Search results

This paper deals with the application of a Tabu Search (TS) metaheuristic to the design of digital filters, with coefficient values expressed as the sum of power of two. The performances of the algorithm are heavily affected by the choice of its parameters, which in turn are related to different implemented strategies. The tuning of these parameters has been performed after many tests. The results of the proposed technique are compared to those obtained by simply rounding the optimal values of coefficients obtained by means of Remetz algorithm, and to those obtained using a simulated annealing algorithm.

Rapid advancements in internet technology have made it possible to develop electronic commerce in general and internet shopping in particular. Easy access to a vast number of existing internet stores enables buyers to customize their shopping processes to minimize the total purchase cost. This paper aims to investigate a novel internet shopping problem, which consists of the diversification of a given list of products to buy among many stores and to use discounts offered by the stores.

The adequate discrete optimization problem referred to as internet shopping optimization problem with price sensitivity discounts (ISOPwD) is investigated, which turned out to be strongly nondeterministic polynomial (NS)-hard. Two heuristic solution algorithms have been derived using the tabu search (TS) and the simulated annealing (SA) metaheuristics for having a solution in a reasonable time. The algorithms have been assessed via computational experiments, and they have been compared with another algorithm known from the literature that has been elaborated for a simpler version of ISOPwD.

The conducted evaluation has shown the advantage of both heuristic algorithms on the algorithm known from the literature. Moreover, the TS-based algorithm outperformed the other one in terms of the total cost incurred by customers and the computational time.

The special primary piecewise linear discounting function is only taken into account. Other possible discounts connected, for example, with bundles of products and (or) coupons are not considered.

The elaborated algorithms can be recommended for internet shopping providers who want to introduce the ability to search a cost-optimized set of products in their databases or for applications that combine offers from various online retailers, e.g. internet price comparison services and auction sites.

The novelty of considered ISOPwD, in comparison with similar problems discussed in the literature, deals with an arbitrary number of purchased products, the possibility to buy an identical product in different stores and the consideration of the weight, the amount and the availability of goods as parameters of ISOPwD.

The purpose of this paper is to generate a daily operating theater schedule aiming to minimize completion time and maximum overtime while integrating real-life surgeon constraints, such as their role, specialty, qualification and availability.

The paper deals with complete surgery process using multi-objective surgery scheduling approach. Furthermore, the combinatorial nature of the studied problem does not allow to solve it to optimality. Therefore, the authors developed two approaches embedded in a tabu search metaheuristic, namely, weighted sum and e-constraint, to minimize completion time and maximum overtime.

The integration of the upstream and downstream services of an intervention and the consideration of the specific constraints related to surgeons are very essential to obtaining more closed schedules to the realty.

The paper includes implications for the development of efficient schedules for a significant number of operations coming from different specialties throughout its complete surgery process under multi-resource constraints.

The paper can help hospital managers and decision makers to well manage the budget by minimizing the overtime cost and by offering efficient daily operating theater schedule.

The results of the paper will help hospital managers and decision makers to well manage the budget by minimizing the overtime cost and offering efficient daily operating theater schedule.

Operating theaters are considered as the most sensitive health department within hospital centers due to their significant cost/necessity for patients and their economic benefits for hospitals. In this paper, the authors consider patients that may require more than one surgery on the same day in the surgery scheduling problem which is a major technology enhancement in the health industry.

The surgery scheduling includes both the preoperative and the postoperative units of the operative stage. Two objectives are considered in a lexicographic way: the minimization of the makespan while prioritizing the patients having two surgeries and the total completion time to perform. An adapted tabu-search algorithm is used to tackles this NP-hard scheduling problem.

The proposed schedule is more relevant for operating theaters as it integrates all stages of the surgical procedure and considers patients with more than one operation during the same day.

This paper is original as it considers patients who need more than one operation, which responds to real challenge faced by decision-makers' in hospitals. The application of the time lags between stages of the surgical procedure generates a good utilization of the hospital resources and makes the scheduling task more flexible.

This study aims to address the hybrid open shop problem (HOSP) with respect to the minimization of the overall finishing time or makespan. In the HOSP, we have to process n jobs in stages without preemption. Each job must be processed once in every stage, there is a set of m_k identical machines in stage k and the production flow is immaterial.

Computational experiments carried out on a set of randomly generated instances showed that the minimal idleness heuristic (MIH) priority rule outperforms the longest processing time (LPT) rule proposed in the literature and the other proposed constructive methods on most instances.

The proposed mathematical model outperformed the existing model in the literature with respect to computing time, for small-sized instances, and solution quality within a time limit, for medium- and large-sized instances. The authors’ hybrid iterated local search (ILS) improved the solutions of the MIH rule, drastically outperforming the models on large-sized instances with respect to solution quality.

The authors formalize the HOSP, as well as argue its NP-hardness, and propose a mixed integer linear programming model to solve it. The authors propose several priority rules – constructive heuristics based on priority measures – for finding feasible solutions for the problem, consisting of adaptations of classical priority rules for scheduling problems. The authors also propose a hybrid ILS for improving the priority rules solutions.

The aim of the paper is to compare two different approaches to multi‐objective optimisation in magnetostatics; in this way, the case study is investigated as a possible benchmark.

A Tabu search method modified with ε‐constraint algorithm is compared with a multi‐objective multi‐individual evolution strategy. The case study is the automated shape design of a magnetic pole. In order to reduce the computational cost of solving the direct problem, which requires repeated analyses of the magnetic field, a neural network has been used to approximate the objective functions that depend on the design variables.

An approximation of the Pareto front for each method is obtained. A twofold comparison between the two methods is made, based on both the result accuracy and the computational cost.

Two different methods were already tested on a case study proposed as a benchmark for multi‐objective optimization in magnetostatics. The paper represents a contribution to bridge the gap between analytical and numerical benchmarks in electromagnetism.

The purpose of this paper is to maximize the total demand covered by the established additive manufacturing and distribution centers and maximize the total literal weight assigned to the drones.

Disaster management or humanitarian supply chains (HSCs) differ from commercial supply chains in the fact that the aim of HSCs is to minimize the response time to a disaster as compared to the profit maximization goal of commercial supply chains. In this paper, the authors develop a relief chain structure that accommodates emerging technologies in humanitarian logistics into the two phases of disaster management – the preparedness stage and the response stage.

Solving the model by the genetic and the cuckoo optimization algorithm (COA) and comparing the results with the ones obtained by The General Algebraic Modeling System (GAMS) clear that genetic algorithm overcomes other options as it has led to objective functions that are 1.6% and 24.1% better comparing to GAMS and COA, respectively.

Finally, the presented model has been solved with three methods including one exact method and two metaheuristic methods. Results of implementation show that Non-dominated sorting genetic algorithm II (NSGA-II) has better performance in finding the optimal solutions.

In this paper, the authors introduced a real world new problem, the multi-trip vehicle routing problem with time windows and the possible use of a less-than-truckload carrier to satisfy customer demands. The purpose of this paper is to develop a heuristic algorithm to route the private trucks with time windows and to make a selection between truckload and less-than-truckload carriers by minimizing a total cost function.

Both mathematical model and heuristic algorithm are developed for routing the private trucks with time windows and for selecting of less-than-truckload carriers by minimizing the total cost function.

In all, 40 test problems were examined with the heuristics. Computational results show that the algorithm obtains the optimal or near-optimal solutions efficiently in terms of time and accuracy.

The research described in this paper differs from the previous one on fleet planning or vehicle routing, in that it modifies the Clarke and Wright method by shifting the performance measure from a distance to cost and also incorporates the fixed cost of different types of trucks into the model. In addition, the authors simultaneously consider the multiple trip vehicle routing problems with time windows and the selection of less-than-truckload carriers that is an integrated scenario of real-world application. To the best of the authors’ knowledge, this scenario has not been considered in the literature.

The purpose of the paper is to present a novel design method for the optimal finite word length (FWL) finite impulse response (FIR) filters.

The design method is based on a parallel tabu search (TS) algorithm which uses the crossover operator of the genetic algorithm.

Three design examples have been presented to show that the proposed method can provide a good solution to the design problem of a FWL FIR filter. In order to show the validity of the proposed method, the performance of the suggested method has been compared to those of widely‐used other methods. From the comparison results, it was concluded that the proposed method can be efficiently used for the optimal FWL FIR filter design.

The number of examples can be increased and also the performance of the proposed method might be compared to other design methods, apart from those presented in this work, developed for the design of optimal FWL FIR filters.

The use of this method produces optimal digital FWL FIR filters with low complexity and therefore provides advantages in the terms of speed and cost.

The originality is the application of the parallel TS algorithm described by the authors to the FWL FIR filter design. The work presented in the paper is particularly important for the researchers studying on the design methods for FWL FIR filter design and the applications of these type filters.

The purpose of this paper is to study the problem of a routing problem with uncertain try-on service time (VRPUS) for apparel distribution, and to devise solution strategies coping with the uncertainty by an evolutionary algorithm. VRPUS belongs to the category of practical routing models integrated with uncertain service times. However, in the background of apparel distribution, it has distinct features. The try-on service will improve the customer satisfaction by providing experiences to customers; the return cost is saved; the customer loyalty is improved for experiencing face-to-face try-on services. However, the uncertainty of try-on service time makes the apparel distribution process uncertain and incurs additional risk management cost, such that the logistics companies should optimally make decisions on the choice of the service and the service processes.

This paper devised a mixed-integer programming (MIP) model for the base vehicle routing problem (VRP) and then it is extended to support the solution strategies for uncertain try-on times. A try-on time estimation parameter and a time reservation parameter are used to cope with the uncertain try-on time, and the try-on rejection strategy is applied when the uncertain try-on time is realized at customer and no surplus time can be used for try-on service besides distributing to remainder customers. Due to the computational complexity of VRPUS, an evolutionary algorithm is designed for solving it. These parameters and strategy options are designed for the operational decisions by logistics companies. Finally, a decision support system (DSS) is designed.

Five experimental scenarios are performed to reveal the impacts of parameters and solution strategies coping with uncertain try-on time on the distribution cost, return cost, and the try-on service failure. The tuning methods are designed to assist the decisions by logistics companies.

A new routing problem is addressed for apparel distribution in fashion industry especially in the context of booming apparel e-commerce, which is a VRP with uncertain try-on service time for apparel distribution; three strategies are developed to cope with the try-on time uncertainty. The proposed method is also a theoretical base for designing a practical DSS for logistics companies to provide try-on service to customers.