Modelling sequence decision inventory management problem under fuzzy environment

Abstract

This study considers a specific inventory management problem, where a retailer could arrange a series of sequential orders, and the next order is arranged after the yield of present stage is known. For this matter, a credibilistic dynamic programming model is formulated under uncertain supply and demand, and the mean cost convex programming model of each ordering stage is derived. The goal is to obtain the optimal order quantity at each stage under the uncertain supply and demand that are represented by fuzzy variables. By extending the analytical solutions of the optimal order quantities, the general expression of the optimal order quantity is obtained. Finally, some numerical experiments are conducted to illustrate the effectiveness of the solution, and the optimum results and sensitivity of parameters are illustrated both numerically and graphically. It showed that the effect of supply uncertainty could be mitigated by placing sequential orders in advance, and more bookings occur at the earlier stage due to the uncertainty of the next stage.

Multi-depot vehicle routing programming for hazmat transportation with weight variation risk

“…For future work, we plan to integrate aspects such as inventory or location into MDVRP modelling, for hazmat transportation. We will consider more realistic factors, e.g., evacuations (Yang, Liu and Yang 2020) during the process of hazmat transportation, more efficient algorithm (Vidal et al 2012;Zhang, Liu, and Lim 2015;Achamu, Berhan and Geremaw 2021), and analyse hazmat transportation risk using flexible mathematical programming models such as credibilistic programming (Li 2013;Tian and Guo 2021) or stochastic programming (Long, Szeto, and Ding 2019;Ma et al 2021). As the number of attributes that require addressing increases, it would be interesting to investigate how attribute evaluation techniques (Li et al 2018) may be utilised to prioritise the sequence in which different attributes are considered, to raise the efficiency of the overall modelling and solution process.…”