New completion time algorithms for sequence based scheduling in multiproduct batch processes using matrix

The scheduling of batch process is a kind of NP-complete problem and has been the interest of research for many years. Various methods have been applied to solve such problems and simulated annealing (SA) is the most efficient algorithm. But SA is very slow when the problem size is large. In this work, particle swarm optimization (PSO) was applied to solve the scheduling problem of multiproduct batch process. The results show that PSO is a powerful method for solving the batch process scheduling problems and is superior to the widely used simulated annealing.

We consider batch processing jobs to minimize the mean completion time. A batch processing machine can handle up to $B$ jobs simultaneously. Each job is represented by an arrival time and a processing time. Jobs processed in a batch have the same completion time, i.e., their common starting time plus the processing time of their longest job. For batch processing, non-preemptive scheduling is usually required and we discuss this case. The batch processing problem reduces to the ordinary uniprocessor system scheduling problem if $B=1$. We focus on the other extreme case $B=+\infty$. Even for this seemingly simple extreme case, we are able to show that the problem is NP-hard for the weighted version. In addition, we establish a polynomial time algorithm for a special case when there are only a constant number of job processing times. Finally, we give a polynomial time approximation scheme for the general case.

Determination of completion times for serial multiproduct processes—3. Mixed intermediate storage systems

Data-based modelling for predicting the completion time of batch processes

This paper considers single-machine just-in-time scheduling of jobs that may be grouped into batches subject to a constraint on batches' weights. A job has a weight, due date, and earliness and tardiness penalties per unit time. A batch's processing time is determined by its jobs. Each job inherits its batch's completion time. The objective is to minimize the weighted sum of earliness and tardiness penalties of all jobs. This problem is challenging: Jobs-to-batch assignment changes the batch's processing time; thus, affects the structure of the entire solution and most importantly of its cost components. This problem is an excellent benchmark for testing linkage learning techniques, which exploit a problem's structure. We propose a matheuristic LTGA, which integrates evolutionary algorithms with mixed-integer programming (MIP). It builds a linkage tree that extracts the dependency among decision variables of MIP solutions. We compare its performance to a state of the art matheuristic CMSA, which combines the learning component of an ant colony system (ACS) with MIP within a construct, solve, merge, and adapt framework. It uses knowledge extracted from ACS' ants to construct a restricted MIP, solves it efficiently, and feeds it back to ACS. Computational tests indicate that LTGA outperforms MIP solvers and CMSA.

The scheduling of batch processes is a widely researched field of chemical engineering. Over the last few decades, a great number of tools have been developed for industrial examples and literature problems. These methods vary not only in their applied model, but in their representation of the problem inputs as well. The most well-known representations are the State-Task Network, the Resource-Task Network, the State Sequence Network, and the S-graph. While the latter also serves as the mathematical model for the related optimization approaches, the others only act as an intermediate model between the raw problem data and the model used for optimization, e.g., a mixed-integer linear programming model. The eS-graph model is a generalization of the S-graph, where the one-to-one relation between nodes and tasks has been relaxed, allowing a much wider range of scheduling problems to be tackled. Processes may simultaneously occupy several units, and release them at different stages of execution, and certain stages of separate processes can be forced to overlap in time. As in the case of the S-graph, the eS-graph models can be solved to optimality by specially designed combinatorial algorithms or serve as a basis for precedence based linear programming formulations. In this work, the modelling capacities of the eS-graph framework are illustrated via a Polymer production case study, where complex timing constraints are present.

Method to incorporate energy integration considerations in multiproduct batch processes

A batch process is a discontinuous and concurrent process which is suitable for multi-product, small-sized production. The distinctive feature of a batch process is that various decision making processes, such as scheduling, design, operation, etc. are strongly connected with each other. Interaction among these processes is necessary to dynamically and flexibly cope with a variety of unplanned events. This paper aims at presenting a batch scheduling technique based on Petri net models and showing the possibilities of integration between scheduling and design of batch processes. For this purpose, it first views the behavior of a batch operating system as a discrete event system and presents a Petri net model to be used for scheduling, design and operation. It next formulates batch scheduling problems based on Petri net partial languages, proposes their solution technique and last discusses the integration between scheduling and design of batch systems.

Hybrid method integrating agent-based modeling and heuristic tree search for scheduling of complex batch processes

A general MILP model for scheduling of batch process with sequence-dependent changeover

Intelligent scheduling and monitoring for multi-product networked batch processes

Batch processing reduces processing time in a business process at the expense of increasing waiting time. If this trade-off between processing and waiting time is not analyzed, batch processing can, over time, evolve into a source of waste in a business process. Therefore, it is valuable to analyze batch processing activities to identify waiting time wastes. Identifying and analyzing such wastes present the analyst with improvement opportunities that, if addressed, can improve the cycle time efficiency (CTE) of a business process. In this paper, we propose an approach that, given a process execution event log, (1) identifies batch processing activities, (2) analyzes their inefficiencies caused by different types of waiting times to provide analysts with information on how to improve batch processing activities. More specifically, we conceptualize different waiting times caused by batch processing patterns and identify improvement opportunities based on the impact of each waiting time type on the CTE. Finally, we demonstrate the applicability of our approach to a real-life event log.

GAMIT is one of the best baseline solution software for high precision GPS data processing. For a certain high precision GPS network with massive data and multiple sessions of single day, the script sh_gamit or others approach fails to achieve batch processing for GPS observations. It is still solved via step by step. However, it's so cockamamie and inefficient for solving. Base on deathly studying and discussing the approach to baseline solution batch processing for high precision GPS network with massive data, this paper proposed an algorithm of baseline solution batch processing for high precision GPS network-named GBSBPA algorithm. And then, we realized the GBSBPA algorithm base on Bash programming language. Finally, it integrated with baseline surveying engineering high precision GPS control network for the experiment of the GBSBPA algorithm analysis and test, and effective and feasible of the GBSBPA algorithm was verified and automation batch processing of GPS network with massive data was achieved, and we obtained satisfying results.

Software design for image mapping and analytics for high throughput phenotyping

Using water cascade analysis to synthesize water use network in batch process