Purpose – Monitoring the real-time temperature, humidity, and physical position status of goods is vital in the cold chain. Diverse logistics technologies and systems have been adopted in the cold chain for monitoring perishable goods. However, these technologies and systems are independent from each other. Data and information in them are not integrated so that information control is not effective. The paper aims to discuss these issues. Design/methodology/approach – By integrating Internet of Things and tracking technologies, this paper proposes an intelligent tracking system, which is designed to achieve effective and fast live monitoring of goods in the cold chain at the lowest cost and with the largest network capacity and simplest protocols. Findings – Structure and information platform design mechanism are introduced. The key part of this system is a wireless sensor network built on Zigbee. Wireless sensors located in cold storages or refrigerated trucks are able to collect and transmit live data quickly and efficiently. Originality/value – Users of the proposed system can easily monitor goods transported in cold chains. In addition, the system assigns specific servers to save historical data for inquiries.

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This paper proposes an effective fireworks algorithm (FWA), which is a new heuristic algorithm inspired by the phenomenon of a fireworks display, to solve the warehouse-scheduling problem. First, a real-world warehouse-scheduling problem is described in detail, and it is formulated as a constrained single-objective optimization problem. Then an effective FWA, FWA-LSCM, is developed by combining with a local search method and chaotic mutation, which are used to balance the exploration and exploitation of FWA. The experimental results show that FWA-LSCM is a competitive algorithm for a set of 2013 Congress on Evolutionary Computation (CEC) benchmark functions. Finally, the proposed FWA-LSCM is successfully applied to the warehouse-scheduling problem and outperforms other FWA algorithms studied in this paper.

Multipopulation is an effective optimization strategy which is often used in evolutionary algorithms (EAs) to improve optimization performance. However, it is of remarkable difficulty to determine the number of subpopulations during the evolution process for a given problem, which may significantly affect optimization ability of EAs. This paper proposes a simple multipopulation management strategy to dynamically adjust the subpopulation number in different evolution phases throughout the evolution. The proposed method makes use of individual distances in the same subpopulation as well as the population distances between multiple subpopulations to determine the subpopulation number, which is substantial in maintaining population diversity and enhancing the exploration ability. Furthermore, the proposed multipopulation management strategy is embedded into popular EAs to solve real-world complex automated warehouse scheduling problems. Experimental results show that the proposed multipopulation EAs can easily be implemented and outperform other regular single-population algorithms to a large extent.