 |
Conference papers
Lot-sizing for production planning in a recovery system with returns
Abstract : This paper deals with the production planning and control of a single product involving combined manufacturing and remanufacturing operations. We investigate here a lot-sizing problem in which the demand for items can be satisfied by both the new and the remanufactured products. We assume that produced and recovered (repaired or remanufactured) items are of the same quality. Two types of inventories are involved in this problem. The manufactured and remanufactured items are stored in the first inventory. The returned products are collected in the second inventory and then remanufactured. The objective of this research is to propose a manufacturing/remanufacturing policy that would minimize the holding, the set up and preparation costs for manufacturing and remanufacturing products. The decision variables are the production rates of the manufacturing and the remanufacturing machines. The paper proposes an extension of the reverse Wagner/Whitin dynamic production planning and inventory control model, an adaptation of the well-known Silver and Meal (SM) and Part Period Balancing (PPB) heuristics, a memetic algorithm (MA) and a greedy randomized adaptive search procedure (GRASP). In this later, an exact resolution was used as a local search to find the best production rates for the periods determined by the randomized heuristics. Numerical experiments were conducted on a set of 300 instances with up to 48 periods. The results show that both metaheuristics give high-quality solutions in moderate computational time.
https://hal-utt.archives-ouvertes.fr/hal-02495911
Contributor : Daniel Gavrysiak Connect in order to contact the contributor
Submitted on : Monday, March 2, 2020 - 4:06:33 PM
Last modification on : Friday, August 27, 2021 - 3:14:07 PM
Contributor : Daniel Gavrysiak Connect in order to contact the contributor
Submitted on : Monday, March 2, 2020 - 4:06:33 PM
Last modification on : Friday, August 27, 2021 - 3:14:07 PM