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Mixed Integer Programming (mixed + integer_programming)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting60%
Engineering40%

Selected Abstracts

Integrating genetic algorithms and spreadsheets: a capital budgeting application

INTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE & MANAGEMENT, Issue 3 2006
R. H. Berry
The role of the tax system in generating interactions between the post-tax cash flows of different projects is discussed. When such interactions can occur, the capital budgeting process should be based around project combinations rather than individual projects. Evaluation of a project combination in net present value terms can easily be done using a spreadsheet. If the number of individual projects is large, then project combinations can be generated and an optimum combination of projects searched for using a genetic algorithm. The genetic algorithm approach has an advantage over alternative computational approaches, such as mixed integer programming, because of the more understandable representation of the problem it allows. Copyright © 2007 John Wiley & Sons, Ltd. [source]

A mixed integer programming for robust truss topology optimization with stress constraints

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 13 2010
Yoshihiro Kanno
Abstract This paper presents a mixed integer programming (MIP) formulation for robust topology optimization of trusses subjected to the stress constraints under the uncertain load. A design-dependent uncertainty model of the external load is proposed for dealing with the variation of truss topology in the course of optimization. For a truss with the discrete member cross-sectional areas, it is shown that the robust topology optimization problem can be reduced to an MIP problem, which is solved globally. Numerical examples illustrate that the robust optimal topology of a truss depends on the magnitude of uncertainty. Copyright © 2010 John Wiley & Sons, Ltd. [source]

A genetic algorithm for a bicriteria supplier selection problem

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 2 2009
Chuda Basnet
Abstract In this paper, we discuss the problem of selecting suppliers for an organisation, where a number of suppliers have made price offers for supply of items, but have limited capacity. Selecting the cheapest combination of suppliers is a straightforward matter, but purchasers often have a dual goal of lowering the number of suppliers they deal with. This second goal makes this issue a bicriteria problem , minimisation of cost and minimisation of the number of suppliers. We present a mixed integer programming (MIP) model for this scenario. Quality and delivery performance are modelled as constraints. Smaller instances of this model may be solved using an MIP solver, but large instances will require a heuristic. We present a multi-population genetic algorithm for generating Pareto-optimal solutions of the problem. The performance of this algorithm is compared against MIP solutions and Monte Carlo solutions. [source]

Short-term harvest planning including scheduling of harvest crews

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 5 2003
J. Karlsson
Abstract The problem we consider is short-term harvesting planning for a total planning period of 4,6 weeks where we want to decide the harvest sequences or schedules for harvest crews. A schedule is an order or sequence of harvest areas assigned to each crew. The harvesting of areas is planned in order to meet industrial demand. The total cost includes harvesting, transportation, and storage. One considerable cost is due to the quality reduction of logs stored at harvest areas. There are a number of restrictions to be considered. Areas are of varying size and the composition of assortments in each area is different. Each harvest team has different skills, a different home base, and different production capacity. Another aspect is the road network. There is a cost related to road opening (restoring, snow removal). In this paper, we develop a mixed integer programming (MIP) model for the problem. The schedules are represented by 0/1 variables. With a limited number of schedules, the problem can be solved by a commercial MIP solver. We have also developed a heuristic solution approach that provides high-quality integer solutions within a distinct time limit to be used when more schedules are used. Computational results from a major Swedish forest company are presented. [source]

A MODEL FOR BATCH ADVANCED AVAILABLE-TO-PROMISE

PRODUCTION AND OPERATIONS MANAGEMENT, Issue 4 2002
CHIEN-YU CHEN
The available-to-promise (atp) function is becoming increasingly important in supply chain management since it directly links production resources with customer orders. In this paper, a mixed integer programming (mip) ATP model is presented. This model can provide an order-promising and -fulfillment solution for a batch of orders that arrive within a predefined batching interval. A variety of constraints, such as raw material availability, production capacity, material compatibility, and customer preferences, are considered. Simulation experiments using the model investigate the sensitivity of supply chain performance to changes in certain parameters, such as batching interval size and customer order flexibility. [source]