智能快递载运装置设计

目的将传统快递物流、移动互联网和机械创新设计相结合,提出一种快递派送最后环节的载运装置应用设计方案。方法该设计主要包括快递载运机械装置、自动控制系统和快件信息管理APP系统。快递员手持终端APP扫描快递二维码信息,并记录存放所在机械装置的具体位置;取件人收到通知取件的信息后,根据信息查找到快递位置;控制系统步进电机带动机械装置运动,将快递所在存放装置自动运至出口位置。结论将新型机械装置与智能手机结合,实现了快递放置的整齐有序,快递派送过程中能被准确查找,降低了快递员的劳动强度,提高了物流工作效率,具有一定的应用价值。该装置的应用可以促进智慧物流的发展。     

A two-layer dynamic scheduling method for minimising the earliness and tardiness of a re-entrant production line

This study focuses on the dynamic scheduling problem of a re-entrant production line, in which all of the parts are assumed to have the same processing routes and be processed on every machine. A two-layer dynamic scheduling method is proposed for the dynamic scheduling of the re-entrant line with the objective of minimising total earliness and tardiness. This method consists of two layers. The top layer is to select the appropriate scheduling policy, and the bottom layer is to generate the scheduling by using the policy selected in the top layer. In the top layer, three different rules are constructed for selecting scheduling policies, namely the lateness comparison rule, the lateness variation comparison rule, and the equal interval switching rule. In the bottom layer, three different scheduling policies are proposed to generate the real-time scheduling for manufacturing, namely the FCFS (first come first service) scheduling policy, the PI (proportional-integral) scheduling policy, and the fuzzy PI scheduling policy. Considering that the real-time status of manufacturing changes constantly, it is necessary to switch among different scheduling policies to adapt to this change. Numerical experiments are performed in the situations with and without urgent jobs. The results show that the proposed two-layer dynamic scheduling method outperforms any single scheduling policy (e.g., the FCFS policy, the PI policy and the fuzzy PI policy) for the dynamic scheduling of a re-entrant production line.     

Approximation of single-class queueing networks with downtime-induced traffic variability

Queuing networks have been used with partial success for analytical modelling of manufacturing systems. In this paper, we consider a tandem system with high traffic variability caused by downtime events in the first queue. We propose improved approximation for departure variability in order to predict the waiting duration at the bottleneck queue located last in the line. We demonstrate that existing methods do not properly approximate such systems and provide some reasons and insights. Thus, a new decomposition method which employs the variability function principles is proposed. We differentiate between two components of the departure variability in multi-class systems: the ‘within-class effect’ – the variability caused by the class’ own inter-arrival and service time distributions – and the ‘between-class effect’ – the variability caused by interactions with other classes. Our analysis shows that the first effect can be approximated by existing multi-class decomposition methods, while the second effect requires a new development. Our proposed approximation for between-class effect is based on simulating a proper sub-system. The method enables modelling different policies of downtimes (e.g. FCFS, Priority). Numerical experiments show relative errors much smaller vs. existing procedures.     

Simulation study of an automated storage/retrieval system

In this paper we present a simulation study of an automated storage/retrieval system and examine a wide variety of control policies. We compare several storage location assignment policies. For the class-based storage policy, we apply a recent algorithm that enables us to evaluate the trade-off between storage space requirements and travel times. We also study a new storage location policy which combines low storage space requirements with short mean travel times. Furthermore, we study the sequencing of storage and retrieval requests whereby we focus on the trade-off between efficient travel of the S/R machines and response time performance.     

Dynamic admission control for two customer classes with stochastic demands and strict due dates

We study a dynamic capacity allocation problem with admission control decisions of a company that caters for two demand classes with random arrivals, capacity requirements and strict due dates. We formulate the problem as a Markov decision process (MDP) in order to find the optimal admission control policy that maximises the expected profit of the company. Such a formulation suffers a state-space explosion. Moreover, it involves an additional dimension arising from the multiple possible order sizes that customers can request which further increases the complexity of the problem. To reduce the cardinality of possible policies, and, thus, the computational requirements, we propose a threshold-based policy. We formulate an MDP to generate such a policy. To deal with the curse of dimensionality, we develop threshold-based approximate algorithms based on the state-reduction heuristics with aggregation proposed previously. Our results reveal that for the majority of instances considered the optimal policy has a threshold structure. We then demonstrate the superiority of the proposed threshold-based approximate algorithms over two benchmark policies in terms of the generated profits and the robustness of the solutions to changes in operational conditions. Finally, we show that our proposed policies are also robust to changes in actual demand from its estimation.     

Synchronized routing of active and passive means of transport

This paper addresses a routing problem where the fulfillment of transport requests requires two types of transport resources, namely, passive and active means of transport. The passive means are used for holding the cargo that is to be shipped from pickup to delivery locations. The active means take up the passive means and carry them from one location to another. Compared to classical vehicle routing problems, the additional challenge in this combined routing problem is that the operations of both transport resources have to be synchronized. In this paper, we provide a modeling approach for the joint routing of passive and active means of transport. We solve the problem by large neighborhood search meta-heuristics that utilize various problem-specific components, for example local search techniques for the routes of active and passive means. A computational study on a large set of benchmark instances is used for assessing the performance of the meta-heuristics.     

Scheduling policies,batch sizes,and manufacturing lead times

In this paper we examine the impact of scheduling policies on batch sizing decisions in a multi-item production system. We also investigate the joint effect of scheduling policies and batch sizing decisions on production lead times. In particular we compare the performance of a first-come-first-served (FCFS) policy with that of a group scheduling (GS) policy and study the effect of each on the optimal batch size. We show that a GS policy can lead to significant performance gains, as measured by reduced lead times and higher production rates, relative to the FCFS policy, and characterize conditions under which these gains are realized. We also study the impact of the GS policy on other system operating parameters. In particular we find that using a GS policy eliminates the need for batching, preserves system capacity despite the presence of setups, and accommodates higher product mix variety. These results are shown to be very different from those obtained for the FCFS policy and to have important implications for the management and control of multi-item production facilities.     

拣货方式、存储策略与路径策略协同研究

对拣货方式、路径策略与存储策略进行协同研究,设计了具有代表性的策略组合。推出了不同路径策略下,实际拣货路径长度的计算公式。通过对各种策略组合仿真结果的比较分析,确定了3种策略的相对重要性:1）分批策略对减少拣货作业总时间影响最大;2）分类存储策略比随机存储策略所需的行走路径缩短很多;3）路径策略对拣货行走的时间的减少明显小于分批拣货方式和分类存储策略带来的拣货作业时间的减少。具体决策时,应优先考虑分类存储策略和分批拣货方式,在确定其已经有效的情况下再考虑路径策略,以使拣货效率达到整体最优。     

Transmission Control under Multi-Service Disciplines in Wireless Sensor Networks

The wireless sensor network (WSN), as the terminal data acquisition system of the 5G network, has attracted attention due to advantages such as low cost and easy deployment. Its development is mainly restricted by energy. The traditional transmission control scheme is not suitable for WSNs due to the significant information interaction. A switchable transmission control scheme for WSNs based on a queuing game (SQGTC) is proposed to improve network performance. Considering that sensor nodes compete for the resources of sink nodes to realize data transmission, the competitive relationship between nodes is described from the perspective of a game. Different types of sensor node requests require a sink node to provide different service disciplines. Mathematical models of social welfare are established for a sink node under the service disciplines of first-come, first-served (FCFS), egalitarian processor sharing (EPS), and shortest service first (SSF). The optimal service strategies are obtained by maximizing social welfare. The sensor nodes provide the expected benefits and satisfy the service requirements of the requests, and the sink node switches the transmission control strategy for the service. Simulation results show that the proposed scheme improves the data transmission efficiency of WSNs and achieves the optimal allocation of resources.     

On sequencing policies for unit-load automated storage and retrieval systems

Automated Storage and Retrieval System (AS/RS) performance highly depends on the characteristics of the mechanical equipment. However, once the system has been physically implemented, achieving its maximum efficiency depends on the way the system is operated. This paper shows that request sequencing (i.e. planning the order in which storage and retrieval requests are performed) is of paramount importance in AS/RS performance. This paper reviews and adapts the most popular storage and sequencing policies to dynamic contexts, and then it proposes a ‘sequencing mathematical model’ (SMM) to simultaneously solve the sequencing and storage location problems. Extensive computational results based on a thorough simulation experiment plan confirm that performing the requests in the right sequence can have a positive impact on AS/RS performance. Our results show that the proposed SMM regularly outperforms other methods. When used in a dynamic context, the proposed SMM may yield up to a 25% reduction in average travel-time compared to the situation where a no-sequencing method is applied.     

Route planning for automated guided vehicles in a manufacturing facility

Automated guided vehicles (AGVs) are widely used in manufacturing and transporting facilities for the movement of material from one location to another. Research in this area is directed toward the development of a path layout design and routing algorithms for movements of materials. The problem is to design a path layout and a routing algorithm that will route the AGVs along the bi-directional path so that the distance travelled will be minimized. This paper presents a bi-directional path flow layout and a routing algorithm that guarantee conflict-free, shortest-time routes for AGVs. Based on the path layout, a routing algorithm and sufficient, but necessary, conditions, mathematical relationships are developed among certain key parameters of vehicle and path. A high degree of concurrency is achieved in vehicle movement. Routing efficiency is analysed in terms of the distance travelled and the time required for AGVs to complete all pickup and drop-off jobs. Numerical results are presented to compare the performance of the proposed model. The research provides the foundation for a bi-directional path layout design and routing algorithms that will aid the designer to develop complicated path layouts.     

Revenue management for a make-to-order company with limited inventory capacity

We consider the application of revenue management to a make-to-order manufacturing company with limited inventory capacity. Orders with different profit margins arrive stochastically over an infinite time horizon and the company has to decide which orders to accept and which orders to reject. We model the problem with a discrete-time Markov decision process and propose a heuristic procedure. In numerical tests we show the potential benefit of using revenue management instead of a FCFS policy and assess the performance of the heuristic procedure.We thank two anonymous referees for their valuable suggestions that helped to improve the quality of the paper.     

International express courier routing and scheduling under uncertain demands

The purpose of this study is to develop a planning model and a real-time adjustment model to help an international express company facing uncertain demands to plan courier routes and schedules and adjust the planned routes in actual operations. A solution procedure is then developed to efficiently solve the real-time adjustment model. A simulation-based evaluation method is also developed to compare the performance of the proposed models. The test results, related to an international express company's operations, show the good performance of the proposed models. Sensitivity analysis is also performed to gain better insights into knowing how several important parameters affect the solutions.     

基于线性时序逻辑的移动端快递派送路径规划

研究了快递派送的路径规划。针对目前快递派送任务点较多,快递员不熟悉派送区域的问题,提出了一种基于线性时序逻辑(LTL)的移动端多点快递派送路径规划方法。该方法利用移动端的百度地图应用包实现快递员的定位与导航任务,提出两层(顶层、底层)规划策略。顶层规划避开百度地图实际道路环境繁琐又庞大的建模,将快递派送问题转化为旅行推销员问题(TSP),仅将快递员派送任务地点建模为一个有限状态的加权切换系统,状态之间的切换权重基于百度地图的自驾导航距离,而非简单的直线距离,以达到底层规划结果符合实际环境,确保最后搜索的路径最优性,同时利用线性时序逻辑语言描述多点快递派送任务,从而将切换系统信息与派送任务信息相融合,构建一个任务可行网络拓扑,并在该网络拓扑上基于Dijkstra算法搜索出快递员离散的最优路径。底层规划完成离散路径的连续化,离散路径的任意相邻任务点间的路径规划基于百度地图实现,从而实现顶层规划的离散路径连续化,获得快递员可派单的实际派送路线。实验结果表明该方法能够解决多点派送任务与派送区域受约束的问题,并保证快递员派单路径的最优性。     

An optimal path of a moving vehicle on a sphere

We consider the problem of finding an optimal path of a moving vehicle, which during its journey provides a certain type of service to a group of existing facilities on a sphere. Service requests from each facility are assumed to follow a Poisson process and the conditional expected cost incurred by a service call is assumed to be proportional to the shortest arc distance between the facility and the vehicle's location at the time of the service request. This problem is formulated as a variational problem and solved by repeatedly applying a dynamic programming procedure to a sequence of staged networks that are constructed via perturbation.     

Cloud Data Center Selection Using a Modified Differential Evolution

The interest in selecting an appropriate cloud data center is exponentially increasing due to the popularity and continuous growth of the cloud computing sector. Cloud data center selection challenges are compounded by ever-increasing users’ requests and the number of data centers required to execute these requests. Cloud service broker policy defines cloud data center’s selection, which is a case of an NP-hard problem that needs a precise solution for an efficient and superior solution. Differential evolution algorithm is a metaheuristic algorithm characterized by its speed and robustness, and it is well suited for selecting an appropriate cloud data center. This paper presents a modified differential evolution algorithm-based cloud service broker policy for the most appropriate data center selection in the cloud computing environment. The differential evolution algorithm is modified using the proposed new mutation technique ensuring enhanced performance and providing an appropriate selection of data centers. The proposed policy’s superiority in selecting the most suitable data center is evaluated using the CloudAnalyst simulator. The results are compared with the state-of-arts cloud service broker policies.     

Optimising two dwell point policies for AS/RSs with input and output point at opposite ends of the aisle

Automated storage and retrieval systems (AS/RSs) are widely used for storing and retrieving products in all types of warehouses. Dwell point policy is a vital control policy that can greatly affect the performance of AS/RSs. In this paper, we study dwell point policies in AS/RSs with input and output stations at opposite ends of the aisle. We first propose two dwell point policies. We find that five existing dwell point policies in the literature are special cases of exactly one of our policies. We then develop expected travel time models for the proposed policies, solve these models with the objective of minimising expected travel time, and obtain closed-form solutions for the optimal dwell location(s). We prove that one proposed policy dominates the other in terms of expected travel time. Numerical experiments are performed to quantify the percentage gap of expected travel time between the proposed policies and policies in the literature. We find that, in some situations, the better proposed policy can achieve up to 8%–10% reduction in expected travel time in comparison with the best literature policy. A real-data case study validates that these situations arise with high probability in typical daily warehouse operations.     

Assess the effects of different operations policies on warehousing reliability

In this study, an assessment model for analysing the reliability of a warehouse system, focusing on resource capability, under given combinations of storage, routing, batching and zoning process policies, is proposed. A reliability assessment model is developed in order to evaluate the effect of different combinations of operations policies on warehouse reliability. To better reflect the real order pick up operations at the warehouse, a simulation model based on the operation scenarios of a Hong Kong logistics service company is developed. The simulation results are used for supporting the evaluation of warehouse reliability through using the proposed reliability assessment model. The most important finding indicates that order batching together with zoning policies yields greater warehouse reliability particularly when the daily order volume is large. In addition, another important research finding shows that warehouse performance is not constant and can drop when certain combinations of operations policies are adopted. The degrading rate of the resource (forklift) when used according to a given combination of polices is also defined by the proposed assessment method. Hence, the result helps logistics service providers enhance resource durability through modifying operations policies and by implementing proper preventive maintenance policies within a dynamic operations environment.     

Providing scalable location service in wireless sensor networks with mobile sinks

Location-based routing has been considered as an efficient routing paradigm for wireless sensor networks (WSNs). The performance of location-based routing highly depends on the way the position information of mobile sinks are updated and managed, which is the typical function of location service. Frequent location updating can improve the location accuracy at the expense of additional communication overhead. In this study, the authors propose a scalable location service for supporting efficient location-based routing in WSNs with mobile sinks. The proposed location service enables each sensor node to locate its closest (mobile) sink with low overhead. Analytical and simulation results show that it can significantly reduce the communication overhead for providing location service while maintaining high routing performance.     

ECC: Edge Collaborative Caching Strategy for Differentiated Services Load-Balancing

Due to the explosion of network data traffic and IoT devices, edge servers are overloaded and slow to respond to the massive volume of online requests. A large number of studies have shown that edge caching can solve this problem effectively. This paper proposes a distributed edge collaborative caching mechanism for Internet online request services scenario. It solves the problem of large average access delay caused by unbalanced load of edge servers, meets users’ differentiated service demands and improves user experience. In particular, the edge cache node selection algorithm is optimized, and a novel edge cache replacement strategy considering the differentiated user requests is proposed. This mechanism can shorten the response time to a large number of user requests. Experimental results show that, compared with the current advanced online edge caching algorithm, the proposed edge collaborative caching strategy in this paper can reduce the average response delay by 9%. It also increases the user utility by 4.5 times in differentiated service scenarios, and significantly reduces the time complexity of the edge caching algorithm.