Optimizing wheat storage and transportation system using a mixed integer programming model and genetic algorithm: A case study

10.1109/IEEM.2009.5373152IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management2109-211

Logistics strategic decisions

Logistics has an important economic role because it swallows the biggest part of capital and supports the flow and movement of many economic transactions. Therefore, designing the best logistics strategies is vital. In this chapter, we take a look at different kinds of logistics decisions, especially investigate the strategic ones. First, we define strategy and its levels, and then we explain strategic planning in a business. There are three levels for logistics decision: strategic, tactical, and operational. Each one is defined in this chapter. Because the focus of this chapter is on logistics strategic decisions, we introduce their main categories: customer service, logistics network design, and, last but not least, outsourcing versus vertical integration. An entire book can be devoted to each category, but here we offer a brief description for each one. In addition, we introduce important tools of strategic decision making. Moroever, at the end of this chapter, we describe the term flexibility in logistics decision making. We indicate that future logistics systems and platforms are not optimized to minimize cost minimization but to maximize strategic flexibility

Multi-criteria location problem

No abstract

Partial Interval Set Cover -- Trade-offs Between Scalability and Optimality

Given an interval I = {1, 2,..., n} of points, a collection I of subintervals of I and a fraction 0 ≤ r ≤ 1, we consider the following variation of partial set cover. We wish to find an optimal subset of I covering at least an r-fraction of I. While this problem is easily solved exactly in quadratic time using classical methods, we focus on developing scalable algorithms which return near-optimal solutions and run in near-linear time. We give a (1 + ɛ)-approximation algorithm running in O ( 1 ɛ · min{n + |I|, |I | log |I|}}) time. We also prove a tight approximation ratio of 2 for a simple greedy algorithm for this problem, improving on the bound of 9 given in [10]