The paper deals with the sequencing problems in which job processing times, along with a processing order, are decision variables having their own associated linearly varying costs. The existing results in this area are surveyed and some new results are provided. In the paper, an attention is focussed on the computational complexity aspects, polynomial algorithms and the worst-case analysis of approximation algorithms. 1.

In the field of machine scheduling problems with controllable processing times, it is often assumed that the possible processing time of a job can be continuously controlled, i.e. it can be any number in a given interval. In this paper, we introduce a discrete model in which job processing times are discretely controllable, i.e. the possible processing time of a job can only be controlled to be some specified lengths. Under this discrete model, we consider a class of single machine problems with the objective of minimizing the sum of the total processing cost and the cost measured by a standard criterion. We investigate most common criteria, e.g. makespan, maximum tardiness, total completion time, weighted number of tardy jobs, and total earliness-tardiness penalty. The computational complexity of each problem is analyzed, and pseudo-polynomial dynamic programming algorithms are proposed for hard problems.

Most deterministic machine scheduling models assume that the processing time of a job on a machine is fixed externally and known in advance of scheduling. However, in most realistic situations, apart from the machines, it requires additional resources to process jobs, and the processing time of a job is determined internally by the amount of the resources allocated. In these situations, both the cost associated with the job schedule and the cost of the resources allocated should be taken into account. Therefore, job scheduling and resource allocation should be carefully coordinated in order to achieve an overall cost-effective schedule. In this paper, we study a parallel-machine scheduling model involving simultaneously job processing and resource allocation. The processing time of a job is non-increasing with the cost of the allocated resources. The objective is to minimize the total cost including the cost measured by a scheduling criterion and the cost of all allocated resources. W...

A multistage algorithm is proposed that will solve the scheduling problem in a flexible manufacturing system by...

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Low volume production associated with space systems manufacturing is inherently expensive, time-consuming, and risk-laden. At the root of this problem is an inability to adequately predict, monitor, and control the product development and sustainment process. This paper describes the ASSIST system, an intelligent knowledge management system designed to address inefficient information management processes and improve space system affordability. ASSIST is designed for collaborative engineering, manufacturing, and testing within a company, between companies, and between local and remote individuals. ASSIST uses web-based standards (including HTTP and XML) as the common message approach connecting its components. Automated process planning and production scheduling is a key component of ASSIST. This paper describes an integrated process planning and production scheduling problem and discusses the solution approach. Keywords: Internet based design and manufacturing. 1.

The optimizing sequence of production for a set of customer orders – in order to minimize machine set-up time and costs – is one of the typical problems found in many manufacturing systems. In this paper, we develop a simulation model to capture a practical system of a metal casting company in Queensland, Australia, and optimize the production sequence for a set of customer orders. The method addressed in the paper can be applied to other optimization

this paper is to construct the trade-off surface between three scheduling criterions: maximal lateness, total flow time and resource consumption for 2 the single-machine scheduling problem where the processing time of each job, p j , is a convex function of a nonrenewable and continuous resource, u j , that is to be allocated to the job. We assume that the processing time function, which is also known as the resource consumption function, obeys the following three essential characteristics: (1) Each job has a processing time that is a decreasing function of the amount of resource allocated to the corresponding job, i.e., 0 / ) ( # j j j du u dp ; (2) The marginal processing time decreases as the resource consumption increases, i.e., 0 ) /( ) ( 2 2 j j j du u p d ; and (3) For each job, the job processing time is infinite if no resources are allocated, ) ( lim u u p . The lateness of job j is defined as j j j d C L # # , where j C and j d are the completion time and due-date of job j, respectively and the total flow time is defined as the completion time of all participating jobs, which can also be defined as the completion time of the last job, ) ( max ..., 2 , 1 . Previous studies (Janiak(1987)) suggested an optimal algorithm to minimize the total flow time where the jobs processing time are resource dependent. Studies concerning maximal Lateness with resource allocation include algorithms which apply only to linear resource consumption function (Wassenhove and Baker (1982)), a special case of a convex resource consumption function (Shabtay (2003)) and an algorithm that can be applied to any convex resource consumption function. (Yedidsion, Shabtay and Kaspi (2003)). The multiple-criteria problem we analyze is the minimization of the group # ...