Hyoung-Ho Doh

Generic production planning model for remanufacturing systems

Abstract 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 two-stage heuristic for single machine capacitated lot-sizing and scheduling with sequence-dependent setup costs

This paper considers a single machine capacitated lot-sizing and scheduling problem. The problem is to determine the lot sizes and the sequence of lots while satisfying the demand requirements and the machine capacity in each period of a planning horizon. In particular, we consider sequence-dependent setup costs that depend on the type of the lot just completed and on the lot to be processed. The setup state preservation, i.e., the setup state at the end of a period is carried over to the next period, is also considered. The objective is to minimize the sum of setup and inventory holding costs over the planning horizon. Due to the complexity of the problem, we suggest a two-stage heuristic in which an initial solution is obtained and then it is improved using a backward and forward improvement method that incorporates various priority rules to select the items to be moved. Computational tests were done on randomly generated test instances and the results show that the two-stage heuristic outperforms the best existing algorithm significantly. Also, the heuristics with better priority rule combinations were used to solve case instances and much improvement is reported over the conventional method as well as the best existing algorithm.

A priority scheduling approach for flexible job shops with multiple process plans

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.

Iterative algorithms for part grouping and loading in cellular reconfigurable manufacturing systems

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.

Multi-period disassembly levelling and lot-sizing for multiple product types with parts commonality:

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.

Capacity and production planning for a hybrid system with manufacturing and remanufacturing facilities

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.

Loading algorithms for flexible manufacturing systems with partially grouped unrelated machines and tooling constraints

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.

Decision Tree based Scheduling for Static and Dynamic Flexible Job Shops with Multiple Process Plans

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.

An integrated model and its extension for disassembly leveling and lot-sizing for multiple product types

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.