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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.