Maximum Service Time Research Articles

Enhancing home delivery of emergency medicine and medical supplies through clustering and simulation techniques: A case study of COVID-19 home isolation in Bangkok

This study investigates the enhancement of the home delivery distribution network for COVID-19 Home Isolation (HI) kits during the Delta variant outbreak of the SARS-CoV-2 virus in Bangkok Metropolitan Area, Thailand. It addresses challenges related to limited resources and delays in delivering HI kits, which can exacerbate symptoms and increase mortality rates. A k-means clustering approach is utilized to optimize the assignment of service areas within the COVID-19 HI program, while discrete event simulation (DES) evaluates potential changes in the home delivery logistics network. Real-world data from the peak outbreak is used to determine the optimal allocation of resources and propose a new logistics network based on proximity to patients' residences. Experimental results demonstrate a significant 44.29 % improvement in overall performance and a substantial 40.80 % decrease in maximum service time. The findings offer theoretical and managerial implications for effective HI management, supporting practitioners and policymakers in mitigating the impact of future outbreaks.

An Adjustable Wireless Backhaul Link Selection Algorithm for LEO-UAV-Sensor-Based Internet of Remote Things Network.

Internet of Remote Things (IoRT) networks utilize the backhaul links between unmanned aerial vehicles (UAVs) and low-earth-orbit (LEO) satellites to transfer the massive data collected by sensors. However, the backhaul links change rapidly due to the fast movement of both the UAVs and the satellites, which is different from conventional wireless networks. Additionally, due to the various requirements of IoRT multiservices, the system performance should be comprehensively considered. Thus, an adjustable wireless backhaul link selection algorithm for a LEO-UAV-sensor-based IoRT network is proposed. Firstly, an optimization model for backhaul link selection is proposed. This model uses Q, which integrates the remaining service time and capacity as the objective function. Then, based on the snapshot method, the dynamic topology is converted into the static topology and a heuristic optimization algorithm is proposed to solve the backhaul link selection problem. Finally, the proposed algorithm is compared with two traditional algorithms, i.e., maximum service time and maximum capacity algorithms. Numerical simulation results show that the proposed model can achieve better system performance, and the overload of the satellites is more balanced. The algorithm can obtain a trade-off between remaining service time and capacity by dynamically adjusting model parameters. Thus, the adjustable backhaul link selection algorithm can apply to multiservice IoRT scenarios.

Hub location with congestion and time-sensitive demand

This work studies the effect of hub congestion and time-sensitive demand on a hub-and-spoke location/allocation system. The Hub Location with Congestion and Time-sensitive Demand Problem is introduced, which combines these two main characteristics. On the one hand, hubs can be activated at several service levels, each of them characterized by a maximum capacity, expressed as the amount of flow that may circulate through the hub, which is associated with a hub transit time. On the other hand, alternative levels are available for served commodities, where each demand level is characterized by its amount of demand, unit revenue, and maximum service time. In this problem the efficiency of a hub-and-spoke system is given by the maximum net profit it may produce. To the best of our knowledge this is the first work where hub congestion and time-sensitive demand are jointly considered.Two alternative mixed-integer linear programming formulations are proposed. They include a new set of constraints, which are necessary to guarantee the consistency of the obtained solutions under the presence of the capacity-type constraints derived from hub service levels and served demand levels. The efficiency of the formulations is analyzed through a set of computational experiments. The results of the computational experiments allow to study the structure of the obtained solutions and to analyze how the different parameters affect them.

Multi-Traveler Salesman Problem for Unmanned Vehicles: Optimization through Improved Hopfield Neural Network

In response to the COVID-19 pandemic, communities utilize unmanned vehicles to minimize person-to-person contact and lower the risk of infection. This paper addresses the critical considerations of these unmanned vehicles’ maximum load capacity and service time, formulating them as constraints within a multi-traveling salesman problem (MTSP). We propose a comprehensive optimization approach that combines a genetic simulated annealing algorithm with clustering techniques and an improved Hopfield neural network (IHNN). First, the MTSP is decomposed into multiple independent TSPs using the fuzzy C-means clustering algorithm based on a genetic simulated annealing algorithm (SA-GA-FCM). Subsequently, the HNN is employed to introduce the data transformation technique and dynamic step factor to prepare more suitable inputs for the HNN training process to avoid the energy function from falling into local solutions, and the simulated annealing algorithm is introduced to solve multiple TSP separately. Finally, the effectiveness of the proposed algorithm is verified by small-scale and large-scale instances, and the results clearly demonstrate that each unmanned vehicle can meet the specified constraints and successfully complete all delivery tasks. Furthermore, to gauge the performance of our algorithm, we conducted ten simulation comparisons with other combinatorial optimization and heuristic algorithms. These comparisons indicate that IHNN outperforms the algorithms mentioned above regarding solution quality and efficiency and exhibits robustness against falling into local solutions. As presented in this paper, the solution to the unmanned vehicle traveling salesman problem facilitates contactless material distribution, reducing time and resource wastage while enhancing the efficiency of unmanned vehicle operations, which has profound implications for promoting low-carbon sustainable development, optimizing logistics efficiency, and mitigating the risk of pandemic spread.

Terraforming – Environment Manipulation during Disruptions for Multi-Agent Pickup and Delivery

In automated warehouses, teams of mobile robots fulfill the packaging process by transferring inventory pods to designated workstations while navigating narrow aisles formed by tightly packed pods. This problem is typically modeled as a Multi-Agent Pickup and Delivery (MAPD) problem, which is then solved by repeatedly planning collision-free paths for agents on a fixed graph, as in the Rolling-Horizon Collision Resolution (RHCR) algorithm. However, existing approaches make the limiting assumption that agents are only allowed to move pods that correspond to their current task, while considering the other pods as stationary obstacles (even though all pods are movable). This behavior can result in unnecessarily long paths which could otherwise be avoided by opening additional corridors via pod manipulation. To this end, we explore the implications of allowing agents the flexibility of dynamically relocating pods. We call this new challenging problem Terraforming MAPD (tMAPD) and develop an RHCR-based approach to tackle it. As the extra flexibility of terraforming comes at a significant computational cost, we utilize this capability judiciously by identifying situations where it could make a significant impact on the solution quality. In particular, we invoke terraforming in response to disruptions that often occur in automated warehouses, e.g., when an item is dropped from a pod or when agents malfunction. Empirically, using our approach for tMAPD, where disruptions are modeled via a stochastic process, we improve throughput by over 10%, reduce the maximum service time (the difference between the drop-off time and the pickup time of a pod) by more than 50%, without drastically increasing the runtime, compared to the MAPD setting.

ANALISA WAKTU PELAYANAN GARDU TOL CENGKARENG 2 DENGAN PTV VISSIM

Toll roads are affected by the service time on toll roads which have been stipulated by the Regulation of the Minister of Public Works No. 16 concerning Minimum Service Standards for Toll Roads, which were later implemented by the BUJT (Toll Road Business Entity). At the Cengkareng 2 toll gate, electronic toll collection has been implemented and at one of the gates, a Single Lane Free Flow (SLFF) system has been implemented. SLFF implemented in Indonesia is a payment technology system using Radio Frequency Identification (RFID) and Dedicated Short Range Communications (DSRC) readings which are expected to speed up service time. With the implementation of SLFF, toll road operators at toll gates can be minimized, on the other hand, will use a fee collection system at the end where the reduction of toll collection and usage accounts will be handled by the toll road operator's office. Modeling the existing road and inputting vehicle data and speed plans is then carried out by a simulation process by entering variations in the volume of vehicles in Vissim to get the maximum capacity from the toll gate. From the results of the analysis carried out, the service time that occurs at the Cengkareng 2 toll gate is 9 seconds/vehicle, this is the maximum service time limit for closed system substations at exit substations based on Minimum Service Standards (SPM) with accessibility, and the maximum capacity of toll gates. Cengkareng with the SLFF system is 316 vehicles/hour with a speed of 17 km/hour and the maximum capacity of the toll booths with a tapping system is substation 5 316 vehicles/hour with a speed of 15 km/hour, substation 6 308 vehicles/hour with a speed of 15 km/hour and substation 7 313 vehicles/hour at a speed of 15 km/hour

Mathematical modeling of the workload processing in the information and communication network

The functioning of a modern information and communication network is impossible without software, which partially replaces specialized equipment when solving network tasks. These tasks form a workload that needs to be processed by a distributed server infrastructure within the network. There are requirements for each type of workload regarding the quality of its processing (maximum service time, permissible losses, etc.). Thus, there is a need to establish relationships between quality indicators and parameters of the processing system to ensure the fulfillment of these requirements using mathematical modeling. The paper proposes a mathematical model of a distributed workload processing system in the information and communication network as a queuing system. Unlike known models, the proposed model considers the possible variable nature of the input workload arrival rate and parallelization methods that may be applied to the software that implements it. Within the modeling process, a method of transition from a non-stationary non-ordinary incoming queries’ flow to an ordinary stationary flow is proposed. Based on the constructed mathematical model, a complex method of energy-efficient workload processing has been developed. A laboratory experiment has proven the efficiency of the proposed complex method and the mathematical model’s adequacy in its basis.

Post-Disaster Temporary Shelters Distribution after a Large-Scale Disaster: An Integrated Model

This paper develops an integrated model for the distribution of post-disaster temporary shelters after a large-scale disaster. The proposed model clusters impacted areas using an Adaptive Neuro-Fuzzy Inference System (ANFIS) method and then prioritizes the points of clusters by affecting factors on the route reliability using a permanent matrix. The model’s objectives are to minimize the maximum service time, maximize the route reliability and minimize the unmet demand. In the case of ground relief, the possibility of a breakdown in the vehicle is considered. Due to the disaster’s uncertain nature, the demands of impacted areas are considered in the form of fuzzy numbers, and then the equivalent crisp counterpart of the non-deterministic is made by Jimenez’s method. Since the developed model is multi-objective, the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Firefly Algorithm (MOFA) are applied to find efficient solutions. The results confirm higher accuracy and lower computational time of the proposed MOFA. The findings of this study can contribute to the growing body of knowledge about disaster management strategies and have implications for critical decision-makers involved in post-disaster response projects. Furthermore, this study provides valuable information for national decision-makers in countries with limited experience with disasters and where the destructive consequences of disasters on the built environment are increasing.

Time dependent orienteering problem with time windows and service time dependent profits

This paper addresses the time dependent orienteering problem with time windows and service time dependent profits (TDOPTW-STP). In the TDOPTW-STP, each vertex is assigned a minimum and a maximum service time and the profit collected at each vertex increases linearly with the service time. The goal is to maximize the total collected profit by determining a subset of vertices to be visited and assigning appropriate service time to each vertex, considering a given time budget and time windows. Moreover, travel times are dependent of the departure times. To solve this problem, a mixed integer linear model is formulated and a metaheuristic algorithm based on variable neighborhood search (VNS) is developed. This algorithm uses three specifically designed neighborhood structures able to deal with the variable service times and profits of vertices. Extensive computational experiments are conducted on test instances adapted from the TDOPTW benchmarks, to validate the performance of our solution approach. Furthermore, a real instance for the city of Shiraz (Iran) is generated. Experimental results demonstrate the suitability of the TDOPTW-STP in practice, and demonstrate that the proposed algorithm is able to obtain high-quality solutions in real-time. Sensitivity analyses clearly show the significant impact of the service time dependent profits on the route plan, especially in the presence of travel time dependency and time windows.

Discrete Time Multi-servers Loss Systems and Stochastic Approximation with Delayed Groups of Customers

The stochastic approximation procedure with delayed groups of delayed customers is investigated. The Robbins-Monro stochastic approximation procedure is adjusted to be usable in the presence of delayed groups of delayed customers. Two loss systems are introduced to get an accurate description of the proposed procedure. Each customer comes after fixed time-intervals with the stage of the following customer is accurate according to the outcome of the preceding one, where the serving time of a customer is assumed to be discrete random variable. Some applications of the procedure are given where the analysis of their results is obtained. The analysis shows that efficiencies of the procedure can be increased by minimizing the number of customers of a group irrespective of their service times that may take maximum values. Efficiencies depend on the maximum service time of the customer and on the number of customers of the group. The most important result is that efficiencies of the procedure are increased by increasing the service time distributions as well as service times of customers .This new situation can be applied to increase the number of served customers where the number of served groups will also be increased. The results obtained seem to be acceptable. In general, our proposal can be utilized to other stochastic approximation procedures to increase the production in many fields such as medicine, computer sciences, industry, and applied sciences.

The Method of Operational Planning of Group Actions of Aircraft in the "Air Taxi" Mode

The article is devoted to the development of algorithms for operational planning of routes for a group of aircraft (AC). We consider group actions of small and unmanned aircraft in the "air taxi" mode, when there is no regular flight schedule between the points of destination, and requests are received "on call" for flights to points whose composition is unknown in advance and is of a random nature. The multicriteria task of planning a group flight in the "air taxi" mode is being solved. The solution to this problem is proposed using the apparatus of the queuing theory, according to which the system under consideration belongs to the class of multichannel queuing systems with waiting. A method for solving the problem of operational planning of aircraft actions is proposed. An algorithm for group target distribution of new claims between aircraft is developed on the basis of a modified minimax criterion for assigning the nearest aircraft for an object with a maximum service time. The developed algorithm is based on the following four main operations: in the first operation, priority unserved targets are selected according to the criterion of assigning a dynamic priority; in the second operation, the formed list is ranked according to another criterion, taking into account the importance and total distance of each ground object from the aircraft group, in three this operation selects the object with the maximum rank, and for it the task of assigning "own" aircraft is solved according to the third criterion of maximum proximity, in the fourth operation the conditions of non-intersection of the group flight routes are checked. A computer model of the system for servicing requests in the air taxi mode has been developed. The developed model makes it possible to analyze various scheduling algorithms, as well as to determine at each step the number of free claims and the number of free and busy aircraft. A comparison is made between the well-known in the theory of queuing and the proposed minimax approaches. It is shown that, in comparison with the known variants of scheduling in the queuing theory, on the basis of the proposed approach, the optimal number of used aircraft is achieved.

Test Loading of Structures with a Suspect Resistance

The reliability of load-bearing structures is normally secured through codes, a competent structural design and proper execution inspection. Alternatively, the reliability can be obtained via skilled test loading, which is a feasible technique both in the construction of new structures and in the load-bearing verification of existing ones. Although the current codes lack instructions for test loading, they are, however, used in special cases; for example, when the reliability of the structures is doubtful due to a defect, or when the structure is suspected to have especially high resistance variability. Test loading involves significant research questions that need to be addressed, including: What is the test load in comparison with the expected maximum service time load or the characteristic load? How can the instantaneous test load be compared with the actual long-term service-time load? Does the test loading harm the structure, and what is the target reliability in the test loading calculation? In this paper, we approach these questions from a theoretical point of view and propose how a suitable test load can be chosen in practice using an approximate and a precise approach.

Thermo-Hydraulic Analysis of a Horizontal HTS ZFC Levitating Bearing Concerning Its Autonomy Safety Service Time

Electromagnetic heating of high-temperature superconductors (HTSs) and non-perfect thermal insulation of cryostats cause liquid nitrogen (LN2) vaporization and consumption. This paper presents results from the thermo-hydraulic analysis of the maximum safety service time of the prototype of a horizontal axis radial levitation bearing with zero-field cooled (ZFC) HTS bulks. It first studies the temperature sensitivity of yttrium barium copper oxide (YBCO) bulks to the LN2 level, particularly when partially immersed. This, when the main component of heat flux is according to the c-axis of the bulk crystal structure, that is the case of the studied bearing prototype and in most of all cryostats of maglev vehicles. Measurements used two platinum resistance thermometers leaning the top and side surfaces of a non-magnetized ZFC YBCO bulk. Correlations between the temperatures measured by the two platinum resistors are made to determine the average temperature of YBCO bulk. The thermal-time constant associated with a YBCO bulk losing contact with LN2 is also determined for two conditions: 1) initial ZFC and 2) initial field-cooled (FC) under the influence of typical magnetic field values generated by the studied bearing permanent magnet (PM) rotor. Results indicate that the YBCO temperature evolution is not notably affected by Joule losses from initial magnetization currents for this bearing. Due to continuous PM rings, the magnetic field perceived by the YBCO bulks does not change in the ideal case of spinning with no vibrations. Another experiment shows that the heating by induction from vibration dynamics does not contribute notably to LN2 consumption. Finally, the safety service time is verified from measured LN2 mass and level evolution in the HTS bearing's stator, due mainly to defective thermal insulation from the room temperature. The process consisted of calculating the times that different leveled YBCO bulks experiments reached the critical temperature and using the conclusions taken from the initial study about the temperature sensitivity of single bulks to the LN2 level. 3D finite element simulations reinforce our experimental results.

Joint planning for battery swap and supercharging networks with priority service queues

Existing network planning models for electric vehicle (EV) services usually treat the battery swap and the on-board supercharging as two independent processes. This study makes an early attempt to design an EV charging network where battery swap and supercharging are jointly coordinated. The swap and supercharge processes are characterized by Erlang B and Erlang C priority queues, respectively. A strategic location-allocation model is formulated to optimize the station sites, battery stock level, and the number of superchargers at chosen sites. Three design criteria, namely, battery state-of-charge, maximum service time, and power grid constraint, are simultaneously taken into account. Meta-heuristics algorithms incorporating Tabu search are developed to tackle the proposed non-linear mixed integer optimization model. Computational results on randomly generated instances show that the priority battery service scheme outperforms the pure battery swap station in terms of spare battery investment cost and charging flexibility. The case study on a real-world traffic network comprised of 0.714 million households further shows the efficacy and advantage of the dual battery charging process for ensuring state-of-charge, service time commitment, and network-wide grid stability.

Latency-Based Transmission Point Selection Algorithms in Wireless Networks

In this paper, we propose transmission point selection algorithms to provide lower service time between an application server to end users by selecting the transmission point used by a newly identified metric, i.e., service latency. In particular, the proposed ordered service latency-based transmission point selection algorithm is beneficial in a sense that it does not increase average service time but decrease the maximum service time although it brings additional computational complexity slightly. We also provide the performance bounds with proven approximation ratios and verify that the proposed algorithm achieves lower maximum service time without degrading the average service time as well as waiting time through simulations in practical environments.

A multi-objective open set orienteering problem

We have constructed a multi-objective set orienteering problem to model real-world problems more fittingly than the existing models. It attaches (i) a predefined profit associated with each cluster of customers, and (ii) a preset maximum service time associated with each customer of all the clusters. When a customer from cluster visits, it allows the earning of a profit score. Our purpose is primarily to search for a route that (i) on the one hand allows us to service each cluster for maximizing customer satisfaction and (ii) on the other hand allows us to maximize our profits, too. The model assumes that the more time we spend on servicing, the more customer satisfaction it yields. It tries to cover as many clusters as possible within a specified time budget. In this paper, we also consider third-party logistics to allow the flexibility of ending our journey at any cluster of our choice. The proposed model is solved using the nondominated sorting genetic algorithm and the strength Pareto evolutionary algorithm. Here, we also generate the dataset to test the proposed model by using instances from the literature of the generalized traveling salesman problem. Finally, we present a comparative result analysis with the help of some statistical tools and discuss the results.

Different aspects of the accelerated oxidation of polypropylene at increased pressure in an autoclave with regard to temperature, pretreatment and exposure media

The aim of this study is to investigate and compare three different factors, temperature, pretreatment and exposure media, on the oxidation and aging behavior of polypropylene (PP) bulk samples in the autoclave test under increased oxygen pressure. The aging of polypropylene, in this case syringe material, was accelerated under different conditions. The samples were aged at 75°, 80° and 85 °C under pure oxygen and at 75 °C under ultrapure water. Further samples treated with 15 kGy (e-beam) were aged at 75 °C under pure oxygen. All experiments were carried out at an oxygen partial pressure of 50 bar. The different courses of aging were evaluated and compared. Color and transparency were used to assess the visual changes; weight, geometry and tensile test for the changes in technical properties; and DSC, FTIR and TDS-GC-MS to improve our physico-chemical understanding of the aging processes.The investigations showed that all factors tested influence oxidation/aging behaviour. Dry or wet exposure has no effect on aging during the induction time. However, after the point of maximum service time the degradation process of polymer changes significantly in the presence of water. The increase of aging temperature decreases the maximum service time; however a temperature higher than 80 °C changes the relative ratio of surface and bulk oxidation. The pretreatment with irradiation decreases the maximum service time further, because the irradiation partly damages the antioxidant, and the mobility of antioxidant fragments inside the polymer changes. These diffusion effects influence the relative ratio of bulk and surface oxidation of the polymer sample.

Study on an Energy-aware Routing Algorithm for Agriculture WSN

As precision farming requires real-time, accurate and sustainable monitoring of the biotope of the field, energy consumption and network delay constitute the major factors affecting the performance of ZigBee-based Wireless Sensor Network (WSN) applied in agricultural production. Based on the AODVjr algorithm, this paper presents a new algorithm, namely NS-AODVjr. Featuring emphasis on energy control and dynamic routing, as well as balance between energy consumption and shortest path routing, this algorithm realizes maximum service time of the WSN while ensuring timely and effective transmission of the data in the monitoring process. After a simulation experiment with the Network Simulator tool, results show that, compared to the original algorithm, although the packet delivery rate and network delay witness no improvement, the new algorithm not only significantly reduces the device energy consumption, but also effectively expand the service time of the network. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2799 Full Text: PDF

Balancing responsiveness and economics in process supply chain design with multi‐echelon stochastic inventory

AbstractThis article is concerned with the optimal design of multi‐echelon process supply chains (PSCs) under economic and responsive criteria with considerations of inventory management and demand uncertainty. The multi‐echelon inventory systems are modeled with the guaranteed service approach to handle the uncertain demands at each echelon. The maximum guaranteed service time of the last echelon of the PSC is proposed as a measure of a PSC's responsiveness. The problem is formulated as a bi‐criterion mixed‐integer nonlinear program (MINLP) with the objectives of minimizing the annualized cost (economic objective) and minimizing the maximum guaranteed service times of the markets (responsiveness objective). The model simultaneously predicts the optimal network structure, transportation amounts, and inventory levels under different specifications of the PSC responsiveness. An example on acetic acid supply chain is presented to illustrate the application of the proposed model and to comprehensively compare different measures of PSC responsiveness. © 2010 American Institute of Chemical Engineers AIChE J, 2011

Head loss performance and modeling of burnt oil palm shell in deep bed filtration

Filtration is a common unit operation in a water treatment plant. The effectiveness of granular filter media in treating surface water and removing suspended solids is already well established. However, less effort in the mathematical modeling towards the design of the filtration unit is observed. The current study involved head loss performance evaluation and development of a mathematical model to correlate maximum service time of filter at a maximum allowable head loss with various effective sizes and flow rates. Sand and burnt oil palm shell (BOPS) used as filter media in single and dual media filters were subjected to different effective sizes and flow rates. The average influent water turbidity for each filter (at specific effective size and flow rate) before entering the filtration unit was from 1 to 5 NTU. This was a pilot a study on settled water that was conducted in a Malaysian water treatment plant. Results suggest that all filters are capable of producing water with acceptable turbidity unit (<1 NTU). The total running time and filtrate water quality produced by BOPS/sand dual media filter was much better (i.e. the running time of BOPS/sand was four to nine times greater than sand single media filter) than BOPS and sand single media filters. The experimental data were well represented by a newly developed model and results of fitting show a high correlation coefficient (R2) of 0.991. The maximum allowable head loss was selected as 240 cm with an acceptable filtrate water quality (less than 1 NTU). In addition, the current results will be a supplementary tool for engineers in designing a proper filtration unit.