Cluster tools are extensively used for processing wafers in semiconductor manufacturing. To enhance the throughput of cluster tools, concurrent processing of multiple types of wafers has become a popular approach. In practice, due to mass customization and the shrinking of chips’ feature sizes, the lot size of wafers tends to become smaller, leading to more frequent transient and non-cyclic periods. Therefore, the scheduling problem of single-armed cluster tools that concurrently process two types of wafers from start-up to close-down period should be urgently addressed. To do so, a mixed integer programming (MIP) model is established to optimize the release sequence of wafer types and robot task sequence so that the total completion time of the two wafer types can be minimized. Since solving the MIP model for large-sized instances is hard, a two-combined backward sequence (2-CBS) is further proposed. Experimental results show that the 2-CBS can save a significant amount of computing time and increase the total completion time by only 1.09% on average. However, by quickly determining the completion time of each wafer lot, the throughput of cluster tools could be enhanced to some extent, as the idle time caused by the arrival delay of overhead hoist transports can be reduced. Note to Practitioners—When considering the concurrent processing of multiple wafer types, almost all existing studies focus on cyclic scheduling and thus fail to address the growing importance of non-cyclic scenarios. To fill this gap, the present work develops an MIP model to optimize the release sequence of wafer types and robot task sequence such that the total completion time of two wafer types can be minimized. Such a model is efficient for small-sized instances. Meanwhile, a 2-CBS strategy is developed for large-sized instances, which can be solved quickly and results in an increase in the total completion time by only 1.09%, demonstrating its effectiveness and applicability.

Cleaning operations are required to clear particles and residual chemical gases in processing chambers of cluster tools in semiconductor manufacturing. To clean the chambers, condition-based chamber cleaning operations considering the actual state of chambers have been developed in practice. For single-armed cluster tools with such operations and wafer residency time constraints, the scheduling problem has been tackled. However, for dual-armed ones, this problem remains open and it is difficult to solve since their scheduling strategies are different from and more complex than single clusters’. To address this challenging problem, this work presents an efficient scheduling strategy to find the schedules that meet wafer residency time constraints and maximize their productivity. Two examples are given to show the effectiveness of the developed strategies. Note to Practitioners—Dual-armed cluster tools are extensively employed in semiconductor manufacturing processes. For residency-time-constrained dual-armed cluster tools with condition-based chamber cleaning operations, this work derives efficient scheduling strategies and algorithms for different cleaning cases. They can be used to realize feasible schedules that meet the wafer residency time constraints and restore a cluster tool to its steady-state as soon as possible. Hence, this work is significant and promising in helping semiconductor manufacturers enhance their fab’s throughput.

In semiconductor manufacturing, chamber cleaning operations (CCOs) are performed from time to time to clear particles and chemical gases that remain in the processing chambers of cluster tools (CTs) to ensure wafer quality. For single-armed CTs with just one CCO that could be completed in one system cycle time, researchers have proposed several scheduling approaches. In practice, more than one CCO may be required concurrently, thus failing the existing ones under such cases. Furthermore, the duration of a cleaning operation may be larger than one system cycle time, which highly complicates the scheduling of such tools. This work addresses this challenge by proposing several scheduling approaches and algorithms for single-armed CTs with several CCOs, each of which exceeds the duration of one system cycle. Suggestions are given for engineers in semiconductor fabrication plants to take the most suitable scheduling approach and algorithm for enhancing the productivity of CTs given their specific scenarios. Finally, two illustrative examples are provided to showcase the power of the proposed concepts and techniques.

This paper extends a traditional supply chain network design problem through two perspectives, i.e., the multiperiod decision environment and the environmental concerns. A planning horizon contains multiple periods. Different periods have different demands of products and hence should have different supply chain decisions, which mainly include facilities' locations, the environment protection level in facilities, as well as the transportation flows of products in the supply chain. For each facility in two consecutive periods, the changes of the location and environment protection level related decisions may incur adjustment cost. A bi-objective mixed-integer programming model is proposed to minimize both the total cost and the CO2 emissions of the supply chain during the planning horizon with multiple periods. A solution approach is proposed to solve the model. Numerical experiments are conducted to validate the effectiveness of the proposed model and algorithm.

Both interference alignment (IA) and interference neutralization (IN) are exploited for coordinated multi-point (CoMP) communications. With the cooperation of the base station (BS), a transmit precoder and a receive filter are jointly designed, and concurrent transmissions of multiple data streams are then enabled. In the design of a precoder, the IN is applied to the interferences carrying the same data so as to align the interfering signals in the opposite direction in a subspace. On the other hand, for interferences carrying different information, IA is employed to align them in the same direction in a subspace, thus reducing the interference signal observed at the receiver side. Based on different precoding schemes at the transmitter's side, receivers adopt zero forcing (ZF) so as to recover the desired data. The proposed IA- and IN-based CoMP (IAN-CoMP) mechanism can achieve effective interference cancellation and suppression by exploiting limited and flexible collaboration at the BS side. It can also make a flexible tradeoff between the cooperation overhead and the system's achievable degrees of freedom (DoFs). We extend the mechanism to general cases where the antenna configurations at both the transmitter and receiver sides, the number of transmitters participating in CoMP, and that of simultaneously served users are variable. Moreover, we discuss both single-location and multi-location-based realizations of the IAN-CoMP. Finally, by defining the average transmit and receive cooperation order, we analyze the upper bound for IAN-CoMP. Our in-depth simulation shows that the IAN-CoMP can significantly improve the spectral efficiency (SE) for cell-edge users.

Interference management (IM) is essential to wireless communication networks, but interference suppression, a key component of IM, is known to degrade users' achievable spectral efficiency (SE). It is thus important to select an appropriate IM method with optimal operating parameters according to diverse network deployments, transmit power differences of various communication equipments, and dynamically changing channel conditions so as to balance the benefits brought by and the cost of IM. Interference neutralization (IN) has recently been receiving considerable attention, with which a duplicate of interference of the same strength and opposite phase w.r.t. the original interfering signal is generated to neutralize the disturbance at the intended receiver. However, to the best of our knowledge, all existing IN schemes assume that interference is completely neutralized without accounting for their power consumption. To remedy this deficiency, we propose a novel scheme, called dynamic interference neutralization (DIN). By intelligently determining the appropriate portion of interference to be neutralized, we balance the transmitter's power used for IN and the desired signal's transmission. We then present a new way to adaptively select one of DIN and other IM methods by taking into account the cost of multiple IM methods and their benefits. Our analysis has shown that DIN can include complete IN and non-interference management (non-IM) as special cases. The proposed strategy is shown via simulation to be able to make better use of the transmit power than existing IM methods, hence enhancing users' SE.

An A daptive tran S mission mechanism exploiting both interference lo C ality and the relationship between d E sired sig N al and in T erference ( ASCENT ) is proposed for uplink transmission in heterogeneous networks. The authors adopt both X channel and Z channel models according to which spatial signal processing is designed. In the X channel, the picocell base station (PBS) exploits information the macrocell base station (MBS) shares to cancel local interference, and cooperatively decodes the data carried by strong interference from a macro‐user (MU), which is then fed to the MBS. As a result, a pico‐user (PU) can transmit simultaneously with an MU on the same channel. In addition, adaptive reception is employed to achieve good tradeoff between interference suppression and the desired level of signal distortion. For the Z channel, the PU and the PBS adopt signal processing suitable for their own channel state. At a PBS, interference cancellation is adopted to eliminate disturbance from an MU via inter‐base station collaboration. ASCENT is also extended to the case of multiple picocells. The authors’ simulation results show that in X channel mode, the achievable uplink rate of an MU can be significantly enhanced. In the case of Z channel, the PU's rate is improved while guaranteeing the MU's data rate.

In supply chain networks of large companies, how to determine the location for establishing distribution centers is an important strategic-level decision problem. This paper proposes a stochastic programming model to determine distribution centers' locations as well as their scales. The objective of the model is to minimize the expected total transportation cost under uncertain demands of customers. An improved particle swarm optimization algorithm and a Lagrangean relaxation-based solution approach are proposed to solve the model. This paper also performs a case study on applying this model to the largest retailer in China. In addition, some numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution methods.

This paper studies an integrated optimization problem on production and procurement for a manufacturer who faces stochastic demands of multiple products made by multiple components. This paper first proposes a deterministic model for the problem when the demands of products are known. Then the model is extended to the case of uncertain demands. A stochastic programming model and a robust optimization model are proposed to handle the production and procurement decisions under uncertain demands. The stochastic programming model can cope with arbitrary probability distributions of products' random demands; while the robust optimization model applies to situations in which limited information about probability distributions is available. Some numerical experiments are performed to investigate the efficiency of the proposed models. We also compare the above two models and evaluate the potential benefits in practice.

This paper studies a strategic level decision problem for disaster relief facility network planning in metropolises. This problem is concerned with some long-term decisions on locating the emergency shelters, locating the supply and medical centers, and maintaining fleets of ambulances and transportation vehicles. These established facilities and deployed vehicles constitute a humanitarian logistic network for rapid responses and reliefs. An integer programming model is proposed to minimize the total cost of establishing such a disaster response network. A Lagrangian relaxation method is developed for solving the problem in large scales. To illustrate the proposed method's usage in practice, a demo example about its application in Shanghai is given. Some numerical experiments are also performed to validate the effectiveness and the efficiency of the proposed model and method.

This paper introduces a new type of automated container terminal system, which utilizes multistory frame bridges and rail-mounted trolleys to transport containers between the quay and the yard. For this new design concept, this paper makes an explorative study to identify the challenges and opportunity for it to be applied in transshipment hubs. We compare the transport efficiency and stacking capacity between this new system and the widely used AGV-based system. Several analytical models and performance measures are proposed for the quantitative comparisons between these two ACT systems.

This study is oriented to a general problem context for most manufacturers that produce products for uncertain market demands. Manufacturers usually have two alternative modes for the production: One is to outsource parts from outside suppliers and then assemble them, and the other is to in-house manufacture parts and then assemble them. Each mode has its relative merits. This paper studies how to balance the tradeoff between the two modes. We utilize the dynamic programming approach to investigate the optimal decision on the aforementioned two modes during each planning period.