Programming (CLP) and Mixed Integer Linear Programming (MILP) in an integrated CLP-MILP framework. The proposed model uses decomposition strategies, continuous time representation, and intervals that represent time constraints (time windows). Real cases were solved in a reduced computational time (order of seconds). The computational results have demonstrated that the model is able to define new operational points to the pipeline, providing significant cost saving. Indeed the CLP-MILP model is an efficient tool to aid the operational decision-making within this real-world pipeline scenario.