Efficient Production Schedule Research Articles

An online dynamic dual bin packing with lookahead approach for server-to-cell assignment in computer server industry

Efficient production scheduling in computer server industry remains a critical challenge. This challenge becomes particularly acute during the testing phase, where servers are assigned for testing in a modular environment consisting of test banks. Unique characteristics such as online arrival of servers known only over a short time window, skewed arrival pattern, power and cooling compatibility constraints, and dynamic assignment make it difficult for decision making, a situation we observed at our industry partner’s site, which often leads to missing due dates and loss of customer trust. Motivated by this challenge, we introduce the Online Dynamic Dual Bin Packing with Lookahead problem and propose an integer linear programming model that maximizes the number of assigned servers. To efficiently solve this model, we decompose the problem into a set of subproblems for a given length of the lookahead. A ‘2-phase’ computational framework is proposed that seamlessly integrates mathematical programming with genetic algorithm. Based on realistic data available from a server manufacturer, our findings suggest that solutions are sensitive to (i) the length of the lookahead window, (ii) testing capacity and server arrival pattern, (iii) test processing time requirement, and physical allocation of servers.

Production planning in an indoor farm: Using time and space requirements to define an efficient production schedule and farm size

Abstract Indoor agriculture is an innovative and environmentally sustainable approach to high-quality food production, utilizing advanced technology to reduce water usage by 95% and achieve a 100-fold increase in production per unit of land compared to conventional farming systems. These enclosed systems provide year-round production of pesticide-free fresh food, even in cities with less favourable climates, addressing food deserts and creating employment opportunities in urban areas. However, the industry faces significant challenges, primarily stemming from substantial investment and operating costs, exacerbated by a limited understanding of the input-output relationship within these systems. This study employs a bioeconomic framework to establish a foundational production function based on growth cycle duration (time) and required growing area (space). Through a partial budget analysis, a 19-day production schedule was identified to provide the highest contribution margin to profits. Results set the minimum size of this hypothetical lettuce indoor farm at 273 m2, rendering it suitable for installation in urban areas. The farm harvests 118 kg per day, within an 800 m2 growing area distributed across four vertically stacked shelves. Estimates of economic output sensitivity to exogenous factors in the US context are also presented, along with a comparison between cost-minimizing and revenue-maximizing strategies.

Kombinasi Algoritma Priority Scheduling dan Earliest Due Date untuk Sistem Penjadwalan Slitting Produk Berbasis Web

Production scheduling is one of the most critical components of production management that can impact efficiency and resource utilization. This research focuses on the development of a web-based production scheduling system that integrates two algorithms, Earliest Due Date (EDD) and Priority Scheduling. This research includes the analysis, design, implementation and testing of a those two algorithms in a web-based application for production management. The result of this research will be analyzed through the results of how both algorithms affects lateness and tardiness. The research findings indicate that the application can effectively manage Work Orders. The application can generate an optimal schedule when conditions arise where some tasks have higher priority than others by combining both algorithms. The system can identify potentially late tasks by calculating the tardiness of the given jobs. And from the analysis results, the application can generate an efficient production schedule with an on-time accuracy rate of 74%, and the average delay for each job is 0.95 hours.

Production Planning in an Indoor Farm: Using Time and Space Requirements to Define an Efficient Production Schedule and Farm Size

Production Planning in an Indoor Farm: Using Time and Space Requirements to Define an Efficient Production Schedule and Farm Size

A multi-objective segmentation scheduling method for the flexible dismantling of retired power batteries taking into account transfer time

Decommissioned power batteries are prone to dilapidated deformation, which makes it difficult to carry out unified and efficient production scheduling for disassembly production. In this paper, a multi-objective scheduling method applied to the dismantling workshop of retired power batteries is presented. For the characteristics of power battery dismantling, a strategy of scheduling by a battery pack and battery module in stages is proposed. The multi-objective optimal scheduling model of the dismantling workshop is established with the decision objectives of reducing the time of completion, energy dissipation and workstation load, taking into account the transfer time of batteries in the dismantling process, and is solved by genetic algorithm. The chromosome coding method, the crossover variation method and the elite retention strategy in the genetic algorithm were optimized. The method is applied to the dismantling examples of 5 kinds of power batteries, and the simulation results show that the proposed method is feasible and effective.

Approaching flexible production with planning methods

AbstractFlexible production systems are becoming more and more important for the industry. Higher requirements for flexibility in the production process demand further improvements in regards to flexible automation. In this perspective, the RoboCup Logistics League competition was designed to provide a testbed for dynamic production domains. The related challenges range from robust navigation over manipulation to efficient production scheduling. In this paper, we provide an update on how the team GRIPS approches the flexible production challenge using modern planning methods. This includes a new long-term planning and scheduling strategy as well as a hierarchical structure which further develops such plans into Behavior Trees.

A novel hybrid Aquila optimizer for energy-efficient hybrid flow shop scheduling

The global energy demand increases, giving rise to concerns about fuel scarcity and environmental damage. In addition, world energy consumption rises as a result of production. In the industrial sector, efficient production scheduling can help reduce energy use. This article proposed a Hybrid Aquila Optimizer (HAO) for solving the Hybrid Flow Shop Scheduling Problem (HFSSP) to minimize total energy consumption. HAO was implemented to determine the best sequence job with the best total energy consumption. Ten job variations were presented to be analyzed on TEC and computational time to measure algorithm performance. This study compared the proposed HAO algorithm with the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Tiki-taka, Firefly, and Artificial Bee Colony (ABC) algorithms. The result of the experiment indicated that HAO was more efficient in reducing the total energy consumption than the GA, PSO, ACO, Tiki-taka, Firefly, and ABC algorithms. A comparison of computational times for each algorithm is also presented in this study.

ANALISIS PENJADWALAN PRODUKSI MENGGUNAKAN METODE CAMPBELL DUDEK SMITH DAN DANNENBRING DALAM MEMINIMUMKAN TOTAL WAKTU PRODUKSI BERAS

The increasing needs for basic materials has resulted in an increased production process of basic materials in a company. CV. Puspa is a manufacturing company engaged in the production of rice, on the production process, CV. Puspa often has a buildup of work so that it requires an alternative production scheduling optimally. This research was conducted to minimize the total time of completion using the Campbell Dudek Smith and Dannenbring method in determining efficient production scheduling. Based on the scheduling sequence obtained, the calculation results of the total completion time using the Campbell Dudek Smith method are less than or equal to the results of calculation using the Dannenbring method. So the Campbell Dudek Smith method is more efficient than the Dannenbring method to be applied to CV. Puspa.

Optimization of No-Wait Flowshop Scheduling Problem in Bakery Production with Modified PSO, NEH and SA

In bakery production, to perform a processing task there might be multiple alternative machines that have the same functionalities. Finding an efficient production schedule is challenging due to the significant nondeterministic polynomial time (NP)-hardness of the problem when the number of products, processing tasks, and alternative machines are higher. In addition, many tasks are performed manually as small and medium-size bakeries are not fully automated. Therefore, along with machines, the integration of employees in production planning is essential. This paper presents a hybrid no-wait flowshop scheduling model (NWFSSM) comprising the constraints of common practice in bakeries. The schedule of an existing production line is simulated to examine the model and is optimized by performing particle swarm optimization (PSO), modified particle swarm optimization (MPSO), simulated annealing (SA), and Nawaz-Enscore-Ham (NEH) algorithms. The computational results reveal that the performance of PSO is significantly influenced by the weight distribution of exploration and exploitation in a run time. Due to the modification to the acceleration parameter, MPSO outperforms PSO, SA, and NEH in respect to effectively finding an optimized schedule. The best solution to the real case problem obtained by MPSO shows a reduction of the total idle time (TIDT) of the machines by 12% and makespan by 30%. The result of the optimized schedule indicates that for small- and medium-sized bakery industries, the application of the hybrid NWFSSM along with nature-inspired optimization algorithms can be a powerful tool to make the production system efficient.

IIoT implementation challenges: analysis and mitigation by blockchain

PurposeThe purpose of this methodology is to categorise the challenges into cause and effect group. The modern scenario of customization, personalization and multi-restrictive working because of pandemics has affected the operations of manufacturing small and medium enterprises (SMEs). In the new normal, the digitalization of manufacturing SMEs can be the path breaker. Modern digitalization includes a mix of technologies such as the industrial internet of things (IIoT), the internet of things, cyber-physical system and big data analytics. This digitalization can help in achieving new design changes, efficient production scheduling, smart manufacturing and unrestricted on-time delivery of quality products. This research paper aims to recognize and analyze the challenges faced while implementing IIoT technologies in manufacturing SMEs and tries to find the possibility of mitigating challenges by blockchain technology.Design/methodology/approachThere were ten challenges of IIoT implementation identified from the literature review and experts’ opinions. To collect information from Indian manufacturing SMEs, a survey tool was formed in the form of a questionnaire. On the fundament of responses received from industrial experts, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique has been used for categorizing these challenges into cause and effect groups. Further, the authors tried to mitigate observed challenges with the help of blockchain technology.FindingsWith the implementation of IIoT technologies, the manufacturing processes become conciliatory, effective and traceable in real time. Observation of the current study states that the top effect group challenges such as the security of data and reliability of technologies can be mitigated by enabling blockchain technologies. The authors conclude that blockchain-enabled IIoT technologies will be highly beneficial for the Indian SMEs strategically and practically in the current scenario.Research limitations/implicationsMethodology of DEMATEL focuses on responses received from experts. The broader approach of survey from manufacturing organizations is compromised due to small sample size in this methodology. Experts approached for survey were from manufacturing SMEs of Delhi National Capital Region only. Broader survey-based techniques may be applied covering different sectors of SMEs in future work.Practical implicationsTechnologies such as blockchain can facilitate advanced security in the application of IIoT and other such practices. While dealing with significant issues and challenges of new technologies, blockchain gives an edge of balance in the current scenario. Its properties of fixity, temper evident and circumvent fraud make this technology ideal for the digitalization of the manufacturing systems in SMEs.Originality/valueDigitalization of manufacturing facilities is the need of the hour. Pandemic challenges have highlighted the urgency of it. This research will motivate and guide the manufacturing SMEs in planning strategies and long-term policies in implementing modern technologies and coping up with the pandemic challenges.

A Research Review on Job Shop Scheduling Problem

In recent years, the manufacturing industry has developed rapidly with fierce competition. Manufacturing enterprises are faced with the challenge of on-time delivery, multiple product choices and quick response to order modification. Reasonable and efficient production scheduling determines whether the enterprise can continue to survive. Therefore, the study of job shop scheduling problem has practical significance. This paper summarizes the research development and current situation of job shop scheduling problem, classifies the existing research methods, and discusses the future research direction of job shop scheduling problem.

Bi-local search based variable neighborhood search for job-shop scheduling problem with transport constraints

In job-shop manufacturing systems, an efficient production schedule acts to reduce unnecessary costs and better manage resources. For the same purposes, modern manufacturing cells, in compliance with industry 4.0 concepts, use material handling systems in order to allow more control on the transport tasks. In this paper, a job-shop scheduling problem in vehicle based manufacturing facility that is mainly related to job assignment to resources is addressed. The considered job-shop production cell has two types of resources: processing resources that accomplish fabrication tasks for specific products, and transporting resources that assure parts’ transport to the processing area. A Variable Neighborhood Search algorithm is used to schedule product manufacturing and handling tasks in the aim to minimize the maximum completion time of a job set and an improved lower bound with new calculation method is presented. Experimental tests are conducted to evaluate the efficiency of the proposed approach.

Application Case of Safety Stock Policy based on Demand Forecast Data Analysis

The fourth industrial revolution encourages manufacturing industry to pursue a new paradigm shift to meet customers' diverse demands by managing the production process efficiently. However, it is not easy to manage efficiently a variety of tasks of all the processes including materials management, production management, process control, sales management, and inventory management. Especially, to set up an efficient production schedule and maintain appropriate inventory is crucial for tailored response to customers' needs. This paper deals with the optimized inventory policy in a steel company that produces granule products under supply contracts of three targeted on-time delivery rates. For efficient inventory management, products are classified into three groups A, B and C, and three differentiated production cycles and safety factors are assumed for the targeted on-time delivery rates of the groups. To derive the optimized inventory policy, we experimented eight cases of combined safety stock and data analysis methods in terms of key performance metrics such as mean inventory level and sold-out rate. Through simulation experiments based on real data we find that the proposed optimized inventory policy reduces inventory level by about 9%, and increases surplus production capacity rate, which is usually used for the production of products in Group C, from 43.4% to 46.3%, compared with the existing inventory policy.

Open-pit coal mine production sequencing incorporating grade blending and stockpiling options: An application from an Indian mine

ABSTRACTProduction scheduling is a crucial aspect of the mining industry. An optimal and efficient production schedule can increase the profits manifold and reduce the amount of waste to be handled. Production scheduling for coal mines is necessary to maintain consistency in the quality and quantity parameters of coal supplied to power plants. Irregularity in the quality parameters of the coal can lead to heavy losses in coal-fired power plants. Moreover, the stockpiling of coal poses environmental and fire problems owing to low incubation periods. This article proposes a production scheduling formulation for open-pit coal mines including stockpiling and blending opportunities, which play a major role in maintaining the quality and quantity of supplied coal. The proposed formulation was applied to a large open-pit coal mine in India. This contribution provides an efficient production scheduling formulation for coal mines after utilizing the stockpile coal within the incubation periods with the maximization of discounted cash flows. At the same time, consistency is maintained in the quality and quantity of coal to power plants through blending and stockpiling options to ensure smooth functioning.

Use of Excel worksheets with user-friendly interface in batch process (PSBP) to minimize the makespan

In the chemical industry, the necessity for scheduling is becoming more pronounced, especially in batch production mode. Nowadays, planning industrial activities is a necessity for survival. Intense competition requires diversified products and delivery in accordance with the requirements of consumers. These activities require quick decision making and the lowest possible cost, through an efficient Production Scheduling. So, this work addresses the Permutation Flow Shop scheduling problem, characterized as Production Scheduling in Batch Process (PSBP), with the objective of minimizing the total time to complete the schedule (Makespan). A method to approach the problem of production scheduling is to turn it into Mixed Integer Linear Programming- MILP, and to solve it using commercial mathematical programming packages. In this study an electronic spreadsheet with user-friendly interface (ESUFI) was developed in Microsoft Excel. The ease of manipulation of the ESUFI is quite evident, as with the use of VBA language a user-friendly interface could be created between the user and the spreadsheet itself. The results showed that it is possible to use the ESUFI for small problems.

Economics and design of balancing power markets in Germany

This article analyzes the economic fundamentals that govern market design and behavior in German balancing power markets. Then, partly based on theoretical work by Chao and Wilson [3], we illustrate the role of the scoring and the settlement rule as key elements of the market design. With sufficiently competitive markets, a settlement rule based on uniform pricing ensures efficient energy call in the balancing power market. A scoring rule based on capacity prices only ensures an efficient production schedule. Thus, both rules together with rational bidding ensure simultaneous efficiency on the balancing power market and the wholesale electricity market.

Production Scheduling for Sustainable Manufacturing Systems

In this paper we investigate the elaboration of an efficient production schedule for sustainable manufacturing systems. Because renewable energies are irregular by nature as they often depend on meteorological conditions (e.g. wind and solar energy), their use in the competitive field of manufacturing production must be addressed with caution. The challenge is to elaborate a reliable production schedule that accommodates energy stochastic fluctuations while satisfying customer and operational constraints. We propose to solve the problem using a meta-heuristic based on Tabu search and discuss major elements that are critical to the success of this approach.

Implementation of supervised statistical data mining algorithm for single machine scheduling

PurposeMachine scheduling plays an important role in most manufacturing industries and has received a great amount of attention from operation researchers. Production scheduling is concerned with the allocation of resources and the sequencing of tasks to produce goods and services. Dispatching rules help in the identification of efficient or optimized scheduling sequences. The purpose of this paper is to consider a data mining‐based approach to discover previously unknown priority dispatching rules for the single machine scheduling problem.Design/methodology/approachIn this work, the supervised statistical data mining algorithm, namely Bayesian, is implemented for the single machine scheduling problem. Data mining techniques are used to find hidden patterns and rules through large amounts of structured or unstructured data. The constructed training set is analyzed using Bayesian method and an efficient production schedule is proposed for machine scheduling.FindingsAfter integration of naive Bayesian classification, the proposed methodology suggests an optimized scheduling sequence.Originality/valueThis paper analyzes the progressive results of a supervised learning algorithm tested with the production data along with a few of the system attributes. The data are collected from the literature and converted into the training data set suitable for implementation. The supervised data mining algorithm has not previously been explored in production scheduling.

Development of Shipyard Simulation System based on Petri Nets

For production activities very large information on process planning and scheduling is necessary. There are various restrictions in the factory. Most efficient production schedule must be planned in consideration of those restrictions. However information handled by production scheduling is huge and it is very difficult to plan the optimal schedule. Therefore, skill is needed for production scheduling.The authors have been studying on a factory simulator that bases on petri nets and published two papers already. This paper is the 3rd report of the authors' research and presents the enhanced concept of the previous simulator. In the authors' previous papers, the working resources and operations are modeled as petri nets, and the shipyard simulator is implemented by using of petri nets. In this research, authors propose the petri nets that are including the restriction of working resources in shipyard.We aimed to implement the factory simulation system of the shipbuilding more realistic than ever. We have already implemented the production scheduling support system in the shipyard that is part of our research in CIM for shipbuilding. In order to use this result of our research more, we implement the system that can evaluate the effectiveness of generated production schedule by the factory simulator. Time is freely controlled in the computer and we implement the shipyard simulation system that can simulate the process of the production activity in a shipyard.

Development of a shipyard simulator based on Petri nets

There are many restrictions on production in the factory. The most efficient production schedule should be planned taking these restrictions into account. Professional skill is necessary to plan the best schedule. A schedule planner has to deal with so much information during production planning that it is difficult to plan the best production schedule with a consideration of so many restrictions. In this study we introduced the use of Petri nets to support such production planning with a computer. Petri nets have been widely used to simulate production because of their capability of modeling concurrency, synchronization, and sequencing in discrete-event systems. This paper reports on implementing a simulation system for the shipyard. We had already implemented a production planning support system for a shipyard that is a part of the computer integrated manufacturing (CIM) system in shipbuilding. Based on this system, we implemented a shipyard simulator that can simulate the production activity in the shipyard. We therefore have defined a shipyard model. The production conditions can be changed, and the efficiency of a schedule can be evaluated. This paper discusses the effectiveness of using a factory simulator.