A linear programming-based method for job shop scheduling

We present a decomposition heuristic for a large class of job shop scheduling problems. This heuristic utilizes information from the linear programming formulation of the associated optimal timing problem to solve subproblems, can be used for any objective function whose associated optimal timing problem can be expressed as a linear program (LP), and is particularly effective for objectives that include a component that is a function of individual operation completion times. Using the proposed heuristic framework, we address job shop scheduling problems with a variety of objectives where intermediate holding costs need to be explicitly considered. In computational testing, we demonstrate the performance of our proposed solution approach

Ownership and control in a competitive industry

We study a differentiated product market in which an investor initially owns a controlling stake in one of two competing firms and may acquire a non-controlling or a controlling stake in a competitor, either directly using her own assets, or indirectly via the controlled firm. While industry profits are maximized within a symmetric two product monopoly, the investor attains this only in exceptional cases. Instead, she sometimes acquires a noncontrolling stake. Or she invests asymmetrically rather than pursuing a full takeover if she acquires a controlling one. Generally, she invests indirectly if she only wants to affect the product market outcome, and directly if acquiring shares is profitable per se. --differentiated products,separation of ownership and control,private benefits of control

Mollusken aus Ghana

A report on marine molluscs from Ghana, Western Africa is given. Included are two lists of the gastropod and bivalve species found

A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules

© 2018 Massachusetts Institute of Technology. Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes

Designing a Decision Support System for Production Scheduling Task in Complex and Uncertain Manufacturing Environments

Part 2: Knowledge Discovery and SharingInternational audienceThe production planning and control process is performed within complex and dynamic organizations made up of equipment, people, information, IT systems, and influenced by a multitude of external factors. How to effectively schedule in uncertain and complex manufacturing environments, still remains a central question to academics and practitioners. In this paper, we propose a framework that can be utilized to design/enhance decision support systems for scheduling activities in complex and uncertain manufacturing environments. The framework is based on the analysis of the relevant literature that addressed human, organizational, and technological aspects of the production planning and scheduling