Suppliers always provide free-shipping for retailers whose total order value exceeds or equals an explicit promotion threshold. This paper incorporates a shipping fee in the discrete multi-period newsvendor problem and applies Weak Aggregating Algorithm (WAA) to offer explicit online ordering strategy. It further considers an extended case with salvage value and shortage cost. In particular, online ordering strategies are derived based on return loss function. Numerical examples serve to illustrate the competitive performance of the proposed ordering strategies. Results show that newsvendors' cumulative return losses are affected by the threshold of the order value-based free-shipping. Moreover, the introduction of salvage value and shortage cost greatly improves the competitive performance of online ordering strategies.
Integrated berth and quay crane allocation problem (BQCAP) are two essential seaside operational problems in container terminal scheduling. Most existing works consider only one objective on operation and partition of quay into berths of the same lengths. In this study, BQCAP is modeled in a multiobjective setting that aims to minimize total equipment used and overall operational time and the quay is partitioned into berths of different lengths, to make the model practical in the real‐world and complex quay layout setting. To solve the new BQCAP efficiently, a multiobjective hydrologic cycle optimization algorithm is devised considering problem characteristics and historical Pareto‐optimal solutions. Specifically, the quay crane of the large vessel in all Pareto‐optimal solutions is rearranged to increase the chance of finding a good solution. Besides, worse solutions are probabilistic retained to maintain diversity. The proposed algorithm is applied to a real‐world terminal scheduling problem with different sizes from a container terminal company. Experimental results show that our algorithm generally outperforms the other well‐known peer algorithms and its variants on solving BQCAP, especially in finding the Pareto‐optimal solutions range.
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