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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.

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 ...

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.

This paper considers a scheduling problem in a flexible job shop with a reconfigurable manufacturing cell. The flexible job shop has both operation and routing flexibilities, which can be represented in the form of a multiple process plan, i.e. each part can be processed through alternative operations, each of which can be processed on alternative machines. The scheduling problem has three decision variables: (a) selecting operation/machine pairs for each part; (b) sequencing of parts to be fed into the reconfigurable manufacturing cell; and (c) sequencing of the parts assigned to each machine. Due to the reconfigurable manufacturing cell’s ability of adjusting the capacity, functionality and flexibility to the desired levels, the priority scheduling approach is proposed in which the three decisions are made at the same time by combining operation/machine selection rules, input sequencing rules and part sequencing rules. To show the performances of various rule combinations, simulation experiments were done on various instances generated randomly using the experiences of the manufacturing experts, and the results are reported for the objectives of minimizing makespan, mean flow time and mean tardiness, respectively.

This study addresses a multi-period part selection problem for flexible manufacturing systems in which processing times are controllable. The problem is to determine the set of parts and their processing times while satisfying the processing time and the tool magazine capacities in each period of a planning horizon. The objective is to minimize the sum of processing, earliness/tardiness, subcontracting and tool costs. Practical considerations such as available tool copies and tool lives are also considered. An integer programming model is developed, and two-phase heuristics are proposed in which an initial solution is obtained by a greedy heuristic under initial processing times and then it is improved using local search methods while adjusting processing times. Computational experiments were done on a number of test instances, and the results are reported.

A reconfigurable manufacturing system (RMS), one of state-of-the-art manufacturing system technologies, is the one designed at the outset for rapid changes in its hardware and software components in order to quickly adjust its production capacity and functionality in response to market or system changes. In this study, we consider a cellular RMS with multiple reconfigurable machining cells (RMCs), each of which has numerical control machines, a setup station, and an automatic material handling and storage system. Each machine within the RMC has an automatic tool changer and a tool magazine of a limited capacity. Two important operational problems, part grouping and loading, are considered in this study. Part grouping is the problem of allocating parts to RMCs, and loading is the problem of allocating operations and their cutting tools to machines within the RMC. An integer programming model is suggested to represent the two problems at the same time for the objective of balancing the workloads assigned to machines. Then, due to the complexity of the problem, we suggest two iterative algorithms in which the two problems are solved repeatedly until a solution is obtained. Computational experiments were done on various test instances and the results are reported.

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.