A hierarchical algorithm for the flexible job shop scheduling problem is described, based on the tabu search metaheuristic. Hierarchical strategies have been proposed in the literature for complex scheduling problems, and the tabu search metaheuristic, being able to cope with different memory levels, provides a natural background for the development of a hierarchical algorithm. For the case considered, a two level approach has been devised, based on the decomposition in a routing and a job shop scheduling subproblem, which is obtained by assigning each operation of each job to one among the equivalent machines. Both problems are tackled by tabu search. Coordination issues between the two hierarchical levels are considered. Unlike other hierarchical schemes, which are based on a one-way information flow, the one proposed here is based on a two-way information flow. This characteristic, together with the flexibility of local search strategies like tabu search, allows to adapt the same basic algorithm to different objective functions. Preliminary computational experience is reported.
tabu
When I stumbled upon tabu some time ago it looked quite interesting on a first glance and I intended to learn more about it when I have the time.But, in the comments section of this question posted on the new german question and answer site texwelt.de Herbert Voß wrote that the tabu package is obsolete in its current version (thereby citing the package author) and is not actively maintained anymore.What are the implications for the usability of the package?Should I refrain from investing time to learn tabu and consider some other packages for the creation of tables instead?
The conclusions I can draw is that using tabu in its current version is dangerous, because its author has announced incompatible changes for the next version. And he doesn't care if people using his package are thrown under the bus.
var preloaded = 'listing': "id":68852,"title":"Tabu","excerpt":"Tabu breaks from tradition with Pan-Latin-inspired cuisine and cocktails, in a unique and eclectic West Loop space. Executive Chef Sa\u00fal Rom\u00e1n, a Cuernavaca, Mexico native\u2026","link":"https:\/\/www.choosechicago.com\/listing\/tabu\/","thumb":"https:\/\/chicago.simpleviewcrm.com\/images\/listings\/IMG_0503-2-_517124AC-5056-A36F-2334AE4E08D586F3.jpg","region":"West Loop","date":"12\/15\/2022","lat":"41.8894427000","lng":"-87.6517285000","addr1":"401 N Morgan","addr2":"","addr3":"","cats":[],"open_status":"Service available Dine-inService unavailable TakeoutService unavailable DeliveryService unavailable Patio","booking_cta":false,"concierge_cta":"Book now","owned":false,"is_hood":73317,"deals":[],"amens":""
The graph is represented as a program-defined MyGraph object. The graph is loaded from an external text file that uses the DIMACS file format. The key method of MyGraph is AreAdjacent, which returns true if two nodes are connected. I set maxTime to 50 iterations to establish an absolute stopping condition for the greedy algorithm. This is artificially small. In a real maximum clique problem, the max time is typically in the range of 1,000 to 100,000. I set targetClique size to establish a second stopping condition; if a clique is found that has the same number of nodes as there are in the graph, the maximum clique must have been found. Most of the work is done by the FindMaxClique method, which uses a greedy, adaptive tabu algorithm to search for the largest possible clique. The helper method ListAsString simply creates a string representation of a List object.
Is TS always deterministic? From reading some sources I got the idea that moves could be random, like in SA. But then there are some papers that talk about "Deterministic Tabu Search". How does deterministic tabu search know which moves to take and how does it escape local optima? It must accept worse solutions sometimes, right?
Tabu Search gets out of local optima, here is an image showing how. It accept worse solutions if all improving solutions are tabu (and not aspired) and either all moves have been evaluated or a selectionLimit or acceptedLimit has been reached.
This case study is one in a series that highlights the resilience (tabu soro) of Fijian workplaces in times of crisis. The aim of the Tabu Soro case studies is to showcase good practice in managing the impact of COVID-19, and disasters such as tropical cyclones, to foster safe and flexible workplaces for all employees.
Two-mode binary data matrices arise in a variety of social network contexts, such as the attendance or non-attendance of individuals at events, the participation or lack of participation of groups in projects, and the votes of judges on cases. A popular method for analyzing such data is two-mode blockmodeling based on structural equivalence, where the goal is to identify partitions for the row and column objects such that the clusters of the row and column objects form blocks that are either complete (all 1s) or null (all 0s) to the greatest extent possible. Multiple restarts of an object relocation heuristic that seeks to minimize the number of inconsistencies (i.e., 1s in null blocks and 0s in complete blocks) with ideal block structure is the predominant approach for tackling this problem. As an alternative, we propose a fast and effective implementation of tabu search. Computational comparisons across a set of 48 large network matrices revealed that the new tabu-search heuristic always provided objective function values that were better than those of the relocation heuristic when the two methods were constrained to the same amount of computation time. 2ff7e9595c
Comments