Minimizing maximal regret in the single machine sequencing problem with maximum lateness criterion

In this paper, the single machine sequencing problem with maximum lateness criterion is discussed. The parameters of the problem are imprecise and they are specified as intervals. The maximal regret criterion is applied to calculate the optimal sequence. A polynomial algorithm for the studied problem is constructed.     

Minimizing sum of completion times on a single machine with sequence-dependent family setup times

This paper presents a branch-and-bound (B&B) algorithm for minimizing the sum of completion times in a single-machine scheduling setting with sequence-dependent family setup times. The main feature of the B&B algorithm is a new lower bounding scheme that is based on a network formulation of the problem. With extensive computational tests, we demonstrate that the B&B algorithm can solve problems with up to 60 jobs and 12 families, where setup and processing times are uniformly distributed in various combinations of the [1,50] and [1,100] ranges.     

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.     

Minimizing makespan on a single machine subject to random breakdowns

We investigate optimal sequencing policies for the expected makespan problem with an unreliable machine, where jobs have to be reprocessed in their entirety if preemptions occur because of breakdowns. We identify a class of uptime distributions under which LPT minimizes expected makespan.     

Pareto optima for total weighted completion time and maximum lateness on a single machine

We consider the single-machine bicriterion scheduling problem of enumerating the Pareto-optimal sequences with respect to the total weighted completion time and the maximum lateness objectives. We show that the master sequence concept originally introduced for 1|r_j|∑w_jU_j by Dauzère-Pérès and Sevaux is also applicable to our problem and a large number of other sequencing problems. Our unified development is based on exploiting common order-theoretic structures present in all these problems. We also show that the master sequence implies the existence of global dominance orders for these scheduling problems. These dominance results were incorporated into a new branch and bound algorithm, which was able to enumerate all the Pareto optima for over 90% of the 1440 randomly generated problems with up to n=50 jobs. The identification of each Pareto optimum implicitly requires the optimal solution of a strongly NP-hard problem. The instances solved had hundreds of these Pareto solutions and to the best of our knowledge, this is the first algorithm capable of completely enumerating all Pareto sequences within reasonable time and space for a scheduling problem with such a large number of Pareto optima.     

Improving the preemptive bound for the single machine dynamic maximum lateness problem

The preemptive lower bound is known to be the main available bound for the single machine dynamic maximum lateness problem. We propose an O(nlogn) time improvement to this bound which reduces by more than 80% the average the gap between the preemptive solution value and the optimal solution value.     

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.     

Minimizing the total completion time on-line on a single machine, using restarts

We give an algorithm to minimize the total completion time on-line on a single machine, using restarts, with a competitive ratio of 3/2. The optimal competitive ratio without using restarts is 2 for deterministic algorithms and e/(e−1)≈1.582 for randomized algorithms. This is the first restarting algorithm to minimize the total completion time that is proved to be better than an algorithm that does not restart.     

Minimizing the weighted number of tardy jobs on a single machine with release dates

In this paper, we describe an exact algorithm to minimize the weighted number of tardy jobs on a single machine with release dates. The algorithm uses branch-and-bound; a surrogate relaxation resulting in a multiple-choice knapsack provides the bounds. Extensive computational experiments indicate the proposed exact algorithm solves either weighted or unweighted problems. It solves the hardest problems to date. Indeed, it solves all previously unsolved instances. Its run time is the shortest to date.     

Minimizing total completion time on a single machine with step improving jobs

Production systems often experience a shock or a technological change, resulting in performance improvement. In such settings, job processing times become shorter if jobs start processing at, or after, a common critical date. This paper considers a single machine scheduling problem with step-improving processing times, where the effects are job-dependent. The objective is to minimize the total completion time. We show that the problem is NP-hard in general and discuss several special cases which can be solved in polynomial time. We formulate a Mixed Integer Programming model and develop an LP-based heuristic for the general problem. Finally, computational experiments show that the proposed heuristic yields very effective and efficient solutions.     

Minimizing maximum lateness of jobs in inventory constrained scheduling

This paper focuses on a machine scheduling problem having applications in truck scheduling at transshipment terminals. Jobs increase and decrease, respectively, the level of a central inventory. Naturally, jobs decreasing the inventory level can be processed only if the level of the inventory is high enough not to drop below zero. We consider the problem to find a schedule for jobs such that the maximum lateness among all jobs is minimized. We develop properties of optimal solutions, lower bounds, and heuristic methods in order to find upper bounds. These are incorporated in four branch and bound algorithms that are based on fixing sequences of jobs in forward or backward direction in two different types of representations. By means of a computational study, we compare these approaches with each other in order to show their efficiency.     

Minimizing maximum lateness in a flow shop subject to release dates

We consider the problem of minimizing the maximum lateness in a m-machine flow shop subject to release dates. The objective of this paper is to develop a new branch-and-bound algorithm to solve exactly this strongly NP-hard problem. The proposed branch-and-bound algorithm encompasses several features including a procedure for adjusting heads and tails, heuristics, and a lower bounding procedure, which is based on the exact solution of the two-machine flow shop problem with time lags, ready times, and delivery times. Extensive computational experiments show that instances with up to 6000 operations can be solved exactly in a moderate CPU time.     

A note on minimizing the sum of squares of machine completion times on two identical parallel machines

In this short note, we address the coherence between minimizing the sum of squares of machine completion times and minimizing makespan on two identical parallel machines. We show equivalence of the two objectives and identify interesting and useful relations which allow us to transfer worst-case ratios of approximation algorithms from one problem to the other.     

Minimizing makespan on a single batching machine with release times and non-identical job sizes

We consider the problem of scheduling jobs with release times and non-identical job sizes on a single batching machine; our objective is to minimize makespan. We present an approximation algorithm with worst-case ratio 2+ε, where ε>0 can be made arbitrarily small.     

Minimizing maximum cost on a single machine with two competing agents and job rejection

The classical Lawler’s Algorithm provides an optimal solution to the single-machine scheduling problem, where the objective is minimizing maximum cost, given general non-decreasing, job-dependent cost functions, and general precedence constraints. First, we extend this algorithm to allow job rejection, where the scheduler may decide to process only a subset of the jobs. Then, we further extend the model to a setting of two competing agents, sharing the same processor. Both extensions are shown to be solved in polynomial time.     

Minimizing flow time variance in a single machine system using genetic algorithms

In this paper, we address an n-job, single machine scheduling problem with an objective to minimize the flow time variance. We propose heuristic procedure based on genetic algorithms with the potential to address more generalized objective function such as weighted flow time variance. The development and implementation of the algorithm is supported with literature review and statistical analysis of the results. Some general guidelines to select the parameter values of the genetic algorithm are also developed using an experimental design approach.     

Minimizing the sum of many rational functions

We consider the problem of globally minimizing the sum of many rational functions over a given compact semialgebraic set. The number of terms can be large (10 to 100), the degree of each term should be small (up to 10), and the number of variables can be relatively large (10 to 100) provided some kind of sparsity is present. We describe a formulation of the rational optimization problem as a generalized moment problem and its hierarchy of convex semidefinite relaxations. Under some conditions we prove that the sequence of optimal values converges to the globally optimal value. We show how public-domain software can be used to model and solve such problems. Finally, we also compare with the epigraph approach and the BARON software.     

两台平行机完工时间平方和最小的排序问题

在两个竞争公司进行零和博弈过程中, 最大化两个公司收益的乘积, 在两台平行机的离线排序问题中相当于最小化两台机器完工时间的平方和. 给出了该问题修改的延缓开始\ LPT\ 算法: 首先, 将工件按照加工时间$\p_j\ $的\ LPT\ 序重新标记; 若加工时间最长的前\ $2m$\ 个工件的总加工时间\ $P(2m)< (2m+1)p_{2m+1}$, 最优的安排加工前\ $2m+1$\ 个工件, 一旦有机器空闲, 依次从第\ $2m+2$\ 个工件安排加工; 否则,\ $P(2m)\geq (2m+1)p_{2m+1}$, 最优的安排加工前\ $2m$\ 个工件, 一旦有机器空闲, 依次从第\ $2m+1$\ 个工件安排加工. 证明了该算法的最差性能比不超过\ $1+ ( \frac{1}{2m+2} )^2$, 且界是紧的.     

Minimizing the makespan in a single machine scheduling problems with flexible and periodic maintenance

This paper deals with a single machine scheduling problems with availability constraints. The unavailability of machine results from periodic maintenance activities. In our research, a periodic maintenance consists of several maintenance periods. We consider a machine should stop to maintain after a periodic time interval or to change tools after a fixed amount of jobs processed simultaneously. Each maintenance period is scheduled after a periodic time interval. We study the problems under deterministic environment and flexible maintenance considerations. Preemptive operation is not allowed. In addition, we propose a more reasonable flexible model for the real production settings. The objective is to minimize the makespan. The proposed problem is NP-hard in the strong sense and some heuristic algorithms are provided. The purpose is to present an efficient and effective heuristic algorithm so that it will be straightforward and easy to implement. Computational results show that the proposed algorithm first fit decreasing (DFF) performs well.     

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.