Search

This research studies the Flow shop - Job shop methodologies, which play a fundamental role in the reduction of costs in factories when planning the production sequence. The selection methodology of articles used in this research took into account that the papers were indexed or approved, in addition to being in a range of no more than ten years of publication. The research results showed a higher percentage of articles on the job shop methodology. We concluded that minimizing production costs is the main objective of production scheduling based on Flow shop - Job shop methodologies. However, in the last decade, the objective has been focused on reducing energy costs since they allow to evaluate the processes to program when to turn on or off a machine. [ABSTRACT FROM AUTHOR]

We address in this paper the two-machine job shop scheduling problem with a single server that sets up the jobs before they get processed on the machines. The server is only needed during the set-up and becomes free at the end of this phase. Moreover, the set-ups are non-anticipatory and the set-up times are sequence-independent. We seek a schedule that minimizes the overall completion time, also called the makespan. We propose several lower bounds to the problem and prove the N P -hardness in the strong sense of two restricted cases. In addition, we present a linear time algorithm for a special case. In order to solve the general problem, we develop a genetic and simulated annealing algorithms that use feasibility guaranteed procedures. An experimental study is carried out to analyze the performance of these meta-heuristic methods. [ABSTRACT FROM AUTHOR]

Since 2000, the field of machine scheduling—an integral part of computer science and operations research—has seen significant advancements. This paper explores the dynamic progression of machine scheduling, offering a detailed overview of its past advancements, current practices, and future directions. Anchoring the research in robust data analysis and statistical methodologies, the paper reveals the subtle yet impactful changes that have characterized the field in the last two decades. It examines the prominence of various scheduling problems, identifies leading research journals, and highlights international contributions and collaborations, thereby offering a thorough guide to the machine scheduling ecosystem. The study delves into specific problem characteristics and assesses performance criteria and solution methods to provide an in-depth view of the field's multifaceted nature. Ultimately, this paper captures the essence of machine scheduling's evolution and suggests new paths for exploration. The insights gained contribute significantly to academic discussions and equip practitioners with a comprehensive understanding of the dynamic landscape of machine scheduling. [ABSTRACT FROM AUTHOR]

• The importance of discrete event simulation for training and testing DRL techniques is discussed. • The ineffectiveness of DRL implemented without a simulation environment is demonstrated. • An object-oriented agent-based discrete event simulator of the job shop is presented. • The simulator is designed to be integrated with DRL agents. • It is possible to define customized features, dispatching rules, reward function. The job shop scheduling problem, which involves the routing and sequencing of jobs in a job shop context, is a relevant subject in industrial engineering. Approaches based on Deep Reinforcement Learning (DRL) are very promising for dealing with the variability of real working conditions due to dynamic events such as the arrival of new jobs and machine failures. Discrete Event Simulation (DES) is essential for training and testing DRL approaches, which are based on the interaction of an intelligent agent and the production system. Nonetheless, there are numerous papers in the literature in which DRL techniques, developed to solve the Dynamic Flexible Job Shop Problem (DFJSP), have been implemented and evaluated in the absence of a simulation environment. In the paper, the limitations of these techniques are highlighted, and a numerical experiment that demonstrates their ineffectiveness is presented. Furthermore, in order to provide the scientific community with a simulation tool designed to be used in conjunction with DRL techniques, an agent-based discrete event simulator is also presented. [ABSTRACT FROM AUTHOR]

Deteriorating items are found in a wide variety of productive environments and have been extensively reported in the literature. However, the evolution of markets demand the development of new products and new forms of work are made necessary to adapt production systems to those changes. The present work focuses on the production control of perishable products in a job shop environment. Specifically, in those products that have an expiration date within the production interval (internal caducity) that must be delivered before a certain date. As far as we know, there are no previous works that focus on the internal caducity of products at the production-control level. Two systems of different nature have been compared: Workload Control (WLC) and Kanban. WLC is usually a benchmark in job shop and made to order environments. Recent studies show that Kanban, traditionally used in JIT (Just in Time) environments, performs similarly or even better than WLC. The study was performed by discrete events simulation using Python© language, SimPy© and DEAP© modules, and considering several responses of the systems. The results show that both systems have a good performance in a variety of scenarios, with overall performance of Kanban in terms of internal caducity and tardy deliveries. [ABSTRACT FROM AUTHOR]

In the field of production scheduling, due to the influence of many factors such as production technology, each production process usually requires multiple machines to simultaneously participate in processing. Meanwhile, the number of workpieces to be processed is large, and each type of workpiece needs to be processed in batches for shortening the production cycle. Aiming at the above problems, in a job shop environment, this paper adopts a variable batching scheme according to the load of the machines involved in each processing process, and proposes a non-mixed multi-processor combined production batch scheduling model and integrate the brainstorming algorithm to search the shortest processing time. Moreover, an improved brainstorming algorithm is proposed by introducing greedy thinking and dynamic discussion mechanism. The number of discussions is changed adaptively with the iteration and the global search and local search are utilized to strengthen the search ability of the proposed algorithm. Finally, it is shown via the test results that the improved brainstorming algorithm is more efficient and convergent than the basic brainstorming algorithm. [ABSTRACT FROM AUTHOR]

COntrol of BAlance by CArd-BAsed NAvigation (COBACABANA) is a card-based system for job shop control. This paper presents a real industrial application of COBACABANA aimed at solving production control problems in an Italian manufacturer of valves and flanges, configured as a job shop system. To the best of our knowledge, this is the first industrial implementation of COBACABANA. Although COBACABANA can be extended to estimating delivery time allowances, this research focuses only on its shop floor control mechanism. In the case study, the COBACABANA boards and cards are carefully reviewed. The criticalities and limitations of traditional COBACABANA are fully explored and, subsequently, two new boards and a new form are introduced to facilitate its practicality and ease of implementation. The new tools enable planners to better monitor the pre-shop pool status and the progress of orders. [ABSTRACT FROM AUTHOR]

Introduction: The present research addresses a bi-objective problem, the classic Job-Shop Scheduling problem with the extension of energy efficiency which seeks to minimize the brand and energy consumption. In this, fixed machines are handled by operations, there are variations in energy consumption and processing time depending on speed, which resembles real environments present in industries. This problem is considered NP-Hard due to its computational complexity. Objective. The objective of this research is to design a hybrid algorithm for the solution to minimize computational time and establish the best sequence and speed assignment seeking to reduce the completion time of the last task (Makespan) and energy consumption. Materials and methods. The research approach is quantitative of the Technological Applied Research type since it serves to generate knowledge that can be put into practice in the productive sector, to promote a positive impact on daily life. Results. The proposed algorithm was evaluated with benchmarking problems in the literature, achieving good performance in computational terms. Conclusions. The Simulated Annealing algorithm improved the results obtained by the genetic algorithm, being the union of the two a good alternative for solving the problem. [ABSTRACT FROM AUTHOR]

Machine failures are the common disturbances in production scheduling, whose appearances are generally random and uncertain. Rescheduling strategies have been proposed to deal with them. However, performances of these rescheduling strategies depend on status of machine failures, and there is no single strategy for every failure status. Hence, how to select the optimal strategy intelligently when a machine failure occurs becomes an important issue. Since the development of artificial intelligence (AI) and machine learning (ML) techniques, intelligent rescheduling has become possible. In this paper, we propose a new rescheduling decision model based on random forest, an effective machine learning method, to learn the optimal rescheduling strategy in different machine failures. We adopt a genetic algorithm (GA) to generate an initial scheduling scheme. Then we design simulation experiments to obtain data of different machine failures which could influence the initial scheme. In each machine failure, all rescheduling strategies are executed respectively and their performances are evaluated based on delay and deviation, then the best strategy is selected as a label. The random forest is trained based on these data samples with labels. Thus the internal mechanism between machine failures and rescheduling strategies can be learned. We conduct experiments to verify the effectiveness of this proposed method and the results show that accuracy can be as high as 97%. Moreover, compared with decision tree (DT) and support vector machine (SVM), the proposed method illustrates the best performance. [ABSTRACT FROM AUTHOR]

In this research, an integrated scheduling problem of job shop systems with an assembly stage and transportation to minimize the total tardiness time is studied. In this problem, the parts are processed in a job shop system and then assembled in the assembly stage. Ultimately, the products are shipped in packages to customers. Setup time is assumed to depend on sequence. At first, a mixed-integer linear model is developed. Since the problem is NP-hard, a hybrid imperialist competitive and simulated annealing (ICA-SA) algorithm is proposed to solve the problems with the medium and large sizes. To validate the performance of the proposed algorithm, results are compared to an imperialist competitive algorithm and a hybrid imperialist competitive and tabu search (ICA-TS) algorithm. Analysis of variance random block design is used to compare the results of the algorithms. P-values of algorithms and blocks in this test are smaller than the significance level of 0.05. The computational results show that the proposed hybrid algorithm achieves better performance than the imperialist competitive algorithm and hybrid imperialist competitive and tabu search.

Additive manufacturing technology has been evolving for several years. New material options, better processing speeds and greater autonomy are some of the characteristics of this technology that are still under research. However, in its current state, many commercially available 3D printers are competing with traditional manufacturing techniques in the fabrication of enduse products. In this paper, different additive manufacturing technologies are compared with injection moulding in a real-world case study. The comparison is conducted in terms of lead time and total production cost. From the case under study, it becomes obvious that none of the additive manufacturing technologies examined is yet able to practically replace injection moulding for medium- and high production volumes. However, when considering low-volume production, both rapid tooling and additive manufacturing may offer an alternative that could result into shorter lead times and decreased total production costs. In addition, the introduction of Additive Manufacturing in a producer's production portfolio can increase flexibility, reduce warehousing costs and assist the company towards the adoption of a mass customisation business strategy. [ABSTRACT FROM AUTHOR]

In this research, we model a job shop that produces a set of discrete parts in a make-to-stock setting. The intent of the research is to develop a planning model to determine the optimal tactical policies that minimise the relevant manufacturing costs subject to workload variability and capacity limits. We consider two tactical decisions, namely the production lot size for each part and the planned lead time for each work station. We model the relevant manufacturing costs, entailing production overtime costs and inventory-related costs, as functions of these tactical decisions. We formulate a non-linear optimisation model and implement it in the Excel spreadsheet. We test the model with actual factory data from our research sponsor. The results are consistent with our intuition and demonstrate the potential value from jointly optimising over these tactical policies. [ABSTRACT FROM AUTHOR]

In this paper, we consider the surrogate measures of robustness for the integration of production scheduling and preventive maintenance planning problem with stochastic machine failures. First, the influence of two categories of maintenance on operation completion time is analyzed. Since it does not come with an exact solution, a novel measure algorithm for evaluating two types of robustness simultaneously is proposed, based on the internal relationships among the scheduling structure, the number and sequence of preventive maintenance activities, the probability and downtime of failures, and the expected completion time of operation. Extensive experiments are conducted on 19 benchmark problems with random machine breakdowns. Experimental results first show that the correlation between our algorithm and Monte Carlo simulation in two robustness indicators is above 99%, and the former computes much faster than the latter. A thorough comparison is made with the other three surrogate robustness measures, which further proves the accuracy of our algorithm. Additional experiments also confirm the benefits by integrating preventive maintenance.

In this paper, we investigate the impact of rescheduling policies in the event of both rework and reconditioning in job shop manufacturing systems. Since these events occur in unplanned and disrupting manner, to address them properly, it is required to manage real-time information and to have flexible reaction capacity. These capabilities, of data acquisition and robotics, are provided by Industry 4.0 Technologies. However, to take full advantage of those capabilities, it is imperative to have efficient decision-making processes to deliver adequate corrective actions. In this sense, we propose an event-driven rescheduling approach. This approach consists of an architecture that integrates information acquisition, optimization process, and rescheduling planning. We study the performance of the system with several algorithms with two performance criteria, namely, (i) relative performance deviation (RPD) in terms of objective function and (ii) schedule stability. We also propose a hybrid policy that combines full rescheduling regeneration with stability-oriented strategies aimed to balance both criteria. We conducted extensive computational tests with instances from the literature under different scenarios. The results show that a sophisticated algorithm can obtain better quality schedules in terms of the objective function but at the expense of sacrificing stability. Finally, we analyze and discuss the results and provide insights for its use and implementation. [ABSTRACT FROM AUTHOR]

Production control systems (PCSs) control the flow of jobs in a production system. The selection of a suitable PCS in the context of make-to-order (MTO) is challenging, due to the characteristics of MTO businesses and the number of parameters or factors that comprise a PCS. The literature that compares PCSs in the MTO context reported contradictory results. In fact, there is a gap in the literature concerning which factors or parameters explain a PCS performance. This paper presents an analysis of comparative studies on PCS in the MTO context, using a systematic literature review, to reveal which control factors and manufacturing conditions influence a PCS performance. The analysis concentrates on studies that use simulation to assess the performance of PCSs. Our results indicate that the main difference in PCSs performance is the design of the control loops. Other important factors that must be considered in the choice of a PCS are the order release mechanism, the workload aggregation approach, and the workload estimation method used on control loops. A framework for choosing a suitable PCS for MTO companies is presented, considering these factors. [ABSTRACT FROM AUTHOR]

Maintenance in a stochastic job shop scheduling environment significantly impacts real-time scheduling problems with the environmental aspect. This simulation research assesses the effect of a reliability-based preventive maintenance approach to system performance for considering job shop scheduling problems with sequence-dependent setup time (SDST). Two types of reliability-based maintenance approaches are considered, i.e., reliability-centered preventive maintenance (RCPM) and a reliability-centered periodic preventive maintenance approach (RCPPM). The shop comprises 10 different machines and six job types. Six scheduling and two energy-oriented performance measures (Pms) are considered for evaluating the system's performance. Results reveal that lower levels of reliability, namely, 0.74, 0.78, and 0.82 recommended for mean flow time, makespan, average operation energy consumption, average idle energy consumption, and total setups Pms. A 0.74 level of reliability is recommended for mean tardiness and the number of tardy jobs Pms for the RCPM approach. RCPPM approach provides the best system Pms by mean flow time, makespan, mean tardiness, average operation energy consumption, average idle energy consumption, and the number of tardy jobs when maintenance time is 5% of operation time. In contrast, mean setup, and total setups Pms are independent of maintenance time. Both approaches get compared, and its statistical analysis shows that if maintenance time is 15% or more than 15% of the operation time, the RCPM method is recommended. If maintenance time is 10% or less than 10% of the operation time, maintenance planning is recommended using the RCPPM approach. Considering real-time scheduling work with the environmental aspect represents the novelty of the present study. [ABSTRACT FROM AUTHOR]

Nowadays, scheduling problem in the field of production planning has been considered by many researchers and this issue is used in various industries. Using the same parallel machines reduces the queue of WIP behind the machine and also increases production. Since scheduling problems in large scale are NP-hard, to solve them, metaheuristic algorithms are used. In this paper, Ant colony optimisation and Harmony Search algorithm are used to solve the problem as well as comparison with two other algorithms and finally, their performance is analysed and the efficient algorithm is investigated. [ABSTRACT FROM AUTHOR]

Copyright of Industrial Management Perspective / Chashm/&āz-I Mudīriyyat-I Ṣan̒atī is the property of Shahid Beheshti University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A suitable sequencing and dispatching of jobs on machines is very much essential to improve the performance of any industry. Sequencing is the prioritizing of a set of jobs in a queue based on some decision rule to determine the order in which they will be processed. This paper aims at detailed study and analysis of hybrid rule selection to reduce lead time, waiting time and to increase machine utilization and throughput. In this study four static and eight hybrid dynamic rules were considered and prioritization of jobs is done using TOPSIS algorithm. The hybrid rules are obtained by combining certain dynamic rules. The data includes 47 jobs and 17 machines with different monthly order quantities. Discrete event simulation tool is used for the study and validation of actual data of a manufacturing plant. The results shows that the proposed hybrid dispatching rule, which is a combination of Earliest Creation time (ECT), Shortest waiting time (SWT) and Most work remaining time (MWRT) are very effective in minimizing lead time and maximizing throughput. The another hybrid dispatching rule, which is a combination of Longest Waiting Time (LWT), ECT and MWRT are effective in maximizing the machine utilization.

This paper deals with the multi-objective job shop scheduling problem in a robotic cell (MOJRCSP). All the jobs are processed according to their operations order on workstations. Different from classical job shop scheduling problem, the studied problem considers that jobs' transportation is handled by a robot. Also, the jobs are expected to be finished in a time window, instead of a constant due date. A mixed Integer Programming (MIP) model is proposed to formulate this problem. Due to the special characteristics of the studied problem and its NP-hard computational complexity, a metaheuristic based on Teaching Learning Based Optimization (TLBO) algorithm has been proposed. The proposed algorithm determines simultaneously the operations' assignments on workstations, the robot assignments for transportation operations, and the robot moving sequence. The objective is to minimize the makespan and the total earliness and tardiness. Computational results further validated the effectiveness and robustness of our proposed algorithm.

Nowadays, quickly changing customer demands are a big challenge in the manufacturing industry, especially for job shops, which are typical coupling and nonlinear multi-input–multi-output (MIMO) systems. In order to achieve good shop floor performance in the presence of short-term demand fluctuations, a key performance indicator—work in process (WIP)—is required to be effectively controlled in the vicinity of the desired levels. For this purpose, a machinery-oriented capacity adjustment approach via a reconfigurable machine tool (RMT) is employed to flexibly balance capacity and load in the case of a bottleneck. A mathematical model concerning the RMT and WIP was first established in the presence of uncertainty and delays. The operator-based robust right coprime factorization (RRCF) method was adopted to stabilize the uncertain system, and adaptive integral separated proportional–integral (ISPI) tracking controllers were further designed to improve the transient and robustness performance. The performance of the proposed ISPI-RRCF was analyzed and compared with that of a state-of-the-art method in a simulation. The results showed that both control systems could ensure that the WIP was within an allowed bound, while the former had lower overshoots, shorter setting times, and more concentrated distributions facing stochastic demands. This further indicated the effectiveness of the proposed algorithm in the avoidance of serious bottlenecks and unbalanced capacity distributions.

Collaborative Manufacturing Scheduling (CMS) is not yet a properly explored decision making practice, although its potential for being currently explored, in the digital era, by combining efforts among a set of entities, either persons or machines, to jointly cooperate for solving some more or less complex scheduling problem, namely occurring in job shop manufacturing environments. In this paper, an interoperable scheduling system integrating a proposed scheduling model, along with varying kinds of solving algorithms, are put forward and analyzed through an industrial case study. The case study was decomposed in three application scenarios, for enabling the evaluation of the proposed scheduling model when envisioning the prioritization of internal–makespan-or external–number of tardy jobs-performance measures, along with a third scenario assigning a same importance or weight to both kinds of performance measures. The results obtained enabled us to realize that the weighted application scenario permitted reaching more balanced, thus a potentially more attractive global solution for the scheduling problem considered through the combination of different kinds of scheduling algorithms for the resolution of each underlying sub problem according to the proposed scheduling model. Besides, the decomposition of a global more complex scheduling problem into simpler sub-problems turns them easier to be solved through the different solving algorithms available, while further enabling to obtain a wider range of alternative schedules to be explored and evaluated. Thus, contributing to enriching the scheduling problem-solving process. A future exploration of the application in other types of manufacturing environments, namely occurring in the context of extended, networked, distributed or virtual production systems, integrating an increased and variable set of collaborating entities or factories, is also suggested.

One of the key functions of Workload Control is order release. Jobs are not released immediately onto the shop floor – they are withheld and selectively released to create a mix of jobs that keeps work-in-process within limits and meet due dates. A recent implementation of Workload Control’s release method highlighted an important issue thus far overlooked by research: How to accommodate re-entrant flows, whereby a station is visited multiple times by the same job? We present the first study to compare the performance of Workload Control both with and without re-entrant flows. Simulation results from a job shop model highlight two important aspects: (i) re-entrant flows increase variability in the work arriving at a station, leading to a direct detrimental effect on performance; (ii) re-entrant flows affect the release decision-making process since the load contribution of all visits by a job to a station has to fit within the norm. Both aspects have implications for practice and our interpretation of previous research since: (i) parameters given for work arriving may significantly differ from those realised; (ii) increased workload contributions at release mean that prior simulations may have been unstable, leading to some jobs never being released. [ABSTRACT FROM PUBLISHER]

The workload control literature highlights the importance of balancing the shop floor workload, but also acknowledges that this can conflict with processing the most urgent orders – hence, there is a trade-off. In practice, shops contain many complexities, e.g. simultaneous batching and sequence-dependent set-up times that may conflict with processing the most urgent orders and require other solutions than workload balancing to avoid capacity losses. This adds to the trade-off dilemma, which traditionally only considers timing and balancing. This paper develops a framework that determines whether to address a complexity through order release or dispatching. It comprises two dimensions: (i) the typical position of a complexity in the routing of an order and (ii) the criticality of the complexity. A case study is presented, which demonstrates the framework’s utility and illustrates the development of specific solutions designed to handle the complexities. Most complexities present in the case require handling at the order release stage. The challenges of handling multiple complexities at this decision level are evaluated. Finally, the implications for managers and future research are outlined. [ABSTRACT FROM PUBLISHER]

Recent research has highlighted the potential impact of pool sequencing on order release performance but it suffered from two shortcomings. First, arguably the best release solution for workload control in practice combines periodic with continuous release. Although the two types of releases serve different functions, recent work assumed the same sequencing rule should be used for both. Here, the use of different sequencing rules for periodic and continuous releases is evaluated. Using a job-shop simulation, we demonstrate that the rule applied during continuous releases has only a negligible impact on performance. Therefore, jobs can be pulled intermediately from the pool by workers using a more straightforward sequencing rule than the one applied for periodic release. Second, it was assumed that all jobs in the pool are sequenced and then a subset is selected for release. But for some load-oriented sequencing rules, the priority value used for sequencing jobs should be updated after each job selection from the pool. Our simulation results show that although this may improve load balancing at release, it does not in fact improve overall shop performance. Therefore, the greedy heuristic of first sequencing and then selecting jobs can be maintained, which allows the release decision-making process to retain its simplicity. The work has important implications for the use of sequencing rules in practice. [ABSTRACT FROM PUBLISHER]

The paper investigates the effects of production scheduling policies aimed towards improving productive and environmental performances in a job shop system. A green genetic algorithm allows the assessment of multi-objective problems related to sustainability. Two main considerations have emerged from the application of the algorithm. First, the algorithm is able to achieve a semi-optimal makespan similar to that obtained by the best of other methods but with a significantly lower total energy consumption. Second, the study demonstrated that the worthless energy consumption can be reduced significantly by employing complex energy-efficient machine behaviour policies. [ABSTRACT FROM PUBLISHER]

In most small manufacturing enterprise (SME), production planning and scheduling are crucial operational management tasks required. A typical make-to-order company is plagued by frequent absenteeism and abrupt resignation of skilled workers, leading to an increase in late delivery of jobs and rework of returned jobs. The effects of some human factors, for example, job skill, job satisfaction, and job fatigue to mention a few were studied using statistical analysis. It was concluded that human factors may have significant effects on job performance in a SME. To our best knowledge, this conclusion is less known in enterprise systems in general. [ABSTRACT FROM AUTHOR]

This article presents an integrated computer simulation–stochastic data envelopment analysis (SDEA) approach to deal with the job shop facility layout design problem (JSFLD) with stochastic outputs and safety and environmental factors. Stochastic outputs are defined as non-crisp operational and deterministic inputs. At first, feasible layout alternatives are generated under expert decision. Then, computer simulation network is used for performance modelling of each layout design. The outputs of simulation are average time-in-system, average queue length and average machine utilisation. Finally, SDEA is used with Lingo software for finding the optimum layout alternative amongst all feasible generated alternatives with respect to stochastic, safety and environmental indicators. The integrated approach of this study was more precise and efficient than previous studies with the stated outputs. The results have been verified and validated by principal component analysis. The unique features of this study are the ability of dealing with multiple inputs (including safety) and stochastic (including environmental) outputs. It also uses mathematical programming for optimum layout alternatives. Moreover, it is a practical tool and may be applied in real cases by considering safety and environmental aspects of the manufacturing process within JSFLD problems. [ABSTRACT FROM PUBLISHER]

Order release is a key component of the Workload Control concept. Jobs do not enter the shop floor directly – they are retained in a pre-shop pool and released in time to meet due dates while keeping work-in-process within limits or norms. There are two important groups of release methods: continuous methods, for which the workload falling to a specified level can trigger a release at any moment in time; and, periodic release methods, for which releases take place at fixed intervals. Continuous release methods in general have been shown to outperform periodic release methods. Yet, there is incongruence in the results presented in the literature on the relative performance of the various continuous release methods. We use a job shop simulation model to examine the performance of continuous release methods from the literature and find that the contradictory results are explained by the different rules applied to sequence jobs in the pool – a factor neglected in previous work. Finally, a new breed of continuous release methods has recently emerged, but these have not been compared with prior approaches. Therefore, we also examine these methods and show that they significantly improve overall performance, although this is to the detriment of jobs with large processing times. [ABSTRACT FROM PUBLISHER]

Genetic algorithms are widely used in various mathematical and real world problems. They are approximate metaheuristic algorithms, commonly used for solving NP-hard problems in combinatorial optimisation. Industrial scheduling is one of the classical NP-hard problems. We analyze three classical industrial scheduling problems: job-shop, flow-shop and open-shop. Canonical genetic algorithm is applied for those problems varying its parameters. We analyze some aspects of parameters such as selecting optimal parameters of algorithm, influence on algorithm performance. Finally, three strategies of algorithm – combination of parameters and new conceptualmodel of genetic algorithm are proposed.

Abstract: This paper aims at presenting a structure of implementation of the Workload Control (WLC) approach on a commercial discrete event simulation software by comprehensively demonstrating how to construct and simulate this approach. This research was developed considering the lack of studies fully describing this implementation on this kind of software, which hinders the dissemination and use of the WLC approach by managers. Initially, the logic was developed on a dedicated and structured language and then converted to the simulation software. A detailed description of the WLC implementation contributes to the business domain by facilitating its replication and application in other manufacturing environments, reducing the project time needed to develop the base model and allowing managers and/or researchers to focus directly on the adjustments of the model to the environment being modeled. In the academic domain, this paper addresses a gap in the WLC literature concerning the lack of tutorials for simulation and the lack of information, in the existing papers, regarding the development of the computational model.

For the job shop with variable processing speeds, the aim of energy saving and consumption reduction is implemented from the perspective of production scheduling. By analyzing the characteristics of the workshop, a multi-objective mathematical model is established with the objective of reducing the total energy consumption and shortening the makespan. A multi-objective discrete water wave optimization (MODWWO) algorithm is proposed for solving the problem. Firstly, a two-vector encoding method is adopted to divide the scheduling solution into two parts, which represent speed selection and operation permutation in the scheduling solution, respectively. Secondly, some dispatching rules are used to initialize the population and obtain the initial scheduling solutions. Then, three operators of the basic water wave optimization algorithm are redesigned to make the algorithm adaptive for the multi-objective discrete scheduling problem under study. A new propagation operator is presented with the ability of balancing global exploration and local exploitation based on individual rank and neighborhood structures. A novel refraction operator is designed based on crossover operation, by which each individual can learn from the current best individual to absorb better information. And a breaking operator is modified based on the local search strategy to enhance the exploitation ability. Finally, extensive simulation experiments demonstrate that the proposed MODWWO algorithm is effective for solving the considered energy-saving scheduling problem. [ABSTRACT FROM AUTHOR]

This paper addresses the job shop scheduling problem including time lag constraints. This is an extension of the job shop scheduling problem with many applications in real production environments, where extra (minimum and maximum) delays can be introduced between operations. It belongs to a category of problems known as NP-hard problems due to the large solution space. Biogeography-based optimization (BBO) is an evolutionary algorithm which is inspired by the migration of species between habitats, recently proposed by Simon (IEEE Trans Evol Comput 12:702–713, 2008) to optimize hard combinatorial optimization problems. BBO has successfully solved optimization problems in many different domains and has demonstrated excellent performance. We propose a hybrid biogeography-based optimization (HBBO) algorithm for solving the job shop scheduling problem with additional time lag constraints while minimizing total completion time. In the proposed HBBO, an effective greedy constructive heuristic is adapted to generate the initial habitat population. A local search metaheuristic is investigated in the mutation step in order to improve the solution quality and enhance the diversity of the population. To assess the performance of the HBBO, a series of experiments are performed on well-known benchmark instances for job shop scheduling problems with time lag constraints. The results prove the efficiency of the proposed algorithm in comparison with various other algorithms. [ABSTRACT FROM AUTHOR]