•This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems.•Given the ...hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions which are otherwise too expensive to compute or mathematically not well-defined. Thus, machine learning looks like a natural candidate to make such decisions in a more principled and optimized way. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.
This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are otherwise too expensive to compute or mathematically not well defined. Thus, machine learning looks like a natural candidate to make such decisions in a more principled and optimized way. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.
Analytic hierarchy process (AHP) is widely used in group decision making (GDM). There are two traditional aggregation methods for the collective preference in AHP-GDM: aggregation of the individual ...judgments (AIJ) and aggregation of the individual priorities (AIP). However, AHP-GDM is sometimes less reliable only under the condition of AIJ and AIP because of the consensus and consistency of the individual pair-wise comparison matrices (PCMs) and prioritization methods. In this paper, we propose aggregation of the nearest consistent matrices (ANCM) with the acceptable consensus in AHP-GDM, simultaneously considering the consensus and consistency of the individual PCMs. ANCM is independent of prioritization methods while complying with the Pareto principal of social choice theory. Moreover, ANCM is easy to program and implement in resolving highly complex group decision making problems. Finally, two numerical examples illustrate the applications and advantages of the proposed ANCM.
As the people are becoming conscious about protection of the environment from pollutant caused by human beings, businesses are adopting green technology to procure green products to save the ...environment from pollution. Consequently, it is a challenging task at the firm manager to capture the market providing best green quality at fair price in a given economy. The paper plans to discuss two situations in two models. In model 1, the optimal green quality and sales prices of the manufacturer and the retailer in both decentralize and centralize systems of a two-echelon supply chain system are investigated. The profit functions of the manufacturer and the retailer include procurement costs, selling prices and cost for green level development and then it is analyzed by calculus method to obtain the optimal values of the decision variables. The model 2 focuses on price competition of two substitute products where demand of the end customers depends on price and quality of green product. Both the firms of green and regular products manufacturer are corporate social responsible. In this model, profit functions of the firms 1 & 2 are formulated separately considering revenues from sales items, cost of green quality and contribution for activities of social responsibility. The main objective is to find out optimal prices and green quality in order to maximize the profit functions of individual and integrated systems. The proposed models are analyzed mathematically and numerical examples are illustrated to justify the feasibility of the model in reality.
Viability is the ability of a supply chain (SC) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts. In this ...paper, we theorize a new notion—the viable supply chain (VSC). In our approach, viability is considered as an underlying SC property spanning three perspectives, i.e., agility, resilience, and sustainability. The principal ideas of the VSC model are adaptable structural SC designs for supply–demand allocations and, most importantly, establishment and control of adaptive mechanisms for transitions between the structural designs. Further, we demonstrate how the VSC components can be categorized across organizational, informational, process-functional, technological, and financial structures. Moreover, our study offers a VSC framework within an SC ecosystem. We discuss the relations between resilience and viability. Through the lens and guidance of dynamic systems theory, we illustrate the VSC model at the technical level. The VSC model can be of value for decision-makers to design SCs that can react adaptively to both positive changes (i.e., the agility angle) and be able to absorb negative disturbances, recover and survive during short-term disruptions and long-term, global shocks with societal and economical transformations (i.e., the resilience and sustainability angles). The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic. We emphasize that resilience is the central perspective in the VSC guaranteeing viability of the SCs of the future. Emerging directions in VSC research are discussed.
Benchmarking with DEA, SFA, and R
International series in management science/operations research/International series in operations research & management science
eBook
•Reviews 164 papers from five internationally-renowned journals between 2004 and 2015.•Papers employ the Real Options approach to Operations Research settings/problems.•Catalogues 6 subject themes ...where the Real Options approach has been most valuable.•Identifies, per theme, the main modeling approaches, key issues and sub-categories.•Documents current trends and suggests promising directions for future research.
The Real Options approach to decision-making has been useful in capturing and valuing the flexibility inherent in many operating decisions that decision makers are faced with. In Operations Research (OR), a field that encompasses a plethora of problem-solving techniques for improving decision-making, we note an observable steady increase in contributions that apply the Real Options approach to model, analyze and evaluate flexible operating alternatives available to decision-makers or to optimize the operational efficiency of decision timing. We review 164 papers that appeared in five internationally-renowned OR journals in the last twelve years, cataloguing the main subject themes and contributions where the Real Options approach has been most valuable to Operations Research. We classify the reviewed papers into six main subject themes, and identify their main characteristics, the modeling approaches used, key issues and sub-categories. We further document current trends and suggest promising opportunities for future research.
•This study draws on dynamic capability theory and builds on eight OR case studies.•It identifies 14 micro-foundations to manage OR activities in dynamic environments.•It identifies 6 cross-cutting ...propositions and a roadmap for future OR research.
While the topic of analytics is rapidly growing in popularity across various domains, there is still a relatively low amount of empirical work in the field of operations research (OR). While studies of various technical and business aspects of analytics are emerging in OR, little has been done to address how the OR community can leverage business analytics in dynamic and uncertain environments – the very place where OR is supposed to play a key role. To address this gap, this study draws on the dynamic capabilities view of the firm and builds on eight selected case studies of operations research activity in large organisations, each of which have invested significantly in analytics technology and implementation. The study identifies fourteen analytics-enabled micro-foundations of dynamic capabilities, essentially highlighting how organisations can use analytics to manage and enhance their OR activities in dynamic and uncertain environments. This study also identifies six key cross-cutting propositions emerging from the data and develops a roadmap for future OR researchers to address these issues and improve the use and value of analytics as enablers of organisational dynamic capabilities.
On the grounds of the long discussion in the literature for the construction of regional input–output (I/O) tables, the present study endeavors to identify the best performing method among the most ...applied location quotient based non-survey techniques. The analysis uses actual data—in particular, the EU input–output tables for the years 2010 and 2014—and by employing three different statistics, it compares the tables created by the quotients FLQ, AFLQ, SLQ, CILQ and RLQ. Comparison is made in both the technical coefficient tables and the Leontief ones. The results indicate that the AFLQ and FLQ provide better results for values of
δ
from 0.1 to 0.3, whereas for values of
δ
from 0.4 to 0.9, the results of the two quotients are not satisfactory. At the same time, using another statistic, the SLQ–CILQ technique seemed to yield satisfactory results. Similar results are also presented for the Leontief tables. Finally, using a linear regression model, the imports do not appear to be related to the value of
δ
in the FLQ.
The coronavirus (COVID-19) outbreak shows that pandemics and epidemics can seriously wreak havoc on supply chains (SC) around the globe. Humanitarian logistics literature has extensively studied ...epidemic impacts; however, there exists a research gap in understanding of pandemic impacts in commercial SCs. To progress in this direction, we present a systematic analysis of the impacts of epidemic outbreaks on SCs guided by a structured literature review that collated a unique set of publications. The literature review findings suggest that influenza was the most visible epidemic outbreak reported, and that optimization of resource allocation and distribution emerged as the most popular topic. The streamlining of the literature helps us to reveal several new research tensions and novel categorizations/classifications. Most centrally, we propose a framework for operations and supply chain management at the times of COVID-19 pandemic spanning six perspectives, i.e., adaptation, digitalization, preparedness, recovery, ripple effect, and sustainability. Utilizing the outcomes of our analysis, we tease out a series of open research questions that would not be observed otherwise. Our study also emphasizes the need and offers directions to advance the literature on the impacts of the epidemic outbreaks on SCs framing a research agenda for scholars and practitioners working on this emerging research stream.
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