Material handling allows matching vendor supply with customer demand, smoothing demand for seasonal products, consolidating products, customizing product, or packaging and arranging distribution activities [10]. Many optimization models have been proposed to improve the material handling. Nazzal et al. [11] develop a closed queuing network approach to analyze the multivehicle material handling systems. Lau et al. [12] present an artificial immune system (AIS)-based model to provide an effective methodology to coordinate and control multiagent systems. Lau and Woo [13] introduce a dynamic routing strategy for determining the optimal route for material flow under a distributed agent-based material handling system. Tuzkaya et al. [14] apply an integrated fuzzy multicriteria decision-making methodology to solve the material handling equipment selection problem. To minimize the machine operation, material handling, and machine setup costs and maximize the machine utilization, Mahdavi et al. [15] designed a simulation-based optimization for controlling operation allocation and material handling equipment selection. To minimize the sum of the expected long-run average transport job waiting cost, Lin et al. [16] propose a Markov decision model–based dynamic vehicle allocation control for automated material handling system (AMHS) in semiconductor manufacturing. Dai et al. [17] explores the economic feasibility of a flexible material handling system using free-ranging automated guided vehicles (AGV) with a local positioning system (LPS) for the apparel industry. Poon et al. [18] develop a RFID-GA-based warehouse resource allocation system to solve stochastic production material demand problems. To effectively analyze and evaluate the performances of closed-loop AMHS with shortcut and blocking in semiconductor wafer fabrication system, a modified Markov chain model (MMCM) has been proposed [19]. Drießel and Mönch [20] suggest an extension of the shifting bottleneck heuristic for complex job shops that takes the operations of AMHS into account. By a combination of the concept of compromise solution and grey relational model, a new fuzzy grey multicriteria decision-making method is presented to deal with the evaluation and selection problems of material handling equipment under the condition of uncertain information [21]. Chung [22] develops a new heuristic method based on a stochastic approach to estimate the arrival times of transportation jobs to their final destinations for automated material handling in an LCD fabrication facility. Because of the need for steadiness and stability in the automated manufacturing systems, a biobjective stochastic programming model is used to evaluate material handling systems with automated guide vehicles [23]. A variant of particle swarm optimization (PSO) with a self-learning strategy is provided for vehicle routing problem of multiple products with material handling in multiple cross-docks [24]. Choe et al. [25] investigates how cognitive automation and mechanical automaton in the material handling system affect manufacturing flexibility. Their simulation program shows that cognitive automation has a very critical effect on manufacturing flexibility in the material handling system. Hadi-Vencheh et al. [26] propose a new hybrid fuzzy multicriteria decision-making model for solving the equipment selection problem in material handling system. The standard deviation, variance, and the downside risk of the cost distribution are investigated as the risk measures of material handling system by Mital et al. [27].

As a novel paradigm, IoT is rapidly gaining ground in the scenario of modern wireless telecommunications [28]. By enabling instant identification and automatic information transfer, IoT technologies (e.g., RFID, auto ID) provide visibility of a dynamically moving object in a constantly changing environment [29]. Experts have done lots of researches about applications of IoT technologies in manufacturing process. RFID has been widely used in supporting the logistics management on manufacturing shop floors where production resources attached with RFID facilities are converted into smart manufacturing objects (SMOs) which are able to sense, interact, and reason to create a ubiquitous environment [30]. Lee et al. [31] present an RFID-based resource allocation system (RFID-RAS), integrating RFID technology and fuzzy logic concept for achieving better resource allocation with particular reference to garment manufacturing. Zhang et al. [32] provide an innovative all-in-one Smart Gateway technology for capturing real-time production data from various manufacturing resources attached to different types of RFID/auto-ID devices. Zhang et al. [33] use the RFID-enabled reconfigurable manufacturing sources to achieve real-time agent-based workflow management. Tu et al. [34] propose an agent-based distributed production control framework with UHF RFID technology to help firms adapt to such a dynamic and agile manufacturing environment. Guo et al. [35] design RFID-based intelligent decision support system architecture to handle production monitoring and scheduling in a distributed manufacturing environment. To monitor and control dynamic production flows and also to improve the traceability and visibility of mass customization manufacturing processes, Chen et al. [36] developed an agent-based manufacturing control and coordination (AMCC) system, an agent-based framework using ontology and RFID technology. Huang et al. [37] developed RFID-based real-time collaborative manufacturing shop-floor service platforms to address automotive manufacturing standards and practices within automotive parts and accessories manufacturers. A study has been made to show the capability of a RFID-based information system in the international distribution process of a car manufacturer [38]. Zhou and Piramuthu [39] consider RFID tags and their applications from a recycling/remanufacturing perspective and propose a novel framework to assist such process based on item-level information visibility and instantaneous tracking/tracing ability enabled by RFID. An intelligent and real-time multiobjective decision-making model is developed to provide timely and effective solutions for multiobjective production planning problem by integrating RFID technology with intelligent optimization techniques [40]. Zhong et al. [41] propose an RFID-enabled real-time advanced production planning and scheduling shell (RAPShell, in short) to coordinate different decision makers across production processes. To address important logistics operations aspects, Mejjaouli and Babiceanu use an integrated RFID-sensor network system to detect the condition of perishable products as they are moved downstream the supply chain before undesired total loss of products occurs [42]. Tang et al. [43] propose a value-driven uncertainty-aware data-processing method that considers RFID detection reliability, timeliness, and the throughput of an assembly line to characterize the potential benefits of RFID implementation in a mixed-model assembly system. Fan et al. [44] implement a research on the impact of RFID technology adoption on supply chain decisions with shrinkage and misplacement problems in the IoT. Oliveira et al. [45] propose a model for logistics management based on geofencing algorithms and radio-frequency technology to improve services, reduce costs, and ensure the safety in cargo transportation.

This part is responsible for making trolleys get the capability of active perception. As shown in the middle of Fig. 6.4, some hardware devices (e.g., industrial control computer, RFID reader and antennas) are attached to the trolley. They are employed to timely capture the real-time information. The employed hardware devices mainly include industrial control computer, RFID reader and antennas. The industrial control computer is used to store temporary real-time information and develop some software. RFID reader and antennas are employed to capture real-time information of manufacturing resources (e.g., pallets, operators, and key work-in-progress) which are attached RFID tags. Their detailed information is described as follows:

This part is responsible for completing the real-time information exchange between move tasks server and trolleys. The worklogic of real-time information exchanging mechanism is shown in the lower-left of Fig. 6.4. Service-oriented architecture (SOA) is employed in this part to send and receive the real-time information during material handling. First, an XML-based schema about the real-time information of trolley will be formed. It contains the information such as trolley ID, maximum volume of trolley, current location, and current operator of trolley. Second, the formed XML-based instance will be sent to move task server by the sendCurrentStatus() method. Then, the web service of move task server side can receive the real-time information of the requested trolley, and the taskOptimizationMethod() will be invoked to get the optimal move task according to the real-time information of trolley and move tasks. Finally, the sendMoveTask() method will be used to send the optimal result to the trolley side. The detailed information of the mentioned optimization method will be illustrated in Section 6.6.

This part is responsible for providing trolleys and operators with the self-decision capability and real-time information-driven guidance. To achieve this aim, workflow is introduced to this part. The key processes during material handling are shown in the right of Fig. 6.4. They are as follows: (1) request and obtain task, (2) go to load location, (3) arrive at the load location, (4) pick up materials, (5) go to unload location, (6) arrive at the unload location, and (7) unload materials. The operations (2) to (4) or operations (5) to (7) is repeated when multimove tasks are assigned to the trolley.

The information model of move tasks is essential to describe the optimization method. Assume that there are N tasks in the move task pool. The information of each move task contains following nodes: ID of move task, from and to locations of move task, due time of move task, priority of move task, material index number of move task, and material information (i.e., ID of material, the quantity of material and volume of material) matched with its index number. The matrix N stores the information model of N move tasks. The defined notations are listed in Table 6.2.

There are lots of move tasks in the move tasks server side. To reduce the calculating complexity, it is necessary to implement the preoptimization for candidate task set to select better tasks for further combination optimization. The preoptimization for candidate tasks set could be executed according to the priorities of move tasks, which are defined by their due time. In general, the earlier the due time, the higher the priority.

As a decision making tool, AHP has been widely used to make decisions in different engineering fields [46]. Due to the promising features of AHP in solving engineering problems, an AHP-based combination method is used to get the optimal move tasks combination from candidate tasks set. To implement the green material handling, the transport distance of trolley, the priorities of move tasks and the used volume of trolley are considered in the presented AHP-based combination method.