https://ira.lib.polyu.edu.hk/bitstream/10397/1269/1/SetupSurvey-2006%28June-19%29.pdf

A survey of scheduling problems with setup times or costs

The scheduling of computer and manufacturing systems has been the subject of extensive research for over forty years. In addition to computers and manufacturing, scheduling theory can be applied to many areas including agriculture, hospitals and transport. The main focus is on the efficient allocation of one or more resources to activities over time. Adopting manufacturing terminology, a job consists of one or more activities, and a machine is a resource that can perform at most one activity at a time. We concentrate on deterministic machine scheduling for which it is assumed that all data that define a problem instance are known with certainty.

A Review of Machine Scheduling: Complexity, Algorithms and Approximability

A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times

This paper presents a scheduling problem for unrelated parallel machines with sequence-dependent setup times, using simulated annealing (SA). The problem accounts for allotting work parts of L jobs into M parallel unrelated machines, where a job refers to a lot composed of N items. Some jobs may have different items while every item within each job has an identical processing time with a common due date. Each machine has its own processing times according to the characteristics of the machine as well as job types. Setup times are machine independent but job sequence dependent. SA, a meta-heuristic, is employed in this study to determine a scheduling policy so as to minimize total tardiness. The suggested SA method utilizes six job or item rearranging techniques to generate neighborhood solutions. The experimental analysis shows that the proposed SA method significantly outperforms a neighborhood search method in terms of total tardiness.

Unrelated parallel machine scheduling with setup times using simulated annealing

This paper reviews the literature related to the class of scheduling problems that involve sequence-dependent setup times (costs), an important consideration in many practical applications. It focuses on papers published within the last decade, addressing a variety of machine configurations including single machine, parallel machine, flow shop, and job shop systems and reviews the optimization and heuristic solution methods used for each category. Since lot sizing is so intimately related to scheduling, this paper reviews work that integrates these issues in relationship to each configuration. This paper provides a perspective of this line of research, gives conclusions, and discusses fertile research opportunities posed by this class of scheduling problems.

Scheduling and lot sizing with sequence-dependent setup: A literature review

Unrelated parallel machine scheduling with setup consideration and a total weighted completion time objective

In this article the problem of minimizing the completion time variance in n-job, single-machine scheduling is considered. The release times for all jobs are assumed to be zero. A new quadratic integer programming formulation is introduced. A Lagrangian relaxation LR procedure is developed to find a lower bound LB to the optimal objective value. When the number of jobs is between 100 and 500, our computational study shows that the lower bounds obtained by the LR procedure are very close to the best known objective values. A new heuristic algorithm is also described. The first phase of the heuristic algorithm is a construction procedure whose purpose is to identify a good initial sequence. The second phase is an improvement procedure based on pairwise interchanges. The new heuristic algorithm provides improved solutions compared to the best known heuristic.

Minimizing single-machine completion time variance

Kramer and Lee recently addressed a common due window scheduling problem with earliness and tardiness penalties, where earliness and tardiness penalty factors are constant and the common window size is given. They showed that the problem is polynomial when the location of the due window is a decision variable. For the case where the location of the due window is given, the problem is also polynomial when the latest due date is greater than or equal to the makespan, and they proposed a pseudopolynomial dynamic programming algorithm to find an optimal schedule when the latest due date is less than the makespan. In this note we address the problem for the case where the location of the due window is given. Specifically, we show that the problem is polynomial if the window location is unrestricted, and present a more efficient dynamic program algorithm to optimally solve the problem if the window location is restricted. The concepts of unrestricted and restricted window locations are defined in this note.

A note on “common due window scheduling”

In this paper we consider the single machine scheduling problem of minimizing the mean absolute deviation (MAD) of job completion times from a restricted common delivery window. This problem is NP-hard. A Lagrangian relaxation procedure is proposed to solve the problem. Two efficient heuristics are also proposed. An experimental study on randomly generated problems is carried out to test the performance of the proposed methods. The computational results show that the obtained lower bounds are very good and the proposed heuristics generate near-optimal solutions.

https://link.springer.com/content/pdf/10.1057%2Fjors.1996.41.pdf

Michael X. Weng

Papers

Unrelated parallel machine scheduling with setup consideration and a total weighted completion time objective

Minimizing single-machine completion time variance

A note on “common due window scheduling”

Single Machine Scheduling with a Common Delivery Window

Single-machine earliness-tardiness scheduling about a common due date with tolerances