The third comprehensive survey on scheduling problems with setup times/costs

Two-agent single-machine scheduling with assignable due dates

We consider scheduling problems involving two agents (agents A and B), each having a set of jobs that compete for the use of a common machine to process their respective jobs. The due dates of the A-jobs are decision variables, which are determined by using the common (CON) or slack (SLK) due date assignment methods. Each agent wants to minimize a certain performance criterion depending on the completion times of its jobs only. Under each due date assignment method, the criterion of agent A is always the same, namely an integrated criterion consisting of the due date assignment cost and the weighted number of tardy jobs. Several different criteria are considered for agent B, including the maxima of regular functions (associated with each job), the total (weighted) completion time, and the weighted number of tardy jobs. The overall objective is to minimize the performance criterion of agent A, while keeping the objective value of agent B no greater than a given limit. We analyze the computational complexity, and devise polynomial or pseudo-polynomial dynamic programming algorithms for the considered problems. We also convert, if viable, any of the devised pseudopolynomial dynamic programming algorithms into a fully polynomial-time approximation scheme.© 2016 Wiley Periodicals, Inc. Naval Research Logistics, 2016

CON/SLK due date assignment and scheduling on a single machine with two agents

This paper presents a Hybrid Evolutionary Algorithm (HEA) to solve the Job Shop Scheduling Problem (JSP). Incorporating a tabu search procedure into the framework of an evolutionary algorithm, the HEA embraces several distinguishing features such as a longest common sequence based recombination operator and a similarity-and-quality based replacement criterion for population updating. The HEA is able to easily generate the best-known solutions for 90 % of the tested difficult instances widely used in the literature, demonstrating its efficacy in terms of both solution quality and computational efficiency. In particular, the HEA identifies a better upper bound for two of these difficult instances.

A hybrid evolutionary algorithm to solve the job shop scheduling problem

Two-agent single-machine scheduling with deteriorating jobs

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.

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

This paper investigates some single-machine scheduling problems with a general learning effect and the group technology assumption. A setup time is incurred whenever the single machine transfers job processing from a family to another family. By the general learning effect, we mean that the actual group setup time depends not only on the total setup time of the groups already processed but also on its scheduled position, and the actual processing time of a job in a certain group depends not only on the total normal processing time of the jobs already processed in the group but also on its scheduled position. We show that the makespan minimisation problem remains polynomially solvable under the proposed models. We also show that the problems of minimising the total completion time, the total weighted completion time and the discounted total weighted completion time of all jobs have polynomial optimal solutions under certain conditions.

Single-machine group scheduling with a general learning effect

Electricity demand forecasting plays a vital role in power system planning, operation, transmission design, and financial risk management. In this study, we employ a Gaussian Process model to estimate the probability distributions of short term electricity demand. To account for input uncertainty in multi-period ahead forecast, we adopt an error propagation procedure that improves upon the naive recursive forecasting. This method is shown to perform well in terms of multi-step ahead point and probabilistic forecasts. We illustrate the usefulness of the suggested estimator with an application to short term demand forecasting of Texas electricity market. 

Wen Hung Wu

Papers

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

Single-machine group scheduling with a general learning effect

Electricity Demand Forecasting and Risk Management Using Gaussian Process Model with Error Propagation