International Journal of Information Technology & Decision Making

The increase in e-commerce and omnichannel commerce is having a significant impact on the supply chain sector and its warehouses. Fluctuations in demand and priorities, the requirement for value-added service, government regulations and other factors put pressure on the operational decision makers on the warehouse floor and the systems that support them. The increasing complexity of daily warehouse operations means that decision support systems will need to become more sophisticated and intelligent to assist decision makers in real-time. The aim of this literature review is to investigate how decision support in warehousing and distribution operations is examined in the research literature. The objective of this review is to understand how this decision support research can assist operational decision makers to manage and complete the daily volume of work through the warehouse. Fifty-one articles were obtained by the literature search. Articles were categorized by type of warehouse, decision support target, operational task and problem type, research article methodology, architecture and technology. Decision support is examined in almost all areas of warehousing operations with the use of a variety of methods and technologies within the research literature. Most “daily warehouse operational” decision support deals with expertise transfer and reacting to real-time events. This paper highlights the lack of research into human–machine collaboration in adaptive decision support systems to assist warehouse operational decision makers.

The technique for order performance by similarity to ideal solution (TOPSIS) is one of the most well-known methods in multiple criteria decision making (MCDM) problems. The classical TOPSIS method employs a similarity index to rank alternatives. However, the chosen alternative sometimes does not have the shortest distance to the positive ideal solution (PIS) and remotest distance from the negative ideal solution (NIS), simultaneously. Besides, in some cases, TOPSIS cannot assign a unique rank to alternatives. The purpose of this paper is to propose a new similarity TOPSIS index based on the relative distance to the best and worst points. In the proposed method, by treating the separations of an alternative from the PIS and the NIS as negative criterion and positive criterion, respectively, we reduce the original MCDM problem to a new one with two criteria. The proposed index, based on different weights, in optimistic, pessimistic, and apathetic cases, easily determines the score of each alternative. Finally, we illustrate the proposed index using four numerical examples. The results are compared with those published in the literature.

Shapelets are subsequences of time-series that represent local patterns and can improve the accuracy and the interpretability of time-series classification. The major task of time-series classification using shapelets is to discover high quality shapelets. However, this is challenging since local patterns may have various scales/lengths rather than a unified scale. In this paper, we resolve this problem by discovering shapelets with multiple scales. We propose a novel Multi-Scale Shapelet Discovery (MSSD) algorithm to discover expressive multi-scale shapelets by extending initial single-scale shapelets (i.e., shapelets with a unified scale). MSSD adopts a bi-directional extension process and is robust to extend single-shapelets obtained by different methods. A supervised shapelet quality measurement is further developed to qualify the extension of shapelets. Comprehensive experiments conducted on 25 UCR time-series datasets show that multi-scale shapelets discovered by MSSD improve classification accuracy by around 10% (in average), compared with single-scale shapelets discovered by counterpart methods.

In this paper, a multi-criteria set-covering methodology is proposed to select suitable locations for a set of data centers. First, a framework of criteria, with social, economic and environmental dimensions, is presented. The framework is used to calculate the suitability of potential data center locations in Iran. To that end, a sample of specialists in Iran was asked to take part in an online questionnaire, based on best–worst method (BWM), to determine the weight of the criteria included in the proposed framework, after which a number of potential locations are evaluated on the basis of the criteria. The proposed model is evaluated under a number of settings. Using the proposed multi-criteria set-covering model, not only the utility of candidate places is evaluated by sustainability criteria but also all service applicants are covered by at least one data center with a specific coverage radius.

The selection of software programmer applicants based on multiperspective evaluation criteria (grade point average (GPA) and soft skills of the applicants) is needed instead of an interview because an interview does not necessarily lead to hiring the best candidate amongst the applicants. The selection of a suitable software programmer is considered a challenging task owing to the following factors: (1) data variation, (2) multiple evaluation criteria and (3) criterion importance. A general framework for the selection of the best software programmer applicants is not available in the existing literature. The present study aims to propose a novel multiperspective hiring framework based on multicriteria analysis to select the best software programmer amongst several applicants. A decision matrix (DM) is constructed for the selection of the best programmer applicants according to multiple criteria, namely, structured programming, object-oriented programming, data structure, database system and courseware engineering. Each criterion includes two parameters, namely, GPA and soft skills, and these criteria cross over with programmer applicants as alternatives. The standard and expert opinion of the Software Engineering Body of Knowledge is used to distribute the criteria in the DM. The two commonly used techniques of multicriteria decision-making are analytic hierarchy process (AHP) for weighing the criteria and technique for order performance by similarity to ideal solution (TOPSIS) for ranking the alternatives (programmer applicants). The data used in this study include 60 software engineering students who graduated in 2016 from Universiti Pendidikan Sultan Idris. Results show that integrating multilayer analytic hierarchy process (MLAHP) and group TOPSIS are effective for solving applicant selection problems. Group TOPSIS uses different contexts — internal and external aggregation — and indicates similar results. Objective validation is used for the ranking of the results, which are equally divided into four parts. Furthermore, the applicants are systematically ranked. This study benefits application software, system software and computer programming tool companies by providing a method that improves software quality whilst reducing time and cost in the selection process.

The knowledge of interschema properties (e.g., synonymies, homonymies, hyponymies and subschema similarities) plays a key role for allowing decision-making in sources characterized by disparate formats. In the past, wide amount and variety of approaches to derive interschema properties from structured and semi-structured data have been proposed. However, currently, it is esteemed that more than 80% of data sources are unstructured. Furthermore, the number of sources generally involved in an interaction is much higher than in the past. As a consequence, the necessity arises of new approaches to address the interschema property derivation issue in this new scenario. In this paper, we aim at providing a contribution in this setting by proposing an approach capable of uniformly extracting interschema properties from a huge number of structured, semi-structured and unstructured sources.

Best Worst Method (BWM) is a multi-criteria decision-making method that is based on a structured pairwise comparison system. It uses two pairwise comparison vectors (best-to-others and others-to-worst) as input for an optimization model to get the optimal weights of the criteria (or alternatives). The original BWM involves a nonlinear model that sometimes results in multiple optimal weights meaning that the weight of each criterion is presented as an interval. The aim of this paper is to introduce a ratio, called concentration ratio, to check the concentration of the optimal intervals obtained from the nonlinear BWM. The relationship between the concentration ratio and the consistency ratio is investigated and it is found that the concentration ratio along with the consistency ratio of the model provides enhanced insights into the reliability and flexibility of the results of BWM.

The increasing demand for image dehazing-based applications has raised the value of efficient evaluation and benchmarking for image dehazing algorithms. Several perspectives, such as inhomogeneous foggy, homogenous foggy, and dark foggy scenes, have been considered in multi-criteria evaluation. The benchmarking for the selection of the best image dehazing intelligent algorithm based on multi-criteria perspectives is a challenging task owing to (a) multiple evaluation criteria, (b) criteria importance, (c) data variation, (d) criteria conflict, and (e) criteria tradeoff. A generally accepted framework for benchmarking image dehazing performance is unavailable in the existing literature. This study proposes a novel multi-perspective (i.e., an inhomogeneous foggy scene, a homogenous foggy scene, and a dark foggy scene) benchmarking framework for the selection of the best image dehazing intelligent algorithm based on multi-criteria analysis. Experiments were conducted in three stages. First was an evaluation experiment with five algorithms as part of matrix data. Second was a crossover between image dehazing intelligent algorithms and a set of target evaluation criteria to obtain matrix data. Third was the ranking of the image dehazing intelligent algorithms through integrated best–worst and VIseKriterijumska Optimizacija I Kompromisno Resenje methods. Individual and group decision-making contexts were applied to demonstrate the efficiency of the proposed framework. The mean was used to objectively validate the ranks given by group decision-making contexts. Checklist and benchmarking scenarios were provided to compare the proposed framework with an existing benchmark study. The proposed framework achieved a significant result in terms of selecting the best image dehazing algorithm.