Shortest Processing Time Rule Research Articles

An Energy-Efficient Unrelated Parallel Machine Scheduling Problem with Batch Processing and Time-of-Use Electricity Prices

The extensive consumption of energy in manufacturing has led to a large amount of greenhouse gas emissions that have caused an enormous effect on the environment. Therefore, investigating how to reduce energy consumption in manufacturing is of great significance to cleaner production. This paper considers an energy-conscious unrelated parallel batch processing machine scheduling problem under time-of-use (TOU) electricity prices. Under TOU, electricity prices vary for different periods of a day. This problem is grouping jobs into batches, assigning the batches to machines and allocating time to the batches so as to minimize the total electricity cost. A mixed-integer linear programming model and two groups of heuristics are proposed to solve this problem. The first group of heuristics first forms batches, assigns the batches to machines and finally allocates time to the batches, while the second group of heuristics first assigns jobs to machines, batches the jobs on each machine and finally allocates time to each batch. The computational results show that the SPT-FBLPT-P1 heuristic in the second group can provide high-quality solutions for large-scaled instances in a short time, in which the jobs are assigned to the machines based on the shortest processing time rule, the jobs on each machine are batched following the full-batch longest processing time algorithm, and the time is allocated to each batch following an integer programming approach. The MDEC-FBLPT-P1 heuristic that uses the minimum difference of the power consumption algorithm to assign the jobs also performed well.

Fast Evolutionary Algorithm for Flow Shop Scheduling Problems

Being complex and combinatorial optimization problems, Permutation Flow Shop Scheduling Problems (PFSSP) are difficult to be solved optimally. PFSSP occurs in many manufacturing systems i.e. automobile industry, glass industry, paper industry, appliances industry, and pharmaceutical industry, and the generation of the best schedule is very important for these manufacturing systems. Evolution Strategy (ES) is a subclass of Evolutionary algorithms and in this paper, we propose an Improved Evolution Strategy to reduce the makespan of PFSSP. Two variants of the Improved Evolution Strategy are proposed namely ES5 and ES10. The initial solution is generated using the shortest processing time rule. In ES5, four offsprings are generated from one parent while in ES10, nine offsprings are generated from one parent. The selection pool consists of both the parents and offsprings. Quad swap mutation operator has been proposed to minimize computational time and for the maximum search of solution space. Also, a variable mutation rate is used for the fine-tuning of results, with the increasing number of iterations the mutation rate is reduced. The performances of both ES variants were tested on two test domains. First, it is applied to benchmark the PFSSP of Carlier and Reeves. Computational results are matched with other well-known techniques available in the literature, and the results show the effectiveness and robustness of the proposed techniques. Secondly, ES is applied to the real-life problem for the manufacturing of batteries to demonstrate its effectiveness. Data was taken from Pakistan Accumulator for NS30-40 Plates battery, the company is daily producing 1400 units of NS30-40 Plates battery. ES is applied to different batch sizes i.e. 35, 140, 1120 & 1400. Our results show that a Min %GAP of 1.25 is found using ES10. Hence the company can increase monthly 450 units of NS30 batteries using the ES10 algorithm.

Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates

Manufacturing industry reflects a country’s productivity level and occupies an important share in the national economy of developed countries in the world. Jobshop scheduling (JSS) model originates from modern manufacturing, in which a number of tasks are executed individually on a series of processors following their preset processing routes. This study addresses a JSS problem with the criterion of minimizing total quadratic completion time (TQCT), where each task is available at its own release date. Constructive heuristic and meta-heuristic algorithms are introduced to handle different scale instances as the problem is NP-hard. Given that the shortest-processing-time (SPT)-based heuristic and dense scheduling rule are effective for the TQCT criterion and the JSS problem, respectively, an innovative heuristic combining SPT and dense scheduling rule is put forward to provide feasible solutions for large-scale instances. A preemptive single-machine-based lower bound is designed to estimate the optimal schedule and reveal the performance of the heuristic. Differential evolution algorithm is a global search algorithm on the basis of population, which has the advantages of simple structure, strong robustness, fast convergence, and easy implementation. Therefore, a hybrid discrete differential evolution (HDDE) algorithm is presented to obtain near-optimal solutions for medium-scale instances, where multi-point insertion and a local search scheme enhance the quality of final solutions. The superiority of the HDDE algorithm is highlighted by contrast experiments with population-based meta-heuristics, i.e., ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). Average gaps 45.62, 63.38 and 188.46 between HDDE with ACO, PSO and GA, respectively, are demonstrated by the numerical results with benchmark data, which reveals the domination of the proposed HDDE algorithm.

Determination of Order Delivery Time in Event Organizer Industry Using a Non-Delay Scheduling Approach

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

An Adaptive SPT Rule for Scheduling and Testing Heterogeneous Jobs

Motivated by common practices in maintenance and healthcare operations, in which diagnostic activities precede service, we study the problem of scheduling jobs with random processing times on a server that can test jobs (i.e., perform a diagnostic procedure) prior to serving them in order to observe their durations. On one hand, testing utilizes the server and increases service delays, but on the other hand, testing reduces uncertainty and informs future scheduling decisions, which contributes to reducing overall delays. We consider two cases in which tests are either optional or mandatory prerequisites for processing heterogeneous jobs whose random processing times (and in some cases weights) are statistically different. For several interesting cases of optional testing problems, we develop an adaptive shortest processing time (SPT) rule, which characterizes the optimal policy using intuitive testing thresholds given by closed-formulas. We then show that a generalization of these thresholds forms an optimal index policy for mandatory testing problems. Our work provides tools for analyzing similar problems, as well as practical insights on how to prioritize uncertainty reduction efforts, in order to reduce delays in service systems.

Using a variable neighborhood search to solve a bi-objective identical parallel machine scheduling problem

We developed a variable neighborhood search heuristic and a mixed integer programming model for the identical parallel machine scheduling problem with sequence dependent setup time. For this problem, we consider minimizing two objectives, which are the makespan and the flow time. The heuristic proposed has a constructive procedure to build initial solutions, five neighborhood structures, and a local search based on the variable neighborhood descent. Computational experiments indicate that the heuristic is very fast and can return better solutions than the model since it found 90% of the best solutions. It also outperformed all solutions computed with the longest processing time and the shortest processing time rules, both commonly adopted for scheduling problems.

Due Date Assignment in a Dynamic Job Shop with the Orthogonal Kernel Least Squares Algorithm

Meeting due dates is a key goal in the manufacturing industries. This paper proposes a method for due date assignment (DDA) by using the Orthogonal Kernel Least Squares Algorithm (OKLSA). A simulation model is built to imitate the production process of a highly dynamic job shop. Several factors describing job characteristics and system state are extracted as attributes to predict job flow-times. A number of experiments under conditions of varying dispatching rules and 90% shop utilization level have been carried out to evaluate the effectiveness of OKLSA applied for DDA. The prediction performance of OKLSA is compared with those of five conventional DDA models and back-propagation neural network (BPNN). The experimental results indicate that OKLSA is statistically superior to other DDA models in terms of mean absolute lateness and root mean squares lateness in most cases. The only exception occurs when the shortest processing time rule is used for dispatching jobs, the difference between OKLSA and BPNN is not statistically significant.

Message Scheduling for Personal Biomedical Sensing System in a Health Care Center

As various biomedical electronic devices supported by different network protocols are springing up in our living space, many development and researches focus on building a digital biomedical system by using modern techniques. To realize such a theme, the system architecture supporting biomedical services in a health care center should include a broker built in a single computer or an embedded system to coordinate various devices. The main challenge is how the future-proof biomedical devices can be easily integrated into the system. In this paper, we propose a practical skeleton to build a personal biomedical sensing system featuring integrability, extendibility and flexibility. The construction guidelines in the skeleton can ease the development and maintenance for developers. A practically realized case study validates the effectiveness of the proposed skeleton. Besides, the proposed scheduling scheme can stabilize the biomedical sensor messages. The period of sending messages could be changeable based on the shortest processing time rule. From the experimental results, the proposed scheme can reduce the waiting time and stabilize messages from biomedical sensors. The central control broker is very essential to handle the main process in the proposed scheme and to control the scheduling scheme of four biomedical sensors used in this research.

An Orderly Untangling Model against Arching Effect in Emergency Evacuation Based on Equilibrium Partition of Crowd

To untangle the arching effect of a crowd as much as possible in emergency evacuations, we employ a theoretical model of equilibrium partition of crowd batch. Based on the shortest time arrangement of evacuation, the crowd is divided into appropriate batches according to the occupied time of evacuation channel in order to determine the occupant number of every evacuation passageway. The number of each batch crowd is calculated under the condition that the time of entering the evacuation passageway is equal to the time of crossing over the evacuation passageway. Subsequently, the shortest processing time (SPT) rule establishes the evacuation order of each batch. Taking a canteen of China Three Gorges University as a background, we obtain the waiting time from the first person to the last one entering the evacuation channel in every batch by simulation. This research utilizes data from simulations to observe an untangling process against the arching effect based on the SPT rule. More specifically, evacuation time only lasts for 180.1 s in order and is 1.6 s longer than that in disorder, but the arching effect disappears. Policy recommendations are offered to improve the evacuation scheme in disaster operations.

Simulation Modeling and Analysis for Investigation of Part Launching Rule in a FMS Scheduling

Background/Objectives: The intention of this simulation study is to inspect the effect of multi-level scheduling rules on the four different performance measures of proposed FMS (flexible manufacturing system). Methods/Statistical Analysis: Input data has been collected from the real existing system and proposed FMS simulation model has been developed using JAVA programming. Findings: The flow of parts through the system has been managed with multi-level scheduling rules related to launching of parts in to a system. The proposed simulation model has been modeled for the existing system and a common optimum part selection rule for the performance parameters such as MFT(mean flow time), make span, mean machine utilization, and mean Automated Guided Vehicle (AGV) utilization has been developed. Application/Improvements: Case study has been conducted on the existing system and the proposed simulation model has been created. The shortest processing time rule was found to be optimum upon comparing other two scheduling rules when output performance measures considered. The proposed simulation model aids in better scheduling of automobile and electronics applications.

Reglas de despacho en la programación de procedimientos quirúrgicos electivos: impacto en los indicadores de ocupación y oportunidad

Introduction: The performance of a surgery service is highly impacted by its schedule. This study measured the impact of the artificial variability caused by a manual scheduling of surgeries at “Hospital Universitario Mayor - Mederi (HUM)” (Colombia). The hypotheses were that the proposed algorithm is able to (i) reduce daily service variation, (ii) increase the availability of service resources, and (iii) improve the opportunity time for each surgery. Materials and methods: The studied surgical service performs around 35,000 annual surgeries and its scheduling process is presently manually made. Actual scheduling records of a regular month were compared to schedules generated by a Bin Packing (BP) algorithm hybridized with the Longest Processing Time (LPT) and Shortest Processing Time (SPT) dispatching rules. Results: It was found that the BP algorithm with LPT rule could improve service performance, reducing the variation coefficients of patients’ flow and daily service occupation by 25.09 % and 36.71 %, respectively. The programmed surgeries were also moved ahead 6.2 days, and the overall occupation rate increased by 26.72 %. Results were not better when a SPT rule was used, boosting the variability on patient flow by 22.7 % and reducing the occupation by 2.28 %. Conclusions: Semiautomatic scheduling of the surgical service at the HUM, a BP algorithm with LPT rule, may substantially increase service performance in terms of service occupation and opportunity.

Production Planning Method Using the Push-back Heuristic Algorithm: Implementation in a Micro Filter Manufacturer in South Korea

In this paper, we present a modeling approach to production planning for an actual production line and a heuristic method. We also illustrate the successful implementation of the proposed method on the production line. A heuristic algorithm called the push-back algorithm was designed for a single machine earliness/tardiness production planning with distinct due date. It was developed by combining a minimum slack time rule and shortest processing time rule with a push-back procedure. The results of a numerical experiment on the heuristic’s performance are presented in comparison with the results of IBM ILOG CPLEX. The proposed algorithm was applied to an actual case of production planning at Woongjin Chemical, a leading manufacturer of filter products in South Korea. The seven-month execution of our algorithm led to a 24.5% decrease in the company’s inventory level, thus demonstrating its practicality and effectiveness.

Patients scheduling problems with deferred deteriorated functions

In the survey scheduling theory, if a surgical patient is regarded as a job waiting to be processed, and the anesthesiologist care unit nurses as machines that are needed before the survey. Due to the lack of operating rooms or the accidental extension of operation time, the patients will have a longer wait time, and can not do operation following the effects of the anaesthetic. The patient needs to be re-anesthesia so that the effects are required to undergo surgery. But this will cause operation time prolonged. Based on the performance of medical procedures under deteriorating health conditions, this paper presents a deterministic approach to schedule patients in surgery scheduling problems with the deferred parameter. We take the patients as jobs. By the deferred deteriorating effect, it means that the actual processing time of a job is a function of its starting time and a control parameter. We prove that the classical SPT (the shortest processing time) rule remains optimal when we minimize the makespan or the total completion time. For problems of minimizing the total weighted completion time and the maximum lateness, we present heuristic sequencing rules and analyze the worst-case bounds for performance ratios. We also show that these heuristic rules can be optimal under some agreeable conditions between the normal processing times and job due dates or weights.

Stochastic Dynamic Job Shop Scheduling with Sequence-Dependent Setup Times: Simulation Experimentation

Stochastic dynamic job shop (SDJS) scheduling problems with sequence-dependent setup times are among the most difficult classes of scheduling problems. This paper assesses the performance of five dispatching rules (DRLs) in such shop from makespan, mean flow time, mean tardiness, number of tardy jobs, total setups, and mean setup time performance measures viewpoint. A discrete event simulation model of a SDJS manufacturing system is developed for investigation purpose. Five DRLs, that is, first come first serve (FCFS), shortest processing time (SPT), shortest setup time (SIMSET), earliest due date (EDD) and shortest sum of setup times and processing time (SSPT) are incorporated in the simulation model. The simulation experiments are conducted under due date tightness factor of 3, shop utilization percentage of 90 and setup time less than processing time. Results indicate that SIMSET rule provides best performance for mean flow time and number of tardy jobs performance measures. The SPT rule provides best performance for mean tardiness measure. The EDD rule is best-performing DRL for makespan, total setups and mean setup time measures.

Due date assignment and dynamic scheduling of one-of-a-kind assembly production with uncertain processing time

In this paper, dynamic scheduling and due date assignment of one-of-a-kind assembly production is studied when the processing time is uncertain and normally distributed. New product arrives randomly with exponentially distributed inter-arrival times. Each product has its unique tree-like process route. To solve this problem, first, a method is proposed to calculate the probability that a product can be completed before its due date. Based on this method, start times of cross-points are optimised to obtain the largest in-time completion probability (ITCP). Second, two new combined dispatch rules are constructed by considering critical path (CP), the latest start time of operation and the impact from newly arrived products. Lastly, a binary search-based due date assignment method is proposed, which assigns a relative early due date to every product and meanwhile guarantee its probability of being completed before the due date. Results of numerical experiments indicate that two proposed rules are more effective in increasing the ITCP than other dispatch rules such as the first-come-first-service rule and the shortest processing time rule, and also indicate that the binary search-based due date assignment method can assign relatively early due date to newly arrived products than other due date assignment methods.

Development of a Production Planning Method for the NIFOR Digester Screw Press

In most production environments, the problem of efficiently scheduling production jobs on several machines is an important consideration when attempting to design a work plan that makes effective use of the available resources because job scheduling in manufacturing is at the crux of production planning because of its impact on revenues. This work examined the production planning and scheduling of jobs at the Nigerian Institute for Oil Palm Research (NIFOR) with the aim of developing an effective production planning method for the production of one of its products the Digester Screw Press (DSP). The material flow pattern and current manufacturing practice were observed and production data was collected on machine set up and run times. The data collected was used to estimate the actual average run times for all the machines and the total processing times for each of the three components that make up the Digester Screw Press. The Microsoft Project 2003 was used as a tool in producing an improved job schedule based on the shortest processing time rule. The technique has reduced the general processing time for the production of DSP from 157hours to 99hours. It provides low machine waiting time, high machine utilization and reduced job tardiness across the shop floor.

Otimização por colônia de formigas para o problema de sequenciamento de tarefas em uma única máquina com terceirização permitida

Este artigo trata do problema de sequenciamento de tarefas em um ambiente de máquina única com possibilidade de terceirização. O problema apresentado busca minimizar a soma ponderada dos custos totais de terceirização e do somatório dos tempos de finalização de cada tarefa e é definido na literatura como 1 / Budget / (1 - δ) Σ Cj + δ. OC. Para esta resolução do referido problema, é proposto um método formado por dois estágios: no primeiro estágio, propõe-se que as tarefas sejam sequenciadas, utilizando-se a regra SPT (Shortest Processing Time - Menor Tempo de Processamento) para que se consiga a redução do espaço de busca no grafo gerado, enquanto que, no segundo estágio, é proposto um algoritmo baseado em ACO (Ant Colony Optimization - Otimização por Colônia de Formigas). O algoritmo aqui proposto incorpora ao ACO quatro características específicas do problema estudado, a saber: i) uma representação em forma de grafo para problemas de scheduling que envolvam terceirização, obtida por meio da aplicação do primeiro estágio do método; ii) uma regra de pré-seleção, que garante a viabilidade da solução; iii) uma nova regra de visibilidade específica para o problema; e iv) uma estratégia de busca local. Os resultados obtidos neste trabalho mostram que o algoritmo baseado em ACO desenvolvido é mais eficiente que o algoritmo de Lee e Sung (2008a) que foi o único trabalho encontrado na literatura que trata do problema em discussão. Adicionalmente, a busca local proposta melhorou o resultado para problemas de tamanho médio e grande.

Improving the Performance of Aeronautical Machine Shop with Functional Layout by Simulation Method

The manufacture of aeronautical parts has the characteristics of short preparation time and small batch with varied types. To satisfy these requirements, the manufacturer must select efficient machine layout and proper dispatching rule, so they can fabricate the expected products and quantity under the least cost. This research used the machine shop of the biggest domestic aeronautical company as the research object and focused on the manufacturing procedures of 7 parts of the landing gear. The research rearranged the production layout and conducted the production simulation to evaluate the performance by comparing current machine layout with modified functional machine layout. Historical order schedules are applied to be the releasing policy of the simulation. Furthermore, the simulation also compared the performance with different dispatching rules, SPT (Shortest Processing Time) and LPT (Longest Processing Time) rule separately. This research found out that the modified functional machine layout adopting SPT rule is the best for machine efficiency. The results of the simulation would provide the machine shop for a reference for improvements.

The Study on Production Performances of Part Transporter in Flexible Manufacturing System by Using Simulation Method

Material handling is one of major components in Flexible Manufacturing System (FMS). Any improvement of material handling capability is to affect the performance of the whole system. This paper discusses the simulation study on the effect of part arrival rate and dispatching rules to the average waiting time and production rate of the FMS. The facilities of the system were modeled into simulation environment by using Arena Simulation Software. The production parameters such as machine processing times, part transportation speed and type of products were put into the model to represent the behaviors of the real system. Two rules have been considered in the study, i. e. first come first served (FCFS), and shortest processing time (SPT). Average waiting time and productivity were taken into account as performance measures of the system. The result of the study showed that SPT rule gives shorter average waiting time and higher productivity. Based on this result, the SPT rules would be used to control part transporter in order to have a better performance of the FMS.

A new Lagrangian Relaxation Algorithm for scheduling dissimilar parallel machines with release dates

In this article we investigate the parallel machine scheduling problem with job release dates, focusing on the case that machines are dissimilar with each other. The goal of scheduling is to find an assignment and sequence for a set of jobs so that the total weighted completion time is minimised. This type of production environment is frequently encountered in process industry, such as chemical and steel industries, where the scheduling of jobs with different purposes is an important goal. This article formulates the problem as an integer linear programming model. Because of the dissimilarity of machines, the ordinary job-based decomposition method is no longer applicable, a novel machine-based Lagrangian relaxation algorithm is therefore proposed. Penalty terms associated with violations of coupling constraints are introduced to the objective function by Lagrangian multipliers, which are updated using subgradient optimisation method. For each machine-level subproblem after decomposition, a forward dynamic programming algorithm is designed together with the weighted shortest processing time rule to provide an optimal solution. A heuristics is developed to obtain a feasible schedule from the solution of subproblems to provide an upper bound. Numerical results show that the new approach is computationally effective to handle the addressed problem and provide high quality schedules.