The problem of scheduling groups of jobs on a single machine under the group technology assumption is studied. Jobs of the same group are processed contiguously and a sequence independent setup time precedes the processing of each group. All jobs have a common fixed due date, which can be either unrestrictively large or restrictively small. The objective is to minimize the total weighted earlin...

The problem considered in this work consists scheduling a set of tasks on single machine, around an unrestrictive common due date to minimize the weighted sum earliness and tardiness. This can be formulated as compact mixed integer program (MIP). In article, we focus neighborhood-based dominance properties, where neighborhood is associated insert swap operations. We derive from these properties...

We consider the single-machine problem of scheduling n jobs to minimize the total weighted deviation from a given common due date, where the weights for early and tardy completion are asymmetric. First, we assume that the common due date is large. We formulate this problem as an integer linear program with an exponential number of variables and present a column generation algorithm to solve e c...

Yet, the few existing models considering learning effects in scheduling concentrate on learning-by-doing (autonomous learning). But recent contributions to the literature on learning in manufacturing organizations emphasize the important impact of proactive investments in technological knowledge on the learning rate (induced learning). In the present paper, we focus on a scheduling problem wher...

This paper studies the single machine scheduling problem for minimizing the expected total weighted deviations of completion times from random common due date. Jobs have exponentially distributed processing times and the common due date is a generalized Erlang distribution. The optimal schedules are shown to be ⁄-shaped. Moreover, we give the optimal schedules when the machine is subject to sto...