Rescheduling Activities in Face of Disruption in House Hold Goods Manufacturing Supply Chain

This present paper speaks about Flexible Job Shop scheduling Problem using a software. Flexible Job shop scheduling is most typical and complex manufacturing environments in production planning issues. A case study was conducted on a manufacturing business set in Vijayawada. Information collected from the producing company to get the time taken by the conventional scheduling method and gathered data for 5 jobs requiring 8 machines that arrived during the analysis period. The Project additionally speaks about the study of varied programming strategies, learning of LEKIN programming package is a software tool has a flexibility to develop new heuristic models to produce effective schedules be applied practically. Input data, taken from the manufacturing company, converted to tables and routing sequences. The LEKIN programming package [3], develops the different schedules for given data with reference to priority rules, such as First Come First Serve (FCFS), Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Due Date (EDD), Critical Ratio (CR) [6]. The schedules obtained from priority rules analysed through performance measures like Make Span, No of late jobs, Total Flow Time, Total Tardiness, Maximum Tardiness, Total Weighted Tardiness, Total Weighted Flow Time. Our goal is to come up with a optimized schedule with in the process of flexible job shop scheduling by using LEKIN scheduling software, using various priority rules as mentioned above and to minimize the make span i.e. the time length of the schedule, during which all the operations for all jobs is completed in an engineering company.

Stochastic proportionate flowshop scheduling with setups

Scheduling problem exists almost everywhere in our day to day real life and at the same time industrial problems related to service sector. Two machine flow shop scheduling problem (FSSP) was solved by Johnson in the mid of 1954. In case of tie, he has chosen the processing times arbitrarily. In this paper, we have given crystal clear idea that how to solve two machine FSSP when tie occurs by giving two priority rules to check for consistency in solving the problem for the objective of minimizing the total completion time. Computational results show that the algorithm with Longest Processing Time (LPT) priority rule provides better results than the algorithm with Shortest Processing Time (SPT) priority rule when the machine order is reversed.

Bounds on Multiprocessing Timing Anomalies

Scheduling in the presence of machine eligibility restrictions when not all machines can process all the jobs is a practical problem into which there has been little research. Pinedo demonstrated that the least flexible job (LFJ) rule was optimal for minimizing makespan in a parallel machine environment (with equal processing times) when there are machine eligibility restrictions, the machine eligibility sets are nested, and no release time constraint exists. The results presented in this paper demonstrate that for the more realistic case when the machine eligibility sets are not nested (with unequal processing times known when a job is released), the longest processing time (LPT) rule performs better than the LFJ rule in the presence or absence of release time stipulations. The experimental results show that the order (job selection first or machine selection first) does not matter, which is consistent with Pinedo’s observation. The new heuristics that are evaluated in this paper provide important results for the parallel machine scheduling problem and their applications in the semiconductor industry, which motivated this research.

This paper compares the waiting times of customers under three priority rules: the order-of-arrival rule, the shortest processing time (within a generation) first, and the longest processing time (within a generation) first. In particular, the covariance matrix of the trivariate distribution of the equilibrium waiting times is obtained explicitly.

This paper looks as initial results of research into the validity of application of selected priority rules in the development of a levelled production plan. The development of a levelled production plan is the key stage of the authors’ own methodology of levelling production to mitigate the adverse impact of variable demand. A levelled production plan permits to maximize effects, defined as being able to deliver diverse products in a timely manner and at the same time reduce stocks and optimize efficient use of manufacturing resources. Application of an appropriate priority rule in the development of a levelled production plan is the key factor determining the effectiveness of the developed methodology. Initial research has been conducted for twenty automatically generated task sets. One hundred manufacturing schedules have been developed in total—five schedules for each set, according to the selected priority rules (shortest task time, longest task time, shortest processing time, longest processing time, first in first out). The schedules have been assessed in terms of meeting the selected key criteria for the objectives of levelled production.

An optimization scheme for minimizing makespan of Gari processing jobs using improved initial population Genetic Algorithm (GA) is proposed. GA with initial population improved by using job sequencing and dispatching rules of First Come First Served (FCFS), Shortest Processing Time (SPT), Longest Processing Time (LPT), and Modified Johnson’s Algorithm form-machines in order to obtain better schedules than is affordable by GA with freely generated initial population and by individual traditional sequencing and dispatching rules was used. The traditional GA crossover and mutation operators as well as a custom-made remedial operator were used together with a hybrid of elitism and roulette wheel algorithms in the selection process based on job completion times. A test problem of 20 jobs with specified job processing and arrival times was simulated through the integral 5-process Gari production routine using the sequencing and dispatching rules, GA with freely generated initial population, and the improved GA. Comparisons based on performance measures such as optimal makespan, mean makespan, execution time, and solution improvement rate established the superiority of the improved initial population GA over the traditional sequencing and dispatching rules and freely generated initial population GA.

The high productivity of a production process has a major impact on the reduction of the production cost and on a quick response to changing demands. Information about a failure-free machine operation time obtained in advance allows the users to plan preventive maintenance in order to keep the machine in a good operational condition. The introduction of maintenance work into a schedule reduces the frequency of unpredicted breaks caused by machine failures. It also results in higher productivity and in-time production. The foregoing of this constitutes the main idea of the predictive scheduling method proposed in the paper. Rescheduling of disrupted operations, with a minimal impact on the stability and robustness of a schedule, is the main idea of the reactive scheduling method proposed. The first objective of the paper is to present a hybrid multi-objective immune algorithm (H-MOIA) aided by heuristics: a minimal impact of disrupted operation on the schedule (MIDOS) for predictive scheduling and a minimal impact of rescheduled operation on the schedule (MIROS) for reactive scheduling. The second objective is to compare the H-MOIA with various methods for predictive and reactive scheduling. The H-MOIA + MIDOS is compared to two algorithms, identified in reference publications: (1) an algorithm based on priority rules: the least flexible job first (LFJ) and the longest processing time (LPT) (2) an Average Slack Method. The H-MOIA + MIROS is compared to: (1) an algorithm based on priority rules: the LFJ and LPT and (2) Shifted Gap-Reduction. This paper presents the research results and computer simulations.

This study discusses the determination of order delivery time in the event organizer (EO) industry. With regard to the characteristics of the EO production process that is identical to the job shop production process in the manufacturing industry, a non-delay scheduling approach is applied. The non-delay schedule is compiled using the non-delay algorithm with the criteria for makespan minimization. Job assignment is done using the shortest processing time (SPT), longest processing time (LPT), and first comes first served (FCFS) priority rules. We consider the situation where all orders arrive simultaneously (offline) and at different time (online). As a case study, the modified non-delay algorithm is examined to solve the problem of an EO in Indonesia. The results of the study show that the nondelay algorithm using SPT rule provides the best schedule performance which results in the shortest makespan and the lowest resource idle time. In addition to determining the delivery time, the resulting non-delay schedule can be used to control the execution of each order. The method of determining order delivery time in this study can be applied to other service industries. Further study can be developed for situations where order arrival and processing time are probabilistic. Furthermore, it is also necessary to consider the balanced distribution of the workload among operators. Determination of Order Delivery Time in Event

This paper develops a scheduling algorithm for the job shop scheduling problem with parallel machines and reentrant process. This algorithm includes two major modules: the machine selection module (MSM) and the operation scheduling module (OSM). An order has several jobs and each job has several operations in a hierarchical structure. The MSM helps an operation to select one of the parallel machines to process it. The OSM is then used to schedule the sequences and the timing of all operations assigned to each machine. A real-life weapons production factory is used as a case study to evaluate the performance of the proposed algorithm. Due to the high penalty of delays in military orders, the on-time delivery rate is the most important performance measure and then makespan is the next most important measure. Well-known performance measures in the scheduling literature, such as maximum lateness and average tardiness, are also evaluated. The simulation results demonstrate that the MSM and OSM using the combination of earliest due date (EDD), the operations’ lowest level code (LLC) of the bill of materials (BOM), and the longest processing time (LPT) outperforms the other scheduling methods.

PurposeThis study aims to address the hybrid open shop problem (HOSP) with respect to the minimization of the overall finishing time or makespan. In the HOSP, we have to process n jobs in stages without preemption. Each job must be processed once in every stage, there is a set of mk identical machines in stage k and the production flow is immaterial.Design/methodology/approachComputational experiments carried out on a set of randomly generated instances showed that the minimal idleness heuristic (MIH) priority rule outperforms the longest processing time (LPT) rule proposed in the literature and the other proposed constructive methods on most instances.FindingsThe proposed mathematical model outperformed the existing model in the literature with respect to computing time, for small-sized instances, and solution quality within a time limit, for medium- and large-sized instances. The authors’ hybrid iterated local search (ILS) improved the solutions of the MIH rule, drastically outperforming the models on large-sized instances with respect to solution quality.Originality/valueThe authors formalize the HOSP, as well as argue its NP-hardness, and propose a mixed integer linear programming model to solve it. The authors propose several priority rules – constructive heuristics based on priority measures – for finding feasible solutions for the problem, consisting of adaptations of classical priority rules for scheduling problems. The authors also propose a hybrid ILS for improving the priority rules solutions.

PVA/Ag nanocomposite films were synthesized via a one-step plasma-solution reduction method with varying processing times. Following synthesis, the PVA/Ag NP solutions were cast to form films, which were subsequently characterized by TEM, XRD, FTIR, XPS, UV–Vis, and Raman spectroscopy. Antimicrobial activity was evaluated using an agar diffusion test. TEM analysis confirmed the presence of Ag NPs with diverse sizes and shapes, averaging around 16.1 nm. XRD analysis showed a distinct peak at 2θ = 38°, corresponding to the Ag (111) plane in films synthesized at 10, 15, and 20 min, indicating the formation of crystalline Ag NPs. XPS results demonstrated an increased O/C ratio in plasma-synthesized PVA/Ag nanocomposite films, along with a new peak at 369.15 eV attributed to the Ag 3d orbital, confirming the presence of Ag NPs within the PVA matrix. FTIR spectra further suggested the formation of coordinate bonds between Ag NPs and PVA polymer chains. UV–Vis analysis revealed a localized surface plasmon resonance (LSPR) peak at approximately 425 nm, with a redshift to 445 nm at a longer processing time (20 min). This suggests alterations in NP size or interaction with the matrix. The optical bandgap of PVA/AgNP films decreased with longer plasma processing time, accompanied by increased film opacity. Raman analysis highlighted the potential use of PVA/Ag NP films (synthesized at 5 min) as substrates for surface-enhanced Raman spectroscopy (SERS). The PVA/Ag NP composite synthesized at a plasma processing time of 20 min exhibited considerable antibacterial activity against S. aureus and C. albicans. Also, the percentage of biofilm inhibition of S. aureus was 65%, compared to the control. Collectively, these findings suggest that plasma-synthesized PVA/Ag NP nanocomposite films are promising candidates for various applications, including optical devices, food packaging, SERS, and wound dressings.

Industry has attributed a decline in quality and product sales of canned lobster (Homarus americanus) meat to a 50% longer retort processing time required by changes in regulations. This study assessed samples of vacuum canned frozen control lobster meat and meat retorted for 28, 35, 40 and 45 min, respectively. Moisture content, expressible moisture (EM) and shear force were determined on claw and tail samples from the various treatments. Concomitantly, samples were evaluated by a trained eight-member quantitative descriptive analysis (QDA) panel for odor, taste, sweetness, saltiness, aftertaste, firmness, chewiness, stringiness, wateriness, springiness, juiciness, inner grey color, inner pink color and overall acceptance. Results show a decrease in moisture content, EM and shear force values with sterilization. In addition, longer operator processing times result in decreased shear force values, increased moisture content and increased EM for both claw and tail muscle. QDA results show increased development of grey color and aftertaste with longer processing time that is congruent with decreased acceptability. These results may reflect microstructural protein damage of the muscle structure and formation of Maillard browning products caused by the excessive cooking at high pressure, temperature, moisture and salt concentration.

Distinctiveness of processing, processing time and memory performance