A model for a textile winding process

This paper solves the problem of determining the efficiency of N machines uni-directionally patrolled by one operative when walking time between the machines is constant, and repair time is constant, but repairs are not always successful. The problem arises in the context of a textile winding process and the results are of value in maintaining controls over the process. The increased efficiency that results from the operative repeatedly trying to repair a stopped machine, rather than leaving it stopped and continuing with his patrol is also calculated, and the design implications of this improved strategy are considered.     

The efficiency of unidirectionally patrolled machines

This paper solves the problem of determining the efficiency of N (not identical) machines which are unidirectionally patrolled by one operative. Generally the machines will be equally spaced in a circular configuration so that the time to walk from one machine to the next will be a constant. However, the case of unequal spacing is just as easy to handle. It is assumed that breakdowns of machine j occur at random at average rate γ_j in running time, and that the time to repair this machine is a constant, c_j. This situation could arise from a mix of new and old machines or, in a textile context, in a situation where different types of yarn are being processed on different machines.     

Two-machine flowshop scheduling with availability constraints

The majority of the scheduling literature carries a common assumption that machines are available all the time. However, this availability assumption may not be true in real industry settings, since a machine may become unavailable during certain periods of time when, for instance, a machine breakdown or a preventive maintenance activity is scheduled. Although the problem is realistic and important, it is relatively new and unstudied. In this paper, we study the two-machine flowshop problem under the assumption that the unavailable time is known in advance. We assume that if a job cannot be finished before the next down period of a machine then the job will have to partially restart when the machine has become available again. We call our model semiresumable. Our model contains two important special cases: resumable where the job can be continued without any penalty and nonresumable where the job needs to totally restart. We study the problem where an availability constraint is imposed only on one machine as well as on both machines. We provide complexity analysis, develop a pseudo-polynomial dynamic programming algorithm to solve the problem optimally and also propose heuristic algorithms with an error bound analysis.     

A polynomially solvable case of the three machine johnson problem

Under study is the classical NP-hard problem with three machines: N tasks must be fulfilled at three machines in minimum time. The processing time is given of each task at each machine. The processing sequences of all tasks are identical. It is impossible to process two tasks at one machine at the same time. We address the properties of this problem, find a new polynomially solvable case, and discuss a corresponding algorithm.     

Due-date assignment on uniform machines

The classical weighted minsum scheduling and due-date assignment problem (with earliness, tardiness and due-date costs) was shown to be polynomially solvable on a single machine, more than two decades ago. Later, it was shown to have a polynomial time solution in the case of identical processing time jobs and parallel identical machines. We extend the latter setting to parallel uniform machines. We show that the two-machine case is solved in constant time. Furthermore, the problem remains polynomially solvable for a given (fixed) number of machines.     

Cost Analysis of the <Emphasis Type="Italic">M/M/R</Emphasis> Machine Repair Problem with Spares and Two Modes of Failure

In this paper we deal with the machine repair problem consisting of M operating machines with S spare machines, and R repairmen where machines have two failure modes under steady-state conditions. Spares are considered to be either cold-standby, warm-standby or hot-standby. The two failure modes have equal probability of repair. Failure time of the machines and repair time of the repairmen are assumed to follow a negative exponential distribution. A cost model is developed in order to determine the optimal values of the number of repairmen and the number of spares simultaneously, while maintaining a minimum specified level of system availability. Numerical results are presented in which several system characteristics are evaluated for three types of standby under optimal operating conditions.     

Single-machine scheduling with maintenance and repair rate-modifying activities

Most papers in the scheduling field are based on the assumption that machines are always available at constant speed. However, in industry applications, it is very common for a machine to be in subnormal condition after running for a certain period of time. Motivated by a problem commonly found in the surface-mount technology of electronic assembly lines, this paper deals with scheduling problems involving repair and maintenance rate-modifying activities. When a machine is running at less than an efficient speed, a production planner can decide to stop the machine and maintain it or wait and maintain it later. If the choice is made to continue running the machine without fixing it, it is possible that the machine will break down and repair will be required immediately. Both maintenance and repair activities can change the machine speed from a sub-normal production rate to a normal one. Hence, we call them rate-modifying activities. Our purpose here is to simultaneously sequence jobs and schedule maintenance activity to optimize regular performance measures. In this paper, we assume that processing time is deterministic, while machine break down is a random process following certain distributions. We consider two types of processing cases: resumable and nonresumable. We study problems with objective functions such as expected makespan, total expected completion time, maximum expected lateness, and expected maximum lateness, respectively. Several interesting results are obtained, especially for the nonresumable case.     

The M/Ek/r machine interference model steady state equations and numerical solutions

This paper studies the machine interference problem in which the running times follow the negative exponential distribution, the repair times the Erlang distribution and the number of operatives is more than one. The steady state equations are derived and it is shown that unlike the case of the M/E_k/r ordinary queueing model, the solution cannot be taken in closed form. An efficient numerical procedure is developed instead, based on a decomposition principle. Tabulated results of the average number of machines running and the operative utilization for a range of the problem parameters are given, for the cases M/E₃/2 and M/E₃/3. A tentative conclusion for a closeness in performance between the models M/M/r and M/E_k/r is drawn.     

Scheduling policies for a repair shop problem

In this paper, we analyze a repair shop serving several fleets of machines that fail from time to time. To reduce downtime costs, a continuous-review spare machine inventory is kept for each fleet. A spare machine, if available on stock, is installed instantaneously in place of a broken machine. When a repaired machine is returned from the repair shop, it is placed in inventory for future use if the fleet has the required number of machines operating. Since the repair shop is shared by different fleets, choosing which type of broken machine to repair is crucial to minimize downtime and holding costs. The optimal policy of this problem is difficult to characterize, and, therefore, is only formulated as a Markov Decision Process to numerically compute the optimal cost and base-stock level for each spare machine inventory. As an alternative, we propose the dynamic Myopic(R) policy, which is easy to implement, yielding costs very close to the optimal. Most of the time it outperforms the static first-come-first-served, and preemptive-resume priority policies. Additionally, via our numerical study, we demonstrate that repair shop pooling is better than reserving a repair shop for each fleet.     

Approximation schemes for parallel machine scheduling with availability constraints

We investigate the problems of scheduling n weighted jobs to m parallel machines with availability constraints. We consider two different models of availability constraints: the preventive model, in which the unavailability is due to preventive machine maintenance, and the fixed job model, in which the unavailability is due to a priori assignment of some of the n jobs to certain machines at certain times. Both models have applications such as turnaround scheduling or overlay computing. In both models, the objective is to minimize the total weighted completion time. We assume that m is a constant, and that the jobs are non-resumable.For the preventive model, it has been shown that there is no approximation algorithm if all machines have unavailable intervals even if w_i=p_i for all jobs. In this paper, we assume that there is one machine that is permanently available and that the processing time of each job is equal to its weight for all jobs. We develop the first polynomial-time approximation scheme (PTAS) when there is a constant number of unavailable intervals. One main feature of our algorithm is that the classification of large and small jobs is with respect to each individual interval, and thus not fixed. This classification allows us (1) to enumerate the assignments of large jobs efficiently; and (2) to move small jobs around without increasing the objective value too much, and thus derive our PTAS. Next, we show that there is no fully polynomial-time approximation scheme (FPTAS) in this case unless P=NP.For the fixed job model, it has been shown that if job weights are arbitrary then there is no constant approximation for a single machine with 2 fixed jobs or for two machines with one fixed job on each machine, unless P=NP. In this paper, we assume that the weight of a job is the same as its processing time for all jobs. We show that the PTAS for the preventive model can be extended to solve this problem when the number of fixed jobs and the number of machines are both constants.     

Vacation policies for machine repair problem with two type spares

This paper studies the machine repair problem consisting of M operating machines with two types of spare machines (S = S₁ + S₂), and R servers (repairmen) who leave for a vacation of random length when there are no failed machines queuing up for repair in the repair facility. At the end of the vacation the servers return and operate two vacation policies. First, the servers take vacations repeatedly until they find the repair facility has at least one waiting failed machine in the queue. Second, the servers do not take a vacation again and remain idle until the first arriving failed machine arrives, which starts a busy period in the repair facility. For both policies, the servers have two service rates for repair-slow and fast. The matrix geometric theory is used to find the steady-state probabilities of the number of failed machines in the system as well as the performance measures. Some special cases are given. A direct search algorithm is used to simultaneously determine the optimal values of the number of two types of spares and the number of servers while maintaining a minimum specified level of system availability.     

On the complexity of scheduling unrelated parallel machines with limited preemptions

We consider the problem of finding a minimum-length preemptive schedule for n jobs on m parallel machines. The problem is solvable in polynomial time, whether the machines are identical, uniform or unrelated. For identical or uniform machines, it is easy to obtain an optimal schedule in which the portion of a job that is assigned to a single machine is processed without interruption. We show that imposing this condition in the case of unrelated machines makes the problem NP-hard.     

A Two-Level Production Control Model with Target Amount Rescheduling

This paper deals with the problem of two-level production control with disturbances. A production system is considered with several machines on the lower level and a section on the upper one. Each machine produces a given target amount by a given due date (common to all machines). Each machine has several possible speeds, which are subject to disturbances. On the machine level, at the routine control point, decision-making centres on introducing the proper speed and determining the next control point. It is assumed that all the target amounts are transferable; i.e. a part of each target amount can be processed by any other machine. In a case when it is realized, for a certain machine, that the target cannot be completed on time, the section reschedules the remaining target amounts among the machines. New target amounts are determined for the machines so that the overall target can be accomplished on time. A stochastic optimization formulation is presented, followed by a heuristic solution and simulation results.     

Cost analysis of the M/M/R machine repair problem with balking,reneging, and server breakdowns

We consider the machine repair problem in which failed machines balk (do not enter) with a constant probability (1 – b) and renege (leave the queue after entering) according to a negative exponential distribution. A group of identical automatic machines are maintained by R servers which themselves are subject to breakdowns. Failure and service times of the machines, and breakdown and repair times of the servers, are assumed to follow a negative exponential distribution. Each server is subject to breakdown even if no failed machines are in the system. This paper presents a matrix geometric method for deriving the steady-state probabilities, using which various system performance measures that can be obtained. A cost model is developed to determine the optimum number of servers. The minimum expected cost, the optimal number of servers, and various system performance measures are provided based on assumed numerical values given to the system parameters. Also the sensitivity analysis is investigated.     

A graph-theoretic approach to interval scheduling on dedicated unrelated parallel machines

We study the problem of scheduling n non-preemptive jobs on m unrelated parallel machines. Each machine can process a specified subset of the jobs. If a job is assigned to a machine, then it occupies a specified time interval on the machine. Each assignment of a job to a machine yields a value. The objective is to find a subset of the jobs and their feasible assignments to the machines such that the total value is maximized. The problem is NP-hard in the strong sense. We reduce the problem to finding a maximum weight clique in a graph and survey available solution methods. Furthermore, based on the peculiar properties of graphs, we propose an exact solution algorithm and five heuristics. We conduct computer experiments to assess the performance of our and other existing heuristics. The computational results show that our heuristics outperform the existing heuristics.     

Optimal on-line flow time with resource augmentation

We study the problem of scheduling n jobs that arrive over time. We consider a non-preemptive setting on a single machine. The goal is to minimize the total flow time. We use extra resource competitive analysis: an optimal off-line algorithm which schedules jobs on a single machine is compared to a more powerful on-line algorithm that has ? machines. We design an algorithm of competitive ratio , where Δ is the maximum ratio between two job sizes, and provide a lower bound which shows that the algorithm is optimal up to a constant factor for any constant ?. The algorithm works for a hard version of the problem where the sizes of the smallest and the largest jobs are not known in advance, only Δ and n are known. This gives a trade-off between the resource augmentation and the competitive ratio.We also consider scheduling on parallel identical machines. In this case the optimal off-line algorithm has m machines and the on-line algorithm has ?m machines. We give a lower bound for this case. Next, we give lower bounds for algorithms using resource augmentation on the speed. Finally, we consider scheduling with hard deadlines, and scheduling so as to minimize the total completion time.     

A class of on-line scheduling algorithms to minimize total completion time

We consider the problem of scheduling jobs on-line on a single machine and on identical machines with the objective to minimize total completion time. We assume that the jobs arrive over time. We give a general 2-competitive algorithm for the single machine problem. The algorithm is based on delaying the release time of the jobs, i.e., making the jobs artificially later available to the on-line scheduler than the actual release times. Our algorithm includes two known algorithms for this problem that apply delay of release times. The proposed algorithm is interesting since it gives the on-line scheduler a whole range of choices for the delays, each of which leading to 2-competitiveness.We also show that the algorithm is 2α competitive for the problem on identical machines where α is the performance ratio of the Shortest Remaining Processing Time first rule for the preemptive relaxation of the problem.     

Exploiting process plan flexibility in production scheduling: A multi-objective approach

When solving a product/process design problem, we must exploit the available degrees of freedom to cope with a variety of issues. Alternative process plans can be generated for a given product, and choosing one of them has implications on manufacturing functions downstream, including planning/scheduling. Flexible process plans can be exploited in real time to react to machine failures, but they are also relevant for off-line scheduling. On the one hand, we should select a process plan in order to avoid creating bottleneck machines, which would deteriorate the schedule quality; on the other one we should aim at minimizing costs. Assessing the tradeoff between these possibly conflicting objectives is difficult; actually, it is a multi-objective problem, for which available scheduling packages offer little support. Since coping with a multi-objective scheduling problem with flexible process plans by an exact optimization algorithm is out of the question, we propose a hierarchical approach, based on a decomposition into a machine loading and a scheduling sub-problem. The aim of machine loading is to generate a set of efficient (non-dominated) solutions with respect to the load balancing and cost objectives, leaving to the user the task of selecting a compromise solution. Solving the machine loading sub-problem essentially amounts to selecting a process plan for each job and to routing jobs to the machines; then a schedule must be determined. In this paper we deal only with the machine loading sub-problem, as many scheduling methods are already available for the problem with fixed process plans. The machine loading problem is formulated as a bicriterion integer programming model, and two different heuristics are proposed, one based on surrogate duality theory and one based on a genetic descent algorithm. The heuristics are tested on a set of benchmark problems.     

Production planning problem with sequence dependent setups as a bilevel programming problem

Each of n products is to be processed on two machines in order to satisfy known demands in each of T periods. Only one product can be processed on each machine at any given time. Each switch from one item to another requires sequence dependent setup time. The objective is to minimize the total setup time and the sum of the costs of production, storage and setup. We consider the problem as a bilevel mixed 0–1 integer programming problem. The objective of the leader is to assign the products to the machines in order to minimize the total sequence dependent setup time, while the objective of the follower is to minimize the production, storage and setup cost of the machine. We develop a heuristics based on tabu search for solving the problem. At the end, some computational results are presented.     

Scheduling jobs with release dates,equal processing times,and inclusive processing set restrictions

We consider the problem of scheduling a given set of n jobs with equal processing times on m parallel machines so as to minimize the makespan. Each job has a given release date and is compatible to only a subset of the machines. The machines are ordered and indexed in such a way that a higher-indexed machine can process all the jobs that a lower-indexed machine can process. We present a solution procedure to solve this problem in O(n²+mnlogn) time. We also extend our results to the tree-hierarchical processing sets case and the uniform machine case.