Single machine parallel-batch scheduling with deteriorating jobs

AbstractWe consider several single machine parallel-batch scheduling problems in which the processing time of a job is a linear function of its starting time. We give a polynomial-time algorithm for minimizing the maximum cost, an O(n5) time algorithm for minimizing the number of tardy jobs, and an O(n2) time algorithm for minimizing the total weighted completion time. Furthermore, we prove that the problem for minimizing the weighted number of tardy jobs is binary NP-hard

Scheduling jobs with agreeable processing times and due dates on a single batch processing machine

AbstractIn this paper we study the problems of scheduling jobs with agreeable processing times and due dates on a single batch processing machine to minimize total tardiness, and weighted number of tardy jobs. We prove that the problem of minimizing total tardiness is NP-hard even if the machine capacity is two jobs and we develop a pseudo-polynomial-time algorithm for an NP-hard special case of this problem. We also develop a pseudo-polynomial-time algorithm for the NP-hard problem of minimizing weighted number of tardy jobs, which suggests that this problem cannot be strongly NP-hard unless P=NP

Minimizing Mean Tardiness Subject To Unspecified Minimum Number Tardy For A Single Machine

In this paper we propose a hybrid branch and bound algorithm for solving the problem of minimizing mean tardiness for a single machine problem subject to minimum number of tardy jobs. Although the minimum number of tardy jobs is known, the subset of tardy job is not known. The proposed algorithm uses traditional branch and bound scheme where lower bounds on mean tardiness are calculated coupled with using the information that the number of tardy jobs is known. It also uses an insertion algorithm which determines the optimal mean tardiness once the subset of tardy jobs is specified. An example is solved to illustrate the developed procedure

Minimizing Mean Tardiness Subject To Unspecified Minimum Number Tardy For A Single Machine

In this paper we propose a hybrid branch and bound algorithm for solving the problem of minimizing mean tardiness for a single machine problem subject to minimum number of tardy jobs. Although the minimum number of tardy jobs is known, the subset of tardy job is not known. The proposed algorithm uses traditional branch and bound scheme where lower bounds on mean tardiness are calculated coupled with using the information that the number of tardy jobs is known. It also uses an insertion algorithm which determines the optimal mean tardiness once the subset of tardy jobs is specified. An example is solved to illustrate the developed procedure

Scheduling Lower Bounds via AND Subset Sum

Given $N$ instances $(X_1,t_1),\ldots,(X_N,t_N)$ of Subset Sum, the AND Subset Sum problem asks to determine whether all of these instances are yes-instances; that is, whether each set of integers $X_i$ has a subset that sums up to the target integer $t_i$. We prove that this problem cannot be solved in time $\tilde{O}((N \cdot t_{max})^{1-\epsilon})$, for $t_{max}=\max_i t_i$ and any $\epsilon > 0$, assuming the $\forall \exists$ Strong Exponential Time Hypothesis ($\forall \exists$-SETH). We then use this result to exclude $\tilde{O}(n+P_{max} \cdot n^{1-\epsilon})$-time algorithms for several scheduling problems on $n$ jobs with maximum processing time $P_{max}$, based on $\forall \exists$-SETH. These include classical problems such as $1||\sum w_jU_j$, the problem of minimizing the total weight of tardy jobs on a single machine, and $P_2||\sum U_j$, the problem of minimizing the number of tardy jobs on two identical parallel machines.Comment: 14 pages, ICALP'2

Competitive two-agent scheduling with deteriorating jobs on a single parallel-batching machine

We consider a scheduling problem in which the jobs are generated by two agents and have time-dependent proportional-linear deteriorating processing times. The two agents compete for a common single batching machine to process their jobs, and each agent has its own criterion to optimize. The jobs may have identical or different release dates. The batching machine can process several jobs simultaneously as a batch and the processing time of a batch is equal to the longest of the job processing times in the batch. The problem is to determine a schedule for processing the jobs such that the objective of one agent is minimized, while the objective of the other agent is maintained under a fixed value. For the unbounded model, we consider various combinations of regular objectives on the basis of the compatibility of the two agents. For the bounded model, we consider two different objectives for incompatible and compatible agents: minimizing the makespan of one agent subject to an upper bound on the makespan of the other agent and minimizing the number of tardy jobs of one agent subject to an upper bound on the number of tardy jobs of the other agent. We analyze the computational complexity of various problems by either demonstrating that the problem is intractable or providing an efficient exact algorithm for the problem. Moreover, for certain problems that are shown to be intractable, we provide efficient algorithms for certain special cases

Minimizing the number of tardy jobs with precedence constraints and agreeable due dates

AbstractMinimizing the number of precedence constrained, unit-time tardy jobs is strongly NP-hard on a single machine. We study a special case of the problem where a job is tardy if it is finished more than a fixed K time units after its earliest possible completion time under the precedence constraints. We prove that the problem remains strongly NP-hard even with these special due dates. We also present polynomial time solutions for the weighted version of the problem if the precedence constraints are out-forests or interval orders. In the process, we also present a polynomial time solution for a special case of the minimum weight hitting set problem