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Corrugated paper is produced by gluing three types of papers of the same breadth. Given a set of orders, we first assign each order to one of the standard breadths, and then sequence those assigned to each standard breadth so that they are continuously manufactured from the three rolls of the specified standard breadth equipped in the machine called corrugator. Here we are asked to achieve multi-goals of minimizing total length of roll papers, total loss of papers caused by the differences between standard breadths and real breadths of the orders, and the number of machine stops needed during production. We use integer programming to assign orders to standard breadths, and then develop a special purpose algorithm to sequence the orders assigned to each standard breadth. This is a first attempt to handle scheduling problems of the corrugator machine.

We consider a vertically integrated papermaking operation composed of an integrated pulp and paper mill with its regional supply network. Considering land procurement and harvest rotation as strategic decision variables, we construct a model to establish a long-range timber supply plan that minimizes the total discounted cost of meeting annual virgin wood fiber demand over an infinite horizon. Under appropriate assumptions on costs and storage, the land procurement and harvest rotation decisions are separable with harvest rotation being determined via a forest economics-type equation and land procurement being determined by a newsvendor-type equation.

In this paper we describe a container loading software package for a pulp and paper manufacturer. The program takes, as input, orders for paper products in sheets or reels. As output it produces precise order quantities, and stowage plans for shipping containers. The program includes a heuristic algorithm for two-dimensional stowing of reels of a common diameter, a bin packing algorithm for combining reels into stacks and a two-dimensional algorithm for stowing pallets of various sizes. Since the order sizes are usually greater than one container load all algorithms are designed for the multi-container case. The program is currently in use and has been effective in several decision situations including (a) good sizes for reel diameters (b) efficient stowage patterns, and (c) production quantities that are compatible with cost efficient shipping. It also reduces the time taken to produce stowage and shipping documents.

In a recently published article in EJOR, Christofides et al. (CAT) present a depth-first search, branch-and-bound solution procedure for the multiple-resource constrained, single project scheduling problem. While there are many important contributions in this paper, we show by counterexample that if the branching strategy described by the authors is used, the optimal solution might not result. Computational experience on a set of test problems appearing in the open literature is reported both with the original branching strategy suggested by the authors and a modified branching strategy that we propose. The modified strategy guarantees the determination of the optimal solution in all instances of the problem at the expense of an increase in node evaluations and average CPU time. Computational results using the revised procedure and a hybrid, breadth-first search procedure also investigated by CAT are reported.

This paper studies a two-machine cross-docking flow shop scheduling problem in which a job at the second machine can be processed only after the processing of some jobs at the first machine has been completed. The objective is to minimize the makespan. We first show that the problem is strongly NP-hard. Some polynomially solvable special cases are provided. We then develop a polynomial approximation algorithm with an error-bound analysis. A branch-and-bound algorithm is also constructed. Computational results show that the branch-and-bound algorithm can optimally solve problems with up to 60 jobs within a reasonable amount of time.

In this paper, we consider single machine scheduling problem in which job processing times are controllable variables with linear costs. We concentrate on two goals separately, namely, minimizing a cost function containing total completion time, total absolute differences in completion times and total compression cost; minimizing a cost function containing total waiting time, total absolute differences in waiting times and total compression cost. The problem is modelled as an assignment problem, and thus can be solved with the well-known algorithms. For the case where all the jobs have a common difference between normal and crash processing time and an equal unit compression penalty, we present an O(n log n) algorithm to obtain the optimal solution.

Consumers are increasingly concerned about pollution and are, at least in part, making their purchases accordingly. In this paper, we consider a supply chain where demand depends on the manufacturer’s environmental reputation, an asset that can only be built over time. We assume that this reputation depends on information emanating from the manufacturer itself about its environmental actions and on its objective environmental record when compared to an industry standard. In this paper, we look at the impact of consumers’ environmental concern on the strategies and outcomes of a supply chain. As the retailer has a direct influence on sales through its pricing policy, it seem more realistic to take a supply chain perspective instead of a single-firm one. Therefore, the contractual arrangement in the supply chain becomes a relevant issue. We model the supply chain as a two-player dynamic game and characterize and compare the equilibrium strategies and outcomes under wholesale-price and revenue-sharing contracts. We analyze the impact of the sharing parameter, the industry pollution standard and consumers’ degree of environmental sensitivity on the results, and in particular, on the region in the parameter space where a revenue-sharing contract is Pareto improving with respect to a wholesale-price contract. Also, we examine whether it is in the best interest of the retailer to behave myopically when setting its price.

This paper focuses on the variance risk premium of project returns and its impacts on the real options to expand and to abandon a project. Variance risk premium measures the hedging costs associated with the time-varying variance of the returns. We find that the variance risk premium increases the real option value to expand and to abandon a project. Hence, the variance premium delays both abandoning and expanding the project. Our setting also enables us to study the multi-stage decisions with real options under the variance risk premium. For example, we consider when there is an abandonment or a re-expansion add-on attached to an expansion project, which is a reminiscence of how our framework facilitates modeling compound real option structures in the presence of the variance risk premium. We present two applications of our models that involve Ford Motor company and battery storage investments for electricity distribution networks. These cases demonstrate how the proposed framework in this paper can be applied to real world decisions, and, moreover, they establish a baseline for how much decisions can be improved when variance risk premium is included in the modeling.

In this paper, we study an inventory model for an omnichannel retailer, that is, a retailer that sells items both via brick-and-mortar stores and online. Online items are delivered from a warehouse, which also replenishes the stores. When the inventory in a store drops below a certain level, the retailer offers customers a discount for purchasing online. In this way, the retailer can save items for customers who need the item immediately and thus avoid lost sales. For this model, we propose an approximation method for calculating the average inventory costs for one store and one warehouse and an optimization procedure for the case of more stores. Using extensive numerical experiments, we show that the approximations are very close to the performance measured via simulation. Finally, we show that by adopting the discounts policy proposed in this paper, the retailer can reduce its total cost, on average, by 8.5% compared to the no-discounts policy.

The maximum weighted submatrix coverage problem is a recently introduced problem with applications in data mining. It is concerned with selecting K submatrices of a given numerical matrix such that the sum of the matrix-entries, which occur in at least one of the selected submatrices, is maximized. In the paper introducing the problem, a problem-specific constraint programming approach was developed and embedded in a large neighborhood-search to obtain a heuristic. A compact integer linear programming formulation was also presented, but deemed inefficient due to its size. In this paper, we introduce new integer linear programming formulations for the problem, one of them is based on Benders decomposition. The obtained Benders decomposition-based formulation has a nice combinatorial structure, i.e., there is no need to solve linear programs to separate Benders cuts. We present preprocessing procedures and valid inequalities for all formulations. We also develop a greedy randomized adaptive search procedure for the problem, which is enhanced with a local search. A computational study using the instances from literature is done to evaluate the effectiveness of our new approaches. Our algorithms manage to find improved primal solutions for ten out of 17 real-world instances, and optimality is proven for two real-world instances. Moreover, for over 700 of 1617 large-scale synthetic instances, our algorithms find improved primal solutions compared to the heuristics from the literature.

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.

Cyber risks are high on the business agenda of every company, but they are difficult to assess due to the absence of reliable data and thorough analyses. This paper is the first to consider a broad range of cyber risk events and actual cost data. For this purpose, we identify cyber losses from an operational risk database and analyze these with methods from statistics and actuarial science. We use the peaks-over-threshold method from extreme value theory to identify “cyber risks of daily life” and “extreme cyber risks”. Human behavior is the main source of cyber risk and cyber risks are very different compared with other risk categories. Our models can be used to yield consistent risk estimates, depending on country, industry, size, and other variables. The findings of the paper are also useful for practitioners, policymakers and regulators in improving the understanding of this new type of risk.

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.