Search

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. [ABSTRACT FROM AUTHOR]

This article considers single-machine problems in which the actual processing time of a job is a function of its position in a sequence (i.e. position-dependent deterioration effects). In this model, a job is either accepted or rejected. If the job is accepted, it is processed on a single machine, but if the job is rejected, a penalty (cost) is imposed. The goal is to minimize the sum of the given scheduling objectives, including the makespan, the total completion time, the total absolute differences in completion times and the total absolute differences in waiting times of the accepted jobs and total rejection penalty of the rejected jobs. It is illustrated that these problems remain polynomially solvable under the proposed models. Finally, computational results demonstrate that the proposed algorithms can solve instances of various size problems in attractive times. An extension to the problems is offered by assuming time-dependent deterioration effects. [ABSTRACT FROM AUTHOR]

We study resource allocation scheduling with job-dependent learning effect on a single machine with or without due date assignment considerations. For a convex resource processing time function, we provide a polynomial time algorithm to find the optimal job sequence, and resource allocations that minimise the schedule criterion (the total compression cost) subject to the constraint that the total compression cost (the schedule criterion) is less than or equal to a fixed amount. [ABSTRACT FROM PUBLISHER]

This article investigates the single-machine common due-window assignment problem with generalized earliness/tardiness penalties and a rate-modifying activity. Job processing times include two cases: one with constant values and another with time-dependent processing times (deterioration effect). Considering the maintenance activity, the maintenance time is a fixed constant. The goal is to determine the position of the common due window and the optimal sequence such that the following objective function is minimized: the weighted penalties for the sum of earliness and tardiness, the weighted number of early and delayed, and the weighted due-window starting time and size. It is proved that the problem can be solved in O (n 6) time, where n is the number of jobs. [ABSTRACT FROM AUTHOR]

This article addresses how to minimize total completion time on a single machine with a position-weighted learning effect. Under non-zero job release dates, a lower bound on the objective function, a number of dominance properties, and an initial solution (i.e. an NEH-based heuristic) are derived, which are used to expedite the elimination process of a branch-and-bound solution procedure. The performance of the branch-and-bound algorithm and the heuristic algorithm are evaluated on a set of randomly generated problem instances. [ABSTRACT FROM AUTHOR]

This article deals with a single-machine resource allocation scheduling problem with deteriorating jobs. Under a group technology environment, the actual processing times of jobs are a convex resource allocation function of their starting time and the amount of resource allocation. The objective is to minimize the weighted sum of makespan and resource allocation cost. For two special cases, it is proved that the problem can be solved in polynomial time. For the general case of the problem, heuristic and branch-and-bound procedures are developed to solve the problem. Computational experiments are also performed to examine the effectiveness and the efficiency of the algorithms. [ABSTRACT FROM AUTHOR]

In 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. [ABSTRACT FROM AUTHOR]

This article deals with an integrated optimization problem of production scheduling and common due-window assignment on a single machine. The objective is to minimize the total cost, which is a weighted sum function of earliness and tardiness, due-window starting time, and due-window size, where the weights only depend on their position in a sequence (i.e. position-dependent weights). A polynomial time solution algorithm is provided for the corresponding problem. The problem can also be extended to slack due-windows assignment and general position-dependent processing times. It is proved that both these extensions remain polynomially solvable. [ABSTRACT FROM AUTHOR]

This article addresses proportionate flowshop scheduling problems with position-dependent weights wherein the weight is not related to the job but to the position in which the job is scheduled. Common and slack due date assignment models are discussed under a due date assignment framework. The goal is to determine a feasible schedule for all jobs and due dates of all jobs to minimize a total cost function wherein the objective function is of the minsum type. Optimal properties for the problems are proposed, based on which polynomial time algorithms are provided to solve these two problems optimally. [ABSTRACT FROM AUTHOR]

This article addresses the no-wait flowshop scheduling problem with simultaneous consideration of common due date assignment, convex resource allocation and learning effect in a two machine setting. The processing time of each job can be controlled by its position in a sequence and also by allocating extra resource, which is a convex function of the amount of a common continuously divisible resource allocated to the job. The objective is to determine the optimal common due date, the resource allocation and the schedule of jobs such that the total earliness, tardiness and common due date cost (the total resource consumption cost) are minimized under the constraint condition that the total resource consumption cost (the total earliness, tardiness and common due date cost) is limited. Polynomial time algorithms are developed for two versions of the problem. [ABSTRACT FROM AUTHOR]

This article addresses permutation flowshop scheduling problems with simple linear deterioration. The objectives are to minimize logarithm of the makespan, total logarithm of the completion time, total weighted logarithm of the completion time, and the sum of the quadratic job logarithms of the completion times. Approximation algorithms and their worst-case bounds are presented and analysed. Branch-and-bound algorithms are also proposed to solve the problems optimally. Computational experiments are performed to illustrate the algorithms. [ABSTRACT FROM AUTHOR]

This study considers a single machine group scheduling problem with deteriorating jobs and resource allocation (controllable processing times). The objective is to have the resource availability limited within a given range, and to minimize the maximum completion time (i.e. the makespan). For two special cases, it is proved that the problem can be solved in polynomial time. For the general case, an heuristic algorithm and a branch-and-bound algorithm are proposed. Computational results show that the proposed heuristic algorithm is generally effective. [ABSTRACT FROM AUTHOR]

In this article, single-machine group scheduling with learning effects and convex resource allocation is studied. The goal is to find the optimal job schedule, the optimal group schedule, and resource allocations of jobs and groups. For the problem of minimizing the makespan subject to limited resource availability, it is proved that the problem can be solved in polynomial time under the condition that the setup times of groups are independent. For the general setup times of groups, a heuristic algorithm and a branch-and-bound algorithm are proposed, respectively. Computational experiments show that the performance of the heuristic algorithm is fairly accurate in obtaining near-optimal solutions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Chinese Institute of Industrial Engineers is the property of Taylor & Francis Ltd 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.)

In this article, we consider a single machine scheduling problem with a time-dependent learning effect and deteriorating jobs. By the effects of time-dependent learning and deterioration, we mean that the job processing time is defined by a function of its starting time and total normal processing time of jobs in front of it in the sequence. The objective is to determine an optimal schedule so as to minimize the total completion time. This problem remains open for the case of −1 < a < 0, where a denotes the learning index; we show that an optimal schedule of the problem is V-shaped with respect to job normal processing times. Three heuristic algorithms utilising the V-shaped property are proposed, and computational experiments show that the last heuristic algorithm performs effectively and efficiently in obtaining near-optimal solutions. [ABSTRACT FROM PUBLISHER]