Service Time Requirements Research Articles

Integrated optimization of pilot and pilot carrier routing in seaports

Pilotage is an important service for vessels to enter and leave seaports. When vessels entering or leaving their berths, pilots are assigned to provide assistance on board to maneuver the vessels. Pilot carriers, such as pilot boats and helicopters are utilized to transport pilots from the pilot station to the vessels at appointed ground. With the increasing number of calling vessels, the pilotage plan should be made properly considering the limited pilots and carriers resources in seaports to enhance timing reliability, which is a key port performance indicator. This paper studies the pilot and pilot carrier routing problem arising in seaports, where both the routes of pilots and carriers are determined jointly to achieve the lowest pilotage costs. The required service time intervals of vessels, the synchronization between pilots and carriers and the limited working duration for both pilots and carriers are considered. The problem is formulated as an arc-flow model, and a hybrid multi-start adaptive large neighborhood search and local search algorithm (mALNS-LS) is developed. Efficient departure time and cost computation methods are designed based on a set of forward and backward functions, and a model-based post optimization is applied. Computational experiments verify the performance of the proposed mALNS-LS in terms of solution quality and efficiency.

Home healthcare staff dimensioning problem for temporary caregivers: A matheuristic solution approach

Staff dimensioning, defined as determining the required numbers of caregivers with different types of skills, is a key decision for home healthcare systems. Home healthcare providers often use a combination of permanent and temporary (casual) caregivers. Determining the required number of temporary caregivers with different skill sets considering uncertainty and routing cost is the main objective of this study. To this end, we propose a two-stage stochastic programming model for the staff dimensioning problem for temporary caregivers, taking into account uncertainties in the required class of service, the required number of visits, and the required service time for each patient. Staff dimensioning decisions are defined in the first stage, and assignment with routing are positioned in the second stage of the model. To solve the problem, a two-phase matheuristic algorithm is developed where an initial solution is generated in the first phase by using an intermediate mathematical model and solving a series of Traveling Salesman Problems (TSPs), then a fix-and-optimize strategy is developed in the second phase to improve the obtained solution. The efficiency of the proposed matheuristic algorithm is examined by various test problems. The results highlight that the proposed model and solution method can be used by HHC providers to effectively utilize the option of recruitment of temporary caregivers in their resource planning considering inevitable uncertain parameters.

How to Deploy Electric Ships for Green Shipping

Maritime transport plays an important role in global economic development but also inevitably faces increasing pressures from all sides, such as ship operating cost reduction and environmental protection. An ideal innovation to address these pressures is electric ships, which are more environmentally friendly than conventional manned fuel oil ships. The electric ship is in its early stages. To provide high-quality transportation services, the service network needs to be designed carefully. Therefore, this research simultaneously studies the location of charging stations, charging plans, route planning, ship scheduling, and ship deployment under service time requirements. The problem is formulated as a mixed-integer linear programming model with the objective of minimizing total cost comprised of charging cost, construction cost of charging stations, and fixed cost of ships. A case study using the data of the shipping network along the Yangtze River is conducted in order to evaluate the performance of the model. Valuable managerial insights are also derived from sensitivity analyses.

Demand prediction based slice reconfiguration using dueling deep Q-network

To satisfy diversified service demands of vertical industries, network slicing enables efficient resource allocation of a common infrastructure by creating isolated logical networks. However, uncertainty and dynamics of service demands will cause performance degradation. Due to operation costs and resource constraints, it is challenging to maintain high quality of user experience while obtaining high revenue for service providers (SPs). This paper develops an optimal and fast slice reconfiguration (OFSR) framework based on reinforcement learning, where a novel scheme is proposed to offer optimal decisions for reconfiguring diverse slices. A demand prediction model is proposed to capture changes in resource requirements, based on which the OFSR scheme is triggered to determine whether to perform slice reconfiguration. Considering the large state and action spaces generated from uncertain service time and resource requirements, deep dueling architecture is adopted to improve the convergence rate. Extensive simulations validate the effectiveness of the proposed framework in achieving higher long-term revenue for SPs.

An Aggregated Data Integration Approach to the Web and Cloud Platforms through a Modular REST-Based OPC UA Middleware.

The Internet of Things (IoT) empowers the development of heterogeneous systems for various application domains using embedded devices and diverse data transmission protocols. Collaborative integration of these systems in the industrial domain leads to incompatibility and interoperability at different automation levels, requiring unified coordination to exchange information efficiently. The hardware specifications of these devices are resource-constrained, limiting their performance in resource allocation, data management, and remote process supervision. Hence, unlocking network capabilities with other domains such as cloud and web services is required. This study proposed a platform-independent middleware module incorporating the Open Platform Communication Unified Architecture (OPC UA) and Representational State Transfer (REST) paradigms. The object-oriented structure of this middleware allows information contextualization to address interoperability issues and offers aggregated data integration with other domains. RESTful web and cloud platforms were implemented to collect this middleware data, provide remote application support, and enable aggregated resource allocation in a database server. Several performance assessments were conducted on the developed system deployed in Raspberry Pi and Intel NUC PC, which showed acceptable platform resource utilization regarding CPU, bandwidth, and power consumption, with low service, update, and response time requirements. This integrated approach demonstrates an excellent cost-effective prospect for interoperable Machine-to-Machine (M2M) communication, enables remote process supervision, and offers aggregated bulk data management with wider domains.

How to achieve a win–win scenario between cost and customer satisfaction for cold chain logistics?

This paper aims to study how to achieve a win–win scenario between distribution cost and customer satisfaction; and propose a reasonable scheme for the terminal distribution of cold chain logistics. The paper focuses on the customers’ time requirements and establishes the penalty costs incurred when service time requirements are not met. In addition, this paper combines different aspects, such as considering the refrigeration energy consumption and the damage costs. Meanwhile, because of the different potential impacts of the various costs, different weights are assigned to each cost. Then, a cold chain logistics vehicle routing optimization model considering time windows is constructed. Finally, the Clarke–Wright (CW) algorithm, which is suitable for the model, is selected to solve the problem, and the validity of the model is verified by an example. The results of the calculation example show that although the transportation cost and energy cost of the optimized distribution route increase slightly, the optimized distribution route greatly reduces the total cost. Customer satisfaction and vehicle load rate have also increased significantly. Cost reduction and satisfaction improvement can sometimes be achieved at the same time. Using mathematical methods to plan routes can help companies save costs. And help planners and delivery staff to improve the timeliness of the last-mile delivery. Simplify complex algorithms without having to use professional software. More ordinary employees can also use this method.

The strategic design of port services based on a total landed cost approach

PurposeThis paper explores the effect of port's service time, particularly the mean and variability, on shippers' total landed costs to determine the competitive position of the port and derive recommendations for the strategic design of port services.Design/methodology/approachThe competitive position of a port is estimated considering the service level offered to the end-users of the port such as port service time, its variability and its effect on the total landed costs observed by the port users. The proposed methodology is meant to help ports to determine the required service time levels to maintain or gain a competitive advantage against other ports, in terms of attracting common hinterland's customers.FindingsResults show the advantages of considering service levels factors to determine the competitive position of a port, and what are the minimum characteristics required to capture more traffic volumes, that can help port managers to take strategic design decisions to better position the port in the current fierce market.Research limitations/implicationsThe proposed methodology is illustrated by considering a case study, which is the Port of Guaymas in Mexico. Data was not directly collected by the port, but based on interviews with shippers and public information, a representative case is presented. Due to a confidentiality agreement with the Port, specific references for most of the data used to estimate the model's parameters are not provided. The analysis is intended to show the potential value of this mechanism and can be used for evaluating the competitive position, from a high-level perspective, of any port to determine potential hinterland by improving the service level of the port.Originality/valueThe existing literature on port choice and port competition has not previously considered the effect of port service levels under the perspective of total landed costs of the users, being this paper a contribution to fulfill this gap.

The Split Delivery Vehicle Routing Problem with Three-Dimensional Loading and Time Windows Constraints

Besides routing and packing plans, synthetically considering the requirements of customers about service time is absolutely necessary. An order split delivery plan can not only better satisfy the service time requirements, but also improve the full-load rate of vehicles. The split delivery vehicle routing problem with three-dimensional loading constraints (3L-SDVRP) combines vehicle routing and three-dimensional loading with additional packing constraints. In the 3L-SDVRP splitting deliveries of customers is basically possible, i.e., a customer can be visited in two or more tours. The vehicle routing problem with three-dimensional loading constraints that are based on the time window and considering split delivery of orders (3L-CVRPTWSDO) and its optimization algorithm are studied in this paper. We established mathematical model of the problem and designed the tabu search algorithm. Based on the examples used in Gendreau et al. (2006), examples was constructed to test our algorithm. The experimental results have expressed that, in the 3L-CVRP problem, the results of split delivery is better than those of non-split delivery, and it is easier to satisfy the time window constraints. The algorithm in this paper generates high quality solutions, it provides a effective method to solve the 3L-CVRPTWSDO problems.

Effective Utilization of Renewable Energy Sources in Fog Computing Environment via Frequency and Modulation Level Scaling

Fog computing introduces a distributed processing capability close to end users. The proximity of computing to end users leads to lower service time and bandwidth requirements. Energy consumption is a matter of concern in such a system with a large number of computing nodes. Renewable energy sources can be utilized to lessen the burden on the main power grid and reduce the carbon footprint, but due to fluctuations, the effective utilization of renewable energy sources needs proper resource management. In this article, we deal with properly managing the resources in a fog environment where the fog nodes are equipped with onsite renewable energy. This article aims to design an efficient mechanism to dynamically dispatch requests among computing nodes and scale frequency and modulation level, based on the current workload and the availability of renewable energy sources, to minimize the service time while keeping the renewable energy utilization and stability at a satisfactory level. We state the problem as the design of a controller for a system with time-varying nonlinear state equations. Accordingly, we borrow the Lyapunov optimization technique from the control theory to design the request dispatching mechanism and prove its asymptotic optimality. We perform extensive simulations to evaluate the effectiveness of the proposed method. The simulation results demonstrate that our proposed method outperforms the naive time-aware baseline scheme up to 26% and 39%, respectively, in terms of service time and renewable energy utilization.

Collaborative Fleet Deployment and Routing for Sustainable Transport

Efficient multi-modal transportation in the hinterland of seaport terminals depends on consolidation of container volumes in support of frequent services of high capacity means of transport, such that sustainable multi-modal transport can compete with uni-modal road transport in cost and time. The tactical design of barge scheduled transport services involves fleet selection and routing through the inland waterway network. The resulting network service design should meet expected demand and service time requirements set by the shippers. We develop a tight MILP formulation for the Fleet Size and Mix Vehicle Routing (FSMVRP) especially adapted for the Port-Hinterland multi-modal barge network design. Also, an analytical model is developed to help understand important design trade-offs made. We consider the case of horizontal cooperation of dry port container terminals that share capacity. Our results show that in case of cooperation, both cost savings and service levels are improved, and allow for sustainable multi-modal transport to be competitive with uni-modal truck transport.

Priority, Capacity Rationing, and Ambulance Diversion in Emergency Departments

We evaluate the practices of priority, capacity rationing, and ambulance diversion in emergency department management. We consider a two-class non-preemptive priority M/M/c queue where high- and low-priority customers correspond to acute and non-acute patients, respectively; the two classes have heterogeneous service time requirements. We model capacity rationing by reserving k servers (beds) to high priority customers, and ambulance diversion by balking acute customers from entering the system when the number of waiting acute patients is higher than m. We provide asymptotic result indicating that for a system with no ambulance diversion and a large number of servers, c, the non-degenerative capacity rationing level is of order O(√c). We also derive exact solutions for different performance measures of interest for this system using queueing Markov chain decomposition (QMCD). These performance measures include expected number of patients, distribution of waiting time, and the rate of ambulance diversion. These are the first exact results for a multi-server queue with non-preemptive priorities and heterogeneous service rates, and thus are of independent interest. We demonstrate numerically how system parameters impact these performance measures and provide insights on the control of capacity rationing and ambulance diversion in emergency departments. Specifically, we show that, as predicted by the asymptotic analysis, a low level of capacity rationing can significantly reduce the waits of high-priority customers with little effect on the waiting of low priority customers.

Improved algorithms for single vehicle scheduling on tree/cycle networks

The single vehicle scheduling problems based on tree/cycle networks are studied in this paper. Each customer, assumed as a vertex on the given network, has a release time and a service time requirement. The single vehicle starts from the depot and aims to serve all the customers. The objective of the problem is to find the optimal routing schedule so as to minimize the makespan. We provide a $$\frac{16}{9}$$ -approximation algorithm and a $$\frac{48}{25}$$ -approximation algorithm for the tour-version and path-version of single vehicle scheduling problem on a tree, respectively. For the tour-version and path-version of single vehicle scheduling problem on a cycle, we present a $$\frac{5}{3}$$ -approximation algorithm and a $$\frac{29}{17}$$ -approximation algorithm, respectively.

Reduce Energy Consumption in SWIPT System by Discontinuous Service Base Stations

In this paper, we investigate energy consumption reduction problem of simultaneous wireless information and power transfer (SWIPT) systems by establishing a discontinuous service base station (DSBS) scheme for SWIPT. Service time of the DSBS is minimized by optimizing the time allocation between energy harvesting (EH) and information transfer (IT), and the power consumption allocation between intermittent IT and continuous target detect. To solve the optimization problem, A method to reduce the dimensions of optimization subject is put forward. Out of service probability of the system is analysed, where the failure event is defined as the terminal required service time is lager then the maximum allowed DSBS service time. Analytical and simulation results reveal that, the system we presented possesses effective energy consumption ability with little reliability lost.

Efficient Benders decomposition algorithms for the robust multiple allocation incomplete hub location problem with service time requirements

Many transportation systems for routing flows between several origin-destination pairs of demand nodes have been widely designed as hub-and-spoke networks. To improve the provided service level of these networks, service time requirements are here considered during modeling, giving rise to a multiple allocation incomplete hub location problem with service time requirements. The problem consists of designing a hub and spoke network by locating hubs, establishing inter-hub arcs, and routing origin-destination demand flows at minimal cost while meeting some service time requirements. As travel times are usually uncertain for most real cases, the problem is approached via a binary linear programming robust optimization model, which is solved by two specialized Benders decomposition algorithms. The devised Benders decomposition framework outperforms a general purpose optimization solver on solving benchmark instances of the hub location literature. The achieved results also show how the probability of violating the travel time requirements decreases with the prescribed protection level, at the expense of the higher costs of the optimal solution for the robust optimization model.

Equilibrium Arriaval Times to Queues: The Case of Last-Come First-Serve Preemptive-Resume

We consider a non-cooperative queueing environment where a finite number of customers independently choose when to arrive at a queueing system that opens at a given point in time and serves customers on a last-come first-serve preemptive-resume (LCFS-PR) basis. Each customer has a service time requirement which is identically and independently distributed according to some general probability distribution, and they want to complete service as early as possible while minimizing the time spent in the queue. In this setting, we establish the existence of an arrival time strategy that constitutes a symmetric (mixed) Nash equilibrium, and show that there is at most one symmetric equilibrium. We provide a numerical method to compute this equilibrium and demonstrate by a numerical example that the social efficiency can be lower than the efficiency induced by a similar queueing system that serves customers on a first-come first-serve (FCFS) basis.

The method for providing quality of service time requirements in reconfigurable computing systems

The process of calculations control in reconfigurable computing systems characterized by the time constraints of both the applications solved and the functional and structural organization of the system is investigated. As part of this, the method is modified and mathematically substantiated for determining the time intervals of tasks. The proposed modification to the analysis of tasks acceleration index allows to evaluate the unproductive time expenditure taking into account the FPGA hardware limitations in the computer algorithms mapping to the reconfigurable computing structure. The method of providing time parameters of service quality in the reconfigurable computing system that provides given application runtime by determining the amount of unproductive time-consuming reconfiguration and the choice of optimal service discipline for each task in terms of time and existing hardware limitations was proposed. The use of the proposed method of determining the time intervals, which the implementation of the method is based on determining the sequence of tasks for which the target architecture of the computer system is effective and the use of additional mechanisms to reduce overhead does not speed up calculations within the advertised time requirements. Application of the proposed method allows to determine and optimize the impact of space limitations the reconfiguration time and reduce the number of rejections of tasks in the dynamic mapping of task flows to the reconfigurable computational structure. The formalization of the method for determining the time intervals of tasks was developed, for which the optimal boundaries of the effective use of the proposed facilities in terms of the adequacy of the offered application time limits to the limits of the reconfigurable computer system were defined and justified. The proposed tools can be used in modern high-performance reconfigurable computing systems during solving the problems of control of various technical and management processes and implementation of multidimensional calculations in complex information systems.

Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements

In “Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements,” Lei and Jasin consider a fundamental dynamic pricing problem when resources are reusable. In this problem, demand arrives according to a price-sensitive nonstationary rate, requesting a service that uses a combination of different types of resources for a deterministic duration of time. The resources are reusable in the sense that they can be immediately used to serve a new customer on the completion of the previous service. Moreover, different customers may have different service time requirement and may book the service in advance. The objective is to construct a dynamic pricing control that maximizes expected total revenues. They develop real-time heuristic controls based on the solution of the deterministic relaxation of the original stochastic problem and show that the proposed controls are near optimal in the regime of large demand and large resource capacity.

Real-Time Dynamic Pricing for Revenue Management with Reusable Resources and Deterministic Service Time Requirements

We consider the setting of a firm that sells a finite amount of resources to price-sensitive customers who arrive randomly over time according to a specified non-stationary rate. Each customer requires a service that consumes one unit of resource for a deterministic amount of time, and the resource is reusable in the sense that it can be immediately used to serve a new customer upon the completion of the previous service. The firm’s objective is to set the price dynamically to maximize its expected total revenues. This is a fundamental problem faced by many firms in many industries. We formulate this as an optimal stochastic control problem and develop two heuristic controls based on the solution of the deterministic relaxation of the original stochastic problem. The first heuristic control is static since the corresponding price sequence is determined before the selling horizon starts; the second heuristic control is dynamic, it uses the first heuristic control as its baseline control and adaptively adjusts the price based on previous demand realizations. We show that both heuristic controls are asymptotically optimal in the regime with large demand and large number of resources. Finally, we consider two important generalizations of the basic model to the setting with multiple service types requiring different service times and the setting with advance service bookings.

The min–max split delivery multi-depot vehicle routing problem with minimum service time requirement

The min–max Split Delivery Multi-Depot Vehicle Routing Problem with Minimum Service Time Requirement (min–max SDMDVRP-MSTR) is a variant of the Multi-Depot Vehicle Routing Problem. Each customer requires a specified amount of service time. The service time can be split among vehicles as long as each vehicle spends a minimum amount of service time at a customer. The objective is to minimize the duration of the longest route (where duration is the sum of travel and service times).We develop a heuristic (denoted by MDS) that solves the min–max SDMDVRP-MSTR in three stages: (1) initialize a feasible solution without splits; (2) improve the longest routes by splitting service times; (3) ensure all minimum service time requirements are satisfied. The first stage of MDS is compared to an existing heuristic to solve the min–max Multi-Depot Vehicle Routing Problem on 43 benchmark instances. MDS produces 37 best-known solutions. We also demonstrate the effectiveness of MDS on 21 new instances whose (near) optimal solutions can be estimated based on geometry. Finally, we investigate the savings from split service and the split patterns as we vary the required service times, the average number of customers per route, and the minimum service time requirement.

Evolutionary Game Theoretic Modeling and Repetition of Media Distributed Shared in P2P-Based VANET

A significant challenge in vehicular networks is to efficiently provide multimedia services with the constraints of limited resources, high mobility, opportunistic contact, and service time requirements. In order to guarantee the vehicle user satisfaction of multimedia service, with the proliferation of the distributed peer-to-peer (P2P) cooperative transmission technologies, P2P-based VANET has recently received a substantial amount of interest. Using the P2P services thought and at the same time, a set of methods should be designed to avoid the disadvantage of P2P system appearing in VANET. Under such a presupposition, in this paper, we study the media provisions in P2P-based VANET and present a repeated game “More Pay for More Work (RGMPMW)” incentive mechanism based on service evaluation information. We also propose evolutionary game-based veracity (EGV) game model which exploits evolutionary game to guarantee the multimedia service share veracity of all vehicles in VANET. In addition, we provide extensive simulation results that demonstrate the effectiveness of our proposed schemes.