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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 p...

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.

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 paper suggests a decision tree based approach for flexible job shop scheduling with multiple process plans. The problem is to determine the operation/machine pairs and the sequence of the jobs assigned to each machine. Two decision tree based scheduling mechanisms are developed for static and dynamic flexible job shops. In the static case, all jobs are given in advance and the decision tree is used to select a priority dispatching rule to process all the jobs. Also, in the dynamic case, the jobs arrive over time and the decision tree, updated regularly, is used to select a priority rule in real-time according to a rescheduling strategy. The two decision tree based mechanisms were applied to a flexible job shop case with reconfigurable manufacturing cells and a conventional job shop, and the results are reported for various system performance measures.

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.

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 adv...

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.

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.

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.

Remanufacturing, one of the most advanced product recovery options, processes used or end-of-life products with disassembly, reprocessing, and reassembly operations in such a way that their qualities are as good as new. This paper focuses on production planning in remanufacturing systems over a given planning horizon with discrete time periods. The main decisions are: (a) the number of used or end-of-life products to be disassembled and disposed; (b) the number of parts or components to be reprocessed, disposed, and newly purchased; and (c) the number of products to be reassembled. The objective is to maximize the total profit, i.e. the difference between the sales revenue and the relevant costs. In particular, set-up costs and times are explicitly considered since set-ups are significant in remanufacturing systems. As an extension of the existing literatures on production planning in remanufacturing systems, a generic mixed integer programming model is suggested that incorporates the detailed processes of the remanufacturing process. Then, owing to the complexity of the problem, two types of heuristics based on the linear programming technique are developed. To show the performances of the heuristics, computational experiments were done on some test instances, and the results are reported.

A reconfigurable manufacturing system (RMS) is an advanced system designed at the outset for rapid changes in its hardware and software components in order to quickly adjust its production capacity and functionally. Among various operational decisions, this study considers the scheduling problem that determines the input sequence and schedule at the same time for a given set of parts. In particular, we consider the practical constraints that the numbers of pallets/fixtures are 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 priority rule based approach in which the two sub-problems are solved using a combination of priority rules. To show the effectiveness of various rule combinations, a simulation experiment was done on the data for a real RMS, and the test results are reported., {"references":["Y. Koren, U. Heisel, F. Jovane, T. 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Disassembly leveling, one of disassembly process planning decisions, is to determine disassembly structures that specify parts/subassemblies to be obtained from 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. In this study, we consider 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: (a) basic problem without parts commonality, i.e., products do not share their parts/subassemblies; and (b) extended problem with parts commonality. For the basic problem, a polynomial-time optimal algorithm is suggested after developing a mathematical programming model. Also, we show that the extended problem is NP-hard and then suggest a heuristic, together with its computational results.

This paper considers the loading problem for flexible manufacturing systems with partially grouped machines, i.e., machines are tooled differently, but multiple machines can be assigned to each operation. 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 where it is allocated. Also, we consider the practical constraints associated with cutting tools: (a) tool life restrictions; and (b) available number of tool copies. An integer linear programming model is suggested for the objective of balancing the workloads assigned to machines. Then, due to the complexity of the problem, we suggest two-stage heuristics in which an initial solution is obtained and then it is improved. The heuristics were tested on some test instances, and the results are reported.