Based on environmental, legal, social, and economic factors, reverse logistics and closed-loop supply chain issues have attracted attention among both academia and practitioners. This attention is evident by the vast number of publications in scientific journals which have been published in recent years. Hence, a comprehensive literature review of recent and state-of-the-art papers is vital to draw a framework of the past, and to shed light on future directions. The aim of this paper is to review recently published papers in reverse logistic and closed-loop supply chain in scientific journals. A total of 382 papers published between January 2007 and March 2013 are selected and reviewed. The papers are then analyzed and categorized to construct a useful foundation of past research. Finally, gaps in the literature are identified to clarify and to suggest future research opportunities.
Sustainability, the consideration of environmental factors and social aspects, in supply chain management (SCM) has become a highly relevant topic for researchers and practitioners. The application of operations research methods and related models, i.e. formal modeling, for closed-loop SCM and reverse logistics has been effectively reviewed in previously published research. This situation is in contrast to the understanding and review of mathematical models that focus on environmental or social factors in forward supply chains (SC), which has seen less investigation. To evaluate developments and directions of this research area, this paper provides a content analysis of 134 carefully identified papers on quantitative, formal models that address sustainability aspects in the forward SC. It was found that a preponderance of the publications and models appeared in a limited set of six journals, and most were analytically based with a focus on multiple criteria decision making. The tools most often used comprise the analytical hierarchy process or its close relative, the analytical network process, as well as life cycle analysis. Conclusions are drawn showing that numerous possibilities and insights can be gained from expanding the types of tools and factors considered in formal modeling efforts.
Risk assessment and management was established as a scientific field some 30–40 years ago. Principles and methods were developed for how to conceptualise, assess and manage risk. These principles and methods still represent to a large extent the foundation of this field today, but many advances have been made, linked to both the theoretical platform and practical models and procedures. The purpose of the present invited paper is to perform a review of these advances, with a special focus on the fundamental ideas and thinking on which these are based. We have looked for trends in perspectives and approaches, and we also reflect on where further development of the risk field is needed and should be encouraged. The paper is written for readers with different types of background, not only for experts on risk.
The solution of several operations research problems requires the creation of a quantitative model. Sensitivity analysis is a crucial step in the model building and result communication process. Through sensitivity analysis we gain essential insights on model behavior, on its structure and on its response to changes in the model inputs. Several interrogations are possible and several sensitivity analysis methods have been developed, giving rise to a vast and growing literature. We present an overview of available methods, structuring them into local and global methods. For local methods, we discuss Tornado diagrams, one way sensitivity functions, differentiation-based methods and scenario decomposition through finite change sensitivity indices, providing a unified view of the associated sensitivity measures. We then analyze global sensitivity methods, first discussing screening methods such as sequential bifurcation and the Morris method. We then address variance-based, moment-independent and value of information-based sensitivity methods. We discuss their formalization in a common rationale and present recent results that permit the estimation of global sensitivity measures by post-processing the sample generated by a traditional Monte Carlo simulation. We then investigate in detail the methodological issues concerning the crucial step of correctly interpreting the results of a sensitivity analysis. A classical example is worked out to illustrate some of the approaches.
► We review the research on dynamic vehicle routing. ► Our taxonomy is based on quality and evolution of information. ► We present several real-world applications of dynamic vehicle routing. ► We survey the current state-of-the-art solution techniques for dynamic routing. ► We describe promising research directions on dynamic routing. A number of technological advances have led to a renewed interest in dynamic vehicle routing problems. This survey classifies routing problems from the perspective of information quality and evolution. After presenting a general description of dynamic routing, we introduce the notion of degree of dynamism, and present a comprehensive review of applications and solution methods for dynamic vehicle routing problems.
Road freight transportation is a major contributor to carbon dioxide equivalent emissions. Reducing these emissions in transportation route planning requires an understanding of vehicle emission models and their inclusion into the existing optimization methods. This paper provides a review of recent research on green road freight transportation.
Remaining useful life (RUL) is the useful life left on an asset at a particular time of operation. Its estimation is central to condition based maintenance and prognostics and health management. RUL is typically random and unknown, and as such it must be estimated from available sources of information such as the information obtained in condition and health monitoring. The research on how to best estimate the RUL has gained popularity recently due to the rapid advances in condition and health monitoring techniques. However, due to its complicated relationship with observable health information, there is no such best approach which can be used universally to achieve the best estimate. As such this paper reviews the recent modeling developments for estimating the RUL. The review is centred on statistical data driven approaches which rely only on available past observed data and statistical models. The approaches are classified into two broad types of models, that is, models that rely on directly observed state information of the asset, and those do not. We systematically review the models and approaches reported in the literature and finally highlight future research challenges.
This paper provides an overview of developments in robust optimization since 2007. It seeks to give a representative picture of the research topics most explored in recent years, highlight common themes in the investigations of independent research teams and highlight the contributions of rising as well as established researchers both to the theory of robust optimization and its practice. With respect to the theory of robust optimization, this paper reviews recent results on the cases without and with recourse, i.e., the static and dynamic settings, as well as the connection with stochastic optimization and risk theory, the concept of distributionally robust optimization, and findings in robust nonlinear optimization. With respect to the practice of robust optimization, we consider a broad spectrum of applications, in particular inventory and logistics, finance, revenue management, but also queueing networks, machine learning, energy systems and the public good. Key developments in the period from 2007 to present include: (i) an extensive body of work on robust decision-making under uncertainty with uncertain distributions, i.e., “robustifying” stochastic optimization, (ii) a greater connection with decision sciences by linking uncertainty sets to risk theory, (iii) further results on nonlinear optimization and sequential decision-making and (iv) besides more work on established families of examples such as robust inventory and revenue management, the addition to the robust optimization literature of new application areas, especially energy systems and the public good.
The design of distribution systems raises hard combinatorial optimization problems. For instance, facility location problems must be solved at the strategic decision level to place factories and warehouses, while vehicle routes must be built at the tactical or operational levels to supply customers. In fact, location and routing decisions are interdependent and studies have shown that the overall system cost may be excessive if they are tackled separately. The location-routing problem (LRP) integrates the two kinds of decisions. Given a set of potential depots with opening costs, a fleet of identical vehicles and a set of customers with known demands, the classical LRP consists in opening a subset of depots, assigning customers to them and determining vehicle routes, to minimize a total cost including the cost of open depots, the fixed costs of vehicles used, and the total cost of the routes. Since the last comprehensive survey on the LRP, published by Nagy and Salhi (2007), the number of articles devoted to this problem has grown quickly, calling a review of new research works. This paper analyzes the recent literature (72 articles) on the standard LRP and new extensions such as several distribution echelons, multiple objectives or uncertain data. Results of state-of-the-art metaheuristics are also compared on standard sets of instances for the classical LRP, the two-echelon LRP and the truck and trailer problem.
With the increasing economic globalization and intensification of market competition, green supply chain management (GSCM), as a new management mode to pursue both economic benefits and the coordinated of environment sustainable development, has become highly relevant topic in modern enterprise production operation management. The green supplier evaluation and selection is the essential core of the GSCM, which can directly impact the manufacturer's performance. The green supplier selection can be regarded as a multiple criteria group decision making (MCGDM) problem that involves many conflict evaluation criteria, both being of qualitative and quantitative nature. Due to the increasing complexity and uncertainty of social economic environment, some evaluations of criteria are not adequately represented by numerical assessments and type-1 fuzzy sets (T1FSs). In addition, decision makers (DMs) usually do not exhibit complete rationality under many practical decision situations. In this paper, we extend the TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) technique to solve MCGDM problems within the context of interval type-2 fuzzy sets (IT2FSs) and present its application to green supplier selection problem. First, we introduce a new distance based on the fuzzy logic and α-cuts of the IT2FSs. Then, an extended novel TODIM method based on prospect theory to solve MCGDM problem under IT2FSs environment is developed. Finally, a green supplier selection example is provided to demonstrate the usefulness of the proposed method. Furthermore, a sensitivity analysis is carried out with the aid of granular computing and the comparative analysis with TOPSIS technique.
Many years have passed since Baesens et al. published their benchmarking study of classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. (6), 627–635.]. The interest in prediction methods for scorecard development is unbroken. However, there have been several advancements including novel learning methods, performance measures and techniques to reliably compare different classifiers, which the credit scoring literature does not reflect. To close these research gaps, we update the study of Baesens et al. and compare several novel classification algorithms to the state-of-the-art in credit scoring. In addition, we examine the extent to which the assessment of alternative scorecards differs across established and novel indicators of predictive accuracy. Finally, we explore whether more accurate classifiers are managerial meaningful. Our study provides valuable insight for professionals and academics in credit scoring. It helps practitioners to stay abreast of technical advancements in predictive modeling. From an academic point of view, the study provides an independent assessment of recent scoring methods and offers a new baseline to which future approaches can be compared.
Supplier evaluation and selection problem has been studied extensively. Various decision making approaches have been proposed to tackle the problem. In contemporary supply chain management, the performance of potential suppliers is evaluated against multiple criteria rather than considering a single factor-cost. This paper reviews the literature of the multi-criteria decision making approaches for supplier evaluation and selection. Related articles appearing in the international journals from 2000 to 2008 are gathered and analyzed so that the following three questions can be answered: (i) Which approaches were prevalently applied? (ii) Which evaluating criteria were paid more attention to? (iii) Is there any inadequacy of the approaches? Based on the inadequacy, if any, some improvements and possible future work are recommended. This research not only provides evidence that the multi-criteria decision making approaches are better than the traditional cost-based approach, but also aids the researchers and decision makers in applying the approaches effectively.
► Operations Research contributes to green logistics in many ways. ► Green transportation also implies efficiency in using transportation equipment. ► Efficiency created by Operations Research helps to reduce greenhouse gas emissions. ► Operations Research methods can show the trade-offs between costs and emissions. ► OR methods can optimize supply chains with the most environmental friendly set-up. The worldwide economic growth of the last century has given rise to a vast consumption of goods while globalization has led to large streams of goods all over the world. The production, transportation, storage and consumption of all these goods, however, have created large environmental problems. Today, global warming, created by large scale emissions of greenhouse gasses, is a top environmental concern. Governments, action groups and companies are asking for measures to counter this threat. Operations Research has a long tradition in improving operations and especially in reducing costs. In this paper, we present a review that highlights the contribution of Operations Research to green logistics, which involves the integration of environmental aspects in logistics. We give a sketch of the present and possible developments, focussing on design, planning and control in a supply chain for transportation, inventory of products and facility decisions. While doing this, we also indicate several areas where environmental aspects could be included in OR models for logistics.
The Orienteering Problem (OP) has received a lot of attention in the past few decades. The OP is a routing problem in which the goal is to determine a subset of nodes to visit, and in which order, so that the total collected score is maximized and a given time budget is not exceeded. A number of typical variants has been studied, such as the Team OP, the (Team) OP with Time Windows and the Time Dependent OP. Recently, a number of new variants of the OP was introduced, such as the Stochastic OP, the Generalized OP, the Arc OP, the Multi-agent OP, the Clustered OP and others. This paper focuses on a comprehensive and thorough survey of recent variants of the OP, including the proposed solution approaches. Moreover, the OP has been used as a model in many different practical applications. The most recent applications of the OP, such as the Tourist Trip Design Problem and the mobile-crowdsourcing problem are discussed. Finally, we also present some promising topics for future research.
► We review the literature on workforce staffing and scheduling from 2004 onwards. ► We present tables that classify each paper with respect to numerous perspectives. ► We identify important trends and opportunities for future research. This paper presents a review of the literature on personnel scheduling problems. Firstly, we discuss the classification methods in former review papers. Secondly, we evaluate the literature in the many fields that are related to either the problem setting or the technical features. Each perspective is presented as a table in which the classification is displayed. This method facilitates the identification of manuscripts related to the reader’s specific interests. Throughout the literature review, we identify trends in research on personnel staffing and scheduling, and we indicate which areas should be subject to future research.
Due to an increased awareness and significant environmental pressures from various stakeholders, companies have begun to realize the significance of incorporating green practices into their daily activities. This paper proposes a framework using Fuzzy TOPSIS to select green suppliers for a Brazilian electronics company; our framework is built on the criteria of green supply chain management (GSCM) practices. An empirical analysis is made, and the data are collected from a set of 12 available suppliers. We use a fuzzy TOPSIS approach to rank the suppliers, and the results of the proposed framework are compared with the ranks obtained by both the geometric mean and the graded mean methods of fuzzy TOPSIS methodology. Then a Spearman rank correlation coefficient is used to find the statistical difference between the ranks obtained by the three methods. Finally, a sensitivity analysis has been performed to examine the influence of the preferences given by the decision makers for the chosen GSCM practices on the selection of green suppliers. Results indicate that the four dominant criteria are Commitment of senior management to GSCM; Product designs that reduce, reuse, recycle, or reclaim materials, components, or energy; Compliance with legal environmental requirements and auditing programs; and Product designs that avoid or reduce toxic or hazardous material use.
This paper surveys recent publications on berth allocation, quay crane assignment, and quay crane scheduling problems in seaport container terminals. It continues the survey of Bierwirth and Meisel (2010) that covered the research up to 2009. Since then, there was a strong increase of activity observed in this research field resulting in more than 120 new publications. In this paper, we classify this new literature according to the features of models considered for berth allocation, quay crane scheduling and integrated approaches by using the classification schemes proposed in the preceding survey. Moreover, we identify trends in the field, we take a look at the methods that have been developed for solving new models, we discuss ways for evaluating models and algorithms, and, finally, we light up potential directions for future research.
Supply chain network design (SCND) is one of the most crucial planning problems in supply chain management (SCM). Nowadays, design decisions should be viable enough to function well under complex and uncertain business environments for many years or decades. Therefore, it is essential to make these decisions in the presence of uncertainty, as over the last two decades, a large number of relevant publications have emphasized its importance. The aim of this paper is to provide a comprehensive review of studies in the fields of SCND and reverse logistics network design under uncertainty. The paper is organized in two main parts to investigate the basic features of these studies. In the first part, planning decisions, network structure, paradigms and aspects related to SCM are discussed. In the second part, existing optimization techniques for dealing with uncertainty such as recourse-based stochastic programming, risk-averse stochastic programming, robust optimization, and fuzzy mathematical programming are explored in terms of mathematical modeling and solution approaches. Finally, the drawbacks and missing aspects of the related literature are highlighted and a list of potential issues for future research directions is recommended.
Multi-criteria decision analysis (MCDA) is a valuable resource within operations research and management science. Various MCDA methods have been developed over the years and applied to decision problems in many different areas. The outranking approach, and in particular the family of ELECTRE methods, continues to be a popular research field within MCDA, despite its more than 40 years of existence. In this paper, a comprehensive literature review of English scholarly papers on ELECTRE and ELECTRE-based methods is performed. Our aim is to investigate how ELECTRE and ELECTRE-based methods have been considered in various areas. This includes area of applications, modifications to the methods, comparisons with other methods, and general studies of the ELECTRE methods. Although a significant amount of literature on ELECTRE is in a language different from English, we focus only on English articles, because many researchers may not be able to perform a study in some of the other languages. Each paper is categorized according to its main focus with respect to ELECTRE, i.e. if it considers an application, performs a review, considers ELECTRE with respect to the problem of selecting an MCDA method or considers some methodological aspects of ELECTRE. A total of 686 papers are included in the review. The group of papers considering an application of ELECTRE consists of 544 papers, and these are further categorized into 13 application areas and a number of sub-areas. In addition, all papers are classified according to the country of author affiliation, journal of publication, and year of publication. For the group of applied papers, the distribution by ELECTRE version vs. application area and ELECTRE version vs. year of publication are provided. We believe that this paper can be a valuable source of information for researchers and practitioners in the field of MCDA and ELECTRE in particular.
As supply chain risk management has transitioned from an emerging topic to a growing research area, there is a need to classify different types of research and examine the general trends of this research area. This helps identify fertile research streams with great potential for further examination. This paper presents a systematic review of the quantitative and analytical models (i.e. mathematical, optimization and simulation modeling efforts) for managing supply chain risks. We use bibliometric and network analysis tools to generate insights that have not been captured in the previous reviews on the topic. In particular, we complete a of the literature that identifies the key research clusters/topics, interrelationships, and generative research areas that have provided the field with the foundational knowledge, concepts, theories, tools, and techniques. Some of our findings include (1) quantitative analysis of supply chain risk is expanding rapidly; (2) European journals are the more popular research outlets for the dissemination of the knowledge developed by researchers in United States and Asia; and (3) sustainability risk analysis is an emerging and fast evolving research topic.