Intelligent scheduling covers various tools and techniques for successfully and efficiently solving the scheduling problems. In this paper, we provide a survey of intelligent scheduling systems by categorizing them into five major techniques containing fuzzy logic, expert systems, machine learning, stochastic local search optimization algorithms and constraint programming. We also review the application case studies of these techniques.

A state of the art review of intelligent scheduling

Simulation model of security control system functioning: A case study of the Wroclaw Airport terminal

The significance of systems configurations has been increased in many applied domains (e.g., software, hardware, manufacturing systems, communication systems, supply chain systems, solving strategies, modular planning, material engineering and combinatorial chemistry). Generally, the following kinds of systems under configuration (reconfiguration) can be examined: (a) initial data/infomation (or row materials); (b) processing systems (machines, computers, algorithms); (c) resultant decisions (e.g., products, plans); (d) applied support systems (e.g., network systems, services); (e) requirements; and (f) standards. In the paper several system configuration problems are investigated: (i) searching for (selection of) a set (structure) of system components, (ii) searching for a set of compatible system components, (iii) allocation of system components, (iv) reconfiguration of a system as redesign of the system structure, (v) multi-stage design and redesign of system configuration, (vi) design or redesign of the system configuration for multi-product systems, and (vii) design of system hierarchy. Combinatorial optimization models (including multicriteria statements) are under examination: multiple choice problem, allocation problem, graph coloring problems, morphological clique problem (with compatibility of system components), multipartite clique and their modifications, spanning trees problems.

Combinatorial optimization in system configuration design

The chromatic sum of a graph is introduced in the dissertation of Ewa Kubicka. It is the smallest possible total among all proper colorings of G using natural numbers. In this article we determine tight bounds on the chromatic sum of a connected graph with e edges,

Tight Bounds on the Chromatic Sum of a

The staff scheduling problem is widely studied in Operational Research. Various surveys are available in the literature dealing with this problem which concerns various objectives and various constraints. In this article, we present a staff scheduling problem in airport security service. First, a modeling of the problem, and a representation of solutions are shown. The problem is solved in three steps, days-off scheduling, shift scheduling, and staff assignment. We focus on the last step, by providing a Memetic Algorithm (MA) which merged an Evolutionary Algorithm and Local Search techniques. We propose a chromosome encoding, a crossover operator and a combined neighborhood function, specially dedicated to this staff assignment problem. Besides providing better solutions than software currently used, this algorithm provides up to 50% of improvement from initial feasible solutions.

A Memetic Algorithm for staff scheduling problem in airport security service

https://hal.archives-ouvertes.fr/hal-00783637/document

An exact method for graph coloring

Greedy algorithms play an important role in the practical resolution of NP-hard problems. A greedy algorithm is a basic heuristic that builds a solution by iteratively adding the locally best element into the solution according to certain criteria. A greedy algorithm can either be used on its own to obtain a “good” solution, or it can be integrated into global optimization methods, for example, to limit the search space in branch and bound algorithms, or to generate initial solutions in metaheuristics. In this paper we are interested in greedy algorithms for the Minimum Sum Coloring Problem (MSCP). Since MSCP is closely related to the basic Graph Coloring Problem (GCP), we start our study with GCP and then turn to MSCP. Concerning GCP, although a lot of work has been reported in the literature, little of it concerns greedy algorithms. The most widely used greedy algorithms remain DSATUR and

https://hal.archives-ouvertes.fr/hal-00451266/document

Greedy Algorithms for the Minimum Sum Coloring Problem

Lower Bounds for the Minimal Sum Coloring Problem

The Minimum Sum Coloring Problem (MSCP) is an extension of the well known Graph Coloring Problem (GCP). Both NP-hard problems consist in assigning a color to each vertex of a graph while respecting the neighborhood constraints: two adjacent vertices cannot share a same color. For MSCP, a weight is associated with each color. While the objective of GCP is to minimize the number of colors, the objective of MSCP is to minimize the sum of the color cost. In this paper, we propose and compare four exact methods to solve MSCP. The first one is a Branch-and-Bound method which uses upper and lower bounds to reduce search space. The second, the third and the fourth methods encode a MSCP instance into a MaxSAT, MinSAT and COP instance respectively, and solve the instance with dedicated solvers.

https://hal.archives-ouvertes.fr/hal-01326666/document

Corinne Lucet

Papers

A Memetic Algorithm for staff scheduling problem in airport security service

An exact method for graph coloring

Greedy Algorithms for the Minimum Sum Coloring Problem

Lower Bounds for the Minimal Sum Coloring Problem

Exact methods for the minimum sum coloring problem