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The purpose of this paper is to provide an improved genetic algorithm to solve 0/1 multidimensional knapsack problem (0/1 MKP), by proposing new selection and crossover operators that cooperate to explore the search space.

The authors first present a new sexual selection strategy that significantly improves the one proposed by (Varnamkhasti and Lee, 2012), while working in phenotype space. Then they propose two variants of the two-stage recombination operator of (Aghezzaf and Naimi, 2009), while they adapt the latter in the context of 0/1 MKP. The authors evaluate the efficiency of both proposed operators on a large set of 0/1 MKP benchmark instances. The obtained results are compared against that of conventional selection and crossover operators, in terms of solution quality and computing time.

The paper shows that the proposed selection respects the two major factors of any metaheuristic: exploration and exploitation aspects. Furthermore, the first variant of the two-stage recombination operator pushes the search space towards exploitation, while the second variant increases the genetic diversity. The paper then demonstrates that the improved genetic algorithm combining the two proposed operators is a competitive method for solving the 0/1 MKP.

Although only 0/1 MKP standard instances were tested in the empirical experiments in this paper, the improved genetic algorithm can be used as a powerful tool to solve many real-world applications of 0/1 MKP, as the latter models several industrial and investment issues. Moreover, the proposed selection and crossover operators can be incorporated into other bio-inspired algorithms to improve their performance. Furthermore, the two proposed operators can be adapted to solve other binary combinatorial optimization problems.

This research study provides an effective solution for a well-known non-deterministic polynomial-time (NP)-hard combinatorial optimization problem; that is 0/1 MKP, by tackling it with an improved genetic algorithm. The proposed evolutionary mechanism is based on two new genetic operators. The first proposed operator is a new and deeply different variant of the so-called sexual selection that has been rarely addressed in the literature. The second proposed operator is an adaptation of the two-stage recombination operator in the 0/1 MKP context. This adaptation results in two variants of the two-stage recombination operator that aim to improve the quality of encountered solutions, while taking advantage of the sexual selection criteria to prevent the classical issue of genetic algorithm that is premature convergence.

The purpose of this paper is to develop an original model and a solution procedure for solving jointly three main strategic fleet management problems (fleet composition, replacement and make-or-buy), taking into account interdependencies between them.

The three main strategic fleet management problems were analyzed in detail to identify interdependencies between them, mathematically modeled in terms of integer nonlinear programing (INLP) and solved using evolutionary based method of a solver compatible with a spreadsheet.

There are no optimization methods combining the analyzed problems, but it is possible to mathematically model them jointly and solve together using a solver compatible with a spreadsheet obtaining a solution/fleet management strategy answering the questions: Keep currently exploited vehicles in a fleet or remove them? If keep, how often to replace them? If remove then when? How many perspective/new vehicles, of what types, brand new or used ones and when should be put into a fleet? The relatively large scale instance of problem (50 vehicles) was solved based on a real-life data. The obtained results occurred to be better/cheaper by 10% than the two reference solutions – random and do-nothing ones.

The methodology of developing optimal fleet management strategy by solving jointly three main strategic fleet management problems is proposed allowing for the reduction of the fleet exploitation costs by adjusting fleet size, types of exploited vehicles and their exploitation periods.

Consulting experts is an effective way to utilize tacit resource. The purpose of the paper is to optimize the matching between panels of experts and groups of demanders to improve the efficiency of tacit knowledge sharing.

Experts and demanders express preferences using linguistic terms. The estimate method based on trust is developed to get missing ratings. Weights of demanders are determined and knowledge needs are identified. Three kinds of satisfaction are measured based on grey relational analysis. To maximize satisfaction of experts and demanders and safeguard meetings of knowledge needs as well as the workload of experts, the optimization model is constructed and the solution is optimal matching results.

The presented approach not only optimizes the matching between demanders and experts but also sets up a panel of experts in case that knowledge needs exceed a single expert’s capacity.

The approach expands research works of methods for tacit knowledge sharing. The continuous updating of matching results and the processing of the data with mixing formats need to be studied further.

The presented approach acts as a valuable reference for the development of knowledge management systems. It can be used in any scene that needs the match between experts and demanders.

The approach provides a new way of helping demanders to find appropriate experts. Both experts’ and demanders’ preferences are considered. A panel of experts is set up when needed. Expert resources are utilized more efficiently and knowledge needs are met more comprehensively.

The purpose of this paper is to tackle a satellite module layout design problem (SMLDP). As a complex engineering layout and combinatorial optimization problem, SMLDP cannot be solved effectively by traditional exact methods. Although evolutionary algorithms (EAs) have shown some promise of tackling SMLDP in previous work, the solution quality and computational efficiency still pose a challenge. This paper aims to address these two issues.

Scatter search (SS) and a cooperative co‐evolutionary architecture are integrated to form a new approach called a cooperative co‐evolutionary scatter search (CCSS). The cooperative co‐evolutionary architecture is characterized by the decomposition and cooperation for dealing with complex engineering problems. SS is a flexible meta‐heuristic method that can effectively solve the combinatorial optimization problems. Designing the elements of SS is context‐dependent. Considering the characteristics of SMLDP, our work focuses on two folds: the diversification method, and the reference set update method. The diversification method is built on the method of coordinate transformation and the controlled randomness. The reference set is updated by the static method on the basis of two dissimilarities. Two test problems for circles packing illustrated the capacity of SS. However, when solving SMLDP, SS shows some limitations in the computational time and quality. This study adopts divide‐conquer‐coordination strategy to decompose SMLDP into several layout sub‐problems. Then CCSS is applied to cooperatively solve these sub‐problems. The experimental results illustrate the capability of the proposed approach in tackling the complex problem with less computational effort.

Applying CCSS to SMLDP can obtain satisfying solutions in terms of quality and computational efficiency. This contrasts with the limiting experimental results of SMLDP with some approaches (including modified SS).

A new CCSS is proposed to provide an effective and efficient way of solving SMLDP. Some elements of SS are improved to address the layout problem. SMLDP is decomposed into several sub‐problems that can be solved cooperatively by CCSS after its characteristics are taken into consideration.

In the last four years, since Volume I of this Bibliography first appeared, there has been an explosion of literature in all the main functional areas of business. This wealth of material poses problems for the researcher in management studies — and, of course, for the librarian: uncovering what has been written in any one area is not an easy task. This volume aims to help the librarian and the researcher overcome some of the immediate problems of identification of material. It is an annotated bibliography of management, drawing on the wide variety of literature produced by MCB University Press. Over the last four years, MCB University Press has produced an extensive range of books and serial publications covering most of the established and many of the developing areas of management. This volume, in conjunction with Volume I, provides a guide to all the material published so far.

The purpose of this study is to solve NP-Hard drone routing problem for the last-mile distribution. This is suitable for the multi-drones parcel delivery for the various items from a warehouse to many locations.

This study conducts as a mission assignment of the single location per flight with the constraint satisfactions such as various payloads in weight, drone speeds, flight times and coverage distances. A genetic algorithm is modified as the concurrent heuristics approach (GCH), which has the knapsack problem dealing initialization, gene elitism (crossover) and gene replacement (mutation). Those proposed operators can reduce the execution time consuming and enhance the routing assignment of multiple drones. The evaluation value of the routing assignment can be calculated from the chromosome/individual representation by applying the proposed concurrent fitness.

This study optimizes the total traveling time to accomplish the distribution. GCH is flexible and can provide a result according to the first-come-first-served, demanded weight or distance priority.

GCH is an alternative option, which differs from conventional vehicle routing researches. Such researches (traveling time optimization) attempt to minimize the total traveling time, distance or the number of vehicles by assuming all vehicles have the same traveling speed; therefore, a specific vehicle assignment to a location is neglected. Moreover, the main drawback is those concepts can lead the repeated selection of best quality vehicles concerning the speed without considering the vehicle fleet size and coverage distance while this study defines the various speeds for the vehicles. Unlike those, the concurrent concept ensures a faster delivery accomplishment by sharing the work load with all participant vehicles concerning to their different capabilities. If the concurrent assignment is applied to the drone delivery effectively, the entire delivery can be accomplished relatively faster than the traveling time optimization.

In the early 1960s a number of people began to investigate the use of computers and quantitative techniques in routing local delivery vehicles. Such vehicles typically leave a central depot early in the day, deliver to a number of customers during the course of the day, and then return to the depot at the end of the day. The well known “savings” technique was developed at this time.

The purpose of this paper is to measure the efficiencies of parallel subsystems with shared inputs/outputs. Each subsystem has not only a set of common inputs and outputs, but also some dedicated inputs and outputs as well as some shared inputs and outputs. A more general data envelopment analysis (DEA) approach is proposed to deal with this efficiency evaluation issue. Based on the proposed approach, mechanisms for shared inputs/outputs distribution and efficiency decomposition among sub-units are presented.

To evaluate the efficiency of the parallel systems, this paper proposes a centralized DEA approach by assuming that the same input/output factor in a decision-making unit (DMU) has the same multiplier for all its sub-units. Furthermore, different proportions of shared inputs/outputs are imposed on sub-units within different DMUs in evaluating each DMU’s efficiency. The proposed approach is applied to evaluate the operational efficiencies of 18 railway firms in China.

By using the proposed DEA approach, the efficiencies of the whole DMU and its sub-units can be measured at the same time, and the optimal allocation strategy of shared inputs/outputs can also be obtained. The proposed model is more reasonable and robust for measuring the operational performance of parallel systems with shared inputs and outputs. The efficiency of railway system in China is relatively low, and its inefficiency is largely caused by lower freight transportation performance. Great disparities among firms can be found in the passenger transportation efficiency and freight transportation efficiency.

This study develops the DEA model under the assumption of constant returns to scale, which can be directly extended to a situation with variable returns to scale.

In this paper, the proposed approach is a more effective way to evaluate the efficiencies of parallel systems with shared inputs/outputs. With respect to the application, to improve the overall efficiency of China’s railway system, more efforts should be taken to improve its operational performance of freight transportation. Furthermore, firms’ disparities should also be considered when making these related policies.

The proposed approach can evaluate the whole DMU and its sub-units at the same time. Considering simultaneously the common/dedicated/shared inputs/outputs, the proposed approach is more general than the existing approaches in the literature. In the described approach, the same type of input or output is assumed to have the same weight for all sub-units within one DMU. More importantly, the proposed model imposes different proportions of shared inputs/outputs on different DMUs’ sub-units when measuring the efficiency for each DMU.

Proper performance measurement is an important issue in library operational management. A data envelopment analysis (DEA) model is applied to evaluate the relative operational efficiency of 25 U.S. private research-university library members of the Association of Research Libraries (ARL). Operations of each library decision-making unit are considered as a production process using four resource input and four service output variables. The model results are analyzed and compared with the efficient group and a peer group by using a t-test. The model provides decision-makers with more accurate information to implement better library services with appropriate resource allocation.

The paper aims to provide an efficient meta-heuristic algorithm to solve the partial set covering problem (PSCP). With rich application scenarios, the PSCP is a fascinating and well-known non-deterministic polynomial (NP)-hard problem whose goal is to cover at least k elements with as few subsets as possible.

In this work, the authors present a novel variant of the ant colony optimization (ACO) algorithm, called Argentine ant system (AAS), to deal with the PSCP. The developed AAS is an integrated system of different populations that use the same pheromone to communicate. Moreover, an effective local search framework with the relaxed configuration checking (RCC) and the volatilization-fixed weight mechanism is proposed to improve the exploitation of the algorithm.

A detailed experimental evaluation of 75 instances reveals that the proposed algorithm outperforms the competitors in terms of the quality of the optimal solutions. Also, the performance of AAS gradually improves with the growing instance size, which shows the potential in handling complex practical scenarios. Finally, the designed components of AAS are experimentally proved to be beneficial to the whole framework. Finally, the key components in AAS have been demonstrated.

At present, there is no heuristic method to solve this problem. The authors present the first implementation of heuristic algorithm for solving PSCP and provide competitive solutions.