건축학과 혹은 건축공학과로 진학을 희망하는 고등학생 친구들을 위해 제작한 자료입니다.
2015 WISET 건축 NO 1 _
이인혜(세종대 대학원 건축학과), 박미영(세종대 건축학과), 주아진(동국대 경찰행정학과)
이은혜(시흥매화고), 심진하(시흥매화고), 김성은(해성여고), 박지연(해성여고)
5 things you need to know to make you a smarter mobile marketeer - Seattle Mo...Paul Booth
Julie Lynn Try (Southard) of Xbox takes us through her 5 important lessons of mobile marketing for Seattle Mobile Mixers.
For more information, please follow @MobileMixers on Twitter
건축학과 혹은 건축공학과로 진학을 희망하는 고등학생 친구들을 위해 제작한 자료입니다.
2015 WISET 건축 NO 1 _
이인혜(세종대 대학원 건축학과), 박미영(세종대 건축학과), 주아진(동국대 경찰행정학과)
이은혜(시흥매화고), 심진하(시흥매화고), 김성은(해성여고), 박지연(해성여고)
5 things you need to know to make you a smarter mobile marketeer - Seattle Mo...Paul Booth
Julie Lynn Try (Southard) of Xbox takes us through her 5 important lessons of mobile marketing for Seattle Mobile Mixers.
For more information, please follow @MobileMixers on Twitter
Industrial Linear/Logic/Logistics and Nonlinear Programming Language (ILP/INL)Alkis Vazacopoulos
The ILP and INL files are considered as “foreign” sub-models that can be used to augment or extend an existing or non-foreign model in IMPL (Industrial Modeling and Programming Language). IMPL supports including only one foreign sub-model i.e., either a single ILP file or a single INL file but not both and multiple ILP or INL files are also not allowed. These foreign files are inputted by the IMPL Modeler only after the entire non-foreign model has been created or generated. These foreign files are useful to add or inject ad hoc, bespoke or custom variables and/or constraints into the overall model or problem.
Hierarchical Decomposition Heuristic for Scheduling: Coordinated Reasoning fo...Alkis Vazacopoulos
This paper presents a new technique for decomposing and rationalizing large decision-making problems into a common and consistent framework. We call this the hierarchical decomposition heuristic (HDH) which focuses on obtaining "globally feasible" solutions to the overall problem, i.e., solutions which are feasible for all decision-making elements in a system. The HDH is primarily intended to be applied as a standalone tool for managing a decentralized and distributed system when only globally consistent solutions are necessary or as a lower bound to a maximization problem within a global optimization strategy such as Lagrangean decomposition. An industrial scale scheduling example is presented that demonstrates the abilities of the HDH as an iterative and integrated methodology in addition to three small motivating examples. Also illustrated is the HDH's ability to support several types of coordinated and collaborative interactions.
Chronological Decomposition Heuristic: A Temporal Divide-and-Conquer Strateg...Alkis Vazacopoulos
The chronological decomposition heuristic (CDH) is a simple divide-and-conquer strategy intended to find rapidly, integer-feasible solutions to production scheduling optimization problems of practical scale. It is not an exact algorithm in that it will not find the global optimum although it does use either branch-and-bound or branch-and-cut. The CDH is specifically designed for production scheduling optimization problems which are formulated using a uniform discretization of time where a time grid is pre-specified with fixed time-period spacing. The basic premise of the CDH is to slice the scheduling time horizon into aggregate time-intervals or “time-chunks” which are some multiple of the base time-period. Each time-chunk is solved using mixed-integer linear programming (MILP) techniques starting from the first time-chunk and moving forward in time using the technique of chronological backtracking if required (Marriott and Stuckey, 1998; for more details see the extensive literature on constraint logic programming). The efficiency of the heuristic is that it decomposes the temporal dimension into smaller size time-chunks which are solved in succession instead of solving one large problem over the entire scheduling horizon. The basic idea of such a decomposition strategy was partially presented in Bassett et. al. (1996) whereby they provided a hierarchical interaction or collaboration between a planning layer and a temporally decomposed scheduling layer. For the CDH, we focus on the time-based decomposition of the scheduling layer without the need for a higher-level coordinating or planning layer.
For many industrial size problems, solving the MILP using commercial branch-and-bound or branch-and-cut optimization can be a somewhat futile exercise even for well-formulated problems of practical interest. Instead, many researchers such as Kudva et. al. (1994), Wolsey (1998), Nott and Lee (1999), Blomer and Gunther (2000) and Kelly (2002) have devised elaborate primal heuristic techniques to enable the solution of problems of large scale and complexity; these techniques can also be augmented by other decomposition strategies such as Lagrangean and Bender’s relaxation. Unfortunately with these heuristics global optimality or even global feasibility cannot be guaranteed, however these methods and others not mentioned, have proven useful for problems which are sometimes too large to be solved using conventional methods alone. Therefore, the CDH should be considered as a step in the direction of aiding the scheduling user in finding integer-feasible solutions of reasonable quality quickly.