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This paper addresses the problem of open shop scheduling on two machines with resources constraints. In the context of our study, in order to be executed, a job requires first, for its preparation for a given period of time, a number of resources which cannot exceed a given resource capacity. Then, it goes onto its execution while the resources allocated to it become available again. We seek a schedule that minimizes the makespan. We first prove the $\mathcal{N}\mathcal{P}$ -hardness of several versions of this problem. Then, we present a well solvable case, lower bounds, and heuristic algorithms along with an experimental study. [ABSTRACT FROM AUTHOR]

This paper reports the results of work that aims to study the practical implementation of Distributed Artificial Intelligence (DAI) and to introduce its capabilities in representing and using knowledge in the area of contractor prequalification. This is accomplished by developing an intelligent Knowledge-Based Decision Support System for a contractor prequalification environment. The resulting DAI architecture comprises a hierarchy of loosely coupled problem solvers, all operating under the supervision of a top-level control mechanism. Each problem solver works as a classifier system. The problem-solving knowledge for each problem solver is developed using (1) machine learning by training neural networks, and (2) heuristic rules of thumb. A learning and refining subsystem is attached to the system for refining the existing knowledge to improve the system performance. [ABSTRACT FROM AUTHOR]

Machine learning exists in many realistic scheduling situations. This study focuses on permutation flow shop scheduling problems, where the actual processing time of a job is defined by a general non-increasing function of its scheduled position, i.e., general position-dependent learning effects. The objective functions are to minimize the total completion time, the makespan, the total weighted completion time, and the total weighted discounted completion time, respectively. To solve these problems, we present approximation algorithms based on the optimal permutations for the corresponding single machine scheduling problems and analyze their worst-case error bound. [ABSTRACT FROM AUTHOR]

Proofs of strong NP-hardness of single machine and two-machine flowshop scheduling problems with learning or aging effect given in Rudek (Computers & Industrial Engineering 61:20-31, ; Annals of Operations Research 196(1):491-516, ; International Journal of Advanced Manufacturing Technology 59:299-309, ; Applied Mathematics and Computations 218:6498-6510, ; Applied Mathematical Modelling 37:1523-1536, ) contain a common mistake that make them incomplete. We reveal the mistake and provide necessary corrections for the problems in Rudek (Computers & Industrial Engineering 61:20-31, ; Annals of Operations Research 196(1):491-516, ; Applied Mathematical Modelling 37:1523-1536, ). NP-hardness of problems in Rudek (International Journal of Advanced Manufacturing Technology 59:299-309, ; Applied Mathematics and Computations 218:6498-6510, ) remains unknown because of another mistake which we are unable to correct. [ABSTRACT FROM AUTHOR]