Minimizing flow-time on a single machine with integer batch sizes

We address a classical minimum flow-time, single-machine, batch-scheduling problem. Processing times and setups are assumed to be identical for all jobs and batches, respectively. Santos and Magazine (Oper. Res. Lett. 4(1985) 99) introduced an efficient solution for the relaxed (non-integer) problem. We introduce a simple rounding procedure for Santos and Magazine's solution, which guarantees optimal integer batches.     

Single machine scheduling with family setups to minimize total earliness and tardiness

This paper considers the problem of scheduling a given number of jobs on a single machine to minimize total earliness and tardiness when family setup times exist. The paper proposes optimal branch-and-bound algorithms for both the group technology assumption and if the group technology assumption is removed. A heuristic algorithm is proposed to solve larger problems with the group technology assumption removed. The proposed algorithms were empirically evaluated on problems of various sizes and parameters. The paper also explores how the choice of procedure affects total earliness and tardiness if an implementation of lean production methods has resulted in a reduction in setup times. An important finding of these empirical investigations is that scheduling jobs by removing the group technology assumption can significantly reduce total earliness and tardiness.     

Single machine scheduling with flow time and earliness penalties

This paper considers the problem of schedulingn jobs on a single machine to minimize the total cost incurred by their respective flow time and earliness penalties. It is assumed that each job has a due date that must be met, and that preemptions are not allowed. The problem is formulated as a dynamic program (DP) and solved with a reaching algorithm that exploits a series of dominance properties and efficiently generated bounds. A major factor underlying the effectiveness of the approach is the use of a greedy randomized adaptive search procedure (GRASP) to construct high quality feasible solutions. These solutions serve as upper bounds on the optimum, and permit a predominant portion of the state space to be fathomed during the DP recursion.To evaluate the performance of the algorithm, an experimental design involving over 240 randomly generated problems was followed. The test results indicate that problems with up to 30 jobs can be readily solved on a microcomputer in less than 12 minutes. This represents a significant improvement over previously reported results for both dynamic programming and mixed integer linear programming approaches.     

On-line scheduling on a batch processing machine with unbounded batch size to minimize the makespan

We present on-line algorithms to minimize the makespan on a single batch processing machine. We consider a parallel batching machine that can process up to b jobs simultaneously. Jobs in the same batch complete at the same time. Such a model of a batch processing machine has been motivated by burn-in ovens in final testing stage of semiconductor manufacturing. We deal with the on-line scheduling problem when jobs arrive over time. We consider a set of independent jobs. Their number is not known in advance. Each job is available at its release date and its processing requirement is not known in advance. This general problem with infinite machine capacity is noted 1∣p − batch, r_j, b = ∞∣C_max. Deterministic algorithms that do not insert idle-times in the schedule cannot be better than 2-competitive and a simple rule based on LPT achieved this bound [Z. Liu, W. Yu, Scheduling one batch processor subject to job release dates, Discrete Applied Mathematics 105 (2000) 129–136]. If we are allowed to postpone start of jobs, the performance guarantee can be improved to 1.618. We provide a simpler proof of this best known lower bound for bounded and unbounded batch sizes. We then present deterministic algorithms that are best possible for the problem with unbounded batch size (i.e., b = ∞) and agreeable processing times (i.e., there cannot exist an on-line algorithm with a better performance guarantee). We then propose another algorithm that leads to a best possible algorithm for the general problem with unbounded batch size. This algorithm improves the best known on-line algorithm (i.e. [G. Zhang, X. Cai, C.K. Wong, On-line algorithms for minimizing makespan on batch processing machines, Naval Research Logistics 48 (2001) 241–258]) in the sense that it produces a shortest makespan while ensuring the same worst-case performance guarantee.     

Minimizing total tardiness in a scheduling problem with a learning effect

Minimizing of total tardiness is one of the most studied topics on single machine problems. Researchers develop a number of optimizing and heuristic methods to solve this NP-hard problem. In this paper, the problem of minimizing total tardiness is examined in a learning effect situation. The concept of learning effects describes the reduction of processing times arising from process repetition. A 0–1 integer programming model is developed to solve the problem. Also, a random search, the tabu search and the simulated annealing-based methods are proposed for the problem and the solutions of the large size problems with up to 1000 jobs are found by these methods. To the best of our knowledge, no works exists on the total tardiness problem with a learning effect tackled in this paper.     

带有固定区间的单机双代理可中断总误工问题

研究带有固定区间的两个代理单机排序问题.第一个代理工件可中断,且工件到达时间与工期满足一致关系,目标函数为最小化总误工.第二个代理工件被安排在固定时间窗口.目标是寻找一个排序,使得满足第二个代理目标可行情况下,第一个代理目标函数值最小.在固定区间等于加工时间的情况下,利用分块原则,提出了一个伪多项式时间动态规划算法,并给出了固定区间大于加工时间情况下的时间复杂度分析.     

Minimizing total tardiness on a single machine with unequal release dates

This study addresses the problem of minimizing total tardiness on a single machine with unequal release dates. Dominance properties established in previous literatures and herein are adopted to develop branch and bound and heuristic procedures. Computational experiments were conducted to evaluate the approaches. The results revealed that the branch and bound algorithm is efficient in solving hard problems and easy problems that involve up to 50 and 500 jobs, respectively. The computational effectiveness of the heuristic is also reported.     

The Delivery Man Problem with time windows

In this paper, a variant of the Traveling Salesman Problem with Time Windows is considered, which consists in minimizing the sum of travel durations between a depot and several customer locations. Two mixed integer linear programming formulations are presented for this problem: a classical arc flow model and a sequential assignment model. Several polyhedral results are provided for the second formulation, in the special case arising when there is a closed time window only at the depot, while open time windows are considered at all other locations. Exact and heuristic algorithms are also proposed for the problem. Computational results show that medium size instances can be solved exactly with both models, while the heuristic provides good quality solutions for medium to large size instances.     

Minimizing makespan and total completion time for parallel batch processing machines with non-identical job sizes

This paper considers the scheduling problem of parallel batch processing machines with non-identical job sizes. The jobs are processed in batches and the machines have the same capacity. The models of minimizing makespan and total completion time are given using mixed integer programming method and the computational complexity is analyzed. The bound on the number of feasible solutions is given and the properties of the optimal solutions are presented. Then a polynomial time algorithm is proposed and the worst case ratios for minimizing total completion time and makespan is proved to be 2 and (8/3–2/3 m) respectively. To test the proposed algorithm, we generate different levels of random instances. The computational results demonstrate the effectiveness of the algorithm for minimizing the two objectives.     

Batch machine production with perishability time windows and limited batch size

This article provides a theoretical analysis of the problem of scheduling jobs in batches by family on a batch-processing machine, in the presence of perishability time windows of equal length. The problem arises in the context of production planning in a microbiological laboratory, and has application in wafer-fab production and for wireless broadcasting. The combined features of multiple families and time windows are new to the literature. The study is restricted to unit job processing times. We prove that the problem is NP-hard, thus solving an open problem by Uzsoy [24]. A Dynamic Programme is developed, with running time polynomial in the input variables of maximum batch size, the number of families and the length of the demand time horizon. In addition, we show that an heuristic approach to minimising the perishability time window can provide a 2-approximation to the optimum.     

工件满足一致性的同类机在线分批排序问题

研究了工件满足一致性,批容量无界的两台同类机在线分批排序问题,目标为极小化工件的最大完工时间和极小化工件的最大流程时间,三元素法分别表示为Q_2|r_ir_j?p_i≤p_j,B=∞, on-line|C_(max),Q_2|r_ir_j?p_i≥p_j,B=∞, on-line|F_(max).不失一般性,假设第一台机器速度为1,第二台机器速度为s,s≥1.对于上述两类问题设计了一个在线算法,并分析了算法竞争比的上界.对第一类问题该在线算法的竞争比不超过s+α,这里α为α~2+sα-1=0的正根,特别地,当s=1时,该算法的竞争比不超过1.618.对第二类排序问题,该在线算法的竞争比不超过1+1/α.     

Minimizing the total completion time on a single machine with the learning effect and multiple availability constraints

This paper considers a single machine scheduling problem with the learning effect and multiple availability constraints that minimizes the total completion time. To solve this problem, a new binary integer programming model is presented, and a branch-and-bound algorithm is also developed for solving the given problem optimally. Since the problem is strongly NP-hard, to find the near-optimal solution for large-sized problems within a reasonable time, two meta-heuristics; namely, genetic algorithm and simulated annealing are developed. Finally, the computational results are provided to compare the result of the binary integer programming, branch-and-bound algorithm, genetic algorithm and simulated annealing. Then, the efficiency of the proposed algorithms is discussed.     

Preemptive scheduling of jobs with agreeable due dates on a single machine to minimize total tardiness

We consider the problem of scheduling n preemptive jobs on a single machine to minimize total tardiness, subject to agreeable due dates, i.e., a later release date corresponds to a later due date. We prove that the problem is -hard in the ordinary sense by showing that it is -hard, and deriving a pseudo-polynomial algorithm for it.     

Optimal methods for batch processing problem with makespan and maximum lateness objectives

In this paper we consider the problem of scheduling n jobs on a single batch processing machine in which jobs are ordered by two customers. Jobs belonging to different customers are processed based on their individual criteria. The considered criteria are minimizing makespan and maximum lateness. A batching machine is able to process up to b jobs simultaneously. The processing time of each batch is equal to the longest processing time of jobs in the batch. This kind of batch processing is called parallel batch processing. Optimal methods for three cases are developed: unbounded batch capacity, b > n, with compatible job groups and bounded batch capacity, b  n, with compatible and non compatible job groups. Each job group represents a different class of customers and the concept of being compatible means that jobs which are ordered by different customers are allowed to be processed in a same batch. We propose an optimal method for the problem with incompatible groups and unbounded batches. About the case when groups are incompatible and bounded batches, our proposed method is considered as optimal when the group with maximum lateness objective has identical processing times. We regard this method, however, as a heuristic when these processing times are different. When groups are compatible and batches are bounded we consider another problem by assuming the same processing times for the group which has the maximum lateness objective and propose an optimal method for this problem.     

ON-LINE PROBLEMS OF MINIMIZING MAKESPAN ON A SINGLE BATCH PROCESSING MACHINE WITH NONIDENTICAL JOB SIZES

The on-line problem of scheduling on a batch processing machine with nonidentical job sizes to minimize makespan is considered. The batch processing machine can process a number of jobs simultaneously as long as the total size of these jobs being processed does not exceed the machine capacity. The processing time of a batch is given by the longest processing time of any job in the batch. Each job becomes available at its arrival time, which is unknown in advance, and its processing time becomes known upon its arrival. The paper deals with two variants： the case only with two distinct arrival times and the general case. For the first case, an on-line algorithm with competitive ratio 119/44 is given. For the latter one, a simple algorithm with competitive ratio 3 is given. For both variants the better ratios can be obtained if the problem satisfies proportional assumption.     

A Hybrid Approach to Scheduling with Earliness and Tardiness Costs

A hybrid technique using constraint programming and linear programming is applied to the problem of scheduling with earliness and tardiness costs. The linear model maintains a set of relaxed optimal start times which are used to guide the constraint programming search heuristic. In addition, the constraint programming problem model employs the strong constraint propagation techniques responsible for many of the advances in constraint programming for scheduling in the past few years. Empirical results validate our approach and show, in particular, that creating and solving a subproblem containing only the activities with direct impact on the cost function and then using this solution in the main search, significantly increases the number of problems that can be solved to optimality while significantly decreasing the search time.     

A new approach based on the surrogating method in the project time compression problems

This paper develops a mathematical model for project time compression problems in CPM/PERT type networks. It is noted this formulation of the problem will be an adequate approximation for solving the time compression problem with any continuous and non-increasing time-cost curve. The kind of this model is Mixed Integer Linear Program (MILP) with zero-one variables, and the Benders' decomposition procedure for analyzing this model has been developed. Then this paper proposes a new approach based on the surrogating method for solving these problems. In addition, the required computer programs have been prepared by the author to execute the algorithm. An illustrative example solved by the new algorithm, and two methods are compared by several numerical examples. Computational experience with these data shows the superiority of the new approach.