Hospitals, as the main customers of medications, typically adopt conservative inventory control policies by keeping large quantities of drugs in stock. Given the perishable nature of medications, such strategies lead ...
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Hospitals, as the main customers of medications, typically adopt conservative inventory control policies by keeping large quantities of drugs in stock. Given the perishable nature of medications, such strategies lead to the expiration of excess inventory in the absence of patients' demand. Consequently, producers are faced with governmental penalties and environmental reputation forfeit due to the negative impact that disposing expired medications pose to the environment. This article aims to improve the sustainability of a pharmaceutical supply chain using a real case study. An analytical model is proposed to explore the effect of implementing a Vendor-Managed Inventory (VMI) system in minimizing the quantity of the expired medications at hospitals. Further, a set of Monte-Carlo simulation tests are conducted to investigate the robustness of the VMI model under demand uncertainty. Experimental results on a real case study under deterministic demand show the efficiency of the VMI model in eliminating the amount of expired medications without compromising customer's satisfaction. The results also demonstrate that the safety stock (SS) level and the capacity assigned to the customer are crucial factors in the overall cost of the pharmaceutical supply chain (PSC). The PSC cost could be reduced by 19% when reducing the SS level by 50%. Moreover, the producer is recommended to increase the capacity assigned to the customer by a factor of 1.5 so as to fully satisfy the customer's demand. Finally, the simulation results confirm the efficiency and robustness of embracing a VMI system under random demand scenarios. More precisely, zero amount of expired medications is obtained in 93% of cases. Thus, adopting this strategy could minimize drug wastage and ultimately improve the reputation of the producer in the market in terms of implementing Lean and sustainable practices. (C) 2019 Elsevier Ltd. All rights reserved.
Given a factorable function f, we propose a procedure that constructs a concave underestimator of f that is tight at a given point. These underestimators can be used to generate intersection cuts. A peculiarity of the...
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ISBN:
(纸本)9783030179533;9783030179526
Given a factorable function f, we propose a procedure that constructs a concave underestimator of f that is tight at a given point. These underestimators can be used to generate intersection cuts. A peculiarity of these underestimators is that they do not rely on a bounded domain. We propose a strengthening procedure for the intersection cuts that exploits the bounds of the domain. Finally, we propose an extension of monoidal strengthening to take advantage of the integrality of the non-basic variables.
This paper presents an optimal dispatch algorithm to coordinate customer-owned controllable loads and smart solar inverters with utility-owned voltage regulators and capacitors to meet voltage control objectives. The ...
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ISBN:
(纸本)9781728119816
This paper presents an optimal dispatch algorithm to coordinate customer-owned controllable loads and smart solar inverters with utility-owned voltage regulators and capacitors to meet voltage control objectives. The optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem. A voltage sensitivity matrix (VSM) is used to linearize the effect of control actions on the voltage at customer nodes when solving the MINLP. The VSM is recalculated at each time step to improve the computational accuracy. Both discrete switching actions of the capacitor and VRs and the continuous adjustment of real and reactive power from load and smart inverters are considered in the MINLP volt-var problem formulation. The objective function minimizes the cost of all control actions and the magnitude of voltage fluctuations from the previous time period. Constraints ensure that the voltage at each node is maintained within ANSI limits and the feeder power factor is controlled within the desired range. The algorithm is tested using an actual 3-phase unbalanced distribution feeder model. Simulation results demonstrate that the proposed algorithm is computationally feasible on real circuits and improves voltage control while minimizing operational costs.
Solution methods for convex mixedintegernonlinearprogramming (MINLP) problems have, usually, proven convergence properties if the functions involved are differentiable and convex. For other classes of convex MINLP ...
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Solution methods for convex mixedintegernonlinearprogramming (MINLP) problems have, usually, proven convergence properties if the functions involved are differentiable and convex. For other classes of convex MINLP problems fewer results have been given. Classical differential calculus can, though, be generalized to more general classes of functions than differentiable, via subdifferentials and subgradients. In addition, more general than convex functions can be included in a convex problem if the functions involved are defined from convex level sets, instead of being defined as convex functions only. The notion generalized convex, used in the heading of this paper, refers to such additional properties. The generalization for the differentiability is made by using subgradients of Clarke's subdifferential. Thus, all the functions in the problem are assumed to be locally Lipschitz continuous. The generalization of the functions is done by considering quasiconvex functions. Thus, instead of differentiable convex functions, nondifferentiable -quasiconvex functions can be included in the actual problem formulation and a supporting hyperplane approach is given for the solution of the considered MINLP problem. Convergence to a global minimum is proved for the algorithm, when minimizing an -pseudoconvex function, subject to -pseudoconvex constraints. With some additional conditions, the proof is also valid for -quasiconvex functions, which sums up the properties of the method, treated in the paper. The main contribution in this paper is the generalization of the Extended Supporting Hyperplane method in Eronen et al. (J Glob Optim 69(2):443-459, 2017) to also solve problems with -pseudoconvex objective function.
This paper describes the extensions that were added to the constraint integerprogramming framework SCIP in order to enable it to solve convex and nonconvex mixed-integernonlinear programs (MINLPs) to global optimali...
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This paper describes the extensions that were added to the constraint integerprogramming framework SCIP in order to enable it to solve convex and nonconvex mixed-integernonlinear programs (MINLPs) to global optimality. SCIP implements a spatial branch-and-bound algorithm based on a linear outer-approximation, which is computed by convex over- and underestimation of nonconvex functions. An expression graph representation of nonlinear constraints allows for bound tightening, structure analysis, and reformulation. Primal heuristics are employed throughout the solving process to find feasible solutions early. We provide insights into the performance impact of individual MINLP solver components via a detailed computational study over a large and heterogeneous test set.
It is a common practice for transportation firms to group together a set of locations that they serve and price their services based on the group that the origin and destination of a service belong to rather than poin...
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It is a common practice for transportation firms to group together a set of locations that they serve and price their services based on the group that the origin and destination of a service belong to rather than point-to-point. Proper grouping of service locations and correct pricing under this policy is essential to the financial success of firms. In this research, we develop a novel model for transportation firms to simultaneously group service locations and determine group-to-group pricing by considering the price elasticity of customers' demand. We formulate the problem as a mixed-integer nonlinear programming and propose two exact solution algorithms based on decomposition principles. The performances of our algorithms are evaluated using computational experiments and results show that proposed methods are effective. (C) 2017 Elsevier B.V. All rights reserved.
This paper seeks to answer questions from the combined bus operator's and users' perspective on how to design limited stop service operation strategies when they are offered along with the normal bus services....
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This paper seeks to answer questions from the combined bus operator's and users' perspective on how to design limited stop service operation strategies when they are offered along with the normal bus services. The passengers' service choice is determined by the common line calculation. The problem is formulated as a mixedintegernonlinear Program (MINLP) with equilibrium constraints. Thereafter, a global optimal solution method applying various linearization and convexification techniques is proposed. Numerical studies are then performed to evaluate the model validity and solution efficiency followed by concluding remarks.
We propose a new deterministic global optimization algorithm for solving mixed-integer bilinear programs. It relies on a two-stage decomposition strategy featuring mixed-integer linear programming relaxations to compu...
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We propose a new deterministic global optimization algorithm for solving mixed-integer bilinear programs. It relies on a two-stage decomposition strategy featuring mixed-integer linear programming relaxations to compute estimates of the global optimum, and constrained non-linear versions of the original non-convex mixed-integernonlinear program to find feasible solutions. As an alternative to spatial branch-and-bound with bilinear envelopes, we use extensively piecewise relaxations for computing estimates and reducing variable domain through optimality-based bound tightening. The novelty is that the number of partitions, a critical tuning parameter affecting the quality of the relaxation and computational time, increases and decreases dynamically based on the computational requirements of the previous iteration. Specifically, the algorithm alternates between piecewise McCormick and normalized multiparametric disaggregation. When solving ten benchmark problems from the literature, we obtain the same or better optimality gaps than two commercial global optimization solvers.
We propose a new medical evacuation (MEDEVAC) model with endogenous uncertainty in the casualty delivery times. The goal is to provide timely evacuation and medical treatment to injured soldiers. The model enforces th...
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We propose a new medical evacuation (MEDEVAC) model with endogenous uncertainty in the casualty delivery times. The goal is to provide timely evacuation and medical treatment to injured soldiers. The model enforces the "Golden Hour" evacuation doctrine, attempts to maximize the expected number of severely injured soldiers evacuated within one hour without delay, and represents the availability of air ambulances as an endogenous source of uncertainty. The MEDEVAC model is a mixed-integer nonlinear programming problem whose continuous relaxation is in general nonconvex and for which we develop an algorithmic method articulated around (i) new bounding techniques obtained through the solution of restriction and relaxation problems and (ii) a spatial branch-and-bound algorithm solving conic mixed-integer programs at each node. The computational study, based on data from Operation Enduring Freedom, reveals that the bounding problems can be quickly solved regardless of problem size, the bounds are tight, and the spatial branch-and-bound dominates the CPLEX and BARON solvers in terms of computational time and robustness. Compared to the MEDEVAC myopic policy, our approach increases the number of casualties treated timely and can contribute to reducing the number of deaths on the battlefield. The benefits increase as the MEDEVAC resources become tighter and the combats intensify. The model can be used at the strategic level to design an efficient MEDEVAC system and at the tactical level for intelligent tasking and dispatching.
We consider the risk-averse uncapacitated facility location problem under stochastic disruptions. By the Conditional-value-at-risk, we control the risks at each individual customer, while previous works usually contro...
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We consider the risk-averse uncapacitated facility location problem under stochastic disruptions. By the Conditional-value-at-risk, we control the risks at each individual customer, while previous works usually control the entire networks. We show that our model provides more reliable solutions than previous ones. The resulting formulation is a mixed-integer nonlinear programming. In response, we develop a multi-dual decomposition algorithm based on the augmented Lagrangian and classic penalty function. A class of decomposed unconstrained subproblems are then solved by an iterative approach not relying on Lagrange multipliers and differentiability. Our experiments show that the algorithm performs well even for some larger problems.
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