Integer programming formulations of discrete hub location problems

This paper proposes variable neighborhood search (VNS) heuristics to solve hub network design problems with profits, which are uncapacitated hub location problems with incomplete hub networks. These problems seek to locate hub facilities, design hub networks, and assign spokes to hubs to maximize total profits. Six problems consisting of multiple allocation, single allocation, and r-allocation strategies, with optional direct connections, are solved. Unlike hub location problems that minimize costs satisfying all service demands, the operators can choose to satisfy a subset of travel demands to maximize profits. Although exact methods and heuristics are both commonly used for solving hub location problems, the problems with profits are mainly solved by the former. Therefore, VNS-based heuristics are proposed to solve six variants of hub location problems. The proposed heuristics have the same shaking procedure to escape local optima, while neighborhood structures in the improvement procedure depend on the allocation strategies. To evaluate the heuristics, this study also designs enhanced Benders decomposition methods which are exact algorithms. Computational experiments on existing benchmark datasets reveal an extraordinary performance of the heuristics. For the instances that can be solved by exact methods, the heuristics solve over 90% to optimality while being one to three orders of magnitude faster than the commercial solver CPLEX and Benders decomposition. Given the outstanding accuracy, with significantly reduced computational cost, the study contributes to the usage of heuristics for hub location problems with profits, especially for larger-scale networks, where exact methods cannot be executed because of limited computational resources.

Solving Hub Location Problems With Profits Using Variable Neighborhood Search

A heuristic approach to the stochastic capacitated single allocation hub location problem with Bernoulli demands

Integrating Industry 4.0 and the Internet of Things (IoT) for eco-friendly manufacturing

The relationship between a company's cost, volume, and profit is important for strategic planning and widely used in business analysis and industrial management. Traditional Cost-Volume-Profit (CVP) analysis is used when a company is trying to determine what single level of sales, prices, and costs is necessary to reach a specific amount of profit. So far, a little attention has been paid to the extension of existing models of CVP analysis to illustrate a set of break even points, profitable sales, prices, and costs. For this purpose, for the first time, we propose a new approach to break even and profitability analysis called sphere packing based on a notion of set of profitability conditions with respect to CVP parameters. This approach uses sphere packing theory [9,10], linear programming and allows industries to handle break even and profitability analysis for multi-product case finding a set of required sales, prices, and costs to ensure profitability of a company. The sphere packing approach also provides practical suggestions and recommendations for managers to choose a set of optimal CVP parameters. The proposed approach is illustrated on some examples providing numerical results. 

A sphere packing approach to break even and profitability analysis

Profit maximizing hub location problem in the airline industry under coopetition

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Profit maximizing hub location problem in the airline industry under coopetition