A Common Medium for Programming Operations-Research Models

Operations research is an interdisciplinary science that develops scientific methods for modeling and solving decision-making problems. Arguably, the two most established and commonly practiced OR methodologies are mathematical programming (also called optimization) and discrete-event system simulation (called simulation). Mathematical programming describes a decision problem as decision variables and as mathematical functions of variables that capture the objective and problem constraints. For dynamic systems that are too complex to allow exact, closed-form solutions in continuous time, simulation can identify and describe events of interest and model their occurrences at discrete points in time. Python can glue together independent software environments to improve the software development cycle for sophisticated operations-research applications, which integrate different modeling methodologies.     

Decentralizing semiconductor capacity planning via internal market coordination

Semiconductor capacity planning is a cross-functional decision that requires coordination between the marketing and manufacturing divisions. We examine the main issues of a decentralized coordination scheme in a setting observed at a major US semiconductor manufacturer: marketing managers reserve capacity from manufacturing based on product demands, while attempting to maximize profit; manufacturing managers allocate capacity to competing marketing managers so as to minimize operating costs while ensuring efficient resource utilization. This cross-functional planning problem has two important characteristics: (i) both demands and capacity are subject to uncertainty; and (ii) all decision entities own private information while being self-interested. To study the issues of coordination we first formulate the local marketing and the manufacturing decision problem as separate stochastic programs. We then formulate a centralized stochastic programming model (JCA), which maximizes the firm's overall profit. JCA establishes a theoretical benchmark for performance, but is only achievable when all planning information is public. If local decision entities are to keep their planning information private, we submit that the best achievable coordination corresponds to an alternative stochastic model (DCA). We analyze the relationship and the theoretical gap between (JCA) and )DCA), thereby establishing the price of decentralization. Next, we examine two mechanisms that coordinate the marketing and manufacturing decisions to achieve (DCA) using different degrees of information exchange. Using insights from the Auxiliary Problem Principle (APP), we show that under both coordination mechanisms the divisional proposals converge to the global optimal solution of (DCA). We illustrate the theoretic insights using numerical examples as well as a real world case.     

A beam search-based algorithm and evaluation of scheduling approaches for flexible manufacturing systems

This paper presents a new algorithm for the flexible manufacturing system (FMS) scheduling problem. The proposed algorithm is a heuristic based on filtered beam search. It considers finite buffer capacity, routing and sequence flexibilities and generates machine and automated guided vehicle (AGV) schedules for a given scheduling period. A new deadlock resolution mechanism is also developed as an integral part of the proposed algorithm. The performance of the algorithm is compared with several machine and AGV dispatching rules using mean flow time, mean tardiness and makespan criteria. It is also used to examine the effects of scheduling factors (i.e., machine and AGV load levels, routing and sequence flexibilities, etc.) on the system performance. The results indicate that the proposed scheduling algorithm yields considerable improvements in system performance over dispatching rules under a wide variety of experimental conditions.