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This paper considers the problem of scheduling jobs in a permutation flow shop with the objective of minimising total earliness and tardiness. A genetic algorithm is proposed for the problem. This procedure and five other procedures were tested on problem sets that varied in terms of number of jobs, machines and the tightness and range of due dates. It was found that the genetic algorithm procedure was consistently effective in generating good solutions relative to the other procedures.

Abstract
This paper presents several procedures for developing non-delay schedules for a permutation flow shop with family setups when the objective is to minimize total earliness and tardiness. These procedures consist of heuristics that were found to be effective for minimizing total tardiness in flow shops without family setups, modified to consider family setups and the total earliness and tardiness objective. These procedures are tested on several problem sets with varying conditions. The results show that variable greedy algorithms are effective when solving small problems, but using a genetic algorithm that includes a neighbourhood defined by the sequence of batches of jobs belonging to the same set-up family is effective when solving medium- or large-sized problems. The results also show that if setup times can be reduced a significant reduction in total earliness and tardiness could result.

Abstract
This paper reports the development of a B2B platform for the personalization of the publicity transmitted during the program intervals. The platform as a whole must ensure that the intervals are filled with ads compatible with the profile, context and expressed interests of the viewers. The platform acts as an electronic marketplace for advertising agencies (content producer companies) and multimedia content providers (content distribution companies). The companies, once registered at the platform, are represented by agents who negotiate automatically the price of the interval timeslots according to the specified price range and adaptation behaviour. The candidate ads for a given viewer interval are selected through a matching mechanism between ad, viewer and the current context (program being watched) profiles. The overall architecture of the platform consists of a multiagent system organized into three layers consisting of: (i) interface agents that interact with companies; (ii) enterprise agents that model the companies, and (iii) delegate agents that negotiate a specific ad or interval. The negotiation follows a variant of the Iterated Contract Net Interaction Protocol (ICNIP) and is based on the price/s offered by the advertising agencies to occupy the viewer's interval. © Springer International Publishing Switzerland 2013.

Abstract
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