By virtue of its cost advantage, online shopping has attracted a substantial population of consumers. At the same time, lacking the first-hand touching experience in online shopping often causes a utility mismatch to consumers, which may not only turn down some potential consumers but also cause a high return rate. A recent business practice for retailers is to encourage showrooming – consumers browsing product in the offline channel but switching to the online channel for purchases, with an aim to leverage both the low cost from the online channel and the real experience from the offline channel. We address such a dual-channel system with omni channels, i.e., concurrently running a mix of online and offline channels by a joint decision on pricing for the two channels as well as the associated return policy. Using an appropriate model we show that a well-designed dual-channel system can indeed increase the profitability of a retailer. Specifically, we find that channel cost difference is the key factor behind the rationale of encouraging showrooming. Retailers can intentionally create a channel price gap to facilitate demand shifting from offline channel to online channel, thus achieving considerable fulfilment cost savings as well as return cost savings. In addition, we reveal that return policy decision is closely related with pricing decisions, and show that return policy design can be viewed as a tool for market segmentation through modulating channel prices. Finally, we identify several barriers for retailers to engage with consumers directly such as consumer valuation uncertainty and cost efficiency of handling returns, and show that it may be beneficial for retailers to engage with consumers indirectly via consumer showrooming.

This paper studies a two-agent scheduling problem on mixed batch machines in parallel. A mixed batch machine can process several jobs simultaneously as a batch, as long as the number of jobs in the batch does not exceed the machine capacity. The processing time of a mixed batch is the weighted sum of the maximum processing time and the total processing time of jobs in the batch. The objective is to minimise the weighted sum of two agents' makespans. We present four approximation algorithms based on two strategies: the machine-centric strategy and the agent-centric strategy. For each strategy, a full batch longest processing time (FBLPT) rule and a longest processing time greedy (LPTG) rule are used. We conduct theoretical analyses based on the worst-case performance ratio to provide the provable guarantees on the performances of the algorithms, and simulation analyses based on randomly generated instances to evaluate the average performances of the algorithms. Furthermore, we verify the consistency between the theoretical and simulation results. The algorithms using agent-centric strategy perform better than ones using machine-centric strategy. Finally, we provide managerial insights for the problem by analysing the technological parameters of batches, importance of agents, and demand seasonality.