Showing papers on "Single-machine scheduling published in 2014"

Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm

Abstract: An integrated production and transportation model, which considers rail transportation, is developed.A heuristic, two metaheuristics and some related procedures are developed.Keshtel algorithm is developed for the first time for a mathematical model.Taguchi experimental design method is utilized to improve their performance.Performance evaluation, comparison, and analysis are employed for the proposed algorithms. Nowadays, scheduling of production cannot be done in isolation from scheduling of transportation since a coordinated solution to the integrated problem may improve the performance of the whole supply chain. In this paper, because of the widely used of rail transportation in supply chain, we develop the integrated scheduling of production and rail transportation. The problem is to determine both production schedule and rail transportation allocation of orders to optimize customer service at minimum total cost. In addition, we utilize some procedures and heuristics to encode the model in order to address it by two capable metaheuristics: Genetic algorithm (GA), and recently developed one, Keshtel algorithm (KA). Latter is firstly used for a mathematical model in supply chain literature. Besides, Taguchi experimental design method is utilized to set and estimate the proper values of the algorithms' parameters to improve their performance. For the purpose of performance evaluation of the proposed algorithms, various problem sizes are employed and the computational results of the algorithms are compared with each other. Finally, we investigate the impacts of the rise in the problem size on the performance of our algorithms.

Due-date assignment and single-machine scheduling with generalised position-dependent deteriorating jobs and deteriorating multi-maintenance activities

Abstract: This paper addresses a single-machine scheduling problem with simultaneous consideration of due-date assignment, generalised position-dependent deteriorating jobs, and deteriorating maintenance activities. It is assumed that the actual processing time of a job is a general non-decreasing function depending on the number of maintenance activities performed before it and its position in a sequence. Moreover, the machine may be subject to several maintenance activities up to a limit over the scheduling horizon. The maintenance activities do not necessarily restore the machine fully to its original perfect state and the duration of a maintenance activity depends on its start time. The objective is to find jointly the optimal job sequence, maintenance frequency and maintenance positions to minimise an objective function that includes the cost of due-date assignment, the cost of discarding jobs that cannot be completed by their due dates and the earliness of the scheduled jobs under the popular CON and SLK due-...

Combining time and position dependent effects on a single machine subject to rate-modifying activities

Abstract: We introduce a general model for single machine scheduling problems, in which the actual processing times of jobs are subject to a combination of positional and time-dependent effects, that are job-independent but additionally depend on certain activities that modify the processing rate of the machine, such as, maintenance. We focus on minimizing two classical objectives: the makespan and the sum of the completion times. The traditional classification accepted in this area of scheduling is based on the distinction between the learning and deterioration effects on one hand, and between the positional effects and the start-time dependent effects on the other hand. Our results show that in the framework of the introduced model such a classification is not necessary, as long as the effects are job-independent. The model introduced in this paper covers most of the previously known models. The solution algorithms are developed within the same general framework and their running times are no worse than those available earlier for problems with less general effects.

Multi-agent based approach for single machine scheduling with sequence-dependent setup times and machine maintenance

Abstract: Scheduling of single machine in manufacturing systems is especially complex when the order arrivals are dynamic. The complexity of the problem increases by considering the sequence-dependent setup times and machine maintenance in dynamic manufacturing environment. Computational experiments in literature showed that even solving the static single machine scheduling problem without considering regular maintenance activities is NP-hard. Multi-agent systems, a branch of artificial intelligence provide a new alternative way for solving dynamic and complex problems. In this paper a collaborative multi-agent based optimization method is proposed for single machine scheduling problem with sequence-dependent setup times and maintenance constraints. The problem is solved under the condition of both regular and irregular maintenance activities. The solutions of multi-agent based approach are compared with some static single machine scheduling problem sets which are available in the literature. The method is also tested under real-time manufacturing environment where computational time plays a critical role during decision making process.

Iterated Local Search for single-machine scheduling with sequence-dependent setup times to minimize total weighted tardiness

Abstract: We present an Iterated Local Search (ILS) algorithm for solving the single-machine scheduling problem with sequence-dependent setup times to minimize the total weighted tardiness. The proposed ILS algorithm exhibits several distinguishing features, including a new neighborhood structure called Block Move and a fast incremental evaluation technique, for evaluating neighborhood solutions. Applying the proposed algorithm to solve 120 public benchmark instances widely used in the literature, we achieve highly competitive results compared with a recently proposed exact algorithm and five sets of best solutions of state-of-the-art metaheuristic algorithms in the literature. Specifically, ILS obtains the optimal solutions for 113 instances within a reasonable time, and it outperforms the previous best-known results obtained by metaheuristic algorithms for 34 instances and matches the best results for 82 instances. In addition, ILS is able to obtain the optimal solutions for the remaining seven instances under a relaxed time limit, and its computational efficiency is comparable with the state-of-the-art exact algorithm by Tanaka and Araki (Comput Oper Res 40:344---352, 2013). Finally, on analyzing some important features that affect the performance of ILS, we ascertain the significance of the proposed Block Move neighborhood and the fast incremental evaluation technique.

Competitive algorithms from competitive equilibria: non-clairvoyant scheduling under polyhedral constraints

Abstract: We introduce and study a general scheduling problem that we term the Packing Scheduling problem (PSP). In this problem, jobs can have different arrival times and sizes; a scheduler can process job j at rate xj, subject to arbitrary packing constraints over the set of rates (x) of the outstanding jobs. The PSP framework captures a variety of scheduling problems, including the classical problems of unrelated machines scheduling, broadcast scheduling, and scheduling jobs of different parallelizability. It also captures scheduling constraints arising in diverse modern environments ranging from individual computer architectures to data centers. More concretely, PSP models multidimensional resource requirements and parallelizability, as well as network bandwidth requirements found in data center scheduling. In this paper, we design non-clairvoyant online algorithms for PSP and its special cases -- in this setting, the scheduler is unaware of the sizes of jobs. Our results are summarized as follows. • For minimizing total weighted completion time, we show a O(1)-competitive algorithm. Surprisingly, we achieve this result by applying the well-known Proportional Fairness algorithm (PF) to perform allocations each time instant. Though PF has been extensively studied in the context of maximizing fairness in resource allocation, we present the first analysis in adversarial and general settings for optimizing job latency. Our result is also the first O(1)-competitive algorithm for weighted completion time for several classical non-clairvoyant scheduling problems. •For minimizing total weighted flow time, for any constant e > 0, any O(n1---e)-competitive algorithm requires extra speed (resource augmentation) compared to the offline optimum. We show that PF is a O(log n)-speed O(log n)-competitive non-clairvoyant algorithm, where n is the total number of jobs. We further show that there is an instance of PSP for which no non-clairvoyant algorithm can be O(n1---e)-competitive with o(√log n) speed. •For the classical problem of minimizing total flow time for unrelated machines in the non-clairvoyant setting, we present the first online algorithm which is scalable ((1 + e)-speed O(1)-competitive for any constant e > 0). No non-trivial results were known for this setting, and the previous scalable algorithm could handle only related machines. We develop new algorithmic techniques to handle the unrelated machines setting that build on a new single machine scheduling policy. Since unrelated machine scheduling is a special case of PSP, when contrasted with the lower bound for PSP, our result also shows that PSP is significantly harder than perhaps the most general classical scheduling settings. Our results for PSP show that instantaneous fair scheduling algorithms can also be effective tools for minimizing the overall job latency, even when the scheduling decisions are non-clairvoyant and constrained by general packing constraints.

Single machine scheduling with truncated job-dependent learning effect

Abstract: In this paper we consider the single machine scheduling problem with truncated job-dependent learning effect. By the truncated job-dependent learning effect, we mean that the actual job processing time is a function which depends not only on the job-dependent learning effect (i.e., the learning in the production process of some jobs to be faster than that of others) but also on a control parameter. The objectives are to minimize the makespan, the total completion time, the total absolute deviation of completion time, the earliness, tardiness and common (slack) due-date penalty, respectively. Several polynomial time algorithms are proposed to optimally solve the problems with the above objective functions.

Novel Genetic Bees Algorithm applied to single machine scheduling problem

Abstract: The proposed novel Genetic Bees Algorithm (GBA) is an enhancement to the swarm-based Bees Algorithm (BA). It is called the Genetic Bees Algorithm because it has genetic operators. The structure of the GBA compared to the basic BA has two extra components namely, a Reinforced Global Search and a Jumping Function. The main advantage of adding the genetic operators to BA is that it will help the algorithm to avoid getting stuck in local optima. In this study the scheduling problem of a single machine was considered. When the basic BA was applied to solve this problem its performance was affected by its weakness in conducting global search to explore the search space. However, in most cases the proposed GBA overcame this issue due to the two new components which have been introduced.

Single-machine scheduling with past-sequence-dependent setup times and general effects of deterioration and learning

Abstract: Scheduling with learning effect and deteriorating jobs has become more popular. However, most of the research assume that the setup time is negligible or a part of the job processing time. In this paper, we propose a model where the deteriorating jobs, the learning effect, and the setup times are present simultaneously. Under the proposed model, the setup time is past-sequence-dependent and the actual job processing time is a general function of the processing times of the jobs already processed and its scheduled position. We provide the optimal schedules for some single-machine problems.

Minimizing worst-case regret of makespan on a single machine with uncertain processing and setup times

Abstract: This research addresses a single machine scheduling problem with uncertain processing times and sequence-dependent setup times represented by intervals. Our objective is to obtain a robust schedule with the minimum absolute deviation from the optimal makespan in the worst-case scenario. The problem is reformulated as a robust traveling salesman problem (RTSP), whereby a property is utilized to efficiently identify worst-case scenarios. A local search-based heuristic that incorporates this property is proposed to solve the RTSP, along with a simulated annealing-based implementation. The effectiveness and efficiency of the proposed heuristic are compared to those of an exact solution method in the literature.

A single-machine scheduling with a truncated linear deterioration and ready times

Abstract: Recently, machine scheduling problems with deteriorating jobs have received interestingly attention from the scheduling research community. Majority of the research assumed that the actual job processing time is an increasing function of its starting time. However, no job can remain undeteriorated indefinitely in real life situations. This paper considers a single-machine scheduling problem with a truncated linear deteriorating effect and ready times. By the truncated linear deteriorating effect, it means that the actual processing time of a job is a function of its starting time and a control parameter. The objective is to minimize the makespan. A mixed integer programming model and a branch-and-bound algorithm coupled with several dominance properties and two lower bounds are developed to search for the optimal solution. In addition, an ant colony and a Tabu search algorithm where each is refined by the three improvements are also proposed for a near-optimal solution, respectively. A computational experiment is then conducted to evaluate the impacts of the used parameters on the performances of the proposed algorithms.

Approximation schemes for single machine scheduling with non-renewable resource constraints

Abstract: In this paper we discuss exact and approximation algorithms for scheduling a single machine with additional non-renewable resource constraints. Given the initial stock levels of some non-renewable resources (e.g., raw materials, fuel, money), and time points along with replenishment quantities, a set of resource consuming jobs has to be scheduled on the machine such that there are enough resources for starting each job, and the makespan is minimized. We show that the problem admits a pseudo-polynomial time algorithm when the number of replenishments is not part of the input, and also present an FPTAS when there is only a single resource, and it is replenished only once. We also describe a PTAS for the problem with a constant number of replenishments.

Single-machine scheduling with periodic maintenance to minimize makespan revisited

Abstract: In this paper, a single-machine scheduling problem with periodic maintenance and non-preemptive jobs is considered. The objective is to minimize the makespan. It shows that the classical list scheduling (LS) algorithm, the longest processing time first (LPT) algorithm, and the modified longest processing time first (MLPT) algorithm all have the same worst-case ratio and the same computational complexity for the considered problem. To compare the performances of three considered algorithms in detail, the worst-case ratios of algorithms are formed as single-variable functions of the total number of maintenance activities needed in the optimal schedule. Analysis results show that the bound associated with the LS algorithm is always dominated by the bound associated with the LPT algorithm, and the latter is always dominated by the bound associated with the MLPT algorithm.

A binary branch and bound algorithm to minimize maximum scheduling cost

Abstract: This paper examines a single machine scheduling problem of minimizing the maximum scheduling cost that is nondecreasing with job completion time. Job release dates and precedence constraints are considered. We assume that each job can be processed exactly once without preemption. This is a classical scheduling problem, and is specifically useful in the scheduling of medical treatments. We develop a simple branch and bound algorithm to solve the scheduling problem optimally. A binary branching technique is developed. We use a preemptive solution approach to locate a lower bound, and design a simple heuristic to find an upper bound. Our algorithm is easy to implement and finds optimal schedules in one CPU minute for almost all instances tested, with up to 1000 jobs.

A 4-approximation for scheduling on a single machine with general cost function

Abstract: We consider a single machine scheduling problem that seeks to minimize a generalized cost function: given a subset of jobs we must order them so as to minimize $\sum f_j(C_j)$, where $C_j$ is the completion time of job $j$ and $f_j$ is a job-dependent cost function. This problem has received a considerably amount of attention lately, partly because it generalizes a large number of sequencing problems while still allowing constant approximation guarantees. In a recent paper, Cheung and Shmoys provided a primal-dual algorithm for the problem and claimed that is a 2-approximation. In this paper we show that their analysis cannot yield an approximation guarantee better than $4$. We then cast their algorithm as a local ratio algorithm and show that in fact it has an approximation ratio of $4$. Additionally, we consider a more general problem where jobs has release dates and can be preempted. For this version we give a $4\kappa$-approximation algorithm where $\kappa$ is the number of distinct release dates.

Single-machine scheduling with common due-window assignment for deteriorating jobs

Abstract: In this paper, we consider a single-machine common due-window assignment scheduling problem with deteriorating jobs. Jobs’ processing times are defined by function of their starting times and job-dependent deterioration rates that are related to jobs and are not all equal. The objective is to determine an optimal combination of sequence and common due-window location so as to minimize the weighted sum of earliness, tardiness and due-window location penalties. We propose an O(n2 log n) time algorithm to solve the problem and discuss several instances to illustrate it.

A general variable neighborhood search for single-machine total tardiness scheduling problem with step-deteriorating jobs

Abstract: This paper studies a single-machine scheduling problem with the objective of minimizing the total tardiness for a set of independent jobs. The processing time of a job is modeled as a step function of its starting time and a specific deteriorating date. The total tardiness as one important objective in practice has not been concerned in the studies of single-machine scheduling problems with step-deteriorating jobs. To overcome the intractability of the problem, we propose a heuristic named simple weighted search procedure (SWSP) and a general variable neighborhood search algorithm (GVNS) to obtain near optimal solutions. Extensive numerical experiments are carried out on randomly generated test instances in order to evaluate the performance of the proposed algorithms. By comparing to the CPLEX optimization solver, the heuristic SWSP and the standard variable neighborhood search, it is shown that the proposed GVNS algorithm can provide better solutions within a reasonable running time.

Single machine scheduling with sum-of-logarithm-processing-times based and position based learning effects

Abstract: In this paper we consider the single machine scheduling problems with sum-of-logarithm-processing-times based and position based learning effects, i.e., the actual job processing time of a job is a function of the sum of the logarithms of the processing times of the jobs already processed and its position in a sequence. The logarithm function is used to model the phenomenon that learning as a human activity is subject to the law of diminishing return. We show that even with the introduction of the proposed model to job processing times, several single machine problems remain polynomially solvable.