Total Completion Time Research Articles

A multi-objective fuzzy programming model for port tugboat scheduling based on the Stackelberg game

To solve the optimization problem of tugboat scheduling for assisting ships in entering and exiting ports in uncertain environments, this study investigates the impact of the decisions of tugboat operators and port dispatchers on tugboat scheduling under the scenario of dynamic task arrival and fuzzy tugboat operation time. Considering the features of the shortest distance tugboat principle, the first available tugboat principle, and the principle of fairness in the task volume of each tugboat, the tugboat company aims to minimize the total daily fuel consumption of tugboat operations, maximize the total buffer time of dynamic tasks, and minimize the total completion time as the objective functions. Due to the limitations of port vessel berthing and departure, as well as the allocation standards for piloting or relocating tugboats, the present study proposes a Stackelberg game-based fuzzy model for port tugboat scheduling with the tugboat operator and port dispatcher acting as decision makers at the upper and lower levels, respectively. A seagull optimization algorithm based on priority encoding and genetic operators is designed as a solution approach. CPLEX, genetic algorithm, standard seagull optimization algorithm, and simulated annealing algorithm are used to compare and analyze the solution results for the 45 problem cases generated from the actual data obtained from the Guangzhou Port. The results verify the efficiency of the proposed seagull optimization algorithm based on priority encoding and genetic operators. Furthermore, additional experiments are conducted to evaluate the changes in fairness coefficient, uncertain parameter correlation coefficients, and objective function correlation coefficients to demonstrate the practicality of the fuzzy programming model. This analysis involves adjusting the confidence level incrementally from 0 to 100% with respect to the model’s uncertain parameters.

Trade-offs between optimal and robust scheduling in one-stage production

Given stochastic disturbances, such as variation in processing times, robust scheduling is recommended over optimal scheduling for production. Different from optimal scheduling that seeks an optimal solution to a key performance indicator (KPI), which relates to the average of a KPI, robust scheduling is to minimize the largest deviation from the optimum for the worst-case scenarios, which relates to the variance of a KPI. However, minimizing the variance does not necessarily optimize the average of a KPI. As one of the fundamental KPIs in production scheduling, total completion time (TCT) drives many other KPIs, such as average flow time, waiting time, due dates, and length of stay. Stochastic processing times and NP-hardness to minimize the variance of TCT, i.e., min(VTCT), are two challenges in production scheduling. To investigate the trade-offs between optimal and robust scheduling, we apply the differentiation method to analyze the first and second moments of TCT. In our approach, we use three statistical measures for processing times, which are the lower bound, the expected value, and the upper bound. We also use three terms for sequencing, which are x(1) the processing time of the initial job, x(i) the processing time of a job in the current position i of a sequence, and x′(i) the difference of processing times between two adjacent jobs. Applying the three measures for processing times to each of the three independent terms for sequencing, we generate 27=3·3·3 sequences to analyze the dynamics of VTCT. Through numerical analysis in our case studies, we show that our sequencing scheme can generate optimal solutions to min(TCT), and solid variation ranges of VTCT. Consequently, we can not only balance the trade-offs between min(TCT) and min(VTCT), but also analyze the trade-offs between optimal scheduling focusing on the first-moment of a KPI and robust scheduling focusing on the second moment. Moreover, our analysis approach using the differentiation method is unique for production scheduling, which enables us to develop analytical methods and heuristics for balancing trade-offs between optimal and robust scheduling.

Two-agent proportionate flowshop scheduling with deadlines: polynomial-time optimization algorithms

AbstractVolatility in the supply chain of critical products, notably the vaccine shortage during the pandemic, influences livelihoods and may lead to significant delays and long waiting times. Considering the capital- and time-intensive nature of capacity expansion plans, strategic operational production decisions are required best to address the supply-demand mismatches given the limited production resources. This research article investigates the production scenarios where the demand of one agent must be completed within a specified due date, hereinafter referred to as the deadline, while minimizing the maximum or total completion time of another agent's demand. For this purpose, the Two-Agent Proportionate Flowshop Scheduling Problem with deadlines is introduced. Two polynomial-time optimization algorithms are developed to solve these optimization problems. This study will serve as a basis for further developing this practical yet understudied scheduling problem.

Multi-objective predictive maintenance scheduling models integrating remaining useful life prediction and maintenance decisions

This paper presents two novel data-driven multi-objective predictive maintenance scheduling models that integrate remaining useful life (RUL) prediction into maintenance planning. First, we propose a deep learning ensemble model that includes a convolutional neural network and bidirectional long short-term memory network with a temporal self-attentional mechanism and Bayesian optimization method to predict system RUL effectively. Then, two novel multi-objective mixed-integer linear programming (MILP) models are developed based on the system-predicted RUL to deal with the system predictive maintenance problem, which aims to minimize the total maintenance completion time and maintenance-related costs simultaneously. To solve this problem, we design an iteration ϵ-constraint method to achieve Pareto solutions. Meanwhile, a fuzzy logic method is proposed to recommend a preferred Pareto solution for decision-makers. Finally, the aircraft engine C-MAPSS dataset from NASA validates that the effectiveness of the proposed data-driven multi-objective predictive maintenance scheduling models are effective. For the predictive maintenance of 20 aircraft engines, both the maintenance completion time and maintenance cost are reduced by more than 40%.

An improved intelligent optimization algorithm for small-batch order production scheduling

Although the critical path method (CPM) is effective for the integrated scheduling of small-batch orders, its overemphasis on vertical process relationships and neglect of horizontal parallel relationships have imposed limitations on scheduling, often leading to suboptimal outcomes in terms of the total product completion time. This study introduces an innovative algorithm designed to overcome these limitations and further optimize the total processing time of products. We propose a strategy of "exchanging adjacent processes on the same device", which operates based on the scheduling results of the CPM. By swapping adjacent and interchangeable processes within the constraints of the problem, this algorithm generates multiple new scheduling schemes, effectively expanding the solution space. This expansion enables the discovery of optimized solutions that leverage "horizontal parallel relationships", which is crucial for reducing the "total processing time of products". Finally, the effectiveness of the proposed algorithm is verified through experiments.

Hybrid loading situation vehicle routing problem in the context of agricultural harvesting: A reconstructed MOEA/D with parallel populations

With the increasing level of agricultural automation, the combination of agriculture and intelligent vehicle technology is propelling the development of smart agriculture. Although this technology has already been applied widely for various agricultural production tasks, inefficient vehicle scheduling still hasn't been resolved satisfactorily. Oriented towards agricultural harvesting scenarios, a hybrid loading situation vehicle routing problem (HLSVRP) model is proposed to minimize total energy consumption and maximum completion time. A reconstructed multi-objective evolutionary algorithm based on decomposition (R-MOEA/D) is developed to solve the problem. Eight solution representations tailored specifically to the problem are introduced by R-MOEA/D, allowing an extensive exploration of the solution space. A modified Clarke & Wright (MCW) heuristic is designed to generate a high-quality initial population. A novel problem-specific parallel population updating mechanism based on the four crossover and two mutation combinations is also provided to improve the exploration ability. A collaborative search strategy is employed to facilitate cooperation among parallel populations. Finally, a series of comparative experiments conducted on various task scales and vehicle scales verify the effectiveness of the proposed algorithmic components and the exceptional performance for solving HLSVRP.

Simulation Model of a Steelmaking-Continuous Casting Process Based on Dynamic-Operation Rules.

The steelmaking-continuous casting process (SCCP) is a complex manufacturing process which exhibits the distinct features of process manufacturing. The SCCP involves a variety of production elements, such as multiple process routes, a wide array of smelting and auxiliary devices, and a variety of raw and auxiliary materials. The production-simulation of SCCP holds a natural advantage in being able to accurately depict the intricate production behavior involved, and this serves as a crucial tool for optimizing the production operation of the SCCP. This paper thoroughly considers the various production elements involved in the SCCP, such as the fluctuation of the converter smelting cycle, fluctuation of heat weight, and ladle operation. Based on the Plant Simulation software platform, a dynamic simulation model of the SCCP is established and detailed descriptions are provided regarding the design of an SCCP using dynamic-operation rules. Additionally, a dynamic operational control program for the SCCP is developed using the SimTalk language, one which ensures the continuous operation of the caster in the SCCP, using the discrete simulation platform. The effectiveness of the proposed dynamic simulation model is verified by the total completion time of the production plan, the transfer time of the heat among the different processes, and the frequency of ladle turnover. The simulation's results indicate that the dynamic simulation model has a satisfactory effect in simulating the actual production process. On this basis, the application effects of different schedules are compared and analyzed. Compared with a heuristic schedule, the optimized schedule based on the "furnace-machine coordinating" mode reduces the weighted value of total completion time by 8.7 min, reduces the weighted value of transfer waiting time by 45.5 min, and the number of rescheduling times is also reduced, demonstrating a better application effect and verifying the optimizing effect of the "furnace-machine coordinating" mode on the schedule.

Minimizing total completion time and makespan for a multi-scenario bi-criteria parallel machine scheduling problem

Multi-criteria scheduling problems under uncertainty remain a relatively unexplored topic in theoretical computer science despite substantial practical interests. This work studies a bi-objective identical parallel machine scheduling problem under uncertainty, in which the first objective is to minimize the total completion time, and the second is to minimize the makespan. Especially a job’s processing time is assumed to be represented by a polynomial function with respect to scenario u∈U, where U⊂R+ is an interval containing an infinite number of scenarios.In this work, we are looking for a compact and complete description of the set of possibly optimal solutions, along with their objective function values, over the set of scenarios or an approximation with a performance guarantee. First, to better understand the characteristics of the studied problem, we consider two single-objective problems: parallel machine scheduling problem under uncertainty with the total completion time and makespan criterion, respectively. For the problem with the total completion time criterion, we demonstrate that the set of possibly optimal schedules can be found in polynomial time. In contrast, for the problem with the makespan criterion, we provide a (1+ϵ)-approximation algorithm. For the bi-objective problem, we provide a 2-approximation algorithm for any number of parallel machines and a (1+ϵ)-approximation algorithm where a fixed number of parallel machines is considered.

Parallel machine scheduling with a total energy consumption limitation for minimizing total completion time

This article investigates an energy-efficient, identical parallel machine scheduling problem. The objective is to minimize the total completion time while total energy costs do not exceed a given limitation. First, a reasonable range of total energy cost limits and the NP-hardness of this problem are analysed. Then, a mixed-integer programming model is presented. Afterwards, an improved simulated annealing (ISA) algorithm is devised. The ISA algorithm incorporates four effective neighbourhood operators, a method for adaptive selection of operators, and an initial feasible solution generation method. Comparison with genetic algorithms (GAs) and simplified swarm optimization (SSO) algorithms verifies the effectiveness and efficiency of the ISA algorithm. Results demonstrate that the ISA algorithm can provide near-optimal or better solutions than CPLEX ® in small- and medium-scale instances. Experimental results on large-scale instances show that ISA significantly outperforms GAs and SSOs. ISA, in particular, can find the best values for all instances of 500 jobs except one.

Scheduling two interfering job sets on identical parallel machines with makespan and total completion time minimization

Scheduling two interfering job sets on identical parallel machines with makespan and total completion time minimization

Earned Value Analysis Terhadap Waktu dan Biaya Pada Proyek Pembangunan Gedung Pasar Padang Panjang

Along with the population and economic growth of West Sumatra, especially in Padang Panjang City, West Padang Panjang District, the need for public facilities is urgently needed, one of which is the Padang Panjang market. In a project, planning and controlling costs and time is part of overall project management, especially at the implementation stage of the case study reviewed by researchers, namely the Padang Panjang market building construction project. In this research regarding time and cost control, the earned value method is one method that can monitor project performance in terms of time and cost. Which aims to determine project performance in terms of time and costs each week and predict the time and costs to complete the remaining work. So that we can get corrective actions to be taken next to progress the project. In this research, to obtain a basic basis for writing, secondary data collection is used as supporting data for the analysis of the results themselves. The analysis and discussion carried out were calculating the BCWS value, BCWP value and ACWP value in order to obtain the time variance value (SV), cost variance value (CV), calculating the productivity index, calculating project performance and calculating the estimated analysis of total project costs and completion time. The results of the research carried out are, the overall BCWS value of the Padang Panjang market project is IDR 96,213,315,046.3, for the BCWP value at the end of the 22nd week of review IDR 12,688,575,967, the ACWP value at the end of the 22nd week of review is IDR. 12,389,476,444.2 . As for the analysis results obtained from several existing indicators, the SPI value was 1.384 > 1, while the CPI was 1.003 > 1, which means that the project was accelerated in terms of schedule but incurred actual costs that were smaller than the work that had been carried out. The ETC value is IDR 84,134,151,328.9 and the EAC value is IDR 96,523,627,773.1, while the ECD value is 373 days for the project completion time.

Analysis of The Earned Value Analysis Method In Construction Projects of Pulantan River Embankment

In the process of a development project, numerous things need to be considered. The time control method used in this research was the Earned Value Analysis method. This research was conducted at the Pulantan River Embankment Improvement Project, Banjarbaru City. The data used in this research include the Time Schedule, Cost Budget Plan, and Weekly Progress Report. This data generates Planned Value and Earned Value. With the help of the Microsoft Excel program, the SV, SPI values, as well as the predicted final project completion time (TEAC and TE) were obtained. The analysis of the timing of the Pulantan River Embankment Improvement project in Banjarbaru City showed that in terms of implementation time, there will be a delay in the work plan schedule with an SPI value below (Negative) <1.00. As of the 25th week, the SPI value is 0.93. The estimated remaining work implementation time from week 25 is 2 weeks of implementation time. The estimated total time for completion of the Pulantan River Embankment Improvement Project in Banjarbaru City is 27 weeks which is 2 weeks longer than the contracted plan schedule of 25 weeks.

Optimal algorithms for preemptive two-agent scheduling on uniform parallel machines

This paper studies two scheduling problems involving two agents A and B, where each agent has an independent set of jobs and all jobs are to be processed preemptively on uniform parallel machines. The aims of the two problems are to minimize the objective function value of agent A with the makespan of agent B not exceeding a threshold T. In the first problem, there are m machines and the objective function of agent A is the makespan. In the second problem, there are two machines and the objective function of agent A is the total completion time. For each of the two problems, we propose an optimal algorithm and obtain some extended results.

The Bead Maze Hand Function Test for Children.

There is a need for a pediatric hand function test that can be used to objectively assess movement quality. We have developed a toy-based test, the Bead Maze Hand Function (BMHF) test, to quantify how well a child performs an activity. This is achieved by assessing the control of forces applied while drawing a bead over wires of different complexity. To study the psychometric properties of the BMHF test and understand the influence of age and task complexity on test measures. A cross-sectional, observational study performed in a single visit. Clinical research laboratory. Twenty-three participants (ages 4-15 yr) were recruited locally. They were typically developing children with no illness or conditions that affected their movement. Interventions/Assessments: Participants performed the BMHF test and the Box and Block test with both hands. Total force and completion time were examined according to age and task complexity using a linear mixed-effects model. We calculated intraclass correlation coefficients to measure interrater reliability of the method and estimated concurrent validity using the Box and Block test. Total force and completion time decreased with age and depended on task complexity. The total force was more sensitive to task complexity. The Box and Block score was associated with BMHF completion time but not with total force. We found excellent interrater reliability. A familiar toy equipped with hidden sensors provides a sensitive tool to assess a child's typical hand function. Plain-Language Summary: We developed the Bead Maze Hand Function (BMHF) test to determine how well a child performs an activity with their hands. The BMHF test is a toy equipped with hidden sensors. Twenty-three typically developing children with no illnesses or conditions that affected their hand movement participated in the study. We asked the children to perform the BMHF test with both hands. Our study found that occupational therapists can reliably use the BMHF test to assess a child's hand function.

S-Graph-Based Reactive Scheduling with Unexpected Arrivals of New Orders

Manufacturing processes are often disrupted by unexpected events, such as machine breakdowns, raw material shortages, and the arrival of new orders. Effectively managing these uncertainties is crucial for maintaining the feasibility and optimality of the production system. The efficiency of a manufacturing system is heavily dependent on the optimality of its scheduling plan. In this study, we present a reactive scheduling approach based on the S-graph framework. The proposed method is specifically designed to handle the arrival of new jobs and generate schedules with the shortest makespan, i.e., the minimum total completion time. Whenever a new order is received, the method dynamically adjusts the production plan through rescheduling. Three distinct scheduling policies are identified that determine which tasks require scheduling or rescheduling and which tasks should remain unchanged in their schedules. To evaluate the effectiveness of the algorithm, we solve several examples from the literature and analyze the results. The findings demonstrate the efficiency and efficacy of the proposed approach. The ability to accommodate new job arrivals and generate schedules with a minimized makespan highlights the practical relevance and benefits of the S-graph-based reactive scheduling method.

Fundamental Technical Skills of Endovascular Surgery: A Preliminary Study on Its Impact on Skills and Stress during Procedures

Our objective is twofold: determining if simulation allows residents to reach proficient surgeons' performance concerning fundamental technical skills of endovascular surgery (FEVS) while investigating effects of the program on surgeons' stress. Using a FEVS training simulator, 8 endovascular FEVS were performed by vascular surgery residents (simulator-naive or simulator-experienced residents [SER]) and seniors. Total time needed to complete the 8 tasks, called total completion time (TCT), was the main evaluation criterion. Analgesia Nociception Index (ANI) was monitored during simulation. Likert scale questionnaire was filled out after each simulation. For each task, TCT was significantly lower for SER and seniors than simulator-naive residents (P=0.0163). After only 5 simulations, SER were able to reach and even exceed the seniors' level in terms of TCT, with a median time of 10.8min for SER and 11.9min for seniors, and wire's movements with a median distance during cannulation of 4.44m for SER and 4.17m for seniors. Seniors remained better than SER in terms of precise wire manipulation (wire movement after cannulation), 4.17m against 4.44m (3.72-5.96), respectively. Based on the Likert scale stress analysis, seniors felt less stressed than both residents' groups (P=0.0618). Seniors' initial ANI and mean ANI over the session were significantly lower than those of the residents, P=0.0358 and P=0.0250, respectively. We showed that 5 simulation sessions allowed residents to reach experienced surgeons' capacities on FEVS concerning TCT. Subjectively, seniors felt less stressed than residents, contrary to the results of our objective measures of stress.

Multi-robot multi-station welding flow shop closed-loop rescheduling with deep reinforcement learning and improved artificial bee colony algorithm

Multi-robot multi-station manufacturing system has gained significant popularity in welding workshop for processing large jobs. The implementation of cooperative welding robots effectively enhances the efficiency and flexibility of the system. However, robot failures also bring challenges for rescheduling to determine the rescheduling timing and re-assign appropriate robots for the affected processes so as to minimize the losses in performance and the maximum completion time. Distinct from open-loop rescheduling approach, this paper proposes a closed-loop rescheduling method via deep reinforcement learning and improved artificial bee colony algorithm. This method addresses two crucial questions: when to trigger a rescheduling, and how to provide a rescheduling scheme. For the first issue, current methods make decisions according to indicators derived from expert experience, failing to adapt to the dynamic environment. Therefore, we propose a deep reinforcement learning method to learn the adaptive trigger policy for the trigger agent under real-time running state. For the second issue, we present an improved artificial bee colony algorithm to re-assign robots without changing processing sequence and feed the performance back to trigger agent. Specially, a variable local search method is incorporated to avoid falling into local optimum. Finally, the proposed method is applied to solve the instances with different scales and a practical case. The experimental results of both demonstrate that the proposed closed-loop rescheduling method can greatly reduce the deviation of the total completion time and the maximum completion time and have superior performance over other algorithms.

Solving tri-criteria: total completion time, total late work, and maximum earliness by using exact, and heuristic methods on single machine scheduling problem

The presented study investigated the scheduling regarding jobs on a single machine. Each job will be processed with no interruptions and becomes available for the processing at time 0. The aim is finding a processing order with regard to jobs, minimizing total completion time , total late work , and maximal tardiness which is an NP-hard problem. In the theoretical part of the present work, the mathematical formula for the examined problem will be presented, and a sub-problem of the original problem of minimizing the multi-objective functions is introduced. Also, then the importance regarding the dominance rule (DR) that could be applied to the problem to improve good solutions will be shown. While in the practical part, two exact methods are important; a Branch and Bound algorithm (BAB) and a complete enumeration (CEM) method are applied to solve the three proposed MSP criteria by finding a set of efficient solutions. The experimental results showed that CEM can solve problems for up to jobs. Two approaches of the BAB method were applied: the first approach was BAB without dominance rule (DR), and the BAB method used dominance rules to reduce the number of sequences that need to be considered. Also, this method can solve problems for up to , and the second approach BAB with dominance rule (DR), can solve problems for up to jobs in a reasonable time to find efficient solutions to this problem. In addition, to find good approximate solutions, two heuristic methods for solving the problem are proposed, the first heuristic method can solve up to jobs, while the second heuristic method can solve up to jobs. Practical experiments prove the good performance regarding the two suggested approaches for the original problem. While for a sub-problem the experimental results showed that CEM can solve problems for up to jobs, the BAB without dominance rule (DR) can solve problems for up to , and the second approach BAB with dominance rule (DR), can solve problems for up to jobs in a reasonable time to find efficient solutions to this problem. Finally, the heuristic method can solve up to jobs. Arithmetic results are calculated by coding (programming) algorithms using (MATLAB 2019a)

A simulated annealing metaheuristic approach to hybrid flow shop scheduling problem

This study investigates a complex hybrid flow shop scheduling problem prevalent in the industrial sector, characterized by dedicated machines, availability dates, and delivery times. The primary objective is to minimize the total completion time (makespan) in a two-stage workshop setting. We conducted a comprehensive literature review, revealing a scarcity of research on this specific configuration, and employed the Simulated Annealing metaheuristic as our main resolution method. Special emphasis was placed on the meticulous parameterization and configuration of this metaheuristic, crucial for navigating the complexity of the problem.Our findings demonstrate the remarkable effectiveness of the Simulated Annealing method, particularly in achieving low deviation from the lower bound in larger problem sizes and specific instance classes. This consistency highlights the method’s robustness and suitability for complex scheduling scenarios. The study also reveals varying degrees of problem solvability across different instance classes, with computation times generally reasonable except in more challenging scenarios.

New bounds for single-machine time-dependent scheduling with uniform deterioration

We consider the single-machine time-dependent scheduling problem with linearly deteriorating jobs arriving over time. Each job i is associated with a release time ri and a processing time pi(si)=αi+βisi, where αi,βi>0 are parameters and si is the job's start time. In this setting, the approximability of both single-machine minimum makespan and total completion time problems remains open. We develop new bounds and approximation results for the special case of the problems with uniform deterioration, i.e. βi=β, for each i. The main contribution is a O(1+1/β)-approximation algorithm for the makespan problem and a O(1+1/β2) approximation algorithm for the total completion time problem. Further, we propose greedy constant-factor approximation algorithms for instances with β=O(1/n) and β=Ω(n), where n is the number of jobs. Our analysis is based on an approach for comparing computed and optimal schedules via bounding pseudomatchings.