Study on Single-Machine Common/Slack Due-Window Assignment Scheduling with Delivery Times, Variable Processing Times and Outsourcing

We study the problem of two-machine no-wait flowshop scheduling with learning effect and convex resource-dependent processing times. Under the condition of the due-date assignment with common flow allowance (i.e. slack (SLK) due-date assignment), we provide a bi-criteria analysis where the first criterion is to minimise the scheduling criteria (i.e. the weighted sum of earliness, tardiness and flow allowance costs), and the second criterion is to minimise the resource consumption cost (i.e. the weighted sum of resource consumption cost). The objective is to determine the optimal job sequence, resource allocations and common (flow allowance) slack time that minimise the three different versions of the two criteria. We prove that these problems can be solved in polynomial time.

Parallel-machine scheduling with past-sequence-dependent delivery times and learning effect

Hybrid flow shop scheduling problems have gained an increasing attention in recent years because of its wide applications in real-world production systems. Most of the prior studies assume that the processing time of jobs is deterministic and constant. In practice, jobs’ processing time is usually difficult to be exactly known in advance and can be influenced by many factors, e.g., machines’ abrasion and jobs’ feature, thereby leading to their uncertain and variable processing time. In this paper, a dual-objective stochastic hybrid flow shop deteriorating scheduling problem is presented with the goal to minimize makespan and total tardiness. In the formulated problem, the normal processing time of jobs follows a known stochastic distribution, and their actual processing time is a linear function of their start time. In order to solve it effectively, this paper develops a hybrid multiobjective optimization algorithm that maintains two populations executing the global search in the whole solution space and the local search in promising regions, respectively. An information sharing mechanism and resource allocating method are designed to enhance its exploration and exploitation ability. The simulation experiments are carried out on a set of instances, and several classical algorithms are chosen as its peers for comparison. The results demonstrate that the proposed algorithm has a great advantage in dealing with the investigated problem.

We considerthe single-machine group scheduling problem with controllable processing times (i.e., resource allocation) under a common due-window (condw) assignment. The objective is to minimize a total cost composed of earliness, tardiness, due-window-related penalties, and resource consumption. Motivated by realistic production settings such as aerospace component machining and electronics batch assembly, the study addresses the joint optimization of group sequence, job sequence, due-window placement, and resource allocation. For linear and convex resource models, we propose a branch-and-bound (BaB^) algorithm and efficient heuristics. Numerical experiments show that the BaB^ algorithm can solve instances with up to 250 jobs and 16 groups. The heuristics (UB^), including a simulated annealing (SA^) algorithm, obtain near-optimal solutions with an average error below 0.05% much faster, demonstrating their practical usefulness for real-time scheduling.

In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artificial Bee Colony algorithm, is proposed for a dynamic flexible job-shop scheduling (DFJSP) problem. This problem consists of n jobs that should be processed by m machines and the processing time of jobs deviates from estimated times. The objective is near-optimal scheduling after any change in tasks in order to minimise the maximal completion time (Makespan). In the proposed model, first, scheduling is done according to the estimated processing times and then re-scheduling is performed after determining the exact ones considering machine set-up. In order to evaluate the performance of the proposed model, some numerical experiments are designed in small, medium and large sizes in different levels of changes in processing times and statistical results illustrate the efficiency of the proposed algorithm.

This paper studies a real-world hybrid flow shop problem arising from the steelmaking continuous casting process, which is the bottleneck of the iron and steel production. There are a variety of features to be taken into account, in particular the batch constraints and the variable processing times in the last stages. Based on a time-index formulation and machine capacity relaxation, three Lagrangian relaxation (LR) approaches are presented for addressing this scheduling problem. The three LR approaches decompose the relaxed problem into job-level problems, batch-level problems and machine-level problems, respectively. These subproblems are solved based on polynomial dynamic programming algorithms. The corresponding Lagrangian dual (LD) problems are solved by an efficient subgradient algorithm with global convergence. Computational results and comparisons demonstrate that the approach adopting job-level decomposition is most efficient among three approaches, whereas the approach adopting batch-level decomposition is most effective.

We study two unrelated machine scheduling problems with machine dependent release dates to minimize the makespan.For the case with fixed processing time where processing job $j$ on machine $i$ requires time $p_{ij}$ and incurs a cost of $c_{ij}$,we derive a $2$-approximation algorithm. For the problem with variable processing times where the cost increases linearly as the processing time decreases, we propose a $(2+\epsilon)$-approximation algorithm.

We consider a number of parallel-machine scheduling problems in which jobs have variable processing times. The actual processing time of each job is described by an arbitrary positive function of the position it holds on a machine. However, the function itself may additionally depend on the job or a machine this job was assigned to. Our aim is to find a schedule that minimizes the objectives of maximum completion time or the total completion time. We present a full set of polynomial solutions for the cases of jobs with no precedence constraints. We also show that the case of single-chained jobs may be not easier in general, but some polynomial results can be obtained, too.

The bowl phenomenon provides a way of increasing the throughput of some production line systems with variable processing times by purposely unbalancing the line in a certain manner. However, previously available numerical results for applying the bowl phenomenon have been quite limited. We extend these numerical results here by obtaining the optimal allocation of work for somewhat larger cases than previously considered. We also develop some guidelines and data for extrapolating these results to estimate the optimal allocation of work for even larger production lines that are beyond the reach of exact solution methods. The results cover a broad cross-section of values of both buffer capacities and the coefficient of variation of processing times.

This paper presents a new self‐position estimation method for an autonomous mobile robot whose processing time can be varied. In this method, the current position of the robot is estimated by image template matching with the normalized correlation coefficient between the input image and stored images. Based on the idea of anytime sensing, the time for self‐position estimation can be varied by changing the image size. In order to realize efficient self‐position estimation, image templates are generated by means of Genetic Algorithm. The usefulness of the proposed method is shown through simulation results using test images and experimental results obtained with a real robot. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(11): 46–58, 2010; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.10223

A topological model of mesh-network of LTE technology presented using hypergraphs and Koenig graphs has been offered. The capabilities of hypergraphs were used to describe the distribution of frequency resources and to assess the characteristics of the initial configurations of the mesh network. Koenig graphs were used for a comparative analysis of the results. This made it possible to describe all possible configurations of the mesh-network, as well as its elements represented in the form of vertices and edges of the hypergraph. A mathematical model of the frequency resource allocation in the LTE technology mesh-network. Its novelty lies in the description of the problem of the frequency resource allocation as subchannel allocation problem in taking into account the interference between the network stations.

ABSTRACTIn this article, the two-machine no-wait permutation flowshop scheduling problem with common due window assignment, learning effect and resource allocation is considered, wherein learning effect and resource allocation mean that the processing time of a job is a function of its position in a sequence and its amount of resource allocation. According to the common due window assignment method, each job is associated with a common due window, which is a decision variable. The goal is to determine the sequence of jobs, common due window starting time, due window size and resource allocation such that the linear weighted sum of job earliness, tardiness, due window size and resource cost is minimized. This article shows that the scheduling problem can be solved in polynomial time.

This paper considers the SLK/DIF due window assignment problems on a single machine with a general job-dependent positional effect, where the actual processing time of a job is subject to a job-dependent and positional effect. Production schedulers may decide to outsource/reject some jobs by paying the corresponding costs. Based on whether to accept the tardiness jobs, we consider two different objectives under slack and different due window assignment, respectively. The first objective function is to minimize the weighted sum of earliness, tardiness, due window starting time, due window size, and outsourcing costs, while the second objective function is to minimize the cost function that includes earliness, due window starting time, due window size and outsourcing costs. We study the structural properties of two due window assignment methods and develop polynomial-time solution algorithms for the considered problems. A numerical example proves the advantage of the outsourcing decision and the distinction between two different objectives.

Sun, G.; Chen, H., and Mao, Y., 2020. The efficiency analysis model of technological innovation resource allocation of coastal enterprises based on genetic algorithm. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 691–695. Coconut Creek (Florida), ISSN 0749-0208.With the continuous development of marine technology, the tasks of marine survey, exploration, navigation, transportation and development are becoming more and more serious. It is necessary to pay attention to the problem of planning resource allocation in coastal areas, so as to design a more reasonable resource allocation scheme. The traditional analysis model can only converge locally, resulting in the deviation of the analysis results of resource allocation efficiency. Therefore, the construction method of the efficiency analysis model of technological innovation resource allocation of coastal enterprises based on genetic algorithm is proposed. According to the relationship between fuzzy operator and fuzzy scale, this method calculates the carrying capacity of coastal environmental resources. In light of the prediction results of the dynamic demand forecast function of emergency resources, it sets the evaluation weight of the efficiency of technological innovation resource allocation of coastal enterprises. Based on the global convergence characteristics of genetic algorithm, it realizes the construction of the efficiency analysis model of technological innovation resource allocation of coastal enterprises. The experimental results show that compared with the traditional method, the model based on genetic algorithm has stronger convergence and can achieve global data convergence. It can be seen that the analysis results of this model accord with the reality and can be applied to the analysis of resource allocation efficiency at this stage.

Process quality and delivery time have received increasing attention in the highly competitive electronics industry. Many studies have proposed process capability indices (PCIs) to assess process effectiveness. However, methods to assess the performance in terms of processing and delivery times of products have seldom been discussed. The conventional PCIs can no longer assess the processing time (PT) and delivery time (DT) performance objectively or identify the relationship between PCIs and the non-conformance rate of PT or the conformance rate of DT. Lacking an effective performance index or an objective testing procedure to assess process/product performance will lead to inefficiency or a high manufacturing management overhead cost. Therefore, this study offers effective performance indices (i.e., PCIs) to assess the PT and DT performance for very large scale integration (VLSI). The uniformly minimum variance unbiased (UMVU) estimators of the proposed PCIs are derived under the assumption of a normal process distribution. The PCI estimators are then employed to construct a one-to-one relationship between the PCIs and the conformance rate of DT or non-conformance rate of PT, respectively. Finally, hypothesis testing procedures for the proposed PCIs are also developed. The testing procedure can be used to determine whether DT or PT can satisfy a customer’s requirements.