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This study examines the scheduling problem for a two-stage flowshop. All jobs are immediately available for processing and job characteristics including the processing times and due dates are known and certain. The goals of the scheduling problem are (1) to minimize the total flowtime for all jobs, (2) to minimize the total number of tardy jobs, and (3) to minimize both the total flowtime and the total number of tardy jobs simultaneously. Lower bound performances with respect to the total flowtime and the total number of tardy jobs are presented. Subsequently, this study identifies the special structure of schedules with minimum flowtime and minimum number of tardy jobs and develops three sets of heuristics which generate a Pareto set of bicriteria schedules. For each heuristic procedure, there are four options available for schedule generation. In addition, we provide enhancements to a variety of lower bounds with respect to flowtime and number of tardy jobs in a flowshop environment. Proofs and discussions to lower bound results are also included.

The development of a rule‐based expert system (ES), driven by a discrete event simulation model, that performs dynamic shop scheduling is described. Based on a flowtime prediction heuristic that has been developed and base‐line runs to establish the efficacy of scheduling strategies such as shortest processing time (SPT), critical ratio, total work, etc., a rescheduling‐based dispatching strategy is investigated in a dynamic job shop environment. The results are discussed and analyzed.

While it is well known that managers make scheduling decisions based on multiple objectives, the majority of sequencing research is directed towards single criterion assessments of scheduling performance. A preference value decision model approach for the selection of job shop sequencing rules using multiple performance criteria is presented. The model is general in nature and may be applied to any of a number of sequencing rules and performance measures. A case example is provided to illustrate the procedure.

Much of the research literature in job shop scheduling deals with pure job shop environments. However, currently most processes involve a hybrid of both the job shop and a flow shop with a combination of flexible and conventional machine tools. Presents a study of such a job shop under varying conditions and performance criteria. Argues that for scheduling in this environment, certain combinations of scheduling rules should be utilized under different arrival rates and for different job types. A simulation model is developed using a hypothetical hybrid job shop to study the performance of rule combinations with variations in arrival rates and processing times. The performance criteria used are flowtime as a measure of work‐in‐process inventory, tardiness for JIT, and throughput for completed items inventory. It was found that rule combination performance varied with the performance criteria. Furthermore, it was found that the combinations were sensitive to arrival rates and processing times. Concludes, from the insights gained in the study, that the rule combination to be implemented should depend on the performance objective and the arrival rate/processing time condition of the hybrid job shop.

Cloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be done optimally to achieve proficient results in a cloud computing environment. While satisfying the user’s requirements in a cloud environment, scheduling has been proven an NP-hard problem. Therefore, it leaves scope to develop new allocation models for the problem. The aim of the study is to develop load balancing method to maximize the resource utilization in cloud environment.

In this paper, the parallelized task allocation with load balancing (PTAL) hybrid heuristic is proposed for jobs coming from various users. These jobs are allocated on the resources one by one in a parallelized manner as they arrive in the cloud system. The novel algorithm works in three phases: parallelization, task allocation and task reallocation. The proposed model is designed for efficient task allocation, reallocation of resources and adequate load balancing to achieve better quality of service (QoS) results.

The acquired empirical results show that PTAL performs better than other scheduling strategies under various cases for different QoS parameters under study.

The outcome has been examined for the real data set to evaluate it with different state-of-the-art heuristics having comparable objective parameters.

This paper aims to develop a method based on artificial intelligence capable of predicting the due date (DD) of job shops in real-time, aiming to assist in the decision-making process of industries.

This paper chooses to use the methodological approach Design Science Research (DSR). The DSR aims to build solutions based on technology to solve relevant issues, where its research results from precise methods in the evaluation and construction of the model. The steps of the DSR are identification of the problem and motivation, definition of the solution’s objectives, design and development, demonstration, evaluation of the solution and the communication of results.

Along with this work, it is possible to verify that the proposed method allows greater accuracy in the DD definition forecasts when compared to conventional calculations.

Some limitations of this study can be pointed. It is possible to mention questions related to the tasks to be informed by users, as they could lead to problems in the performance of the artifact as the input data may not be correctly posted due to the misunderstanding of the question by part of the users.

The proposed artifact is a method capable of contributing to the development of the manufacturing industry to improve the forecast of manufacturing dates, assisting in making decisions related to production planning. The use of real production data contributed to creating, demonstrating and evaluating the artifact. This approach was important for developing the method allowing more reliability.

Examines the planning and scheduling systems in an integrative framework for a dual resource constrained (DRC) job shop. Models a bottleneck shop environment which represents a common situation in practice. Research on workload smoothing mechanisms which integrate the planning and scheduling systems is sparse. Simulates 13 smoothing rules (utilizing aggregate workload and bottleneck information), three despatching rules and two order review/release rules. The results of this study indicate that workload smoothing by the planning system has a significant effect on the performance of the DRC job shop. Pulling work ahead in valley periods improved the tardy measures of performance, while pushing jobs back in peak periods usually deteriorated shop performance. Suggests that combining a planning system effectively with order review/release can improve mean tardiness, percentage tardy and mean flowtime measures of performance.

Reviews a critical aspect of job shop scheduling research, namely the decision regarding release timing of orders to the manufacturing shopfloor. Covers articles specifically addressing the order release problem; the information should prove helpful for researchers employing order release policies in their research. The release decision literature is classified into three areas: descriptive studies including case and survey research, analytical or optimization‐based research, and simulations of theoretical and empirically derived job shops. Presents tables describing the release rules and summarizing the characteristics of the simulation research. Identifies a number of topics within the order release research area in need of further investigation, including the comparison of larger sets of release rules, the use of more realistic simulation models such as the dual resource constrained job shop model, the need for further empirical identification of release policies, and the use of release policies that consider dynamic shop conditions.

In job‐shop scheduling research, the shortest processing time despatching rule (SPT) has received considerable attention. Several truncated shortest processing time (SPT) despatching rules have been published in the literature. The motivation for such rules is that SPT despatching will frequently result in favourable inventory levels but unfavourable due date performance. Thus, the truncated SPT rules periodically switch from SPT despatching to despatching by some other rule. Owing to the complexity associated with these truncated rules, their implementation is doubtful. As a result, a modified truncated rule is presented that is easy to implement and yet performs favourably with respect to inventory and due date performance.

In this paper, the authors investigate the effects bottleneck machines have on the performance of a multi‐stage job shop. The operation performed by the bottleneck and its position in the product Bill of Materials is varied to determine the effects on system performance. It was found that the best place for the bottleneck machine to be located was at gateway operations located low in the Bill of Materials.