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Due to increasing awareness on the treatment of end-of-use/life products, disassembly has been a fast-growing research area of interest for many researchers over recent decades. This paper introduces a novel lot-sizing problem that has not been studied in the literature, which is the service-parts lot-sizing with disassembly option. The disassembly option implies that the demands of service parts can be fulfilled by newly manufactured parts, but also by disassembled parts. The disassembled parts are the ones recovered after the disassembly of end-of-use/life products. The objective of the considered problem is to maximize the total profit, i.e., the revenue of selling the service parts minus the total cost of the fixed setup, production, disassembly, inventory holding, and disposal over a planning horizon. This paper proves that the single-period version of the considered problem is NP-hard and suggests a heuristic by combining a simulated annealing algorithm and a linear-programming relaxation. Computational experiment results show that the heuristic generates near-optimal solutions within reasonable computation time, which implies that the heuristic is a viable optimization tool for the service parts inventory management. In addition, sensitivity analyses indicate that deciding an appropriate price of disassembled parts and an appropriate collection amount of EOLs are very important for sustainable service parts systems.

We consider the problem of determining the lot sizes that satisfy the demands of remanufactured products over a given planning horizon with discrete time periods. Remanufacturing, in which used or end-of-life products are restored to like-new condition, typically consists of disassembly, reprocessing and reassembly processes, and hence the lot sizes are determined for each of the three processes. The objective is to minimise the sum of setup and inventory holding costs occurring at the three processes. To represent the problem mathematically, we suggest a mixed integer programming model by combining the existing ones for disassembly and assembly systems. After proving that the problem is NP-hard, we suggest two dynamic programming based heuristics, called the aggregation and the decomposition type heuristics in this paper. Computational experiments were done on various test instances, and the results show that the two heuristics give near-optimal solutions in a short amount of computation time. Also, the ...

Dynamic lot sizing is the problem of determining the quantity and timing of ordering items while satisfying the demand over a finite planning horizon. This paper considers the problem with two practical considerations: minimum order size and lost sales. The minimum order size is the minimum amount of items that should be purchased and lost sales involve situations in which sales are lost because items are not on hand or when it becomes more economical to lose the sale rather than making the sale. The objective is to minimize the costs of ordering, item , inventory holding and lost sale over the planning horizon. To solve the problem, we suggest a heuristic algorithm by considering trade-offs between cost factors. Computational experiments on randomly generated test instances show that the algorithm quickly obtains near-optimal solutions.

This paper considers disassembly scheduling, which is the problem of determining the quantity and timing of the end-of-use/life products to be disassembled while satisfying the demand for their parts obtained from disassembling the products over a planning horizon. This paper focuses on the problem with stochastic demand of parts/modules, capacity restrictions on disassembly resources, and multiple product types with a two-level product structure. The two-level product structure implies that an end-of-use/life product is hierarchically decomposed into two levels where the first level corresponds to the parts/modules and the second level corresponds to the product. We formulate the problem as a stochastic inventory model and to solve the problem we propose a Lagrangian heuristic algorithm as well as an optimisation algorithm for the sub-problems obtained from Lagrangian decomposition. The test results on randomly generated problems show that the Lagrangian heuristic algorithm demonstrates good performance in terms of solution quality and time.

Disassembly scheduling, one of the important operational problems in disassembly systems, can be generally defined as the problem of determining the quantity and timing of the end-of-use/life products while satisfying the demand of their parts over a planning horizon. This paper presents a literature review on this planning problem in disassembly systems. First, the basic form of the problem is defined with a mathematical formulation. To characterize the differences between assembly and disassembly processes, the effects of the divergence property are also explained with respect to the zero inventory property, indispensable surplus inventory, and mathematical representation. Then, we review the existing research articles on the basic problem and its generalizations. A systematic scheme for classifying problems is also suggested. Finally, we suggest several future research directions.

This paper considers the problem of determining the disassembly schedule (quantity and timing) of products in order to satisfy the demand of their parts or components over a finite planning horizon. The objective is to minimize the sum of set-up, disassembly operation, and inventory holding costs. As an extension of the uncapacitated versions of the problem, we consider the resource capacity restrictions over the planning horizon. An integer program is suggested to describe the problem mathematically, and to solve the problem, a heuristic is developed using a Lagrangean relaxation technique together with a method to find a good feasible solution while considering the trade-offs among different costs. The effectiveness of the algorithm is tested on a number of randomly generated problems and the test results show that the heuristic suggested in this paper can give near optimal solutions within a short amount of computation time.

Disassembly scheduling, one of the important operational problems in disassembly systems, is the problem of determining the timing and quantity of disassembling used or end-of-life products to satisfy the demand of their parts or components over a given planning horizon. This paper focuses on the case of single product type without parts commonality while the resource capacity restrictions are considered explicitly. The problem is formulated as an integer programming model with the objective of minimizing the number of products disassembled, and a solution algorithm is suggested after deriving its optimal solution properties. Computational experiments are done on various test problems, and the results show that the algorithm suggested in this paper gives optimal solutions for the test problems within a very short amount of computation time.

Disassembly scheduling is one of the important planning problems in disassembly systems, and generally defined as the problem of determining the quantity and timing of disassembling products in order to satisfy the demand of their parts or components over a planning horizon. This paper focuses on the case of multiple product types with parts commonality for the objective of minimizing the sum of set-up, disassembly operation and inventory-holding costs, and it suggests a two-phase heuristic in which an initial solution is obtained using a linear-programming relaxation, and then improved by perturbing the initial solution using a dynamic programming algorithm with look-ahead check. Computational experiments are done on a number of randomly generated test problems and the results show that the heuristic suggested in this paper can give near-optimal solutions within a short computation time.

This paper focuses on the problem of determining the ordering and disassembly schedules of used or end-of-life products while satisfying the demand of their parts/components over a planning horizon. To represent and optimally solve the basic case of the problem, an integer programming model is suggested for the objective of minimizing the sum of purchase, set-up, inventory holding and disassembly operation costs. Then, the basic model is extended to consider more general cases with parts commonality, i.e. single and multiple product types with parts commonality. Computational experiments done on the problems obtained from the literature and a number of randomly generated test problems show that the integer programming models suggested in this paper work well up to medium-sized test problems.

We consider the problem of determining the disassembly schedules of used products while satisfying the demand of their parts/components over a planning horizon. The case of multiple product types with parts commonality is considered for the objective of minimizing the sum of setup, disassembly operation, and inventory holding costs. A heuristic algorithm is proposed using the linear programming relaxation approach. Test results of the case study on the data obtained from a disassembly shop show that the heuristic algorithm suggested in this paper works well for practical problems.

This paper considers the problem of determining the quantity and timing of disassembling used or end-of-life products in order to satisfy the demand of their parts or components over a finite planning horizon. We focus on the case of single product type without parts commonality, i.e., assembly product structure. The objective is to minimize the sum of setup, disassembly operation, and inventory holding costs. Several properties of optimal solutions are derived, and then a branch and bound algorithm is developed based on the Lagrangian relaxation technique. Results of computational experiments on randomly generated test problems show that the algorithm finds the optimal solutions in a reasonable amount of computation time.

Disassembly scheduling is the problem of determining the timing and quantity of disassembling used products to satisfy the demands of their parts or components over the planning horizon. This paper focuses on the case of single product type without parts commonality while the resource capacity restrictions are explicitly considered. The problem is formulated as an integer program for the objective of minimizing the number of products to be disassembled, and an optimal algorithm, after deriving the properties of the problem, is suggested. Computational experiments are done on randomly generated test problems, and the results are reported.

We consider the problem of determining the disassembly schedule (quantity and timing) of products in order to satisfy the demand of their parts or components over a finite planning horizon. This paper focuses on the capacitated version of the problem for the objective of minimizing the sum of setup, disassembly operation, and inventory holding costs. The problem is formulated as an integer program, and to solve the problem, a Lagrangean heuristic algorithm is developed after reformulating the integer program. To show the performance of the heuristic algorithm, computational experiments are done on randomly generated test problems, and the test results show that the algorithm suggested in this paper works well.