Yunqiang Yin

Scheduling problems with two agents and a linear non-increasing deterioration to minimize earliness penalties

We consider a scheduling environment with two agents and a linear non-increasing deterioration. By the linear non-increasing deterioration we mean that the actual processing time of a job belonging to the two agents is defined as a non-increasing linear function of its starting time. Two agents compete to perform their respective jobs on a common single machine and each agent has his own criterion to be optimized. The goal is to schedule the jobs such that the combined schedule performs well with respect to the measures of both agents. Three different objective functions are considered for one agent, including the maximum earliness cost, total earliness cost and total weighted earliness cost, while keeping the maximum earliness cost of the other agent below or at a fixed level U. We propose the optimal (nondominated) properties and present the complexity results for the problems addressed here.

Two-agent single-machine scheduling with release times and deadlines

Multiple-agent scheduling has attracted considerable research attention in recent years. However, studies of multiple-agent scheduling with release times and deadlines are few. In the presence of ready times, sometimes it is beneficial to wait for future job arrivals in constructing a schedule. Inspired by the importance of ready times, we study the single-machine two-agent scheduling problem with releases times and deadlines to minimise the number of tardy jobs of one agent under the restriction that the maximum lateness of the jobs of the other agent cannot exceed a given value Q. Having established that the problem is strongly NP-hard, we provide a branch-and-bound and a simulated annealing algorithm to search for the optimal and approximate solutions, respectively. The results of computational experiments reveal that the SA algorithm can generate near-optimal solutions quickly.

Ant colony algorithms for a two-agent scheduling with sum-of processing times-based learning and deteriorating considerations

This paper addresses a two-agent single-machine scheduling problem with the co-existing sum-of-processing-times-based learning and deteriorating effects. In the proposed model, it is assumed that the actual processing time of a job of the first (second) agent is a decreasing function of the sum-of-processing-times-based learning (or increasing function of the sum-of-processing-times-based deteriorating effect) in a schedule. The aim of this paper is to find an optimal schedule to minimize the total weighted completion time of the jobs of the first agent with the restriction that no tardy job is allowed for the second agent. For the proposed model, we develop a branch-and-bound and some ant colony algorithms for an optimal and near-optimal solution, respectively. Besides, the extensive computational experiments are also proposed to test the performance of the algorithms.

Two-agent single-machine scheduling with assignable due dates

Abstract We consider several two-agent scheduling problems with assignable due dates on a single machine, where each of the agents wants to minimize a measure depending on the completion times of its own jobs and the due dates are treated as given variables and must be assigned to individual jobs. The goal is to assign a due date from a given set of due dates and a position in the sequence to each job so that the weighted sum of the objectives of both agents is minimized. For different combinations of the objectives, which include the maximum lateness, total (weighted) tardiness, and total (weighted) number of tardy jobs, we provide the complexity results and solve the corresponding problems, if possible.

An investigation on a two-agent single-machine scheduling problem with unequal release dates

This paper addresses a two-agent scheduling problem on a single machine with arbitrary release dates, where the objective is to minimize the tardiness of one agent, while keeping the lateness of the other agent below or at a fixed level Q. A mixed integer programming model is first presented for its optimal solution, admittedly not to be practical or useful in the most cases, but theoretically interesting since it models the problem. Thus, as an alternative, a branch-and-bound algorithm incorporating with several dominance properties and a lower bound is provided to derive the optimal solution and a marriage in honey-bees optimization algorithm (MBO) is developed to derive the near-optimal solutions for the problem. Computational results are also presented to evaluate the performance of the proposed algorithms.

Common due date assignment and scheduling with a rate-modifying activity to minimize the due date, earliness, tardiness, holding, and batch delivery cost

e consider a single-machine batch delivery scheduling and common due date assignment problem. In addition to making decisions on sequencing the jobs, determining the common due date, and scheduling job delivery, we consider the option of performing a rate-modifying activity on the machine. The processing time of a job scheduled after the rate-modifying activity decreases depending on a job-dependent factor. Finished jobs are delivered in batches. There is no capacity limit on each delivery batch, and the cost per batch delivery is fixed and independent of the number of jobs in the batch. The objective is to find a common due date for all the jobs, a location of the rate-modifying activity, and a delivery date for each job to minimize the sum of earliness, tardiness, holding, due date, and delivery cost. We provide some properties of the optimal schedule for the problem and present polynomial algorithms for some special cases.

Single-machine scheduling with time-dependent and position-dependent deteriorating jobs

In many real-life scheduling situations, the jobs deteriorate at a certain rate while waiting to be processed. This study introduces a new deterioration model where the actual processing time of a job depends not only on the starting time of the job but also on its scheduled position. The objective is to find the optimal schedule such that the makespan or total completion time is minimised. This study first shows that both problems are solvable in O(n log n) time. This study further shows that in both cases there exists an optimal schedule that is the shortest processing time, longest processing time, or V-shaped with respect to the job normal processing times, depending on the relationships between problem parameters.

Single-machine and two-machine flowshop scheduling problems with truncated position-based learning functions

Scheduling with learning effects has received growing attention nowadays. A well-known learning model is called ‘position-based learning’ in which the actual processing time of a job is a non-increasing function of its position to be processed. However, the actual processing time of a given job drops to zero precipitously as the number of jobs increases. Motivated by this observation, we propose two truncated learning models in single-machine scheduling problems and two-machine flowshop scheduling problems with ordered job processing times, respectively, where the actual processing time of a job is a function of its position and a control parameter. Under the proposed learning models, we show that some scheduling problems can be solved in polynomial time. In addition, we further analyse the worst-case error bounds for the problems to minimize the total weighted completion time, discounted total weighted completion time and maximum lateness.

Single-machine common due-date scheduling with batch delivery costs and resource-dependent processing times

In this paper we consider the problem of single-machine batch delivery scheduling with an assignable common due date and controllable processing times. The job processing time is either a linear or a convex function of the amount of a continuously divisible and non-renewable resource allocated to the job. Finished jobs are delivered in batches and there is no capacity limit on each delivery batch. The objective is to find a job sequence, a partition of the job sequence into batches, a common due date, and resource allocation that jointly minimise a cost function based on earliness, weighted number of tardy jobs, job holding, due-date assignment, batch delivery, makespan, and resource consumption. We provide some properties of the optimal solution, and show that the problem with the linear and convex resource consumption functions can be solved in and time, respectively. We also show that some special cases of the problem can be solved by lower-order algorithms.

Two-agent single-machine scheduling to minimize the batch delivery cost

Integrated production and batch delivery scheduling with two agents is considered.Constrained optimization problems on various criteria of the two agents are addressed.Computational complexity status and solution procedures are developed for the problems.Numerical studies are conducted to evaluate the performance of the algorithms. We consider integrated production and batch delivery scheduling in a make-to-order production system involving two competing agents, each of which having its own job set competes to process its jobs on a shared single machine. To save the delivery cost, the jobs of the same agent can be processed and delivered together batches. The completion time of each job in the same batch coincides with the batch completion time. A batch setup time is incurred before the processing of the first job in each batch. Each of the agents wants to minimize an objective function depending on the completion times of its own jobs. The goal is to determine a schedule for all the jobs of the two agents that minimizes the objective function of one agent, while keeping the objective function value of the other agent below or at a given value. For each of the problems under consideration, we either provide a polynomial-time algorithm to solve it or show that it is NP -hard. In addition, for each of the NP -hard problems, we present a mixed integer linear programming (MILP) formulation and provide a pseudo-polynomial dynamic programming algorithm, establishing that it is NP -hard in the ordinary sense only, and show that it admits an efficient fully polynomial-time approximation scheme, if viable. Finally, we compare the performance of the pseudo-polynomial dynamic programming algorithms against the corresponding MILP formulations with randomly generated instances.