Personnel scheduling: A literature review

Simulation for manufacturing system design and operation: Literature review and analysis

Hyper-heuristics have recently emerged as a powerful approach to automate the design of heuristics for a number of different problems. Production scheduling is a particularly popular application area for which a number of different hyper-heuristics have been developed and are shown to be effective, efficient, easy to implement, and reusable in different shop conditions. In particular, they seem to be a promising way to tackle highly dynamic and stochastic scheduling problems, an aspect that is specifically emphasized in this survey. Despite their success and the substantial number of papers in this area, there is currently no systematic discussion of the design choices and critical issues involved in the process of developing such approaches. This paper strives to fill this gap by summarizing the state-of-the-art approaches, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling. This paper also identifies challenges and open questions and highlights various directions for future work.

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Automated Design of Production Scheduling Heuristics: A Review

This paper contains a complete and updated review of the literature for multiobjective flowshop problems, which are among the most studied environments in the scheduling research area. No previous comprehensive reviews exist in the literature. Papers about lexicographical, goal programming, objective weighting, and Pareto approaches have been reviewed. Exact, heuristic, and metaheuristic methods have been surveyed. Furthermore, a complete computational evaluation is also carried out. A total of 23 different algorithms including both flowshop-specific methods as well as general multiobjective optimization approaches have been tested under three different two-criteria combinations with a comprehensive benchmark. All methods have been studied under recent state-of-the-art quality measures. Parametric and nonparametric statistical testing is profusely employed to support the observed performance of the compared methods. As a result, we have identified the best-performing methods from the literature, which along with the review, constitutes a reference work for further research.

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A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem

Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems

A comparative study of a new heuristic based on adaptive memory programming and simulated annealing: The case of job shop scheduling

This paper investigates an intelligent system that selects dispatching rules to apply locally for each machine in a job shop. Randomly generated problems are scheduled using optimal permutations of three different dispatching rules on five machines. A neural network is then trained to associate between a statistical characterization of the job mix in each of these problems, with the best combination of dispatching rules to use. Once trained, the neural network is able to recommend for new problems a dispatching rule to use on each machine. Two networks are trained separately for minimizing makespan and the mean flowtime in the job shop. Test results show that the combinations of dispatching rules suggested by the trained networks produce better results, for both objectives, than the alternative of using a single rule common to all machines.

A neural network for dispatching rule selection in a job shop

A cooperative dispatching approach for minimizing mean tardiness in a dynamic flowshop

In this paper, we present a multi-faceted approach for ranking dispatching rules (DRs) in multi-objective dynamic flow shop scheduling systems using data envelopment analysis (DEA). The merits of t...

Ranking dispatching rules in multi-objective dynamic flow shop scheduling: a multi-faceted perspective

A Genetic Algorithm (GA) is developed for the retail staff scheduling problem. The proposed algorithm is implemented and compared with a conventional integer programming branch-and-bound approach. The performance of the algorithm is tested on six real-world problems. A sensitivity analysis is carried out on three problems for two genetic parameters: population size and mutation rate. Using statistical analysis, the effects of these parameters on the solution quality and computational times are studied. The comparative study shows that GA can produce near-optimal solutions for all of the test problems, and for half of them, it is more successful than the branch-and-bound method.

Ahmed El-Bouri

Papers

A comparative study of a new heuristic based on adaptive memory programming and simulated annealing: The case of job shop scheduling

A neural network for dispatching rule selection in a job shop

A cooperative dispatching approach for minimizing mean tardiness in a dynamic flowshop

Ranking dispatching rules in multi-objective dynamic flow shop scheduling: a multi-faceted perspective

Application of a genetic algorithm to staff scheduling in retail sector