Total Completion Time Research Articles

11 Contributions of Cardiovascular Burden, Peripheral Inflammation, and Brain Integrity on Digital Clock Drawing Performance in Non-Demented Older Adults

Objective:Higher cardiovascular burden and peripheral inflammation are associated with small vessel vascular disease, a predominantly dysexecutive cognitive profile, and a higher likelihood of conversion to vascular dementia. The digital clock drawing test, a digitized version of a standard neuropsychological tool, is useful in identifying cognitive dysfunction related to vascular etiology. However, little is known about the specific cognitive implications of vascular risk, peripheral inflammation, and varying levels of overall brain integrity. The current study aimed to examine the role of cardiovascular burden, peripheral inflammation, and brain integrity on digitally acquired clock drawing latency and graphomotor metrics in non-demented older adults.Participants and Methods:The final prospectively recruited IRB-consented participant sample included 184 non-demented older adults (age: 69±6 years, education: 16±3 years, 46% female, 94% white) who completed digital clock drawing, vascular assessment, blood draw, and brain MRI. Digital clock drawing variables of interest included: total completion time (TCT), pre-first hand latency (PFHL), digit misplacement, hour hand distance from center, and clock face area (CFA). Cardiovascular burden was calculated using the revised version of the Framingham Stroke Risk Profile (FSRP-10). Peripheral inflammation was operationalized using interleukin (IL)-6, IL-8, IL-10, tumor necrosis factor alpha (TNF-a), and high sensitivity C-reactive protein (hsCRP). The brain integrity composite was comprised of bilateral entorhinal cortex volume, bilateral ventricular volume, and whole brain leukoaraiosis.Results:Over and above age and cognitive reserve, hierarchical regressions showed FSRP-10, inflammatory markers, and brain integrity explained an additional 13.3% of the variance in command TCT (p< 0.001), with FSRP-10 (p=0.001), IL-10 (p= 0.019), and hsCRP (p= 0.019) as the main predictors in the model. FSRP-10, inflammatory markers, and brain integrity explained an additional 11.7% of the variance in command digit misplacement (p= 0.009), with findings largely driven by FSRP-10 (p< 0.001).Conclusions:Overall, in non-demented older adults, subtle behavioral nuances seen in digital clock drawing metrics (i.e., total completion time and digit misplacement) are partly explained by cardiovascular burden, peripheral inflammation, and brain integrity over and above age and cognitive reserve. These nuanced behaviors on digitally acquired clock drawing may associate with an emergent disease process or overall vulnerability.Funding sources: Barber Fellowship; K07AG066813; R01 AG055337; R01 NR014810; American Psychological Foundation Dissertation Award; APA Dissertation Research Award

49 A Preliminary Neurocognitive Profile Characterization of Treatment Resistant Depression

Objective:Major Depressive Disorder (MDD) subtypes have been shown to differentially impact psychiatric symptom presentation, clinical features, and functional abilities. While there is extensive research regarding MDD subtypes and clinical characteristics, there has been limited information regarding the relationship between MDD subtypes and neurocognitive functioning. In particular, the neurocognitive impact of the subtype of treatment resistant depression (TRD), defined as MDD that is unresponsive to treatment, is unknown. The aim of this preliminary study was to address this gap by characterizing the neurocognitive profile of TRD. We characterized the performance of older adults with TRD on measures across multiple neurocognitive domains, and explored whether performance varied based on age and education.Participants and Methods:Data utilized were drawn from a broader NIMH-funded, randomized, controlled study conducted at the University of New Mexico that investigated the clinical and cognitive outcomes of varying pulse amplitudes during acute electroconvulsive therapy (ECT) in adults with MDD. Participants in the study were age 50+ with a diagnosis of MDD, and further delineated by subtype as TRD. For this analysis, we utilized demographic and baseline neurocognitive data collected prior to start of treatment for those diagnosed with MDD, recurrent, severe (TRD). Neurocognitive measures included the Delis Kaplan Executive Function System (D-KEFS) Verbal Fluency and Color-Word Interference Subtests, Hopkins Verbal Learning Test-Revised (HVLT-R), and the Wechsler Adult Intelligence Scale 4th Edition (WAIS-IV) Digit Spans. Demographic-adjusted scaled scores were computed, and descriptive statistics were used to characterize the demographic and neurocognitive features of the sample. Multiple Analysis of Variance (MANOVA) was used to investigate difference in performance across neurocognitive measures based on level of education, with age as a covariate.Results:The sample (n = 42) had a mean age of 65 (SD=8), education level between12 and 14 years, 66.6% were female and 93% were Caucasian. DKEFS Verbal Fluency Category Switching Total Switching Accuracy fell in the Average range (Mean SS=9.5, SD=3.1), and Color Word Inhibition Total Completion Time fell in the Average range (Mean SS=8.5, SD=3.3). HVLT-R Total Recall Correct fell in the Mildly Impaired range (Mean T=35.5, SD=9.9) and Delayed Recall Correct fell in the Mildly to Moderately Impaired range (Mean T=32.9, SD=11.0). WAIS-IV Digit Span fell in the Average range (Mean SS=9.5, SD=2.2). Results indicated that age did not adjust outcomes on the neurocognitive variables, Wilks's λ=0.63, F(6, 23)=2.13, p=0.08. We found no evidence for significant effect of level of education on neurocognitive functioning when controlling for the covariate of age, Wilks's λ=-0.16, F(36, 103.7)=1.47, p=0.07.Conclusions:To our knowledge, this is one of two studies to examine neurocognitive functioning in patients with TRD. The analysis indicated generally intact performance in the neurocognitive domains of executive function (inclusive of verbal fluency, cognitive flexibility, and inhibition), auditory attention and working memory, and Impaired performance on indices of verbal learning and memory. Age did not impact performance on neurocognitive measures, and there was no significant effect for level of education. Further research is warranted to confirm these findings and further explicate the neurocognitive profile of TRD.

Integrated maintenance and production scheduling for unrelated parallel machines with setup times

This paper considers jointly scheduling the production and resource-constrained maintenance activities in a manufacturing setting with unrelated parallel machines. In particular, a single maintenance activity needs to be scheduled on each machine in one of its available time windows, and the maintenance activities require a scarce resource, thereby limiting the number of maintenance activities that can be scheduled simultaneously on different machines. In addition, machine- and sequence-dependent setup times, machine eligibility constraints and job-specific release and due dates are considered. A mixed-integer linear program is formulated with objectives including the makespan and, motivated from practice, a weighted sum of total production completion times at machines and total job tardiness. Additionally, a hybrid genetic algorithm with a novel solution representation is proposed for solving industry-scale large instances. A case study is performed with real-world data from a semiconductor manufacturer, where production and maintenance are scheduled separately. The benefit of simultaneously scheduling production and maintenance is investigated. Tests with real-world data show that the proposed model results in schedules that substantially improve the current factory practice.

B - 65 Assessing Methods of Executive Ability from Trail Making Test Part B in Mexican-Americans.

We investigated three different measures of executive ability derived from the Trail Making Test part B (TMT-B) in Mexican-American adults. Forty-seven Mexican-Americans were administered both TMT part A (TMT-A) and TMT-B, and total time for completion was recorded. Additionally, participants completed The Acculturation Rating Scale for Mexican Americans. Participants were divided into two groups: low-acculturation group (n = 19) and high-acculturation group (n = 28). Independent Sample T-tests were used to examine differences in executive abilities between our acculturation groups. Executive measures included the TMT-B raw score (i.e., seconds to complete TMT-B), the raw score difference (in seconds) between TMT-A and TMT-B (TMT-BA), and the difference between a predicted TMT-B score (TMT-BP) and the obtained TMT-B score (TMT-BBP). Correlations between TMT-B, TMT-BA, and TMT-BBP and other executive functioning tests (i.e., letter fluency, animal naming, & Comalli Stroop Test part C) were evaluated. Results revealed the high-acculturation group outperformed the low-acculturation group on all measures of executive ability derived from TMT-B, p's < 0.05. Furthermore, the high-acculturation group TMT-BA significantly correlated with Comalli Stroop Test part C, r = 0.44, p = 0.021. Finally, no significant correlations between the different measures of executive ability from TMT-B and other executive functioning tests were found in the low-acculturation group. We found the high-acculturation group demonstrated better executive functioning abilities on all three TMT executive ability variables compared to the low-acculturation group. In our high-acculturation sample, TMT-BA executive ability variable correlated with at least one executive functioning measure, which suggests that TMT-BA can be a likely better measure of executive ability than TMT-B or TMT-BBP.

Influential process nodes identification strategy for aircraft assembly system based on complex network and improved PageRank

In the manufacturing network, the fluctuation of process operation time can result in the delay of the total completion time, which brings a huge challenge to controlling entire production schedule. For aircraft manufacturing industries, the processes that have a significant impact on the completion time of the assembly line need to be identified and monitored in order to ensure the final assembly efficiency and reliability. However, due to the large number of process nodes and the complexity of resource constraints and relationship constraints, traditional node centrality algorithms cannot identify influential nodes accurately. Therefore, based on complex network theory, this paper studies the influential nodes identification problem in the context of assembly lines. Firstly, The Resource-Process Coupling Network for the assembly line is constructed based on the production background and network resource characteristics. Then, based on PageRank algorithm and the idea of resource iteration, Improved PageRank Algorithm (IPRA) is proposed to identify influential process nodes, which brings the resource and time parameters into the allocation rules. Finally, according to simulation results, a comparative analysis of IPRA and existing algorithms is conducted and concludes that our method can better identify influential nodes in actual complex production networks. Furthermore, this paper identifies influential process nodes of aircraft assembly line based on the case of the commercial aircraft manufactory.

A comparison of brief versus explicit descriptors for verbal rating scales: interrupted time series design

BackgroundVerbal rating scales (VRS) are widely used in patient-reported outcome (PRO) measures. At our institution, patients complete an online instrument using VRSs with a five-point brief response scale to assess symptoms as part of routine follow-up after ambulatory cancer surgery. We received feedback from patients that the brief VRS descriptors such as “mild” or “somewhat” were vague. We added explicit descriptors to our VRSs, for instance, “Mild: I can generally ignore my pain” for pain severity or “Somewhat: I can do some things okay, but most of my daily activities are harder because of fatigue” for fatigue interference. We then compared responses before and after this change was made.MethodsThe symptoms investigated were pain, fatigue and nausea. Our hypothesis was that the explicit descriptors would reduce overall variance. We therefore compared the coefficient of variation of scores and tested the association between symptoms scores and known predictors thereof. We also compared time to completion between questionnaires with and without the additional descriptors.ResultsA total of 17,500 patients undergoing 21,497 operations were assigned questionnaires in the period before the descriptors were added; allowing for a short transition period, 1,417 patients having 1436 operations were assigned questionnaires with the additional descriptors. Symptom scores were about 10% lower with the additional descriptors but the coefficient of variation was slightly higher. Moreover, the only statistically significant difference between groups for association with a known predictor favored the item without the additional language for nausea severity (p = 0.004). Total completion time was longer when the instrument included the additional descriptors, particularly the first and second time that the questionnaire was completed.ConclusionsAdding descriptors to a VRS of post-operative symptoms did not improve scale properties in patients undergoing ambulatory cancer surgery. We have removed the additional descriptors from our tool. We recommend further comparative psychometric research using data from PROs collected as part of routine clinical care.

Contributions of Cardiovascular Burden, Peripheral Inflammation, and Brain Integrity on Digital Clock Drawing Performance in Non-Demented Older Adults.

Greater cardiovascular burden and peripheral inflammation are associated with dysexecutive neuropsychological profiles and a higher likelihood of conversion to vascular dementia. The digital clock drawing test (dCDT) is useful in identifying neuropsychological dysfunction related to vascular etiology. However, the specific cognitive implications of the combination of cardiovascular risk, peripheral inflammation, and brain integrity remain unknown. We aimed to examine the role of cardiovascular burden, inflammation, and MRI-defined brain integrity on dCDT latency and graphomotor metrics in older adults. 184 non-demented older adults (age 69±6, 16±3 education years, 46% female, 94% white) completed dCDT, vascular assessment, blood draw, and brain MRI. dCDT variables of interest: total completion time (TCT), pre-first hand latency, digit misplacement, hour hand distance from center, and clock face area. Cardiovascular burden was calculated using the Framingham Stroke Risk Profile (FSRP-10). Peripheral inflammation markers included interleukin (IL)-6, IL-8, IL-10, tumor necrosis factor-alpha, and high sensitivity C-reactive protein. Brain integrity included bilateral entorhinal cortex volume, lateral ventricular volume, and whole brain leukoaraiosis. FSRP-10, peripheral inflammation, and brain integrity explained an additional 14.6% of the variance in command TCT, where FSRP-10 was the main predictor. FSRP-10, inflammatory markers, and brain integrity explained an additional 17.0% in command digit misplacement variance, with findings largely driven by FSRP-10. Subtle graphomotor behavior operationalized using dCDT metrics (i.e., TCT and digit misplacement) is partly explained by cardiovascular burden, peripheral inflammation, and brain integrity and may indicate vulnerability to a disease process.

How to Deploy Robotic Mobile Fulfillment Systems

Many warehouses involved in e-commerce order fulfillment use robotic mobile fulfillment systems. Because demand and variability can be high, scheduling orders, robots, and storage pods in interaction with manual workstations are critical to obtaining high performance. Simultaneously, the scheduling problem is extremely complicated because of interactions between decisions, many of which must be taken timely because of short planning horizons and a constantly changing environment. This paper models all such scheduling decisions in combination to minimize order fulfillment time. We propose two decision methods for the above scheduling problem. The models batch the orders using different batching methods and assign orders and batches to pods and workstations in sequence and robots to jobs. Order picking and stock replenishment operations are included in the models. We conduct numerical experiments based on a real-world case to validate the efficacy and efficiency of the model and algorithm. Instances with 14 workstations, 400 orders, 300 stock-keeping units (SKUs), 160 pods, and 160 robots can be solved to near optimality within four minutes. Our methods can be applied to large instances, for example, using a rolling horizon. Because our model can be solved relatively fast, it can be used to take managerial decisions and obtain executive insights. Our results show that making integrated decisions, even when done heuristically, is more beneficial than sequential, isolated optimization. We also find that positioning pick stations close together along one of the system’s long sides is efficient. The replenishment stations can be grouped along another side. Another finding is that SKU diversity per pod and SKU dispersion over pods have strong and positive impacts on the total completion time of handling order batches. Funding: This work was supported by National Natural Science Foundation of China [72025103, 72361137001, 71831008, 72071173] and the Research Grants Council of the Hong Kong Special Administrative Region, China [HKSAR RGC TRS T32-707/22-N]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0265 .

Solving Restricted Preemptive Scheduling on Parallel Machines with SAT and PMS

Restricted preemption plays a crucial role in reducing total completion time while controlling preemption overhead. A typical version of restricted preemptive models is k-restricted preemptive scheduling, where preemption is only allowed after a task has been continuously processed for at least k units of time. Though solving this problem of minimizing the makespan on parallel machines is NP-hard in general, it is of vital importance to obtain the optimal solution for small-sized problems, as well as for evaluation of heuristics. This paper proposes optimal strategies to the aforementioned problem. Motivated by the dramatic speed-up of Boolean Satisfiability (SAT) solvers, we make the first step towards a study of applying a SAT solver to the k-restricted scheduling problem. We set out to encode the scheduling problem into propositional Boolean logic and determine the optimal makespan by repeatedly calling an off-the-shelf SAT solver. Moreover, we move one step further by encoding the problem into Partial Maximum Satisfiability (PMS), which is an optimized version of SAT, so that the explicit successive calls of the solver can be eliminated. The optimal makespan of the problem and the performance of the proposed methods are studied experimentally. Furthermore, an existing heuristic algorithm is evaluated by the optimization methods.

ANALISIS PENJADWALAN PRODUKSI MENGGUNAKAN METODE NAWAZ ENSCORE HAM (NEH) DAN HEURISTIC POUR DALAM MEMINIMUMKAN TOTAL WAKTU PRODUKSI

Competition in the industrial world is getting tougher, requiring companies to have the right strategy and planning in every production activity. CV. Puspa is a company engaged in rice production. CV. Puspa uses the First Come First Serve (FCFS) method in scheduling every job that comes. This research aims to minimize the total production time using the Nawaz Enscore Ham (NEH) and Heuristic Pour methods at CV. Puspa. The data used is quantitative data, namely data on the processing time for rice production during January 2021. Based on the results of a comparison of the total completion time with the efficiency index parameter, the NEH method shows better performance compared to the FCFS and Heuristic Pour methods as on January 19, 2021. Meanwhile, based on the results of hypothesis testing with the F test, it shows that the FCFS, NEH and Heuristic methods Pour has the same efficiency. Thus, in general the results of scheduling for January 2021 using the NEH method do not have significant an influence on the production scheduling used by CV. Puspa.

Multiple operation theatre scheduling for mitigating the disturbance caused by emergency patients

Scheduling emergency patients is a problem that most hospitals struggle to solve without disturbing elective surgery patients' schedules. The present work undertakes this problem and considers scheduling elective patients surgeries in multiple operation theatres to mitigate the possible disturbance caused by emergency patient arrivals. The resultant problem has been termed as multiple operation theatre problems with total expected disturbance (MOTED). The number of elective surgeries and their corresponding surgery times are known and given in advance. However, emergency patient arrivals are stochastic in nature, which is tackled through scenario-generation techniques. The model assumes that emergency case scenarios can be predicted from historical data, and determines the sequence of elective patients in a multiple operation theatre such that the sum of the total expected disturbance (TED) caused by emergency patients and the total completion time of elective surgeries is minimized. The disturbance minimization increases the satisfaction level of patients, physicians and other medical staff, and indirectly reduces the overtime costs. The work provides an optimal algorithm for the MOTED problem with a single-operation theatre. Three heuristics and two metaheuristics have been proposed to solve the complete MOTED problem. The metaheuristic involves particle swarm optimization (PSO) and ant colony optimization (ACO). An extensive numerical experiment is performed using 48 randomly generated problem instances.

Joint Velocity and Spectrum Optimization in Urban Air Transportation System via Multi-Agent Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. One big challenge is that new aerial vehicles (AV) will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe air operations. In this paper, we consider an air transportation system where multiple AVs are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) must be achieved at all times to ensure flight safety. Our objective is to minimize the total mission completion time by jointly optimizing the velocities and spectrum allocation for all AVs. We formulate the optimization problem as a multi-stage Markov game where the optimization variables are coupled together. A multi-agent deep reinforcement learning VD3QN algorithm is proposed to enable cooperative learning among AVs. Additionally, we propose a heuristic greedy algorithm (HGA) and an orthogonal multiple access (OMA) solution as baseline solutions. Extensive simulation results show that our learning-based solution outperforms the baseline solutions under different network configurations.

Exact and Near Pareto Optimal Solutions for Total Completion Time and Total Late Work Problem

In this paper, the bi-criteria machine scheduling problems (BMSP) are solved, where the discussed problem is represented by the sum of completion and the sum of late work times simultaneously. In order to solve the suggested BMSP, some metaheurisitc methods are suggested which produce good results. The suggested local search methods are simulated annulling and bees algorithm. The results of the new metaheurisitc methods are compared with the complete enumeration method, which is considered an exact method, then compared results of the heuristics with each other to obtain the most efficient method.

Multi-Objective Production and Scheduling Optimization of Offshore Wind Turbine Steel Pipe Piles Based on Improved Hesitant Fuzzy Method

This paper investigates the multi-objective optimization problem in the production of offshore wind turbine steel pipe piles (OWTSPP). Considering the particularity of the steel pipe pile production process, it is divided into a flexible flow shop scheduling (FFSS) stage and an open parallel shop scheduling (OPSS) stage, respectively. Mathematical models are established for each stage, and the critical path and production time information are obtained using a disjunctive graph model. Due to the inability of existing empirical scheduling methods to balance production goals, an improved Pythagorean hesitant fuzzy method (IPHFM) is proposed to solve the multi-objective optimization problem in steel pipe pile production. Specifically, the maximum completion time, machine total load, and total completion time are taken as optimization objectives. The improved Lagrange multiplier method with penalty terms is used to handle the constraints and objective functions, and a Lagrange objective function is generated. Then, the Lagrange objective function matrix is obtained by normalization and same-scale processing, and an algorithm is designed to obtain the Pareto front solution set. Finally, this paper compares the optimal scheduling plans under the empirical scheduling method and the improved method. The results show that the improved method can significantly improve production efficiency in both small-scale and large-scale production, with improvements of 15.7% and 22.16%, respectively.

A good balance for an online scheduling problem considering both learning and deteriorating effects in the handicraft process

The paper considers an online scheduling problem with the effects of both learning and deterioration to minimize the total completion time. More specifically, we assume that the actual processing length of job Jj is p jr = p j r b + at , where pj is the initial processing time of Jj , t is the starting time of Jj , r is the seating arrangement position of Jj , b is the learning factor and a is the deterioration factor, respectively. For this problem, we show that the performance ratio of any deterministic online algorithm is not <2 and provide a best possible online algorithm DSBPT with a competitive ratio of 2. Furthermore, we also present a concise computational simulation study to verify the effectiveness and efficiency of the proposed algorithm DSBPT, as well as the management implications provided for decision-makers to production optimization.

Analisa Percepatan Proyek Pembuatan Mesin Grinding Cat Dengan Time Cost Trade Off Method (Studi Kasus : PT TDI Sidoarjo)

The case study in this research is a project to manufacture a paint paste grinding machine at PT Pabrik Cat Tunggal Djaja Indah. In its implementation, this project is considered to have a delay of 2 weeks, with a total project completion time of 120 days, so project acceleration is needed. The scheduling method is carried out using PDM (Precendence Diagram Method) with the help of Microsoft Project 2016 software to find the critical path, then after knowing the critical activities, crashing is carried out with the alternative of adding working hours or overtime and adding optimum workers. The Time Cost Trade Off method will analyze the time after crashing. The optimal duration to complete this project is 107 days with an acceleration of 13 working days with a percentage of 10.38% and an optimal total cost of Rp 262,947,693 with a cost efficiency of 3.56%.

Dependent task offloading with deadline-aware scheduling in mobile edge networks

In the field of the Internet of Things (IoT), Edge computing has emerged as a revolutionary paradigm that offers unprecedented benefits by serving the IoT at the network edge. One of the primary advantages of edge computing is that it reduces the job completion time by offloading tasks at the edge server from the IoT. Typically, a job is made up of dependent tasks in which the output of one task is required as the input to the other. This work proposes a directed cyclic graph model that represents the dependencies among these tasks focusing on jointly optimizing task dependencies with deadline constraints for tasks that are delay-sensitive. Thus, dependent tasks are scheduled while considering their deadlines using priority-aware scheduling. For tasks with no deadlines, the processing is done with First-Come-First-Serve (FCFS) scheduling. The tasks with a priority are offloaded to the suitable edge server for processing by using a priority queue to enhance the task satisfaction rate under deadline constraints. To model the suitable edge server decision, we use the Markov decision process (MDP) that minimizes the total completion time. Additionally, we model the mobility of users while offloading tasks to the edge servers. The throughput results demonstrate that the proposed strategy outperforms random offloading, the highest data rate offloading (HDR), the highest computing device (HCD), and delay-dependent priority-aware offloading (DPTO), by 66.67%, 43.75%, 27.78%, and 4.55%, respectively. Furthermore, the proposed strategy surpasses random, HDR, and HCD offloading in terms of task satisfaction rate by 20.48%, 16.28%, and 12.36%, respectively.

A gamified teaching–learning based optimization algorithm for a three-echelon supply chain scheduling problem in a two-stage assembly flow shop environment

The supply chain scheduling problem, which is an integrated production scheduling and distribution through the batch delivery system, has been studied by researchers for more than two decades. So far, all researches in this regard have studied the two-echelon supply chain network. The first novelty of this research is that, for the first time, the three-echelon supply chain scheduling problem, including supplier, manufacturer and customer, is investigated considering two separated batch delivery systems for transportation, simultaneously. To do so, a two-stage assembly flow shop environment is used. In the first stage, there are a number of decentralized suppliers who are responsible for manufacturing components. The components are dispatched to the second stage (assembly stage) with the first batch delivery system and after assembly, the orders are dispatched to the customer through secondary batch delivery system. The purpose of this problem is to minimize the cost of total completion time plus batch delivery costs. First, a mixed integer linear programming model (MILP) is presented that is able to solve small-size instances in a logical time. The second novelty of this research is that, to solve large-size instances, a novel gamified teaching–learning based optimization (GTLBO) algorithm is developed with applying a number of dominance rules and gamification tools. The computational results indicate the superiority of the new TLBO variant over its standard form and two well-known methods, i.e. GA and PSO, especially by applying the dominance rules.

Dynamic Optimization of Conflict-Free Routing of Automated Guided Vehicles for Just-in-Time Delivery

This paper studies the solution procedure to the problem of conflict-free route planning for automated guided vehicles for just-in-time delivery to minimize the earliness and tardiness of a specified delivery time while minimizing the total completion time. The characteristic of the problem is that a vehicle can have an idle time at any nodes considering dispatching of dynamic task arrivals. This problem is modeled by a time-space network where the pickup and delivery tasks are dynamically scheduled. A heuristic solution procedure is developed to derive a conflict-free routing problem such that earliness and tardiness penalties are minimized while the total completion time of each task is also minimized under dynamic task arrivals. The effectiveness of the proposed method is demonstrated by comparing the performance with Gurobi from computational results. The results show that the proposed method can obtain a better solution that is close to an optimal solution with significantly shorter computational time than that derived from Gurobi. The trade-off relationship between earliness/tardiness penalties and the total completion time is investigated in various situations. The effects of the interval of task assignment on the performance of earliness/tardiness and the total completion time are studied. The computational results show the conditions when the trade-off relationship can be obtained. <i>Note to Practitioners</i>&#x2014;The multiple AGVs are used in public transportation and social systems with rapid development of artificial intelligence and optimization methods. The benefits of AGV affect the reduction of congestion, traffic police, legal services, and economic savings in the manufacturing and logistics industries. In order to satisfy the customer&#x2019;s demands, it is requested to deliver the products just-in-time for the desired time without decreasing the total system performance. This paper presents the dispatching and conflict-free routing of AGVs for just-in-time transportation while minimizing the total completion time of the tasks. In many real factories with multiple production processes, the shortest time transportation may sometimes cause the congestions of AGVs waiting for handling or overstocked. Just-in-time transportation is required not only to reduce inventories but also to supply only when it is needed. Computational results show the situations when the just-in-time delivery and the minimization of the total completion time can be achieved.

Joint Communication and Sensing Design in Coal Mine Safety Monitoring: 3-D Phase Beamforming for RIS-Assisted Wireless Networks

This paper investigates the resource allocation of a reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) system in a coal mine scenario. In the JCAS system, an RIS is implemented at the corner of the zigzag tunnels to improve the complicated wireless environment, where ground obstacles frequently block direct links. In addition, a wireless backhaul base station with a limited energy budget is deployed in the depth of the mine to sense the target area and provide internet of things (IoT) services and communication services for users. Furthermore, a data center is placed on the ground to analyze the obtained data and route the communication data. Under this deployment, a joint optimization problem of RIS phase shift matrix, RIS element switches, and area sensing time is proposed. We aim to maximize the successful sensed bits under total completion time, and maximum transmit power constraints. In order to solve this problem, an iterative algorithm is proposed. The successive convex approximation (SCA) based algorithm is used for the RIS phase shift matrix optimization subproblem. For the sensing time optimization subproblem, the quadratic approximation method is proposed to optimize the number of area perceptions. The coordinate descent method is utilized to optimize the RIS element switches. Simulation results show that the energy efficiency is improved by up to 38%, and 7% increases the specific data size compared with the benchmark solutions.