Model and heuristic solutions for the multiple double-load crane scheduling problem in slab yards

This article studies a multiple double-load crane scheduling problem in steel slab yards. Consideration of multiple cranes and their double-load capability makes the scheduling problem more complex. This problem has not been studied previously. We first formulate the problem as a mixed-integer linear programming (MILP) model. A two-phase model-based heuristic is then proposed. To solve large problems, a pointer-based discrete differential evolution (PDDE) algorithm was developed with a dynamic programming (DP) algorithm embedded to solve the one-crane subproblem for a fixed sequence of tasks. Instances of real problems are collected from a steel company to test the performance of the solution methods. The experiment results show that the model can solve small problems optimally, and the solution greatly improves the schedule currently used in practice. The two-phase heuristic generates near-optimal solutions, but it can still only solve comparatively modest problems within reasonable (4 h) computational timeframes. The PDDE algorithm can solve large practical problems relatively quickly and provides better results than the two-phase heuristic solution, demonstrating its effectiveness and efficiency and therefore its suitability for practical use

Coil batching to improve productivity and energy utilization in steel production

This paper investigates a practical batching decision problem that arises in the batch annealing operations in the cold rolling stage of steel production faced by most large iron and steel companies in the world. The problem is to select steel coils from a set of waiting coils to form batches to be annealed in available batch annealing furnaces and choose a median coil for each furnace. The objective is to maximize the total reward of the selected coils less the total coil'coil and coil'furnace mismatching cost. For a special case of the problem that arises frequently in practical settings where the coils are all similar and there is only one type of furnace available, we develop a polynomial-time dynamic programming algorithm to obtain an optimal solution. For the general case of the problem, which is strongly NP-hard, an exact branch-and-price-and-cut solution algorithm is developed using a column and row generation framework. A variable reduction strategy is also proposed to accelerate the algorithm. The algorithm is capable of solving medium-size instances to optimality within a reasonable computation time. In addition, a tabu search heuristic is proposed for solving larger instances. Three simple search neighborhoods, as well as a sophisticated variable-depth neighborhood, are developed. This heuristic can generate near-optimal solutions for large instances within a short computation time. Using both randomly generated and real-world production data sets, we show that our algorithms are superior to the typical rule-based planning approach used by many steel plants. A decision support system that embeds our algorithms was developed and implemented at Baosteel to replace their rule-based planning method. The use of the system brings significant benefits to Baosteel, including an annual net profit increase of at least 1.76 million U.S. dollars and a large reduction of standard coal consumption and carbon dioxide emissions

Preliminary Draft Report: State-of-the-Art Review of Integrated Systems Control in the Steel Industry

This is a preliminary draft version of the report to be issued on the "State-of-the-Art of Integrated Systems Control in the Steel Industry". The draft is incomplete and not necessarily in final form. Its purpose is to provide background material for the IIASA Conference on "Integrated Systems Control in the Steel Industry" scheduled for 30 June to 2 July, 1975. A second purpose is to motivate feedbacks concerning omissions and additions generated by respondents and Conference participants which may be incorporated into the final 'report

Integrated Models and Algorithms for Automotive Supply Chain Optimization

The automotive industry is one of the most important economic sectors, and the efficiency of its supply chain is crucial for ensuring its profitability. Developing and applying techniques to optimize automotive supply chains can lead to favorable economic outcomes and customer satisfaction. In this dissertation, we develop integrated models and algorithms for automotive supply chain optimization. Our objective is to explore methods that can increase the competitiveness of the automotive supply chain via maximizing efficiency and service levels. Based on interactions with an automotive industry supplier, we define an automotive supply chain planning problem at a detailed operational level while taking into account realistic assumptions such as sequence-dependent setups on parallel machines, auxiliary resource assignments, and multiple types of costs. We model the research problem of interest using mixed-integer linear programming. Given the problem’s NP-hard complexity, we develop a hybrid metaheuristic approach, including a constructive heuristic and an effective encoding-decoding strategy, to minimize the total integrated cost of production setups, inventory holding, transportation, and production outsourcing. Furthermore, since there are often conflicting objectives of interest in automotive supply chains, we investigate simultaneously optimizing total cost and customer service level via a multiobjective optimization methodology. Finally, we analyze the impact of adding an additional transportation mode, which offers a cost vs. delivery time option to the manufacturer, on total integrated cost. Our results demonstrate the promising performance of the proposed solution approaches to analyze the integrated cost minimization problem to near optimality in a timely manner, lowering the cost of the automotive supply chain. The proposed bicriteria, hybrid metaheuristic offers decision makers several options to trade-off cost with service level via identified Pareto-optimal solutions. The effect of the available additional transportation mode’s lead time is found to be bigger than its cost on the total integrated cost measure under study

A Special Case of the Multiple Traveling Salesmen Problem in End-of-Aisle Picking Systems

This study focuses on the problem of sequencing requests for an end-of-aisle automated storage and retrieval system in which each retrieved load must be returned to its earlier storage location after a worker has picked some products from the load. At the picking station, a buffer is maintained to absorb any fluctuations in speed between the worker and the storage/retrieval machine. We show that, under conditions, the problem of optimally sequencing the requests in this system with a buffer size of m loads forms a special case of the multiple traveling salesmen problem in which each salesman visits the same number of cities. Several interesting structural properties for the problem are mathematically shown. In addition, a branch-and-cut method and heuristics are proposed. Experimental results show that the proposed simulated annealing-based heuristic performs well in all circumstances and significantly outperforms benchmark heuristics. For instances with negligible picking times for the worker, we show that this heuristic provides solutions that are, on average, within 1.8% from the optimal value

Production scheduling

https://egrove.olemiss.edu/aicpa_comm/1116/thumbnail.jp

Sequence-Based Simulation-Optimization Framework With Application to Port Operations at Multimodal Container Terminals

It is evident in previous works that operations research and mathematical algorithms can provide optimal or near-optimal solutions, whereas simulation models can aid in predicting and studying the behavior of systems over time and monitor performance under stochastic and uncertain circumstances. Given the intensive computational effort that simulation optimization methods impose, especially for large and complex systems like container terminals, a favorable approach is to reduce the search space to decrease the amount of computation. A maritime port can consist of multiple terminals with specific functionalities and specialized equipment. A container terminal is one of several facilities in a port that involves numerous resources and entities. It is also where containers are stored and transported, making the container terminal a complex system. Problems such as berth allocation, quay and yard crane scheduling and assignment, storage yard layout configuration, container re-handling, customs and security, and risk analysis become particularly challenging. Discrete-event simulation (DES) models are typically developed for complex and stochastic systems such as container terminals to study their behavior under different scenarios and circumstances. Simulation-optimization methods have emerged as an approach to find optimal values for input variables that maximize certain output metric(s) of the simulation. Various traditional and nontraditional approaches of simulation-optimization continue to be used to aid in decision making. In this dissertation, a novel framework for simulation-optimization is developed, implemented, and validated to study the influence of using a sequence (ordering) of decision variables (resource levels) for simulation-based optimization in resource allocation problems. This approach aims to reduce the computational effort of optimizing large simulations by breaking the simulation-optimization problem into stages. Since container terminals are complex stochastic systems consisting of different areas with detailed and critical functions that may affect the output, a platform that accurately simulates such a system can be of significant analytical benefit. To implement and validate the developed framework, a large-scale complex container terminal discrete-event simulation model was developed and validated based on a real system and then used as a testing platform for various hypothesized algorithms studied in this work

Single Tower Crane Allocation Models Using Ant Colony Optimization

The construction industry greatly benefits from the utilization of heavy machines and equipment to accomplish successful projects. Tower cranes, in specific, have a crucial role in the transportation of material loads across the site. Because these machines are fixed to the ground, it is essential for planners and managers to position them in a location that provides the most efficient transfer of materials possible. This is commonly known as the tower crane allocation problem, and many researchers have attempted to optimize the tower crane location using mathematical, artificial intelligence, and simulation approaches. However, many works from the literature contain critical errors which make the models infeasible. This research presents the application of ant colony optimization (ACO) and an ACO variation to tower crane allocation models. Results show that the approaches presented in this work are up to par with even the most powerful methodologies used to solve the problem