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This paper proposes a novel matheuristic algorithm for solving the Production Routing Problem (PRP). The PRP is a hard-to-solve combinatorial optimization problem with numerous practical applications in the field of freight transportation, logistics and supply chain management. A manufacturer is responsible for determining production decisions, as well as the timing and the quantity of replenishment services offered to a set of geographically dispersed customers over a multi-period time horizon. The problem calls for jointly optimizing the production, inventory, distribution and routing decisions. The paper provides a novel two-commodity flow formulation and proposes a two-phase infeasible space matheuristic algorithm for solving the examined problem. The first phase deals with a relaxation of the problem to construct production-distribution plans. In the second phase, these are completed with routing information and optimized via a local search framework which oscillates between the feasible and the infeasible solution space. The framework is equipped with mixed integer programming components for restoring infeasibility and for diversifying the conducted search. Computational experiments demonstrate that the infeasibility space exploration significantly contributes to the quality of the final solutions. The results obtained by the proposed matheuristic out-match the results of state-of-the-art approaches. More specifically, 594 and 55 new best solutions out of 1440 and 90 instances of two well-established benchmark data sets of small-medium and large instances are reported, respectively.

Motivated by the commonly observed commercial used apparel collection (UAC) programs in fashion retail supply chain operations, this paper analytically explores the associated operational challenges that firms face. First, in the basic model, we consider the case when a fashion retail brand promotes its UAC program and collects the used apparel from consumers. Depending on the conditions of the collected apparel products, the fashion retail brand will classify and either donate them for charity or send them to remanufacturing. For either case, the fashion retail brand gains a benefit. We analytically derive in closed-form the optimal promotion effort and study the mechanism for “profit” coordination. Our results indicate that many traditional supply chain contracts fail to achieve “profit” coordination. Thus, the effort cost sharing (ECS) contract is proposed and proven to be effective for “profit” coordination. Finally, to check the robustness of findings from the basic model, we examine various UAC practice related extended models, such as i) consumer coupon offering, ii) “no remanufacturing” model, iii) consumer heterogeneity in environmental consciousness, iv) own-brand collection vs any-brand collection. Our findings show that the qualitative insights continue to hold in the extended scenarios. Some novel scenario-dependent findings are also obtained. Insights derived in this paper not only contribute to the literature but also provide scientifically sound managerial guidance and insights to practitioners on how UAC programs can be best operated.

This paper examines the high-dimensional dependence modelling problem in the context of project risk assessment. As the dimension of uncertain performance units (i.e., itemized costs and activity times) in a project increases, specifying a feasible correlation matrix and eliciting relevant pair-wise information, either from historical data or with expert judgement, becomes practically unattainable or simply not economical. This paper presents a factor-driven dependence elicitation and modelling framework with scalability to large-scale project risks. The multi-factor association model (MFAM) accounts for hierarchical relationships of multiple association factors and provides a closed-form solution to a complete and mathematically consistent correlation matrix. Augmented with the structured association (SA) technique for systematic identification of hierarchical association factors, the MFAM offers additional flexibility of utilizing the minimum information available in standardized, ubiquitous project plans (e.g., work breakdown structure, resource allocation, or risk register), while preserving the computational efficiency and the scalability to high dimensional project risks. Numerical applications and simulation experiments show that the MFAM, further combined with extended analytics (i.e., parameter calibration and optimization), provides credible risk assessments (with accuracy comparable to full-scale simulation) and further enhances the realism of dealing with high-dimensional project risks utilizing all relevant information.

Metaheuristics are widely employed to solve hard optimization problems, like vehicle routing problems (VRP), for which exact solution methods are impractical. In particular, local search-based metaheuristics have been successfully applied to the capacitated VRP (CVRP). The CVRP aims at defining the minimum-cost delivery routes for a given set of identical vehicles since each vehicle only travels one route and there is a single (central) depot. The best metaheuristics to the CVRP avoid getting stuck in local optima by embedding specific hill-climbing mechanisms such as diversification strategies into the solution methods. This paper introduces a hybridization of a novel adaptive version of Iterated Local Search with Path-Relinking (AILS-PR) to the CVRP. The major contribution of this paper is an automatic mechanism to control the diversity step of the metaheuristic to allow it to escape from local optima. The results of experiments with 100 benchmark CVPR instances show that AILS-PR outperformed the state-of-the-art CVRP metaheuristics.

Wildfires can pose a major threat to the secure operation of power networks. Chile, California, and Australia have suffered from recent wildfires that have induced considerable power supply cuts. Further, as power systems move to a significant integration of variable renewable energy sources, successfully managing the impact of wildfires on the power supply can become even more challenging due to the joint uncertainty in wildfire trajectories and the power injections from wind and solar farms. Motivated by this, this paper develops a practical decision-support approach that concatenates a stochastic wildfire simulation method with an attacker-defender model that aims to find a worst-case realization for (i) transmission line and generator contingencies, out of those that can potentially be affected by a given wildfire scenario, and for (ii) wind and solar power trajectories, based on a max-min structure where the inner min problem represents a best adaptive response on generator dispatch actions. Further, this paper proposes an evaluation framework to assess the power supply security of various power system topology configurations, under the assumption of limited transmission switching capabilities, and based on the simulation of several wildfire evolution scenarios. Extensive computational experiments are carried out on two representations of the Chilean power network with up to 278 buses, showing the practical effectiveness of the proposed approach for enhancing wildfire resilience in highly renewable power systems.

In this paper, a strategic decision making model for the sustainable development of marine renewable energy is proposed, and a specific application to the United Kingdom (UK) is demonstrated. As an island nation the UK benefits from significant marine energy potential which is providing an increasing contribution to UK's renewable energy portfolio. The paper investigates the question of how decision makers can be aided to reach a decision on which types of marine renewable energy projects should be chosen for development given that strategic energy planning is subject to a number of uncertain parameters and multiple sustainability objectives. In this context, the contribution of this paper lies in the combination of renewable energy portfolio selection and the application of multi-objective methods. Interval coefficient goal programming models are adopted in order to address the impreciseness and uncertainty associated with the goals and coefficients of the models in the context of renewable energy selection. The potential renewable energy projects are clustered in order to aid the decision making process and preferential weight sensitivity methods are employed. Conclusions are drawn for optimistic and pessimistic scenarios in the context of UK marine renewable energy planning.

When a manufacturer outsources the production of a product, it distributes its intellectual property (IP) into a supply chain that it may not be able to fully control. An IP agreement between a manufacturer and its suppliers is a popular solution to address the challenge of supplier-copycatting. The goal of this paper is to examine the impact of copycatting, from both the supplier and third-party firms, and the effectiveness of an IP agreement. Specifically, we use a game-theoretic approach to examine a system where a manufacturer outsources to a supplier. The supplier and a third-party firm decide whether or not to enter the market with copycat products while the manufacturer selects the level of marketing investment. The manufacturer can reduce the threat of supplier-copycatting by signing an IP agreement. However, we find that the manufacturer can be worse off from signing an IP agreement with its supplier, even if the IP agreement is costless and perfectly enforceable. We show that a manufacturer can deter copycat products through vertical integration and IP agreements and we outline the instances where each method is preferred. Furthermore, we find that the manufacturer may choose not to invest in quality improvements as a copycat deterrence strategy. We show that the supplier can benefit from the manufacturer's decision to sign an IP agreement and that the supplier and the consumers can benefit from IP regulations against copycat products. Our paper demonstrates the strengths and limitations of various copycat deterrence strategies when a supplier and third-party may produce copycat products.

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.

This paper aims to investigate the capacity utilization (CU) of selected large Chinese iron and steel enterprises by using the data envelopment analysis (DEA) approach. In this paper, we first propose a DEA model with an unrestricted capacity directional output distance function by incorporating two joint cost disposability relations and differentiating between non-emission-causing inputs and emission-causing inputs. Second, we define the status of decision-making units (DMUs) with excess capacity and design a method to identify insufficient variable inputs. We use a group of 48 largest Chinese iron and steel enterprises as our sample to investigate their performance in terms of CU indicator. The main findings are summarised and can be used to support the corresponding policymaking for the managers of both government and enterprises.

Booming parcel shipping business has raised wide attention. The trunk line is easier to manage, since parcels are consolidated to fully utilize vehicle capacities. However, the first mile handled by individual companies is beset by high empty haul rates and low efficiency. This paper takes the lead in exploring a new business model and possibilities of establishing a common service platform for the first mile. We investigate strategic decisions of two courier logistics companies competing with partially substitutable collection service. They can choose to cooperate with the cost-efficient platform to enjoy a saving, while the market share of their original channel has to be eroded by the outsourcing channel. We propose three scenarios: (i) Both companies have only one direct channel. (ii) Only one company has two channels. (iii) Both companies have two. This paper comprehensively presents the joint effect of internal competition and the cost saving on companies’ welfare and customers’ consuming experience. The findings show that in most cases, cooperating with the platform will help the companies gain a profit increment and customers will benefit from a price decrease compared to the current practice. However, when customers loyalty to the direct channel is moderate, they will suffer a price increase, though companies are better off. Additionally, given the cost advantage of outsourcing is slighter, and customers show less difference to two channels, the internal competition will be the most intensified and overtake the effect of cost sparing. The company running two channels will suffer a profit loss.

Attended home delivery describes the delivery of goods by e-grocers or e-tailers to customers within an agreed time window. Because customers expect narrow time windows, offering such services may lead to expensive fulfillment operations. This has led to research on how to influence customers’ bookings using time window pricing or slotting. In this paper, we reconsider the problem of demand management through dynamic pricing for attended home delivery services. This problem is usually modeled as a stochastic dynamic program, but even small instances cannot be solved to optimality due to the curses of dimensionality. The major challenges consist of finding feasible time windows for an incoming customer, estimating the opportunity costs, i.e., the future monetary loss due to accepting a booking, and optimizing the time window prices in real time. In this paper, we propose a route-based approximate dynamic programming approach to tackle these challenges. The approach carefully combines and partially extends state-of-the-art methods in attended home delivery, dynamic pricing, and dynamic vehicle routing. In an extensive simulation study, we compare its performance with state-of-the-art benchmark heuristics. The results indicate a superior performance of our approach in terms of both profit and number of customers served.

A recently emerging concept, Extended Producer Responsibility (EPR), is being adopted by the government in more and more countries and regions. It shifts the burden of proper disposal of end-of-life consumer products from (local) governments to the producers that bring the products to the market. To comply with governments’ EPR-type legislation, producers form coalitions to have their products recycled in a more efficient way. In this paper, we study how two important determinants of recycling costs, fixed recycling costs and material-stream heterogeneity, influence producers’ recycling network (structure of producers’ recycling coalitions). On one hand, large fixed recycling costs make the coalitions typically larger, due to the economies of scale. On the other hand, large coalitions generate typically more heterogeneous material streams, which increase the variable recycling costs due to additional separation and disassembly efforts. This paper discusses two currently existing scenarios: one exists prior to EPR-type legislation (referred to as the Disparate Problem, or DP) and the other is motivated by EPR-type legislation (referred to as the Endogenous Problem, or EP). In DP, the recycling of end-of-life products is not the responsibility of any producer but the government. Therefore, the recycling network is determined to minimize the total recycling cost, which also maximizes the social welfare, while the outputs are determined by producers without concerns about recycling. In EP, producers who compete in a horizontally differentiated primary market may also collaborate at the same time to organize proper disposal of their products. As each producer is only interested in maximizing its own payoff and there may exist conflict of interests, we use the game-theoretical methodology to analyze the endogenous process of coalition-formation. We find structural differences in these two scenarios and conclude by discussing implications for social welfare of imposing tax or subsidy.

A preventive maintenance scheduling problem is studied on behalf of generation companies (GENCOs) with natural gas power plants, while taking into account their signed natural gas contracts and the opportunities of purchasing and selling natural gas in the spot market. This paper considers the uncertain prices of both natural gas and electricity in the spot market, and proposes a multistage stochastic mixed integer programming (MSMIP) model seeking the optimal operations regarding maintenance outage scheduling and natural gas trading. Large-scale MSMIP problems suffer not only the curse of dimensionality, but also computational difficulties with both discrete and continuous variables at each stage. To this respect, this paper leverages the progressive hedging algorithm based on scenario-based decomposition to solve large MSMIP problems. The solutions obtained from the algorithm exhibit promising quality under our numerical studies. Due to the independence among all the subproblems after the decomposition, the algorithm is amenable to parallel computing, which leads to faster convergence as demonstrated in the numerical results. Computational experiments also show that it is beneficial to use MSMIP while considering both maintenance planning and natural gas contracting. In addition, the results also indicate the GENCOs with a larger number of small generators perform better than those with a smaller number of big generators.

Hyper-heuristics have emerged as a way to raise the level of generality of search techniques for computational search problems. This is in contrast to many approaches, which represent customised methods for a single problem domain or a narrow class of problem instances. The term hyper-heuristic was defined in the early 2000s as a heuristic to choose heuristics, but the idea of designing high-level heuristic methodologies can be traced back to the early 1960s. The current state-of-the-art in hyper-heuristic research comprises a set of methods that are broadly concerned with intelligently selecting or generating a suitable heuristic for a given situation. Hyper-heuristics can be considered as search methods that operate on lower-level heuristics or heuristic components, and can be categorised into two main classes: heuristic selection and heuristic generation. Here we will focus on the first of these two categories, selection hyper-heuristics. This paper gives a brief history of this emerging area, reviews contemporary selection hyper-heuristic literature, and discusses recent selection hyper-heuristic frameworks. In addition, the existing classification of selection hyper-heuristics is extended, in order to reflect the nature of the challenges faced in contemporary research. Unlike the survey on hyper-heuristics published in 2013, this paper focuses only on selection hyper-heuristics and presents critical discussion, current research trends and directions for future research.

In this paper we consider the impact of 3D printing, or additive manufacturing, on a simple supply chain, consisting of a manufacturer and retailer, that serves stochastic customer demand. 3D printing is a relatively new manufacturing technology that is attracting attention from many firms. However, the impact of 3D printing on operations and firm relationships in a supply chain is relatively unexplored in academic research. A unique aspect of 3D printers is that they can be installed at the retailer in a supply chain, a characteristic that we highlight in our paper since it enables the supply chain to be more responsive to demand. Consequently, 3D printers can be adopted by either the manufacturer or, in a more novel situation, the retailer; we analyze the equilibrium of Stackelberg games in both cases. We characterize the economic and competitive conditions where either firm adopts 3D printing, and show that under either scenario, it is possible for both firms to earn more profit than a benchmark system without 3D printing. We identify and quantify the positive benefits associated with 3D printing, for both firms in a simple supply chain, when either firm adopts this new manufacturing technology. In many cases, the scenario where the manufacturer adopts 3D printing and installs 3D printers at the retailer results in the best profit outcomes for the manufacturer. The retailer’s preference, however, depends on problem parameters. Therefore, supply chain managers should carefully consider the possibility of 3D printing products in their supply chain.

To reduce congestion, environmental damage, and negative health impact in large urban areas plenty of novel concepts for last-mile distribution have been innovated in the recent years. The concept treated in this paper are mobile parcel lockers that are able to change their locations during the day, either autonomously or moved by a human driver. By relocating lockers their reach towards addressees also varying their whereabouts over the day can be increased. This paper optimizes the changing locations of lockers, such that customers are at some time during the planning horizon within a predefined range of their designated locker. Our aim is to minimize the locker fleet when satisfying all customers. We formulate the resulting mobile locker location problem and provide suited exact solution procedures. To asses the potential whether mobile lockers are a promising last-mile concept, worth the investment required to develop it to a market-ready solution, we benchmark the necessary fleet size of mobile lockers with the required number of their stationary counterparts. Our results show that considerable reductions are possible.

This paper considers a supply chain consisting of a retailer for short life cycle products facing stochastic customer demand and a manufacturer that initiates production upon receipt of retail orders. Departing from the common view of the newsvendor problem, we assume that the delivery lead time is not fixed, but that both the retailer and the manufacturer have the option to shorten it. Shorter lead times enable the retailer to place orders closer to the start of the selling season where additional information on customer preferences has become available, reducing demand uncertainty. In the work at hand, lead time is assumed to depend on the order quantity, on the supplier's production capacity, and a fixed transportation delay. This paper proposes a model for determining the optimal order quantity and production capacity in centralized and decentralized settings. For the uncoordinated case, we show that if the retailer's ability to gather and analyze additional demand information is revealed to the manufacturer, the arising information asymmetry between the two parties can aggravate the double marginalization effect and, in turn, erode supply chain efficiency. In a coordinated supply chain, however, both parties have an incentive to align both order quantity and investments in lead time reduction. To coordinate the decentralized supply chain, we propose a buy-back contract that helps to leverage supply chain profitability. We conclude with an outlook on future research opportunities.

This paper studies how appointment cancellations affect scheduling strategies in outpatient healthcare clinics. While cancellation rates in outpatient clinics have been reported to be as high as 27%, cancelled appointments are often ignored, or grouped with no-shows in healthcare scheduling models. We find that there exists a value of total demand that, when calculated over a scheduling horizon, marks the boundary between where cancellations hurt or help a clinic. We refer to that value as the switch point. Up to the switch point, clinics can achieve a greater reward when patients do not cancel. However, for values of expected total demand greater than the switch point, the clinic reward is reduced if more patients retain (do not cancel) appointments. To assist us in evaluating the switch point, we construct a mixed-integer nonlinear programming model to solve a multi-day outpatient scheduling problem. The model accounts for both inter-day (appointment day) and intra-day (appointment time slot) scheduling decisions, while balancing service benefits against service costs. We include probabilities of no-show and cancellation, which allows us to discuss how cancellations affect scheduling decisions through the switch point. The knowledge of the switch point allows a clinic to understand when appointment no-shows and cancellations negatively affect clinic service, and can assist the clinic in determining the number of patients for which it is committed to provide service, i.e., its panel size. In this paper, we discuss methodologies for calculating the switch point, and discuss its sensitivity to model parameters.

The problem studied in this paper is the multi-depot open vehicle routing problem, which has the following two differences in relation to the classical vehicle routing problem: there are several depots; the vehicles do not return to the depot after delivering the goods to the customers, i.e., the end of the route is not the starting point. There are many practical applications for this problem, however the great majority of the studies have only addressed the open vehicle routing problem with a single depot. In this paper, we present an iterated local search algorithm, in which the moves performed during the local search are recalled and this historical search information is then used to define the moves executed inside the perturbation procedures. Therefore, it is recorded the number of times that each customer is moved during the local search. Since this information is continuously updated and changes in each iteration, the search is driven to potentially better regions of the solution space, and increases the chance of avoiding cycling, even when using deterministic perturbations. The performance of this algorithm was tested using a large set of benchmark problems and was compared with other algorithms, and the results show that it is very competitive.

Prepositioning of assets and supplies prior to a disaster strike accelerates the response activities as it reduces the supply chain burden associated with humanitarian relief items. Unlike prior survey papers on pre-disaster and post-disaster humanitarian logistics, our paper has a specific focus on prepositioning of assets and supplies in the domain of natural disasters. The first aim of our paper is to review the main Operations Research and Management Science (OR/MS) journal papers published between 2000 and 2018 on this topic. We have statistically analyzed these papers based on contributions in different journals, number of papers per year, and type of disaster. We have also categorized the papers based on their decision variables into three categories: Allocation papers (“A”), Location papers (“L”), and Location-Allocation papers (“LA”). After that, we have assessed our current literature based on some of the methodological issues in Humanitarian Operations that gathered by Kovacs and Moshtari (2018). The second aim of our paper is research gap identification. Our key findings in this domain are that there is a lack of papers that: consider demand-side costs in their proposed model objectives; deal with uncertainty in funding, budget, asset and supply quantities, and infrastructure; considering prepositioning as a risk mitigation strategy; take reliability into account for reducing the risk of loss; consider prepositioning of medical staff and emergency crew; discuss the best time to start prepositioning of supplies and assets in confronting a foreseen disaster; use social media to better prepare for upcoming disasters.

Water resource allocation in multinational river basins has been a major concern for all riparian countries. From the perspective of cooperative and sustainable water resource development, considering the spatiotemporal characteristics of the water demand, namely, the priority of water use due to the geographic location of each riparian country, the seasonality of water demand, and the differences in the net utility of water utilization, this paper constructs a fuzzy coalition game model for multinational water resource allocation and provides the optimal allocation strategy using the example of the Lancang-Mekong River. The study indicates that considering the geographic locations and the differences in the water utility of each country compared with the initial allocation strategy based on agricultural water demand, the cooperative strategy increases the allocated water resources in high-utility countries (expect countries near the estuary) and the overall water utility in the entire basin. Then, considering the seasonality of the water resource reallocation, the fuzzy coalition among riparian countries further increases the net water utility of each country and achieves the optimal allocation. As the preconditions for the establishment of cooperative relationships, upstream countries need to transfer partial water rights to downstream countries in certain seasons. The basic idea that is presented in this paper, the fuzzy coalition model of water resource allocation with spaciotemporal constraints, can be applied to the water allocation of other multinational rivers.

Today's technology facilitates selling strategies that were unthinkable only a few years ago. One increasingly popular strategy uses incompletely specified products (ICSPs). The seller retains the right to specify some details of the product or service after the sale. The selling strategies’ main advantages are an additional dimension for market segmentation and operational flexibility due to supply-side substitution possibilities. Since the strategy became popular with Priceline and Hotwire in the travel industry about two decades ago, it has increasingly been adopted by other industries with stochastic demand and limited capacity as well. At the same time, it is actively researched from the perspectives of strategic operations management, empirics, and revenue management. This paper first describes the application of ICSPs in practice. Then, we introduce the different research communities that are active in this field and relate the terminology they use. The main part is an exhaustive review of the literature on selling ICSPs from the different perspectives. Here, we complement a tabular overview with an introduction into the community and a detailed description of each paper. Finally, possible directions for future research are outlined. We see that strategic operations management has described advantages of ICSPs over other strategies in a variety of settings, but also identified countervailing effects. Today, empirical research is confined to hotels and airlines and largely disconnected from the other perspectives. Operational papers are ample, but mostly concerned with the availability of ICSPs. Research on operational (dynamic) pricing is surprisingly scarce.

Shelter opening and evacuation of vulnerable populations are operations crucial to disaster response, which is one of the four phases of Disaster Operations Management (DOM). Optimisation has tried to capture some of the different issues related to shelter location and evacuation routing: several models have been developed over the years. However, they are still far from being fully comprehensive. The aim of this paper is to identify the current challenges in devising realistic and applicable optimisation models in the shelter location and evacuation routing context, with the ultimate goal of outlining a roadmap for future research in this topical area. A critical analysis of the most recent combined models is provided, including insights from the authors of the existing papers. The analysis highlights numerous gaps and research opportunities, such as the need for future optimisation models to involve stakeholders, include evacuee as well as system behaviour, be application-oriented rather than theoretical or model-driven, and interdisciplinary.

Many institutions have their own vehicle fleet and hired drivers that can be assigned to working trips to ensure that they all take place within the desired time periods. In these situations, it is important that vehicles and drivers are assigned to planned trips in an optimal and automated way. Trips are planned to occur over a given planning horizon and have a number of characteristics that have to be taken into account: a trip is characterized by a starting time, start and end locations, travelling distance and time, number of drivers needed for each assigned vehicle, number of passengers, and so on. This paper considers the problem of assigning both vehicles and drivers to a set of planned trips, having as objective the minimization of total cost. To properly address this problem it is necessary to consider a number of features, like the possibility of vehicles being shared between different trips if the timeframes and destinations are compatible. The mathematical model developed and presented in this paper takes all these features into account. The problem was motivated by a case study in the context of a public Brazilian university. This case study is also described and computational experiments using a general solver are reported. The general solver is capable of calculating optimal solutions in reasonable computational times, but problems where the fleet is mainly composed of small vehicles are more challenging. A matheuristic has been developed and tested, that seems to be competitive for these type of problems.

This paper examines the incentives of firms in exerting research and development (R&D) efforts to reduce the environmental cost of their production processes. Firms in almost every sector face mounting pressures from consumers, competitors and regulators to conduct environmental R&D. However, many are standing still due (mainly in part) to the trade-offs between benefits and costs, uncertainties and effectiveness of R&D activities. This paper presents a two-stage duopoly model that demonstrates how consumer demands, competitor actions and regulatory requirements can impact firms’ environmental R&D incentives. On the policy front, firms are regulated by a constant unit emissions cost under an emissions tax. On the demand side, in addition to deriving economic value from the product, consumers incur heterogeneous intrinsic costs (due to altruistic concerns) to consume environmentally inferior products, and exhibit “egoistic” preferences to consume their favorite brand. On the supply side, the efforts exerted by a firm generate a probability of R&D success with which the firm decreases the environmental cost of its production process, and the R&D cost exhibits diminishing returns to scale for R&D efforts. In this context, two firms compete on their R&D and pricing decisions in a two-stage game framework. Our results showcase a number of scenarios in which those external forces can induce firms to exert higher R&D efforts, some of which are counter-intuitive and contrary to commonly held assumptions and arguments about the role of consumer, competitive and regulatory pressures in environmental development.

In surface mining operations, fleet management systems seek to make optimal decisions to handle material in two steps: path production optimization and real-time truck dispatching. This paper develops a multiple objective transportation model for real-time truck dispatching. The model addresses two major drawbacks of former models. The proposed model dispatches the trucks to destinations trying to simultaneously minimize shovel idle times, truck wait times, and deviations from the path production requirements established by the production optimization stage. To evaluate the performance of the proposed model, we developed a benchmark model based on the backbone of the most widely used fleet management system in the mining industry (Modular Mining DISPATCH). Afterward, we built a discrete event simulation model of the truck and shovel operation using an iron ore mine case study, implemented both of the dispatching models, and compared the results. The implementation of the models suggests that the multiple objective model developed in this paper is able to meet the production requirements of the operation using a fleet at 85% of the size of the deterministically calculated desired fleet. In addition, the model is able to meet the full capacity of the processing plants with a fleet of 30% less trucks than the desired fleet.

A growing amount of municipal solid waste (MSW) is generated worldwide, and common materials (paper, plastic, metals, and glass), which account for more than half of MSW, exhibit low recovery rates. In this paper, we aim to investigate some key questions about recycling across three dimensions: greenhouse gas emissions, operational costs, and aggregate costs (social costs of emissions plus operational costs.) First, we build supply chain models for cradle-to-grave and cradle-to-cradle supply chains to derive an analytical condition for recycling effectiveness, and use US emissions and cost data to empirically validate that recycling is effective in reducing emissions for all the abovementioned materials. Furthermore, our analysis shows that recycling is effective for all materials, with the exception of glass, with respect to both operational and aggregate costs. Second, we study optimal recycling decisions in terms of collection and yield rates in a socially optimal case, as well as in scenarios in which recycling decisions are made by a local government, a product manufacturer, and an independent recycling firm. Unlike some of existing findings, we show that there are instances in which a product manufacturer or an independent firm might be the best choice for organizing recycling operations. Finally, we discuss and analyze incentives that a social planner should offer to recyclers to bring their efforts closer to the socially optimal choice. We obtain a novel result, which shows that a deposit/refund scheme implemented by a social planner with a refund to local governments might lead to a socially optimal collection rate.

In conventional project management, the project manager is given a deadline and is responsible for making decisions assuming this deadline will not change. But in reality, project deadlines frequently change. Conventional project management addresses unanticipated changes in the project deadline with external change control processes. If this external process changes the deadline, the manager will replace the original deadline with the modified deadline. The manager is still suppose to assume this modified deadline is fixed in making project decisions. As this paper will show, recognizing deadline uncertainty can substantially improve the value of the manager’s decisions. Deadline uncertainty could be formally addressed in project management by making dramatic changes in conventional project management. But given the widespread use of project management, making such dramatic changes might not be feasible. This paper presents a much more easily implemented approach to incorporating deadline uncertainty into managerial decision making which makes no changes to conventional project management procedures.

As a new e-commerce phenomenon in financing, peer to peer (P2P) lending has received increased attention recently. For P2P lending platforms, risk control ability (i.e., the ability to accurately assess and screen borrowers to control the credit risk of loans) is a competitive differentiator. The paper models a three-stage game to investigate optimal risk control ability and corresponding optimal prices of P2P lending platforms under different tariffs and agents’ homing choices. Risk-price coefficients for lenders and borrowers are introduced to measure the impact of risk control ability on prices, where higher risk-price coefficients indicate that prices are more sensitive to risk control ability. Moreover, the paper investigates the role of platforms’ scales in deciding optimal risk control ability, prices and market shares. To our best knowledge, this is the first theoretical paper to study the competition in P2P lending considering risk control ability based on game theory. The analytical results show that: 1) In equilibrium, optimal risk control ability decreases in risk-price coefficients; 2) The risk-price coefficient for lenders contributes less than that for borrowers to optimal risk control ability if some lenders multi-home; 3) Smaller platforms have higher risk control ability, prices and attract more borrowers than larger platforms under specific conditions.

Research funding programs are a policy instrument utilized by governments to influence the innovation process. They are usually elaborated, launched and managed by research funding agencies. In order to select the most adequate research projects, agencies often rely on the peer review process. This paper introduces a methodology to support funding decisions based on the peer review process. The methodology involves the use of a multicriteria decision model to support the assessment, evaluation, prioritization and selection of applications, under a multi-step decision-making process, which fits into a strategic management cycle within the agency. The Multiattribute Value Theory, being considered under a Value Focused Thinking approach, provides a basis for the construction of the multicriteria decision model. The good practices in peer review and also a logical framework for program management are considered by the methodology. A pilot study, presented in the paper, involved a retrospective implementation of a peer review process in the context of a program launched by the Ministry for Science, Technology, Innovations and Communications and the National Council of Technological and Scientific Development, in Brazil. The methodology allowed a clear distinction of roles. The agency staff in the role of decision-makers was responsible for making value judgments on behalf of the agency. The experts, in the role of committee members and ad hoc reviewers, contributed with their expertise by providing objective assessments. Such assessments served as a basis for evaluating the applications, characterizing the possible portfolios, and can be considered as data in future program evaluation studies.

Social media may play a critical role in disaster response by identifying needs in a shorter amount of time and thereby improving situational awareness. However, needs identified through social media initially have not been verified and some may be inaccurate. This can create a barrier to its use during disaster response decision making. Consequently, a key tradeoff between the timeliness and accuracy of social data for disaster response logistics planning must be assessed. This study aims to stimulate interest in a research agenda aimed at evaluating this tradeoff. A general framework for investigating whether it is worthwhile to act on user-generated data prior to its absolute verification in the context of disaster response logistics planning is presented. While the framework is applicable to a variety of logistics planning problems, this paper demonstrates its use via an application in mobile delivery of disaster response commodities. A case study motivated by the 2010 Haiti earthquake is developed using real social data, and is presented for use in conjunction with the framework. The types of insights that are possible via the framework are revealed through the small computational study included in the paper.

For over 50 years Operational Researchers have advocated that Operational Research (OR) could be considered as a useful set of ideas and methods for the benefit of society. However, this aspiration, while still chiming today, has yet to realise its full potential. This paper focuses on organisations whose remit is to alleviate social problems, and therefore have a social purpose. They are under considerable pressure to demonstrate the impact of the work they do. However, showing the value of these organisations is not easy. The paper contributes to this research gap by developing a framework for measuring the impact of social purpose organisations. This is accomplished by bringing together current research on Sen's capability approach and configurational theory, and arguing for an integrative view to show the value of social purpose organisations.\ud \ud

This paper explores how theory and methodology from Community Operational Research (Community OR) can enhance Lean initiatives. We are driven by the paucity of the literature discussing the involvement of non-obvious stakeholders, particularly local communities, in the adoption of Lean. We present a project undertaken with a food production company in the Niger Delta Region of Nigeria, where we employed a Systemic Intervention methodology to integrate theory and methods from Community OR with those from Lean. Based on this example, we argue that the inclusion of community representatives is necessary if Lean waste-reduction initiatives are to benefit both organizations and their local communities. Our only proviso is that, in the spirit of Community OR, the involvement of community representatives must be meaningful, so change is agreed through stakeholder engagements that respect their inputs and framings, and do not result in organizations imposing unwanted ‘solutions’ on communities. The paper ends with some reflections on the added value that Community OR can offer Lean practitioners.

Community Operational Research (Community OR), and its disciplinary relation, Community-Based Operations Research, has an increasingly high profile within multiple domains that benefit from empirical and analytical approaches to problem solving. These domains are primarily concentrated within nonprofit services and local development. However, there are many other disciplines and application areas for which novel applications and extensions of Community OR could generate valuable insights. This paper identifies a number of these, distinguishing between ‘emerging trends’ (mostly in well-studied areas of operational research, management science and analytics) and ‘new frontiers’, which can be found in traditions not commonly oriented towards empirical and analytical methods for problem solving, where community-engaged decision modeling represents new ways of generating knowledge, policies and prescriptions. This paper will show how the exploration of emerging trends and new frontiers in Community OR can provide a basis for the development of innovative research agendas that can broaden the scope and impact of the decision sciences.

The processes by which community-members help to shape local agendas can vary from highly-formalised procedures to very informal learning and sharing activities that engage multiple stakeholders in conversations to construct a better understanding of issues and concerns of community members. Community partners sometimes want to work with universities to build university-community research partnerships to support these activities. This paper looks at two such cases and the framework of ideas that we have used to steer and theorise our participatory research approach. This approach uses informal learning combined with a belief in the value and potential of self-organising processes in community research. The result is a contribution to Community OR that develops long-term engagements rather than brief interventions and produces on-going constructed conversations with community members to help articulate and share knowledge about social experiences and expectations. The paper emphasises the need for Community OR researchers to focus not only on the technologies they are producing but also on the processes they create to support the development of the communities they are working with. We present a framework that uses a combination of self-organisation and informal learning theories to support the analysis and development of this process approach.

The purpose of this paper is to present an application of community O.R. (COR), using soft system methodology (SSM) to help a grant-maker to understand how to unlock and release more resources into communities. The problem area under investigation is a perceived lack of connectivity and alignment between and within the public, private and third sectors in Scotland to address the challenges being faced by communities, articulated by third sector organisations. This is of value as the private sector (we focus on "for-profit businesses") hold the majority of these resources (e.g. people, assets and money). The paper argues that a conduit to business and the third sector can support the allocation of business resources into communities and evaluating outcomes as part their responsible business practice. The 'connect' model is proposed and refined to achieve the client's goal and to address a range of stakeholder issues/challenges. Data was collected in 17 workshops with the client, stakeholder groups and a continuous diaological process with 16 key stakeholders and critical friends. Early on in the SSM study, the 'investing in social capital' relevant system was deemed a necessary early intervention, to bring together actors in a 'open system' that addresses the 'whole problem' area (launched as the 'Responsible Business Forum'). Two additional concepts emerge from the study that help refine the 'connect' model and have implications for theory and practice: the co-creation of business-community value, facilitated by a conduit that unlocks a 'shared space' based on a match of shared aspirations and goals.

This paper seeks to explore and illustrate the proposition that although the Community Operational Research field has developed intellectually over recent years, and has generated useful knowledge about how particular OR methods and techniques can be brought to bear upon a wide range of community-based problems, more needs to be known about the underlying experiences and challenges that are associated with carrying out work of this nature. We argue that surfacing, reflecting upon and raising awareness of these aspects is important in furthering the development of the field. The paper focuses on two key processes: client and stakeholder engagement, and the alignment of methodologies and techniques with the socio-cultural context of their application. The argument is framed theoretically and illustrated through reference to a project carried out in an indigenous community setting. Our proposition is that conducting Community Operational Research in indigenous peoples settings can sharpen our awareness and understanding of aspects of practice that present significant challenges and which to varying degrees are more widely applicable in the community OR context.

Motivated by the industrial practices, this paper develops a two-stage model to explore the supply option contract in a two-echelon supply chain, taking into account a stochastic spot market and demand information updating of general correlation. We develop a concept called EUOS (expected unit opportunity saving) by which to examine the expected benefit received per unit of the option under different market scenarios. With the concept of EUOS, we develop intuitive analytical results to characterize the optimal strategies for portfolio procurement via the option contract and spot markets, and to design the appropriate option mechanism for supply chain coordination. Furthermore, we reveal that EUOS can be a viable alternative for real option pricing in the supply chain. Detailed comparisons between the mixed market scenario (in which the spot market and the option contract market co-exist) and the scenario with pure spot market or pure option contract market are made, and thereby we analytically examine the effects of supply competition between the spot market and the option contract market. Our paper contributes to the literature by developing intuitive characterizations for the option contract models and generating some new insights with respect to the use of supply option contracts in the supply chain in the presence of a stochastic spot market and demand information updating.

In the digital age, retail store operations face a variety of novel challenges and complexities. We review 255 papers on retail store operations from 32 operations research, management science, retailing, and general management journals over the period 2008–2016. We assess the current state of research within the context of retail store operations. By discussing the limitations present in these papers, we identify a number of research gaps and propose several opportunities for advancing retail expertise in the operations management community.

Empirical studies attempting to open the ‘black box’ of the practice of operational research (OR) are beginning to appear in the literature, particularly within the area known as behavioural OR. Many scholars within this community share a commitment to both empirically investigate what OR practitioners and users actually do when engaged in OR-supported processes, and evaluate what the effect of these ‘doings’ is. Despite these developments, we still know very little about the complexities and situated specifics of OR practice as it happens on the ground. This is mostly due to the methodological challenges involved in treating real-time OR practice as an analytical problem, which requires making OR practice ‘visible’ by bringing to the fore its material and interactional features for close examination. In this paper we adopt ethnomethodology as one way to address this challenge. Using an empirical vignette drawn from a facilitated modelling workshop in which causal mapping was used with a top management team, we illustrate how an ethnomethodologically-informed perspective can reveal the ways in which OR-supported activity is practically accomplished by those involved, moment by moment, and with what effects. We conclude the paper by summarising the contribution that these kinds of fine-grained studies of OR practice make to the behavioural OR agenda, and outline some potentially useful avenues for future research.

We consider multiple criteria decision aiding (MCDA) in the case of interactions between criteria. In dealing with interactions between criteria, fuzzy measures and integrals have demonstrated great advantages. Nevertheless, the determination of fuzzy measures has proven difficult because the capacities of not only single criterion but also all subsets of criteria need to be identified. Due to the value judgment essence of MCDA, the attitudes of the decision maker (DM) are typically modeled to identify fuzzy measures. In this paper, the tolerance attitudes of the DM, which implies a direct requirement instead of partial preference, are modeled with regard to the determination of fuzzy measures for the first time. With two scales developed in this paper, the DM can directly express the tolerance attitudes to certain criteria other than providing partial preference through pairwise comparison. As a result, it requires less prior knowledge and is more efficient to some extent. Further, the inherent interacting mechanism of criteria under different tolerance attitudes is explored. At last, the tolerance attitudes are applied to the process of multiple criteria analysis using a Choquet integral. A classic student evaluation problem is given as an example. The evaluation results are compared with additive models. This paper not only provides a new inspiration to the determination of fuzzy measures but also improves the descriptive capacity of fuzzy measures to the real world.

The literature suggests that increasing stakeholder engagement has a positive impact on projects using discrete-event simulation in healthcare. This suggests projects should strive to involve the stakeholders in as much of the project as possible, through facilitated workshops. A notable gap in stakeholder involvement is the model coding stage, in which a conceptual model is turned into a discrete-event simulation model running on a computer. This paper investigates how and under what circumstances model coding might also be conducted in facilitated workshops, in particular through the use of the Business Process Model and Notation (BPMN) modelling standard. This work arose from a series of modelling projects with two hospitals, one in Italy and the other in the UK. The paper describes how BPMN can contribute, with a case in which model coding was achieved in a facilitated workshop and a second in which it was not but which highlights further barriers to this in some contexts. These barriers arise from the detail necessary for requisite modelling regarding (i) the level of complexity of the model and (ii) challenges in data access and analysis to populate the model. The relationship between the technical capabilities of tools available and the impact of these barriers is also discussed. We believe this is the first time that discrete-event simulation model coding in a facilitated workshop in healthcare has been described, and we provide a clear view of the further barriers. To indicate when facilitated model coding is currently achievable, we suggest a contextual matrix.

Contains fulltext : 174550.pdf (Publisher’s version ) (Closed access) The role of stakeholders in organizational decision-making is gaining more and more attention. Managers find that in order to create value sustainably and ethically, it is necessary to balance the interests of various stakeholders. This trend is reflected in the management literature, where much advancement has been made in what is known as stakeholder theory. In the founding years of stakeholder theory there was a close connection and interchange of ideas between stakeholder theorists and operational researchers. Yet in recent years, the stream of papers that includes both fields has dwindled to a trickle. This lack of theoretical integration is surprising, as Operational Research is in nature a collaborative discipline. In this paper, we discuss three topics in stakeholder theory that can inform and improve studies involving working with stakeholders in Operational Research: instrumental versus moral stakeholder theory; focusing on trade-offs versus focusing on avoiding trade-offs; and focusing on the decision-making organization versus focusing on stakeholder engagement. We then conduct a systematic review of 144 Operational Research articles on the topic ‘stakeholders’. Content analysis of these articles reveals four distinct traditions of working with stakeholders: optimizing, balancing, structuring and involving. We compare the four traditions on goals, role of the analyst, type of data used, and results. Our analysis provides the basis for recommendations to Operational Research practitioners on how to work with stakeholders. 12 april 2017 9 p.

Local search (LS) is an essential module of most hybrid meta-heuristic evolutionary algorithms which are a major approach aimed to solve efficiently multi-objective optimization (MOO) problems. Furthermore, LS is specifically useful in many real-world applications where there is a need only to improve a current state of a system locally with limited computational budget and/or relying on computationally expensive process simulators. In these contexts, this paper proposes a new neighborhood-based iterative LS method, relying on first derivatives approximation and linear programming (LP), aiming to steer the search along any desired direction in the objectives space. The paper also leverages the directed local search (DS) method to constrained MOO problems. These methods are applied to the bi-objective (cost versus life cycle assessment-based environmental impact) optimization of drinking water production plants. The results obtained show that the proposed method constitutes a promising local search method which clearly outperforms the directed search approach.

In this paper we establish conditions under which uniqueness of market equilibrium is obtained in a setup where prior to trading of electricity, transmission capacities between different market regions are fixed. In our setup, firms facing fluctuating demand decide on the size and location of production facilities. They make production decisions constrained by the invested capacities, taking into account that market prices (partially) reflect scarce transmission capacities between the different market zones. For this type of peak-load pricing model on a network we state general conditions for existence and uniqueness of the market equilibrium and provide a characterization of equilibrium investment and production. The presented analysis covers the cases of perfect competition and monopoly—the case of strategic firms is approximated by a conjectural variations approach. Our result is a prerequisite for analyzing regulatory policy options with computational multilevel equilibrium models, since uniqueness of the equilibrium at lower levels is of key importance when solving these models. Thus, our paper contributes to an evolving strand of literature that analyzes regulatory policy based on computational multilevel equilibrium models and aims at taking into account individual objectives of various agents, among them not only generators and customers but also, e.g., the regulator deciding on network expansion.

In this paper, we develop and compare three distinct models for cybersecurity investment in competitive and cooperative situations to safeguard against potential and ongoing threats. We introduce a Nash equilibrium model of noncooperation in terms of cybersecurity levels of the firms involved, which is formulated, analyzed, and solved using variational inequality theory. The equilibrium of this model then acts as the disagreement point over which bargaining takes place in the setting of the second model, which yields a cooperative solution in which the firms are guaranteed that their expected utilities are no lower than those achieved under noncooperation. Nash bargaining theory is utilized to argue for information sharing and to quantify its monetary and security benefits in terms of reduction in network vulnerability to cyberattacks. The third model in this paper also focuses on cooperation among the firms in terms of their cybersecurity levels, but from a system-optimization perspective in which the sum of the expected utilities is maximized. Qualitative properties are provided for the models in terms of existence and uniqueness results along with numerical solutions to two cases focusing on retailers and financial service firms, since these have been subject to some of the most damaging cyberattacks. Sensitivity analysis results are also provided. We compare the solutions of the models for the cases and recommend a course of action that has both financial and policy-related implications.

In this paper, the effect of behavioural analytics on short-term default predictions at the invoice level is addressed by answering a question that slightly diverges from the traditional probability of default definition: ‘What is the probability that this invoice will be paid within the next 30 days?’ Resultantly improving short-term liquidity planning accuracy and supporting financial management in companies. To provide a valid answer to the research question, a set of issues needs to be resolved, including identifying an appropriate data set, increasing the data predictive power, and creating and testing predictive models. Since the appropriate data set is not yet presented, we primarily focus on the first two issues: identifying appropriate data and raising its predictive power. In this paper, we propose to build predictive models upon a new data source from multiple companies, acquired by business partners' data sharing concept. Furthermore, we upgrade these data with behavioural analysis to test the assumption that the probability of default depends not only on payment capability but also on payment preparedness. The predictive power of shared invoice data and the effects of behavioural analysis are tested in a two-phase experiment: first, basic shared data are used to predict short-term invoice defaults, and in the second phase, the behavioural analysis results are included in the dataset. Lastly, the predictive models’ test results are compared. Both results are positive: the already high accuracy of models, build upon basic data is significantly upgraded in models, using the behaviour analysis extended data set.

The European Union relies on oil for over 90% of its transportation fuels. To reduce this dependency, the EU is developing an integrated market for compressed (CNG) and liquefied (LNG) natural gas as alternatives to diesel for long-haul trucking. This paper develops a modeling approach for optimizing a subsidized European network of LNG truck stops based on the flow-refueling location model (FRLM). The FRLM locates stations to cover the maximum flow volume of origin–destination trips on their shortest paths. The FRLM explicitly accounts for the need for multiple refueling stops to serve long-distance trips given a user-specified vehicle driving range. In this paper, the FRLM maximizes covered flows in terms of ton-kilometers (tkm) rather than tons because tkm are proportional to the amount of diesel that can be replaced by LNG and the associated difference in carbon emissions. Nevertheless, despite the fact that maximizing covered tkm prioritizes longer shipments, the basic FRLM clusters all new stations in Germany, which has the densest concentration of flows. For subsidized stations promoting pan-European trucking, concentrating new stations in a few countries may not be politically desirable. We therefore introduce and compare several new side constraints for producing a more equitable distribution of covered flows across EU members, including transnational corridor constraints that require connectivity between different countries and partial country coverage constraints that require a minimum percentage coverage threshold in each country. The country coverage constraints show the most promise for offering decision-makers distinct alternatives for a network of alternative-fuel stations across countries.

Wright (Wright, M. OR analysis of sporting rules - A survey. European Journal of Operational Research, 232(1):1-8, 2014) recently presented a survey of sporting rules from an Operational Research (OR) perspective. He surveyed 21 sports, which consider the rules of sports and tournaments and whether changes have led to unintended consequences. The paper concludes: "Overall, it would seem that this is just a taster and there may be plenty more such studies to come". In this paper we present one such study. This is an interdisciplinary paper, which cuts across economics, sport and operational research (OR). We recognize that the paper could have been published in any of these disciplines but for the sake of continuity with the paper that motivated this study, we wanted to publish this paper in an OR journal. We look at specific examples where the rules of sports have led to unforeseen and/or unwanted consequences. We hope that the paper will be especially useful to sports administrators, helping them to review what has not previously worked and also encouraging them to engage with the scientific community when considering making changes. We believe that this is the first time that such a comprehensive review of sporting rules, which have led to unexpected consequences, has been published in the scientific literature.

Seriation aims at finding a linear order for a set of objects to reveal structural information which can be used for deriving data-driven decisions. It presents a difficult combinatorial optimization problem with its roots and applications in many fields including operations research. This paper focuses on a popular seriation problem which tries to find an order for a single set of objects that optimizes a given seriation criterion defined on one-mode two-way data, i.e., an object-by-object dissimilarity matrix. Over the years, members of different research communities have introduced many criteria and seriation methods for this problem. It is often not clear how different seriation criteria and methods relate to each other and which criterion or seriation method to use for a given application. These methods are representing tools for analytics and therefore are of theoretical and practical interest to the operations research community. The purpose of this paper is to provide a consistent overview of the most popular criteria and seriation methods and to present a comprehensive experimental study to compare their performance using artificial and a representative set of real-world datasets.