This paper explores selected heuristics methods, namely CDS, Palmer’s slope index, Gupta’s
algorithm, and concurrent heuristic algorithm for minimizing the makespan in permutation
flow shop scheduling problem. Its main scope is to explore how different instances sizes
impact on performance variability. The computational experiment includes 12 of available
benchmark data sets of 10 problems proposed by Taillard. The results are computed and
presented in the form of relative percentage deviation, while outputs of the NEH algorithm
were used as reference solutions for comparison purposes. Finally, pertinent findings are
commented.
In the article, the authors propose a typology of political knowledge from online learning activities and test its validity in an empirical qualitative study. The essence of their proposal is that meaningful study of the process of acquiring knowledge (rational analysis of factors modifying attitudes) must take into account both the perspective of the citizen (the demand for information) and an analysis of the publicly available knowledge (the supply of information). The authors distinguish three main methods of acquiring information: heuristic, reflective, and by-product learning. They note the importance of generational factors in shaping the cognitive activity of Internet users. There has been a gradual increase in the importance of source management, with simultaneous alienation and skepticism towards information obtained on the Internet. While the authors’ analysis is restricted to the Internet, their approach is not reductionist in that they consider the internet to be a medium for traditional media and its influence on civic attitudes.
A method of creating production schedules regarding production lines with parallel machines is presented. The production line setup provides for intermediate buffers located between individual stages. The method mostly concerns situations when part of the production machines is unavailable for performance of operations and it becomes necessary to modify the original schedule, the consequence of which is the need to build a new schedule. The cost criterion was taken into account, as the schedule is created with the lowest possible costs regarding untimely completion of products (e.g. fines for delayed product completion). The proposed method is relaxing heuristics, thanks to which scheduling is performed in a relatively short time. This was confirmed by the presented results of computational experiments. These experiments were carried out for the rescheduling of machine parts production.
The paper presents a novel Iterated Local Search (ILS) algorithm to solve multi-item multi-family capacitated lot-sizing problem with setup costs independent of the family sequence. The model has a direct application to real production planning in foundry industry, where the goal is to create the batches of manufactured castings and the sequence of the melted metal loads to prevent delays in delivery of goods to clients. We extended existing models by introducing minimal utilization of furnace capacity during preparing melted alloy. We developed simple and fast ILS algorithm with problem-specific operators that are responsible for the local search procedure. The computational experiments on ten instances of the problem showed that the presence of minimum furnace utilization constraint has great impact on economic and technological conditions of castings production. For all test instances the proposed heuristic is able to provide the results that are comparable to state-of-the art commercial solver.
In the paper, we present a coordinated production planning and scheduling problem for three major shops in a typical alloy casting
foundry, i.e. a melting shop, molding shop with automatic line and a core shop. The castings, prepared from different metal, have different
weight and different number of cores. Although core preparation does not required as strict coordination with molding plan as metal
preparation in furnaces, some cores may have limited shelf life, depending on the material used, or at least it is usually not the best
organizational practice to prepare them long in advance. Core shop have limited capacity, so the cores for castings that require multiple
cores should be prepared earlier. We present a mixed integer programming model for the coordinated production planning and scheduling
problem of the shops. Then we propose a simple Lagrangian relaxation heuristic and evolutionary based heuristic to solve the coordinated
problem. The applicability of the proposed solution in industrial practice is verified on large instances of the problem with the data
simulating actual production parameters in one of the medium size foundry.
The paper presents a production scheduling problem in a foundry equipped with two furnaces and one casting line, where the line is a bottleneck and furnaces, of the same capacity, work in parallel. The amount of produced castings may not exceed the capacity of the line and the furnaces, and their loads determine metal type from which the products are manufactured on the casting line. The purpose of planning is to create the processing order of metal production to prevent delays in the delivery of the ordered products to the customers. The problem is a mix of a lot-sizing and scheduling problems on two machines (the furnaces) run in parallel. The article gives a mathematical model that defines the optimization problem, and its relaxed version based on the concept of a rolling-horizon planning. The proposed approaches, i.e. commercial solver and Iterated Local Search (ILS) heuristic, were tested on a sample data and different problem sizes. The tests have shown that rolling horizon approach gives the best results for most problems, however, developed ILS algorithm gives better results for the largest problem instances with tight furnace capacity.
Cross-docking is a strategy that distributes products directly from a supplier or manufacturing plant to a customer or retail chain, reducing handling or storage time. This study focuses on the truck scheduling problem, which consists of assigning each truck to a door at the dock and determining the sequences for the trucks at each door considering the time-window aspect. The study presents a mathematical model for door assignment and truck scheduling with time windows at multi-door cross-docking centers. The objective of the model is to minimize the overall earliness and tardiness for outbound trucks. Simulated annealing (SA) and tabu search (TS) algorithms are proposed to solve large-sized problems. The results of the mathematical model and of meta-heuristic algorithms are compared by generating test problems for different sizes. A decision support system (DSS) is also designed for the truck scheduling problem for multi-door cross-docking centers. Computational results show that TS and SA algorithms are efficient in solving large-sized problems in a reasonable time.
The results presented here are twofold. First, a heuristic algorithm is proposed which, through removing some unnecessary arcs from a digraph, tends to reduce it into an adjoint and thus simplifies the search for a Hamiltonian cycle. Second, a heuristic algorithm for DNA sequence assembly is proposed, which uses a graph model of the problem instance, and incorporates two independent procedures of reducing the set of arcs - one of them being the former algorithm. Finally, results of tests of the assembly algorithm on parts of chromosome arm 2R of Drosophila melanogaster are presented.
The problem of sequencing jobs on a single machine to minimize total cost (earliness and
tardiness) is nowadays not just important due to traditional concerns but also due to its
importance in the context of Collaborative Networked Organizations and Virtual Enterprises,
where precision about promptly responses to customers’ requests, along with other
important requirements, assume a crucial role. In order to provide a contribution in this
direction, in this paper the authors contribute with an applied constructive heuristics that
tries to find appropriate solutions for single machine scheduling problems under different
processing times and due dates, and without preemption allowed. In this paper, two different
approaches for single-machine scheduling problems, based on external and internal
performance measures are applied to the problem and a comparative analysis is performed.
Computational results are presented for the problem under Just-in-Time and agile conditions
on which each job has a due date, and the objective is to minimize the sum of holding costs
for jobs completed before their due date and tardiness costs for jobs completed after their
due date. Additional computational tests were developed based on different customer and
enterprise oriented performance criteria, although preference is given to customer-oriented
measures, namely the total number of tardy jobs and the maximum tardiness.
In this paper, we study the constrained exact and approximate controllability of traveling wave solutions of Korteweg-de Vries (third order) and Boussinesq (fourth order) semi-linear equations using the Green’s function approach. Control is carried out by a moving external source. Representing the general solution of those equations in terms of the Frasca’s short time expansion, system of constraints on the distributed control is derived for both types of controllability. Due to the possibility of explicit solution provided by the heuristic method, the controllability analysis becomes straightforward. Numerical analysis confirms theoretical derivations.