The purpose of this article is to model and solve an integrated location, routing and inventory problem (LRIP) in cash-in-transit (CIT) sector. In real operation of cash transportation, to decrease total cost and to reduce risk of robbery of such high-value commodity. There must be substantial variation, making problem difficult to formulate. In this paper, to better fit real life applications and to make the problem more practical, a bi-objective multiple periods, capacitated facilities with time windows under uncertain demand (BO-PCLRIP-TW-FD) in the LRIP, motivated by the replenishment of automated teller machines, is proposed. Then, using the chance constrained fuzzy programming to deal with uncertain parameters, the comprehensive model is formulated as a crisp mixed-integer linear programming. At last, to validate the mathematical formulation and to solve the problem, the latest version of ε-constraint method (i.e., AUGMECON2) is used. The proposed solution approach is tested on a realistic instance in CIT sector. Numerical results demonstrate the suitability of the model and the formulation. The ability of the model to be useful references for security carriers in real-world cases.
Rapid population growth and increasing demand of transportation necessitate more efficient transportation and logistics processes. Efficient logistics processes in a supply chain can help the supplier selection procedure be more proficient in terms of delivery time. This paper studies a three-stage supply chain which enables truck sharing for delivery. All suppliers and the manufacturer have a time window for loading and unloading the material. A nonlinear programming model is developed to find the optimal truck share among different suppliers. A numerical example shows the applicability of the proposed model.
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