Optimizing production scheduling of steel plate hot rolling for economic load dispatch under time-of-use electricity pricing

Time-of-Use (TOU) electricity pricing provides an opportunity for industrial users to cut electricity costs. Although many methods for Economic Load Dispatch (ELD) under TOU pricing in continuous industrial processing have been proposed, there are still difficulties in batch-type processing since power load units are not directly adjustable and nonlinearly depend on production planning and scheduling. In this paper, for hot rolling, a typical batch-type and energy intensive process in steel industry, a production scheduling optimization model for ELD is proposed under TOU pricing, in which the objective is to minimize electricity costs while considering penalties caused by jumps between adjacent slabs. A NSGA-II based multi-objective production scheduling algorithm is developed to obtain Pareto-optimal solutions, and then TOPSIS based multi-criteria decision-making is performed to recommend an optimal solution to facilitate filed operation. Experimental results and analyses show that the proposed method cuts electricity costs in production, especially in case of allowance for penalty score increase in a certain range. Further analyses show that the proposed method has effect on peak load regulation of power grid.Comment: 13 pages, 6 figures, 4 table

Design and operational control of an AGV system

In this paper we first deal with the design and operational control of Automated Guided Vehicle (AGV) systems, starting from the literature on these topics. Three main issues emerge: track layout, the number of AGVs required and operational transportation control. An hierarchical queueing network approach to determine the number of AGVs is decribed. Also basic concepts are presented for the transportation control of both a job-shop and a flow-shop. Next we report on the results of a case study, in which track layout and transportation control are the main issues. Finally we suggest some topics for further research

Inventory routing for dynamic waste collection

We consider the problem of collecting waste from sensor equipped underground containers. These sensors enable the use of a dynamic collection policy. The problem, which is known as a reverse inventory routing problem, involves decisions regarding routing and container selection. In more dense networks, the latter becomes more important. To cope with uncertainty in deposit volumes and with fluctuations due to daily and seasonal e ects, we need an anticipatory policy that balances the workload over time. We propose a relatively simple heuristic consisting of several tunable parameters depending on the day of the week. We tune the parameters of this policy using optimal learning techniques combined with simulation. We illustrate our approach using a real life problem instance of a waste collection company, located in The Netherlands, and perform experiments on several other instances. For our case study, we show that costs savings up to 40% are possible by optimizing the parameters

Ship Routing with Pickup and Delivery for a Maritime Oil Transportation System: MIP Modeland Heuristics

This paper examines a ship routing problem with pickup and delivery and time windows for maritime oil transportation, motivated by the production and logistics activities of an oil company operating in the Brazilian coast. The transportation costs from offshore platforms to coastal terminals are an important issue in the search for operational excellence in the oil industry, involving operations that demand agile and effective decision support systems. This paper presents an optimization approach to address this problem, based on a mixed integer programming (MIP) model and a novel and exploratory application of two tailor-made MIP heuristics, based on relax-and-fix and time decomposition procedures. The model minimizes fuel costs of a heterogeneous fleet of oil tankers and costs related to freighting contracts. The model also considers company-specific constraints for offshore oil transportation. Computational experiments based on the mathematical models and the related MIP heuristics are presented for a set of real data provided by the company, which confirm the potential of optimization-based methods to find good solutions for problems of moderate sizes

The effect of workload constraints in mathematical programming models for production planning

Linear and mixed integer programming models for production planning incorporate a model of the manufacturing system that is necessarily deterministic. Although these eterministic models are the current-state-of-art, it should be recognized that they are used in an environment that is inherently stochastic. This fact should be kept in mind, both when making modeling choices and when setting the parameters of the model. In this paper we study the relation between workload constraints that reflect the finite capacity of the manufacturing system, and the use of planned lead times. It is a common practice in rolling schedule based production planning to limit the periodic output to the average production rate. If lead times are not modeled explicitly, this also implies a restricition on the periodic releases to the average production rate. We demonstrate that this common practice results in inefficient use of the production capacity and show that the use of planned lead times leads to a better trade-off between efficiency and reliability. We analyze a stylized model of a manufacturing system with a single exponential server and two queues in series: an admission queue and a work-in-progress (WIP) queue. The admission queue represents the pool of unreleased orders that is virtually present in the state variables of the planning model. Periodically, jobs from the admission queue are released to the WIP queue such that the number of jobs in WIP and in service does not exceed the workload constraint. We present a simple formula for the maximum utilization rate of such a system, characterize the stationary queue-length distribution by its generating function, and give the distribution of the sojourn time of a job. We use the results to compare various settings of the workload constraint and the planned lead time

Aggregate overhaul and supply chain planning for rotables

We consider the problem of planning preventive maintenance and overhaul for modules that occur in a eet of assets such as trains or airplanes. Each type of module, or rotable, has its own maintenance program in which a maximum amount of time between overhauls of a module is stipulated. Overhauls are performed in an overhaul workshop with limited capacity. The problem we study is to determine aggregate workforce levels, turn-around-stock levels of modules, and overhaul and replacement quantities per period so as to minimize to sum of labor costs, material costs of over- haul, and turn-around-stock investments over the entire life-cycle of the system to be maintained. We prove that this planning problem is strongly NP-hard, but we also provide computational evidence that the mixed integer programming formulation can be solved within reasonable time for real-life instances. Furthermore, we show that the linear programming relaxation can also be used to aid decision making. We apply the model in a case study

Production Scheduling in Integrated Steel Manufacturing

Steel manufacturing is both energy and capital intensive, and it includes multiple production stages, such as iron-making, steelmaking, and rolling. This dissertation investigates the order schedule coordination problem in a multi-stage manufacturing context. A mixed-integer linear programming model is proposed to generate operational (up to the minute) schedules for the steelmaking and rolling stages simultaneously. The proposed multi-stage scheduling model in integrated steel manufacturing can provide a broader view of the cost impact on the individual stages. It also extends the current order scheduling literature in steel manufacturing from a single-stage focus to the coordinated multi-stage focus. Experiments are introduced to study the impact of problem size (number of order batches), order due time and demand pattern on solution performance. Preliminary results from small data instances are reported. A novel heuristic algorithm, Wind Driven Algorithm (WDO), is explained in detail, and numerical parameter study is presented. Another well-known and effective heuristic approach based on Particle Swarm Optimization (PSO) is used as a benchmark for performance comparison. Both algorithms are implemented to solve the scheduling model. Results show that WDO outperforms PSO for the proposed model on solving large sample data instances. Novel contributions and future research areas are highlighted in the conclusion