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Abstract The job shop scheduling literature has been dominated by a focus on regular objective functions – in particular the makespan – in its half a century long history. The last twenty years have encountered a spike of interest in other objectives, such as the total weighted tardiness, but research on non-regular objective functions has always been isolated and scattered. Motivated by this observation, we present a tabu search heuristic for a large class of job shop scheduling problems, where the objective is non-regular in general and minimizes a sum of separable convex cost functions attached to the operation start times and the differences between the start times of arbitrary pairs of operations. This problem definition generalizes a number of problems considered earlier in the literature. A particular notion of “critical paths” derived from the so-called timing problem is at the core of the proposed neighborhood definition exploited successfully in a tabu search algorithm. The computational results attest to the promise of our work.

This paper addresses the problem of minimising the number of moves to unload a set of boxes off a gravity conveyor by a forklift. If the input data are known in advance, the problem is efficiently solvable with a dynamic programming approach. However, this method is rarely applicable in practice for two reasons. First, the problem generally occurs in a real-time environment where the input data are revealed over time. Second, computing devices are in most cases not available in forklifts or gravity conveyors for decision-making. Online approaches that can easily be applied by human operators are therefore sought in practice. With this in mind, we first propose some intuitive approaches and analyse their performance through an extensive experimental study. The results show that these approaches are quite inefficient as they average between 14.7 and 59.3% above the optimum. A less intuitive but still simple approach is then designed that consistently produces good results with an average gap of 6.1% to the ...