Yavari Mohammad, Enjavi Hossein, Geraeli Mohaddese
Department of Industrial Engineering, Faculty of Technology and Engineering, University of Qom, Iran.
Res Transp Bus Manag. 2020 Dec;37:100552. doi: 10.1016/j.rtbm.2020.100552. Epub 2020 Sep 12.
In today's competitive world, with the increase in the complexity of supply chains, supply chain vulnerability to disruptions has increased. In this research, a multi-period location-inventory-routing (LIR) problem of perishable products is investigated under the disruption of routes in some periods. To make a resilient supply chain, two types of pricing namely dynamic pricing and disruptive pricing are applied to manage demands along with location, inventory, and routing decisions. In this regard, an integrated LIR model is developed considering disruption in routes, price-sensitive demand, and a product with a certain life-time. In this model, the price of retailers is a descending function of the time and product lifetime. The proposed model is devised as a mixed-integer non-linear programming model that maximizes the total profit of the supply chain. Due to the NP-hard nature of the problem, the research has developed an efficient genetic algorithm to solve large-sized problems. Computational experiments conducted indicating that the projected GA has an average gap of less than 2.66% from the optimal solution within a reasonable time. The performance of the integrated model, the efficiency of the proposed resilient strategy, and the impact of shelf-life are investigated in a case study. Results revealed that the integrated model for dynamic pricing and LIR decisions enjoys 79.33% improvement in the total expected profit for the supply chain under disruption compared to static pricing. As expected, by increasing the product's shelf-life, the profit of the supply chain increases in all pricing policies. It should be noted that applying the dynamic pricing policy, compared to the product's lifetime, enjoys a greater impact on supply chain profit under disruption. Moreover, there is a necessity to choose an appropriate pricing policy for markets with a different value of price elasticity.
在当今竞争激烈的世界中，随着供应链复杂性的增加，供应链对中断的脆弱性也在上升。在本研究中，研究了在某些时期路线中断情况下易腐产品的多周期选址-库存-路径（LIR）问题。为构建具有弹性的供应链，应用了动态定价和应急定价两种定价方式来管理需求，同时进行选址、库存和路径决策。在此方面，考虑路线中断、价格敏感型需求以及具有一定保质期的产品，开发了一个集成的LIR模型。在该模型中，零售商的价格是时间和产品保质期的递减函数。所提出的模型被设计为一个混合整数非线性规划模型，以最大化供应链的总利润。由于该问题具有NP难的性质，本研究开发了一种高效的遗传算法来解决大规模问题。进行的计算实验表明，所提出的遗传算法在合理时间内与最优解的平均差距小于2.66%。通过一个案例研究，研究了集成模型的性能、所提出的弹性策略的效率以及保质期的影响。结果表明，与静态定价相比，动态定价和LIR决策的集成模型在路线中断情况下使供应链的总预期利润提高了79.33%。正如预期的那样，通过延长产品保质期，在所有定价策略下供应链的利润都会增加。需要注意的是，与产品保质期相比，应用动态定价策略对路线中断情况下的供应链利润影响更大。此外，对于具有不同价格弹性值的市场，有必要选择合适的定价策略。
