Multi-level production planning problems in which multiple items compete for the same resources frequently occur in practice, yet remain daunting in their difficulty to solve. In this paper we propose a heuristic framework that can generate high quality feasible solutions quickly for various kinds of lot-sizing problems. In addition, unlike many other heuristics, it generates high quality lower bounds using strong formulations, and its simple scheme allows it to be easily implemented in the Xpress-Mosel modeling language. Extensive computational results from widely used test sets that include a variety of problems demonstrate the efficiency of the heuristic, particularly for challenging problems.

The Nurse Rostering Problem (NRP) is defined as assigning a number of nurses to different shifts during a specified planning period, considering some regulations and preferences. This is often very difficult to solve in practice particularly by applying a sole approach. In this paper, we propose a novel hybrid algorithm combining the strengths of Integer Programming (IP) and Variable Neighbourhood Search (VNS) algorithms to design a hybrid method for solving the NRP. After generating the initial solution using a greedy heuristic, the solution is further improved by employing a Variable Neighbourhood Descent algorithm. Then IP, deeply embedded in the VNS algorithm, is employed within a ruin-and-recreate framework to assist the search process. Finally, IP is called again to further refine the solution during the remaining time. We utilize the strength of IP not only to diversify the search process, but also to intensify the search efforts. To identify the quality of the current solution, we use a new generic scoring scheme to mark the low-penalty parts of the solution. Based on the computational tests with 24 instances recently introduced in the literature, we obtain better results with our proposed algorithm, where the hybrid algorithm outperforms two state-of-the-art algorithms and Gurobi in most of the instances. Furthermore, we introduce 11 randomly generated instances to further evaluate the efficiency of the hybrid algorithm, and we make these computationally challenging instances publicly available to other researchers for benchmarking purposes.

In this paper, we propose a simple but efficient heuristic that combines construction and improvement heuristic ideas to solve multi-level lot-sizing problems. A relax-and-fix heuristic is firstly used to build an initial solution, and this is further improved by applying a fix-and-optimize heuristic. We also introduce a novel way to define the mixed-integer subproblems solved by both heuristics. The efficiency of the approach is evaluated solving two different classes of multi-level lot-sizing problems: the multi-level capacitated lot-sizing problem with backlogging and the two-stage glass container production scheduling problem (TGCPSP). We present extensive computational results including four test sets of the Multi-item Lot-Sizing with Backlogging library, and real-world test problems defined for the TGCPSP, where we benchmark against state-of-the-art methods from the recent literature. The computational results show that our combined heuristic approach is very efficient and competitive, outperforming benchmark methods for most of the test problems

This paper proposes two new mixed integer programming models for capacitated multi-level lot-sizing problems with backlogging, whose linear programming relaxations provide good lower bounds on the optimal solution value. We show that both of these strong formulations yield the same lower bounds. In addition to these theoretical results, we propose a new, effective optimization framework that achieves high quality solutions in reasonable computational time. Computational results show that the proposed optimization framework is superior to other well-known approaches on several important performance dimensions.

For offshore wind to be competitive with mature energy industries, cost efficiencies must be improved throughout the lifetime of an offshore wind farm (OWF). With expensive equipment hire spanning several years, installation is an area where large savings can potentially be made. Installation operations are subject to uncertain weather conditions, with more extreme conditions as OWF developments tend towards larger sites, further offshore in deeper waters. One approach to reduce the cost of the installation process is to evaluate advanced technologies or operational practices. However, in order to demonstrate cost savings, the impact of these advances on the installation process must be quantified in the presence of uncertain environmental conditions. To addresses this challenge a simulation tool is developed to model the logistics of the installation process and to identify the vessels and operations most sensitive to weather delays. These operations are explored to identify the impact of technological or operational advances with respect to weather delays and the resulting installation duration under different levels of weather severity. The tool identifies that loading operations contribute significantly to the overall delay of the installation process, and that a non-linear relationship exists between vessel operational limits and the duration of installation

In this paper, we analyze a variety of approaches to obtain lower bounds for multi-level production planning problems with big bucket capacities, i.e., problems in which multiple items compete for the same resources. We give an extensive survey of both known and new methods, and also establish relationships between some of these methods that, to our knowledge, have not been presented before. As will be highlighted, understanding the substructures of difficult problems provide crucial insights on why these problems are hard to solve, and this is addressed by a thorough analysis in the paper. We conclude with computational results on a variety of widely used test sets, and a discussion of future research

The aim of this review article is to provide a synoptic and critical evaluation of the extensive research that has been performed in demand forecasting in the scheduled passenger transportation industry, specifically in the last few decades. The review begins with an attempt to classify and tabulate the research according to the properties of proposed models, their objectives and application areas in industry in different stages of the planning cycle. This is followed by an assessment of forecast methodologies with suggestions on different methodologies that industry practitioners can adopt to suit their specific needs and recommendations towards future directions of research. We also provide a look into the cross cutting concerns that need to be addressed by all forecasting systems irrespective of the domain or planning stage, such as demand unconstraining, aggregation and the role of expert judgement to incorporate the effect of other extraneous factors that might affect the demand. We conclude from our study that there is a lack of standardization in the way in which methods are described and tested. As a result, there is a lack of cumulative knowledge building. To redress this concern, we propose open source testbeds to facilitate benchmarking of new models. We also propose a checklist as a guideline to standardize the research reports and suggest that when proposing newer models, researchers may consider including a comparative study with existing standard models in research report.

The coupled lot-sizing and cutting-stock problem has been a challenging and significant problem for industry, and has therefore received sustained research attention. The quality of the solution is a major determinant of cost performance in related production and inventory management systems, and therefore there is intense pressure to develop effective practical solutions. In the literature, a number of heuristics have been proposed for solving the problem. However, the heuristics are limited in obtaining high solution qualities. This paper proposes a new progressive selection algorithm that hybridizes heuristic search and extended reformulation into a single framework. The method has the advantage of generating a strong bound using the extended reformulation, which can provide good guidelines on partitioning and sampling in the heuristic search procedure so as to ensure an efficient solution process. We also analyze per-item and per-period Dantzig-Wolfe decompositions of the problem and present theoretical comparisons. The master problem of the per-period DantzigWolfe decomposition is often degenerate, which results in a tailing-off effect for column generation.We apply a hybridization of Lagrangian relaxation and stabilization techniques to improve the convergence. The discussion is followed by extensive computational tests, where we also perform detailed statistical analyses on various parameters. Comparisons with other methods indicate that our approach is computationally tractable and is able to obtain improved results.