مقاله سال ۲۰۱۶

With the development and wide-spread use of mobile technology, customers can shop anytime and anywhere through a business-to-consumer (B2C) e-commerce shopping platform. However, with the big boom of B2C e-commerce, small lot-size, high frequency, and dynamic arrival of customer orders make order picking and delivery difficult to implement. In order to accelerate the whole order fulfillment process, orders should be picked and delivered to customers in a very short lead time. It is therefore critical to integrate these two operations. This paper focuses on the on-line order batching strategy in relation to the picking process, where orders become available dynamically over time and the known orders can be grouped into several batches based on existing batching rules. Moreover, as many warehouses outsource order delivery to a Third Party Logistics (3PL) provider, orders should be picked and packed up before a fixed departure time. Motivated by these changes, we study the integrated on-line order batching and delivery scheduling (IOOPDS) problem with a fixed delivery departure time.

In the B2C on-line scheduling model, information such as arrival time and customer demand may not be known until the order’s release. Algorithms for the model have to form batches and assign them to appropriate pickers with a fixed departure time in an on-line environment. The objective is to deliver a maximal number of orders within the shortest service time. Four main decision issues need to be solved: 1) when should arrival orders begin to be grouped into batches; 2) which orders should be assigned to the same batch; 3) which orders are urgent and should be satisfied quickly, and which ones can be satisfied later; and 4) how should the batches be assigned to available pickers. However, departure time, multiple pickers, and the on-line environment all challenge classical batching and assigning approaches. For ease of implementation, rule-based solutions are proposed to solve the complex problems: 1) hybrid time window batching rules based on urgency degree and similarity degree; and 2) assigning rules based on departure time and balance strategy.

The remainder of this paper is organized as follows. In section 2, we provide a literature review of the integrated on-line order picking and distribution scheduling problem. In section 3, we establish an IOOPDS optimization model to maximize delivered orders in minimal service time with regard to multiple order-pickers. To solve this problem, rule-based batching and assigning solutions are proposed in section 4. In section 5, several examples are tested to verify the effectiveness of the proposed model and algorithms for IOOPDS. The paper concludes with a summary and an outlook on further research topics in section 6.