We assess alternative cross-training policies for work-teams considering cost, and levels of cross training. The policies are assessed with respect to their robustness to demand-mix variation and absenteeism coverage. We employ simulation to examine instances where cross training can be used to help meet a fixed demand scenario, and with instances where cross-training can help to meet demand mix variability. Current results indicate that when minimizing cross-training costs, policies related to equalizing the cross-training level among the workforce, may provide improvement in terms of robustness without additional cost. We also assess the effects of some environmental factors, demand mix-coverage, absenteeism coverage, and job-task correlation.
We present a reachability graph-based search optimization tool for scheduling.Motivated by the lack of tool support for optimization of TCPNs.Implements an event-driven timed state space with AI heuristic search algorithms.Aimed at supporting flexible decision making process with algorithm portfolio.Comparative study of nine search algorithms on real system demonstrates tool efficiency. The combination of Petri net (PN) modeling with AI-based heuristic search (HS) algorithms (PNHS) has been successfully applied as an integrated approach to deal with scheduling problems that can be transformed into a search problem in the reachability graph. While several efficient HS algorithms have been proposed albeit using timed PN, the practical application of these algorithms requires an appropriate tool to facilitate its development and analysis. However, there is a lack of tool support for the optimization of timed colored PN (TCPN) models based on the PNHS approach for schedule generation. Because of its complex data structure, TCPN-based scheduling has often been limited to simulation-based performance analysis only. Also, it is quite difficult to evaluate the strength and tractability of algorithms for different scheduling scenarios due to the different computing platforms, programming languages and data structures employed. In this light, this paper presents a new tool called TIMSPAT, developed to overcome the shortcomings of existing tools. Some features that distinguish this tool are the collection of several HS algorithms, XML-based model integration, the event-driven exploration of the timed state space including its condensed variant, localized enabling of transitions, the introduction of static place, and the easy-to-use syntax statements. The tool is easily extensible and can be integrated as a component into existing PN simulators and software environments. A comparative study is performed on a real-world eyeglass production system to demonstrate the application of the tool for scheduling purposes.
In real-life logistics and distribution activities it is usual to face situations in
which the distribution of goods has to be made from multiple warehouses or
depots to the nal customers. This problem is known as the Multi-Depot Vehicle
Routing Problem (MDVRP), and it typically includes two sequential and
correlated stages: (a) the assignment map of customers to depots, and (b) the
corresponding design of the distribution routes. Most of the existing work in the
literature has focused on minimizing distance-based distribution costs while satisfying
a number of capacity constraints. However, no attention has been given
so far to potential variations in demands due to the tness of the customerdepot
mapping in the case of heterogeneous depots. In this paper, we consider
this realistic version of the problem in which the depots are heterogeneous in
terms of their commercial oer and customers show dierent willingness to consume
depending on how well the assigned depot ts their preferences. Thus,
we assume that dierent customer-depot assignment maps will lead to dierent
customer-expenditure levels. As a consequence, market-segmentation strategies
need to be considered in order to increase sales and total income while accounting
for the distribution costs. To solve this extension of the MDVRP, we
propose a hybrid approach that combines statistical learning techniques with
a metaheuristic framework. First, a set of predictive models is generated from
historical data. These statistical models allow estimating the demand of any
customer depending on the assigned depot. Then, the estimated expenditure of
each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A
set of computational experiments contribute to illustrate our approach and how
the extended MDVRP considered here diré in terms of the proposed solutions
from the traditional one.
This paper proposes two constructive heuristics, i.e. HPF1 and HPF2, for the blocking flow shop problem in order to minimize the total flow time. They differ mainly in the criterion used to select the first job in the sequence since, as it is shown, its contribution to the total flow time is not negligible. Both procedures were combined with the insertion phase of NEH to improve the sequence. However, as the insertion procedure does not always improve the solution, in the resulting heuristics, named NHPF1 and NHPF2, the sequence was evaluated before and after the insertion to keep the best of both solutions. The structure of these heuristics was used in Greedy Randomized Adaptive Search Procedures (GRASP) with variable neighborhood search in the improvement phase to generate greedy randomized solutions. The performance of the constructive heuristics and of the proposed GRASPs was evaluated against other heuristics from the literature. Our computational analysis showed that the presented heuristics are very competitive and able to improve 68 out of 120 best known solutions of Taillard’s instances for the blocking flow shop scheduling problem with the total flow time criterion
The objective of SALBP-E is to minimize the product on the number of workstations by the cycle time. Recently Wei and Chao [Comput. Ind. Eng. 61 (2011) 824–830] have proposed an exact procedure for solving this problem. It is based on solving iteratively SALBP-2 by means of a MILP model. SALBP-E has not been much studied and hence the high interest of their work. However, the article has several errors that make its understanding harder and, moreover, impede the correct implementation of their procedure for solving SALBP-E. Therefore, it is important to correct them.
Zou, Q.; Zhang, Q.; Yang, J.; Cloutier, A.; Peña-Pitarch, E. Computers and industrial engineering Vol. 63, num. 4, p. 791-801 DOI: 10.1016/j.cie.2012.05.001 Data de publicació: 2012-10-27 Article en revista