6 research outputs found
A multi-criteria model for maintenance job scheduling
This paper presents a multi-criteria maintenance job scheduling model, which is formulated using a weighted multi-criteria integer linear programming maintenance scheduling framework. Three criteria, which have direct relationship with the primary objectives of a typical production setting, were used. These criteria are namely minimization of equipment idle time, manpower idle time and lateness of job with unit parity. The mathematical model constrained by available equipment, manpower and job available time within planning horizon was tested with a 10-job, 8-hour time horizon problem with declared equipment and manpower available as against the required. The results, analysis and illustrations justify multi-criteria consideration. Thus, maintenance managers are equipped with a tool for adequate decision making that guides against error in the accumulated data which may lead to wrong decision making. The idea presented is new since it provides an approach that has not been documented previously in the literature
Single machine scheduling with job-dependent machine deterioration
We consider the single machine scheduling problem with job-dependent machine
deterioration. In the problem, we are given a single machine with an initial
non-negative maintenance level, and a set of jobs each with a non-preemptive
processing time and a machine deterioration. Such a machine deterioration
quantifies the decrement in the machine maintenance level after processing the
job. To avoid machine breakdown, one should guarantee a non-negative
maintenance level at any time point; and whenever necessary, a maintenance
activity must be allocated for restoring the machine maintenance level. The
goal of the problem is to schedule the jobs and the maintenance activities such
that the total completion time of jobs is minimized. There are two variants of
maintenance activities: in the partial maintenance case each activity can be
allocated to increase the machine maintenance level to any level not exceeding
the maximum; in the full maintenance case every activity must be allocated to
increase the machine maintenance level to the maximum. In a recent work, the
problem in the full maintenance case has been proven NP-hard; several special
cases of the problem in the partial maintenance case were shown solvable in
polynomial time, but the complexity of the general problem is left open. In
this paper we first prove that the problem in the partial maintenance case is
NP-hard, thus settling the open problem; we then design a -approximation
algorithm.Comment: 15 page
Scheduling preventive maintenance on a single CNC machine
In this study we attempt to deal with process planning, scheduling and preventive maintenance (PM) decisions, simultaneously. The objective is to minimize the total completion time of a set of jobs on a CNC machine. During the process planning, we decide on the processing times of the jobs which are controllable (i.e. they can be easily changed) on CNC machines. Using shorter processing times (higher production rates) would result in greater deterioration of the machine, and we would need to plan more frequent PM visits to the machine, during which it would not be available. Therefore, the selected processing times determine not only the completion times but also the PM visit times. We first provide optimality properties for the joint problem. We propose a new heuristic search algorithm to determine simultaneously the processing times of the jobs, their sequence and the PM schedule
A note on worst-case performance of heuristics for maintenance scheduling problems
We study a machine scheduling model in which job scheduling and machine maintenance activities have to be considered simultaneously. We develop the worst-case bounds for some heuristic algorithms, including a sharper worst-case bound of the SPT schedule than the results in the literature, and another bound of the EDD schedule. (c) 2006 Elsevier B.V. All rights reserved
On Discrete Hyperbox Packing
Bin packing is a very important and popular research area in the computer
science field. Past work showed many good and real-world packing algorithms. How-
ever, due to the complexity of the problem in multiple-dimensional bin packing, also
called hyperbox packing, we need more practical packing algorithms for its real-world
applications.
In this dissertation, we extend 1D packing algorithms to hyperbox packing prob-
lems via a general framework that takes two inputs of a 1D packing algorithm and
an instance of hyperbox packing problem and outputs a hyperbox packing algorithm.
The extension framework significantly enriches the family of hyperbox-packing algorithms, generates many framework-based algorithms, and simultaneously calls for the
analysis for those algorithms.
We also analyze the performance of a couple of framework-based algorithms from
two perspectives of worst-case performance and average-case performance. In worst-
case analysis, we use the worst-case performance ratio as our metric and analyze the
relationship of the ratio of framework-based algorithms and that of the corresponding
1D algorithms. We also compare their worst-case performance against two baselines:
strip optimal algorithms and optimal algorithms. In average-case analysis, we use
expected waste as a metric, analyze the waste of optimal hyperbox packing algorithms,
and estimate the asymptotic forms of the waste for framework-based algorithms