Total Setup Time Research Articles

An effective two-stage heuristic for scheduling the distributed assembly flowshops with sequence dependent setup times

This paper studies the Distributed Assembly Permutation Flowshop Scheduling Problem with Sequence Dependent Setup Times (DAPFSP-SDST). The optimization objective is minimization of maximal completion time (makespan), and it is shown that minimizing the sum of Total Setup Times for Assembling Products (TSTAP) and Total Idle Times on Assembly Machine (TITAM) is equivalent to minimizing the makespan. Additionally, minimization of TSTAP and TITAM can be transformed into sequencing problems of products and jobs within critical products, respectively. We also find out that a product sequence essentially explores an area in the solution space, with solutions in the area having the same TSTAP but different TITAMs determined by Critical-Jobs-Sequences (CJSs). Based on the new findings, an effective Two-Stage Heuristic Algorithm (TSHA) is proposed to first obtain promising product sequences and then exploit the corresponding areas for the DAPFSP-SDST. At the first stage of TSHA, high-quality initial product sequences are obtained through a constructive method and two Neighborhood Descent for Product Sequence (NDPS) algorithms are presented for more potential searching areas. At the second stage, a Neighborhood Descent for CJS (NDCJS) is designed to find CJSs with TITAM as small as possible. Evaluations on a benchmark instance set show that TSHA achieves better performance compared to the existing meta-heuristic and hyper-heuristic algorithms on the DAPFSP-SDST. Another impressive advantage of the TSHA is its low computational cost, as it is a heuristic algorithm with some neighborhood search operators.

Skin marker combined with surface-guided auto-positioning for breast DIBH radiotherapy daily initial patient setup: An optimal schedule for both accuracy and efficiency.

By employing three surface-guided radiotherapy (SGRT)-assisted positioning methods, we conducted a prospective study of patients undergoing SGRT-based deep inspiration breath-hold (DIBH) radiotherapy using a Sentine/Catalys system. The aim of this study was to optimize the initial positioning workflow of SGRT-DIBH radiotherapy for breast cancer. A total of 124 patients were divided into three groups to conduct a prospective comparative study of the setup accuracy and efficiency for the daily initial setup of SGRT-DIBH breast radiotherapy. Group A was subjected to skin marker plus SGRT verification, Group B underwent SGRT optical feedback plus auto-positioning, and Group C was subjected to skin marker plus SGRT auto-positioning. We evaluated setup accuracy and efficiency using cone-beam computed tomography (CBCT) verification data and the total setup time. In groups A, B, and C, the mean and standard deviation of the translational setup-error vectors were small, with the highest values of the three directions observed in group A (2.4±1.6, 2.9±1.8, and 2.8±2.1mm). The rotational vectors in group B (1.8±0.7°, 2.1±0.8°, and 1.8±0.7°) were significantly larger than those in groups A and C, and the Group C setup required the shortest amount of time, at 1.5±0.3min, while that of Group B took the longest time, at 2.6±0.9min. SGRT one-key calibration was found to be more suitable when followed by skin marker/tattoo and in-room laser positioning, establishing it as an optimal daily initial set-up protocol for breast DIBH radiotherapy. This modality also proved to be suitable for free-breathing breast cancer radiotherapy, and its widespread clinical use is recommended.

Self-growth learning-based machine scheduler to minimize setup time and tardiness in OLED display semiconductor manufacturing

This paper focuses on a scheduling problem of the evaporation and encapsulation (EVEN) line in organic light-emitting diode (OLED) display manufacturing towards minimizing total setup time and tardiness of allocated jobs. This scheduling problem is an unrelated dedicated parallel machine scheduling (UDPMS) problem with practical complex constraints including machine availability, family setup, preventive maintenance (PM), and job splitting. There are three major points to this problem. First, the processing of a job on a machine should be completed before its due date. Second, setups should be reduced to minimize labor costs. Finally, a job is able to be split into one or more sub-jobs for processing on machines independently. To obtain a schedule to minimize total setup time and tardiness of allocated jobs, we suggest two novel machine schedulers such as deep learning-based and self-growth learning-based schedulers, called DLS and SGLS, respectively. Although the structure of the proposed schedulers is identical, especially, SGLS incrementally learns to explore more effective allocation patterns by preserving the obtained knowledge from the trained DLS based on self-growth learning. The comprehensive experiments show that SGLS produces satisfactory schedules in terms of minimizing total setup time and tardiness. In particular, when the number of jobs and the ratio of job sizes are larger, SGLS achieves a schedule with both less setup time and tardiness compared to others.

Evaluation of initial patient setup methods for breast cancer between surface-guided radiation therapy and laser alignment based on skin marking in the Halcyon system

BackgroundThis study was conducted to evaluate the efficiency and accuracy of the daily patient setup for breast cancer patients by applying surface-guided radiation therapy (SGRT) using the Halcyon system instead of conventional laser alignment based on the skin marking method.Methods and materialsWe retrospectively investigated 228 treatment fractions using two different initial patient setup methods. The accuracy of the residual rotational error of the SGRT system was evaluated by using an in-house breast phantom. The residual translational error was analyzed using the couch position difference in the vertical, longitudinal, and lateral directions between the reference computed tomography and daily kilo-voltage cone beam computed tomography acquired from the record and verification system. The residual rotational error (pitch, yaw, and roll) was also calculated using an auto rigid registration between the two images based on Velocity. The total setup time, which combined the initial setup time and imaging time, was analyzed to evaluate the efficiency of the daily patient setup for SGRT.ResultsThe average residual rotational errors using the in-house fabricated breast phantom for pitch, roll, and yaw were 0.14°, 0.13°, and 0.29°, respectively. The average differences in the couch positions for laser alignment based on the skin marking method were 2.7 ± 1.6 mm, 2.0 ± 1.2 mm, and 2.1 ± 1.0 mm for the vertical, longitudinal, and lateral directions, respectively. For SGRT, the average differences in the couch positions were 1.9 ± 1.2 mm, 2.9 ± 2.1 mm, and 1.9 ± 0.7 mm for the vertical, longitudinal, and lateral directions, respectively. The rotational errors for pitch, yaw, and roll without the surface-guided radiation therapy approach were 0.32 ± 0.30°, 0.51 ± 0.24°, and 0.29 ± 0.22°, respectively. For SGRT, the rotational errors were 0.30 ± 0.22°, 0.51 ± 0.26°, and 0.19 ± 0.13°, respectively. The average total setup times considering both the initial setup time and imaging time were 314 s and 331 s, respectively, with and without SGRT.ConclusionWe demonstrated that using SGRT improves the accuracy and efficiency of initial patient setups in breast cancer patients using the Halcyon system, which has limitations in correcting the rotational offset.

Multiobjective Sequence-Dependent Job Sequencing and Tool Switching Problem

This article considers the extension of the job sequencing and tool switching problem with sequence-dependent setup times (SDSSP) and with the requirement to comply with the due date of the processed jobs. This extension is motivated by the practical application in manufacturing environment where the products manufactured may be subjected to a specific due date. This study develops a multiobjective SDSSP model (MO-SDSSP) to simultaneously minimize the total setup time of tool switches and the tardiness of the machining process. Then, a two-stage Multiobjective Adaptive Large Neighborhood Search (MOALNS) and Simulated Annealing (SA) is presented as a heuristic algorithm for the MO-SDSSP, in which the MOALNS is proposed to solve the job sequencing subproblem while the SA is used to solve the tool switching subproblem. Subsequently, the performance of MOALNS-SA is compared to several popular multiobjective optimization algorithms. Comparison on four performance criteria has shown the applicability of the MOALNS. Finally, managerial analysis is performed to analyze the relationship between the total tool setup time and the compliance of due dates.

Comparison between Robotic Single-Site and Laparoendoscopic Single-Site Hysterectomy: Multicentric Analysis of Surgical Outcomes.

We consecutively enrolled 278 women between 2012 and 2019 in this multicentre trial. The patients underwent robotic single-site hysterectomy (RSSH) or laparoendoscopic single-site hysterectomy (LESSH) procedures with or without salpingo-oophorectomy for benign indications. Surgical parameters and surgical outcomes were analysed. There was a statistical difference between the two surgical techniques for total operative time (p = 0.001), set-up time (p = 0.013), and anaesthesia time (p = 0.001). Significant differences in intraoperative blood loss were observed (p = 0.001), but no differences were shown for blood transfusion or intraoperative or postoperative complications in the two groups. LESSH outperformed RSSH in terms of surgical performance and clinical outcomes, with no differences in adverse events.

A GRASP for a real-world scheduling problem with unrelated parallel print machines and sequence-dependent setup times

We consider a real-world scheduling problem arising in the colour printing industry. The problem consists in assigning print jobs to a heterogeneous set of flexographic printer machines and finding a processing sequence for the jobs assigned to each machine. The machines are characterised by a limited sequence of colour groups and can equip additional components (e.g. embossing rollers and perforating rolls) to process jobs that require specific treatments. The process to equip a machine with an additional component or to clean a colour group takes a long time, with the effect of significantly raising the setup times. The aim is to minimise a weighted sum of total weighted tardiness and total setup time. The problem derives from the activities of an Italian food packaging company. To solve it, we developed a greedy randomised adaptive search procedure equipped with several local search procedures. The excellent performance of the algorithm is proved by extensive computational experiments on real-world instances, for which it produced good-quality solutions within a limited computing time. The algorithm is currently in use at the company to support their weekly scheduling decisions.

Perencanaan Produksi Batch pada Industri Makanan untuk Meningkatkan Produktivitas

The batch production system is one of the strategies used by the industry to meet increasingly varied consumer demands. More product variations can result in an increase in the total setup time as a result of the many production processes switching from different families. This study aims to plan batch production at Bakpia Pathok Vista (UBV) SMEs to increase productivity by reducing setup and idle time. Production planning includes job sequencing and operator allocation. A heuristic algorithm was proposed to solve the problem. The results showed that production planning based on the proposed algorithm could reduce the total setup time and idle time by 15.63% and 92.67%, respectively, so that productivity increases by 31.67% from the previous condition. This research can be further developed by considering operator overtime and minimizing makespan.

A study of skin marker alignment using different diamond-shaped light fields for prone breast external-beam radiation therapy.

For breast cancer patients treated in the prone position with tangential fields, a diamond-shaped light field (DSLF) can be used to align with corresponding skin markers for image-guided radiation therapy (IGRT). This study evaluates and compares the benefits of different DSLF setups. Seventy-one patients who underwent daily tangential kilovoltage (kV) IGRT were categorized retrospectively into four groups: (1) DSLF field size (FS)=10×10cm2 , gantry angle=90° (right breast)/270° (left breast), with the same isocenter as treatment tangential beams; (2) same as group 1, except DSLF FS=4×4cm2 ; (3) DSLF FS=4×4-6×8cm2 , gantry angle=tangential treatment beam, off-isocenter so that the DSLF was at the approximate breast center; and (4) No-DSLF. We compared their total setup time (including any DSLF/marker-based alignment and IGRT) and relative kV-based couch shift corrections. For groups 1-3, DSLF-only dose distributions (excluding kV-based correction) were simulated by reversely shifting the couch positions from the computed tomography plans, which were assumed equivalent to the delivered dose when both DSLF and IGRT were used. For patient groups 1-4, the average daily setup time was 2.6, 2.5, 5.0, and 8.3min, respectively. Their mean and standard deviations of daily kV-based couch shifts were 0.64±0.4, 0.68±0.3, 0.8±0.6, and 1.0±0.6cm. The average target dose changes after excluding kV-IGRT for groups 1-3 were-0.2%, -0.1%, and +0.4%, respectively, whereas DSLF-1 was most efficient in sparing heart and chest wall, DSLF-2 had lowest lung Dmax ; and DSLF-3 maintained the highest target coverage at the cost of highest OAR dose. In general, the use of DSLF greatly reduces patient setup time and may result in smaller IGRT corrections. If IGRT is limited, different DSLF setups yield different target coverage and OAR dose sparing. Our findings will help DSLF setup optimization in the prone breast treatment setting.

Unrelated parallel machine scheduling problem with special controllable processing times and setups

The controllable processing times (CPTs) have many practical applications, enabling the length of the job processing to vary within an interval flexibly with additional costs. In this paper, we study an unrelated parallel machine scheduling problem with machine- and sequence-dependent setup times and a special case of CPTs without extra costs. The objective is to maximize the difference between the sum of realized state of processing of all jobs and makespan. To solve the problem, a mixed integer programming (MIP) model is formulated first and then a logic-based Benders decomposition (LBBD) method is developed, in which the master problem is for job assignments and the subproblem is furthered decomposed into a sequencing problem for minimum total setup times on each machine and a processing time determination problem. Several LBBD-based heuristics are also employed by imposing different optimality gaps for the master problem. The performance of the MIP formulation, the exact LBBD method and the LBBD-based heuristics is compared through extensive computational experiments. The results demonstrate that the exact LBBD method with the preprocessing procedure which generates initial feasible solutions and cuts is effective and the proposed LBBD-based heuristics reduce the computation time significantly at the expense of slightly solution quality reduction.

A mixed-integer programming model for an integrated production planning problem with preventive maintenance in the pulp and paper industry

Production planning and scheduling in the pulp and paper industry can be very challenging. In most cases, practitioners address the production planning process manually, which is time-consuming and sub-optimal. This study deals with production planning encountered in a pulp mill company involving different wood species, parallel heterogeneous lines, inventory limits, sequence-independent setup times and preventive maintenance. To tackle the problem, an efficient mixed-integer formulation is proposed that optimizes when, where and how much to produce of different wood species and schedules preventive maintenance to minimize the total setup times. Several computational experiments are conducted to solve a case study in a pulp mill company in Chile. The results show the capability of the model to support the decision-making process in the pulp and paper industry, providing an efficient tool for practitioners to solve the problem in a reasonable amount of time.

Automated CT reformations reduce time and variability in trauma panscan exam completion

To test the hypothesis that an automated post-processing workflow reduces trauma panscan exam completion times and variability. One-hundred-fifty consecutive trauma panscans performed between June 2018 and December 2019 were included, half before and half after implementation of an automated software-driven post-processing workflow. Acquisition and reconstruction timestamps were used to calculate total examination time (first acquisition to last reformation), setup time (between the non-contrast and contrast-enhanced acquisitions), and reconstruction time (for the contrast-enhanced reconstructions and reformations). The performing technologist was recorded and accounted for in analyses using linear mixed models to assess differences between the pre- and post-intervention groups. Exam, setup, and recon times were (mean ± standard deviation) 33.5 ± 4.6, 9.2 ± 2.4, and 23.6 ±4.7min before and 27.8 ± 1.5, 8.9 ± 1.4, and 18.9 ±1.7min after intervention. These reductions of 5.7 and 4.7min in the mean exam and recon times were statistically significant (p< 0.001) while the setup time was not (p= 0.49). The reductions in standard deviation were statistically significant for exam and recon times (p< 0.0001) but not for setup time (p= 0.13). All automated panscans were completed within 36min, versus 65% with the traditional workflow. Automation of image reconstruction workflow significantly decreased mean exam and reconstruction times as well as variability between exams, thus facilitating a consistently rapid imaging assessment, and potentially reducing delays in critical management decisions.

Introducing preferences in scheduling applications

In some applications like fabric dying, the sequence in which jobs are processed on a single machine can influence product quality, i.e., there is a strong preference to run a certain type of job after another for reasons that do not influence regular scheduling objectives like total completion time. These reasons include minimizing changes in color families on machines (schedule preferences) and reducing the difference in shades between consecutive jobs (shade inconsistency). Nonetheless, the scheduling of jobs on machines must consider sequence-dependent setup times as well. In this paper, a mixed integer linear program has been presented to schedule jobs and account for setup times. The scheduling preferences and shade differences have been modeled as two new objectives, along with traditionally used objectives such as makespan, lateness and total setup time. The MILP is found to be useful only for small problems. To tackle larger problems, several heuristic methods are proposed. In order to obtain solutions quickly, multiple construction heuristics are developed based on SPT/LPT/EDD/LFJ rules and tested on a variety of industrially-inspired data sets. To produce schedules that perform well across multiple objectives at the same time, three additional tailored heuristics are developed. Both the original and tailored heuristics are compared to the optimal solutions for small-sized problems. In addition, a problem size reduction heuristic has been developed which can segment the larger MILP into smaller independently solvable MILPs by reducing the possible machines on which a job can be processed. For these larger problems, the heuristics are compared among themselves as well as against the problem size reduction heuristic.

Research on Scheduling Problem of Manufacturing/remanufacturing Hybrid Systems

Remanufacturing plays a significant effect on saving social resources, developing green economy and reducing enterprise cost. Aiming at a production scheduling problem in manufacturing/remanufacturing hybrid systems we investigated, a multi-objective optimal scheduling model is built. The goals of optimization are to minimize total equipment idle time, total delivery delay and total setup time which are consistent with actual needs of the enterprise. An improved NSGA-II is adopted to increase the population diversity and improve the search performance. The similarity degree S is employed to evaluate the diversity of population in this paper. Crossover and mutation operations are adjusted adaptively based on S. This algorithm is applied to an engine manufacturing enterprise compared with the original genetic algorithm. The analysis of experimental results shows that the way in this paper has certain superiority.

Forecasting the Number of Offset Printing Machine Breakdowns Using the Support Vector Machine (SVM) Metdhod

PT. MJT is a company engaged in manufacturing that produces various types of plastic tubes for cosmetic packaging. Production activities at PT. MJT uses an intermittent process, which in the printing division requires a longer total setup time because this process produces various types of specifications of goods to order. This has an effect on the amount of engine breakdown. The purpose of this research is to try the method of forecasting the number of breakdowns for offset printing machines at PT. MJT. One of the methods used in this research is the Support Vector Machine method. Support Vector Machine is a method that can help predict the number of breakdowns that will be experienced by the offset printing machine at PT. MJT. Support vector machine is a method that can reduce the error value in forecasting compared to other methods. From this research, it is hoped that it can produce a forecast of the number of breakdowns for offset printing machines at PT. MJT for a period of one year or twelve periods.

Mathematical Modeling for a Flexible Manufacturing Scheduling Problem in an Intelligent Transportation System

This paper presents a new mathematical model for a production system through a scheduling problem considering a material handling system as an intelligent transportation system by automated guided vehicles (AGVs). The traditional systems cannot respond to the changes and customer’s demands and for this reason, a flexible production system is used. Therefore, for this purpose, automated transportation systems are used for more flexibility in production. Thus, several AGVs are considered to perform various jobs among different machines and warehouses. In this production system, there are possibilities of failure and breakdown of AGVs and machines simultaneously. A modified rate is considered for determining the maintenance duration time as a percentage of the setup time when the maintenance time is dependent on the total setup time of machines and the total transfer jobs time of AGVs. Hence, we consider the probability of breakdown of AGVs and machines simultaneously and show the effect of these problems. The objective function is to minimize the maximum completion time (i.e., makespan or Cmax), the tardiness penalty, and the total transportation cost bearing in mind that the impact of new constraints with mathematical innovation on how failure and repair time are affected by the entire production scheduling. The proposed model belongs to mixed-integer linear programming (MILP). Finally, several small-sized problems are generated and solved by the CEPLEX solver of GAMS software to show the efficiency of the proposed model.

Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Group scheduling problems have attracted much attention owing to their many practical applications. This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time, release time, and due time. It is originated from an important industrial process, i.e., wire rod and bar rolling process in steel production systems. Two objective functions, i.e., the number of late jobs and total setup time, are minimized. A mixed integer linear program is established to describe the problem. To obtain its Pareto solutions, we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods, i.e., an insertion-based local search and an iterated greedy algorithm. The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers. Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

Many manufacturing systems need more than one type of resource to co-work with. Commonly studied flexible job shop scheduling problems merely consider the main resource such as machines and ignore the impact of other types of resource. As a result, scheduling solutions may not put into practice. This paper therefore studies the dual resource constrained flexible job shop scheduling problem when loading and unloading time (DRFJSP-LU) of the fixtures is considered. It formulates a multi-objective mathematical model to jointly minimize the makespan and the total setup time. Considering the influence of resource requirement similarity among different operations, we propose a similarity-based scheduling algorithm for setup-time reduction (SSA4STR) and then an improved non-dominated sorting genetic algorithm II (NSGA-II) to optimize the DRFJSP-LU. Experimental results show that the SSA4STR can effectively reduce the loading and unloading time of fixtures while ensuring a level of makespan. The experiments also verify that the scheduling solution with multiple resources has a greater guiding effect on production than the scheduling result with a single resource.

Multi-objective scheduling in hybrid flow shop: Evolutionary algorithms using multi-decoding framework

Hybrid flow shops are common manufacturing environments applied in many industrial fields. This paper tackles the scheduling problem in hybrid flow shop with unrelated machines, machine eligibility and sequence-dependent setup times (SDST) to minimize the bi-criteria of total tardiness and total setup time. Evolutionary algorithms (EAs) are adopted to solve the problem. Firstly, four efficient decoding algorithms using different machine selection rules are developed for constructing a schedule from a job permutation. These decoding algorithms are able to map the job permutation space to distinct regions in the objective space. Then, we propose a multi-decoding framework (MDF) for taking advantage of multiple decoding algorithms along one evolution path. The hybridization of MDF and EAs leads to a hyper-heuristic approach. The proposed MDF is coupled with a genetic algorithm to solve the problem in “a priori” approach, that is, to optimize a convex combination of the objectives given user preference information. The framework is also embedded to a multi-objective genetic algorithm, known as NSGA-II, to solve the problem in “a posteriori” approach, which aims at approximating the Pareto-optimal set for the user to make posterior decisions. The efficiency of the proposed methods is validated by numerical results. More specifically, when “a priori” approach is used, the proposed MDF helps EAs adjusting the adopted decoding scheme and generating solution aligned to the user preference; when “a posteriori” approach is applied, the MDF extends the search space and improves the solution quality.

MULTI-OBJECTIVE ENERGY EFFICIENT MIXED MODEL ASSEMBLY LINE SEQUENCING FOR SUSTAINABLE MANUFACTURING

The sustainability of manufacturing systems is not thoroughly investigated in the existing literature despite that fact that it affects service-oriented organisations. This paper addresses this gap by incorporating the criterion of energy consumption in mixed model assembly line sequencing (MMALS) to explore the potential for energy saving. Energy consumption was integrated with makespan and sequence dependent setup time of mixed models to achieve the sustainability in sequencing. A mathematical model was developed for the optimisation of energy efficient MMAL (EEMMAL). The multi-objective intelligent genetic algorithm (MOIGA) was proposed for minimisation of three conflicting objectives. A case study was conducted using centrifugal pump assembly line to test the performance of proposed MOIGA and the results were compared with those obtained from multi-objective genetic algorithm (MOGA). Finally, a trade-off analysis was conducted between total setup time, makespan (a service level measure on shop floor) and energy consumption (a factor of environmental sustainability). This analysis indicated the effectiveness of MOIGA (compared to MOGA) for EEMMALS problems.