The economic lot-sizing problem with remanufacturing: analysis and an improved algorithm

Abstract

Remanufacturing is recognized as a major circular economy option to recover and upgrade functions from post-use products. However, the inefficiencies associated with operations, mainly due to the uncertainty and variability of material flows and product conditions, undermine the growth of remanufacturing. With the objective of supporting the design and management of more proficient and robust remanufacturing processes, this paper proposes a generic and reconfigurable simulation model of remanufacturing systems. The developed model relies upon a modular framework that enables the user to handle multiple process settings and production control policies, among which token-based policies. Customizable to the characteristics of the process under analysis, this model can support logistics performance evaluation of different production control policies, thus enabling the selection of the optimal policy in specific business contexts. The proposed model is applied to a real remanufacturing environment in order to validate and demonstrate its applicability and benefits in the industrial settings.

Capacitated Dynamic Lot-sizing for Remanufacturing Systems with Parallel Reprocessing Workstations

“…For the test, 10 instances were generated randomly for some combinations of 7 levels of the number of components (5,10,15,20,30, 40 and 50), 6 levels of the number of periods (5,10,15,20, 30 and 40) and 3 levels of capacity tightness (loose, regular and tight). Here, the test instances were classified into the small-sized ones when the optimal solutions could be obtained using CPLEX within 3600 seconds and the large-sized ones when the optimal solutions could not be obtained.…”

“…Also, the demand requirements are represented by constraint (3). Constraints (4), (5) and (6) ensure that a setup in a period occurs whenever at least one disassembly, reprocessing or reassembly operation is performed in that period. Constraints (7), (8) and (9) represent the capacity constraints.…”

“…Richter and Sombrutzki [3] define a dynamic lot-sizing problem for single-stage remanufacturing systems with return flows and suggest the optimal algorithms that modify the well-known Wagner/Whitin algorithm. Later, it is extended by Richter and Weber [4], Piñeyro and Viera [5] and Cunha and Melo [6]. The single-stage models have a critical limitation in that the detailed material flows through the three processes are not considered.…”

The integrated production-inventory-routing problem in the context of reverse logistics: The case of collecting and remanufacturing of EOL products

“…Constraint (16) ensures that the total pickup load on the last arcs is equal to the total pickup demand, while constraint (17) guaranties that the load on the first arcs is zero. Similarly, constraints (18) and (19) determine the delivery loads on the first and the last arcs. Constraint (20) guaranties that the total load on the vehicle does not exceed its capacity.…”

“…Several extensions of the CDLSR problem can be found in [16] and [17]. Recent, works on heuristics approaches can be found on [18], [19] and [20]. For recent review of mixed integer programming techniques, readers can refer to [21] and [22].…”