Just-in-time batch scheduling problem with two-dimensional bin packing constraints

This paper introduces and approximately solves a multi-component problem where small rectangular items are produced from large rectangular bins via guillotine cuts. An item is characterized by its width, height, due date, and earliness and tardiness penalties per unit time. Each item induces a cost that is proportional to its earliness and tardiness. Items cut from the same bin form a batch, whose processing and completion times depend on its assigned items. The items of a batch have the completion time of their bin. The objective is to find a cutting plan that minimizes the weighted sum of earliness and tardiness penalties. We address this problem via a constraint programming based heuristic (CP) and an agent based modelling heuristic (AB). CP is an impact-based search strategy, implemented in the general-purpose solver IBM CP Optimizer. AB is constructive. It builds a solution through repeated negotiations between the set of agents representing the items and the set representing the bins. The agents cooperate to minimize the weighted earliness-tardiness penalties. The computational investigation shows that CP outperforms AB on small-sized instances while the opposite prevails for larger instances

Packing circles in the smallest circle: an adaptive hybrid algorithm

The circular packing problem (CPP) consists of packing n circles Ci, i∈N={1, …, n}, into the smallest containing circle ℂ. The objective is to determine the coordinates (xi) of the centre of Ci, i∈N, as well as the radius r and centre (x, y) of ℂ. CPP, which is a variant of the two-dimensional open-dimension problem, is NP hard. This paper presents an adaptive algorithm that incorporates nested partitioning within a tabu search and applies some diversification strategies to obtain a (near) global optimum. The tabu search is to identify the n circles’ ordering, whereas the nested partitioning is to determine the n circles’ positions that yield the smallest r. The computational results show the efficiency of the proposed algorithm.

A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems

National Nature Science Foundation of China [60773126]; Province Nature Science Foundation of Fujian; academician start-up fund [X01109]; 985 information technology fund [0000-X07204]; Kuwait University [US01/06]This paper approximately solves the high school timetabling problem using a simulated annealing based algorithm with a newly-designed neighborhood structure. In search for the best neighbor, the heuristic performs a sequence of swaps between pairs of time slots, instead of swapping two assignments as in a standard simulated annealing. The computational results show that the proposed heuristic, which is tested on two sets of benchmark instances, performs better than existing approaches. (C) 2009 Elsevier B.V. All rights reserved