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This study addresses the challenge of accurately forecasting demand for maintenance-related spare parts of the K-X tank, influenced by high uncertainty and external factors. Deep learning models with RobustScaler demonstrate significant improvements, achieving an accuracy of 86.90% compared to previous methods. RobustScaler outperforms other scaling models, enhancing machine learning performance across time series and data mining. By collecting eight years' worth of demand data and utilising various consumption data items, this study develops accurate forecasting models that contribute to the advancement of spare parts demand forecasting. The results highlight the effectiveness of the proposed approach, showcasing its superiority in accuracy, precision, recall, and F1-Score. RobustScaler particularly excels in time series analysis, further emphasizing its potential for enhancing machine learning performance on diverse datasets. This study provides innovative techniques and insights, demonstrating the effectiveness of deep learning and data scaling methodologies in improving forecasting accuracy for maintenance spare parts demand. [ABSTRACT FROM AUTHOR]

Disassembly levelling is to determine disassembly structures that specify components to be obtained from end-of-use/life products, and disassembly lot-sizing is to determine the timing and quantity of disassembling end-of-use/life products to satisfy the demands of their components. As an extension of the previous studies that consider them separately, this study integrates the two problems, especially in the form of multi-period model. Particularly, this study considers a generalized integrated problem in which disassembly levels may be different for the products of the same type. To describe the problem mathematically, we develop an integer programming model that minimizes the sum of setup, operation and inventory holding costs. Then, due to the problem complexity, a heuristic algorithm is proposed that consists of two phases: (a) constructing an initial solution using a priority-based greedy heuristic and (b) improving it by removing unnecessary disassembly operations after characterizing the properties of the problem. To show the performance of the heuristic algorithm, computational experiments were performed on various test instances and the results are reported. [ABSTRACT FROM AUTHOR]

In this paper, we deal with a problem that arises from the oil industry with the need to schedule a pipe laying support vessel fleet responsible for connecting oil wells to production platforms. We model it as an identical parallel machine scheduling problem, considering a particular case where jobs are composed of intersecting sets of operations and with operations partitioned into families. A non-anticipatory family setup time is incurred on three occasions: when a machine changes the execution of operations from one family to another, when the machine reaches its capacity, and before the first operation on each machine. These considerations, along with other scheduling features, make the problem more challenging and attractive to the scheduling literature. We propose three mathematical formulations to solve 72 generated instances, based on actual data, with up to 50 operations to schedule. Among the formulations, the Batch Scheduling presents better results with the smallest gaps, when compared to the best lower bounds obtained. This formulation considers a dispatching rule to sequence operations within batches, generating better solutions in 22 of the 24 largest instances, dominating the other formulations. A Brazilian oil company currently uses this approach in a production system for its tactical planning. [ABSTRACT FROM AUTHOR]

In the conventional setup planning methods, the required fixtures are planned in a sequential order relative to other activities of the setup planning. In the present study, a new approach has been developed and a concurrent method has been proposed for integrating the setup planning and fixture design practice, which paves the way for considering the fixture design principles in parallel to setup planning activities. This eliminates the need to revise the setup plans to remedy the possible conflicts which are common to conventional procedures. The fixture principles have been cast into mathematical relations as a means to developing the proposed method. In the present study, the locating surfaces are determined as the most important step of the fixture system design. The reference faces which cannot be used as locating surfaces are also distinguished, in order for the designer to revise the design of the workpiece. [ABSTRACT FROM AUTHOR]

This study addresses an integrated batching and scheduling problem for a single-machine flexible machining cell in which each pallet can load multiple parts, i.e. multi-fixturing pallets, and part processing times can be changed with different processing costs, i.e. controllable processing times. The batching sub-problem is to select the set of parts to be produced in each period of a planning horizon and the resulting scheduling sub-problem is to determine the set of parts to be loaded on each multi-fixturing pallet, the part processing times and the pallet input/processing sequences for the parts selected in each period. A bi-criterion objective is considered that minimises the total tardiness and the total processing cost simultaneously. A solution approach is proposed that consists of three phases from the first to the last period: (a) generating the whole schedule over the planning horizon; (b) selecting the parts to be produced during the current period using the scheduling information; and (c) determining the final schedule for the selected parts. Simulation experiments were done on a number of test instances and the results are reported. [ABSTRACT FROM AUTHOR]

This study addresses a dynamic lot-sizing problem for remanufacturing systems in which end-of-use/life products are disassembled into their parts on a single disassembly workstation and then each part is reprocessed on one of the parallel reprocessing workstations. The problem, which integrates disassembly and reprocessing lot-sizing for multi-level disassembly structures, determines disassembly and reprocessing lot-sizes as well as the workstation on which each part is reprocessed, while satisfying workstation capacities and reprocessed part demands in each period of a planning horizon. An integer programming model is developed that minimizes the sum of set-up, operation and inventory costs. After the NP hardness is shown, two-stage heuristics are proposed in which an initial solution is obtained and then improved by full and partial bi-directional moves. Computational results show that the full move-based heuristic gives near-optimal solutions for small-sized instances and the partial move-based heuristic gives fast solutions with appropriate qualities. [ABSTRACT FROM AUTHOR]

This study addresses a part selection problem for flexible manufacturing systems in which part processing times are controllable to optimize the total cost associated with energy consumption, operational performance, and so on. The problem is to determine the set of parts to be produced, part processing times and the number of tools for each tool type in each period of a planning horizon while satisfying processing time capacity, tool magazine capacity and tool life restrictions. The objective is to minimize the sum of part processing, earliness/tardiness, tool and subcontracting costs. Tool sharing among part types is also considered. After an integer programming model is developed, two types of solution algorithms are proposed, that is, fast heuristics useful when decision time is critical and variable neighborhood search algorithms when solution quality is important. Computational experiments were conducted on a number of test instances and the best fast heuristics are specified, together with reporting how much the variable neighborhood search algorithms improve the fast heuristics. [ABSTRACT FROM AUTHOR]

This paper considers the job shop scheduling problem with alternative operations and machines, called the flexible job shop scheduling problem. As an extension of previous studies, operation and routing flexibilities are considered at the same time in the form of multiple process plans, i.e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decisions are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. Since the problem is highly complicated, we suggest a practical priority scheduling approach in which the two decisions are done at the same time using a combination of operation/machine selection and job sequencing rules. The performance measures used are minimising makespan, total flow time, mean tardiness, the number of tardy jobs, and the maximum tardiness. To compare the performances of various rule combinations, simulation experiments were done on the data for hybrid systems with an advanced reconfigurable manufacturing system and a conventional legacy system, and the results are reported. [ABSTRACT FROM PUBLISHER]

We consider two interrelated problems that occurred in disassembly systems: disassembly leveling and lot sizing. Disassembly leveling, one of disassembly process planning decisions, is to determine disassembly structures that specify parts and/or subassemblies to be obtained from disassembling used/end-of-life products, and disassembly lot sizing is the problem of determining the amounts of disassembly operations required to satisfy the demands of their parts and/or subassemblies. Unlike the existing studies, this study considers the two problems at the same time for the objective of minimizing the sum of disassembly setup and operation costs. In particular, we consider a generalized version in which disassembly levels may be different even for products of the same type. Two types of the problem are considered in this study. The first one is the basic problem without parts commonality, i.e., products do not share their parts or subassemblies, for which a polynomial time optimal algorithm is suggested after developing a mathematical programming model. The second one is an extended problem with parts commonality. After developing another mathematical programming model for the extension, we prove that it is NP hard. Then, a heuristic algorithm is suggested together with its computational results. [ABSTRACT FROM AUTHOR]

This study proposes an integrated model for capacity and production planning in a hybrid production system with manufacturing and remanufacturing facilities. The manufacturing facility produces new products from raw materials, while the remanufacturing facility produces remanufactured products by disassembling, reprocessing and reassembling end-of-use/life products. The problem is to determine capacity requirements and production quantities at manufacturing and remanufacturing facilities, together with subcontracting quantities, to satisfy the demands over a given planning horizon. The objective is to minimize the sum of shutdown, production, inventory holding and subcontracting costs. In particular, this study considers the budget constraint that restricts the cost required to change manufacturing and remanufacturing capacities. An integer programming model is developed to represent the integrated problem mathematically, and then, due to the problem complexity, two linear programming relaxation–based heuristics, each of which fixes the binary variables using a systematic method, are suggested. Computational experiments were done on a number of test instances, and the test results show that the heuristics give near-optimal solutions. [ABSTRACT FROM AUTHOR]

We consider the input sequencing and scheduling problems in a reconfigurable manufacturing system, a state-of-the-art manufacturing system designed at the outset for rapid changes in its hardware and software components. Due to the inherent operation and routing flexibilities of the system, each part is processed according to a multiple process plan, i.e., each part can be processed through alternative operations, each of which can be processed on alternative machines. The main decisions are: (a) the sequence of parts to be released into the system; (b) the selection of operation/machine pair; and (c) the sequence of the parts assigned to each machine within the system. In particular, we consider the practical constraint that the number of fixtures is limited and hence a part can be released into the system only when the fixture required for the part is available. To solve the integrated input sequencing and scheduling problems, we suggest a practical priority rule based approach in which the three decisions are done using a combination of dispatching rules, i.e., those for input sequencing, operation/machine selection, and part sequencing. To show the performances of various rule combinations, simulation experiments were done on the data derived from a real system, and the test results are reported. [ABSTRACT FROM AUTHOR]

This paper considers the loading problem for flexible manufacturing systems with highly flexible partial machine grouping, i.e., machines are tooled differently, but each operation can be assigned to multiple machines. Loading is the problem of allocating operations and their associated cutting tools to machines for a given set of parts. As an extension of the existing studies, we consider unrelated machines, i.e., processing time of an operation depends on the speed of the machine to which it is allocated, and dedicated machines, i.e., certain part types must be processed on a specific machine. Also, we consider the constraints associated with cutting tools: (a) tool life restrictions and (b) number of available tool copies. An integer linear programming model is suggested for the objective of balancing the workloads assigned to machines and then due to the complexity of the problem, we suggest two-stage heuristics in which an initial solution is obtained using modified bin-packing algorithms and then it is improved by a simple search technique. The two-stage heuristics suggested in this study were tested on various test instances, and the results show that they can give reasonable quality solutions within a very short amount of computation time. Also, a sensitivity analysis was done on the tightness of the tooling constraints, and the results are reported. [ABSTRACT FROM AUTHOR]