Coordinated Replenishment Policy in an Assembly System Based on Supply-Hub

Purpose The purpose of this paper is to investigate a new and practical decision support model of the coordinated replenishment and delivery (CRD) problem with multi-warehouse (M-CRD) to improve the performance of a supply chain. Two algorithms, tabu search-RAND (TS-RAND) and adaptive hybrid different evolution (AHDE) algorithm, are developed and compared as to the performance of each in solving the M-CRD problem. Design/methodology/approach The proposed M-CRD is more complex and practical than classical CRDs, which are non-deterministic polynomial-time hard problems. According to the structure of the M-CRD, a hybrid algorithm, TS-RAND, and AHDE are designed to solve the M-CRD. Findings Results of M-CRDs with different scales show that TS-RAND and AHDE are good candidates for handling small-scale M-CRD. TS-RAND can also find satisfactory solutions for large-scale M-CRDs. The total cost (TC) of M-CRD is apparently lower than that of a CRD with a single warehouse. Moreover, the TC is lower for the M-CRD with a larger number of optional warehouses. Practical implications The proposed M-CRD is helpful for managers to select the suitable warehouse and to decide the delivery scheduling with a coordinated replenishment policy under complex operations management situations. TS-RAND can be easily used by practitioners because of its robustness, easy implementation, and quick convergence. Originality/value Compared with the traditional CRDs with one warehouse, a better policy with lower TC can be obtained by the new M-CRD. Moreover, the proposed TS-RAND is a good candidate for solving the M-CRD.

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Supplier Replacement Model in a One-Level Assembly System under Lead-Time Uncertainty

Applied Sciences ◽

10.3390/app10103366 ◽

2020 ◽

Vol 10 (10) ◽

pp. 3366

Author(s):

Hasan Murat Afsar ◽

Oussama Ben-Ammar ◽

Alexandre Dolgui ◽

Faicel Hnaien

Keyword(s):

Lead Time ◽

Probability Distributions ◽

Assembly System ◽

Lead Times ◽

Real Component ◽

Price Negotiation ◽

Purchase Cost ◽

Replacement Model ◽

Replenishment Policy ◽

Lead Time Uncertainty

Supplier selection/replacement strategies, purchasing price negotiation and optimized replenishment policies play a key role in efficient supply chain management in today’s dynamic market. Their importance increases even more in Industry 4.0. In this paper, we propose a joint model of replenishment planning and purchasing price negotiation in the context of supplier replacement in a one-level assembly system (OLAS) producing one type of finished product. The real component lead times are stochastic. There is consequently a non-negligible risk that the assembly process may be stopped if all components for assembly are not delivered on the due date. This incurs inventory-related costs, holding and backlogging, which should be minimized. We consider a set of suppliers characterized by their prices and the probability distributions of their lead-times, and we present a model and an approach that optimize not only replenishment policy, but also purchasing prices. For a given unit, it is possible to model several alternative suppliers with alternative pricing and lead-time uncertainties, and evaluate their impacts on the total cost: composed of holding, backlogging and purchasing costs for the assembly system. The findings of this study indicate that it can be beneficial to pay suppliers an additional purchase cost in order to reduce the holding and backlogging costs related to uncertainty. In consequence, decision makers can use the proposed approach to negotiate prices and delivery delays or to select suppliers.

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