Integrated capacity and inventory management with capacity acquisition lead times

We model a make-to-stock production system that utilizes permanent and contingent capacity to meet non-stationary stochastic demand, where a constant lead time is associated with the acquisition of contingent capacity. We determine the structure of the optimal solution concerning both the operational decisions of integrated inventory and flexible capacity management, and the tactical decision of determining the optimal permanent capacity level. Furthermore, we show that the inventory (either before or after production), the pipeline contingent capacity, the contingent capacity to be ordered, and the permanent capacity are economic substitutes. We also show that the stochastic demand variable and the optimal contingent capacity acquisition decisions are economic complements. Finally, we perform numerical experiments to evaluate the value of utilizing contingent capacity and to study the effects of capacity acquisition lead time, providing useful managerial insights.     

两服务商竞争的研发外包知识转移信息披露决策

针对两服务商竞争的研发外包知识转移,考虑两个服务商知识吸收能力、信息获取能力均不同,且信息劣势方不知道信息优势方的知识吸收能力。通过建立以企业为领导者,两个服务商为跟随者的Stackelberg主从博弈模型,研究了企业和信息优势方的知识吸收能力披露决策,以及信息劣势方的信息处理决策,并用算例对结论加以说明。研究表明:仅当知识吸收能力较弱的服务商索取的单位报酬相对较低时,企业应披露信息优势方的知识吸收能力;仅当信息优势方知识吸收能力较弱时,其应披露自身知识吸收能力;信息劣势方应始终遵循信息优势方的信息披露策略;各方信息披露决策对企业和服务商均有不同影响。     

The line index and minimum cut of weighted graphs

In this paper we discuss the line index and the minimum capacity, sum of the weights in a minimum cut, of weighted graphs, relationships between the line index and the minimum capacity, between the two kinds of minimum capacities, and between graphs with only negative weights and graphs with both positive weights and negative weights. We provide linear algorithms for both the line index and the minimum capacity of series-parallel graphs. We discuss simplifications in computing the line index and the minimum capacity of weighted graphs. We show that the generalization of algorithm of Orlova and Dorfman, and Hadlock to mixed graphs is just one step. We also discuss the k-approximation of the minimum capacity.     

Representations of simple games by social choice functions

We investigate possible constructions of choice procedures (social choice functions) for committees (simple games). The notion of a capacity of a committee is derived from our construction. We determine the capacity of strong, symmetric and weak simple games. We also provide an upper bound on the capacity of a simple game without veto players.     

Flexible capacity strategy with multiple market periods under demand uncertainty and investment constraint

We establish a flexible capacity strategy model with multiple market periods under demand uncertainty and investment constraints. In the model, a firm makes its capacity decision under a financial budget constraint at the beginning of the planning horizon which embraces n market periods. In each market period, the firm goes through three decision-making stages: the safety production stage, the additional production stage and the optimal sales stage. We formulate the problem and obtain the optimal capacity, the optimal safety production, the optimal additional production and the optimal sales of each market period under different situations. We find that there are two thresholds for the unit capacity cost. When the capacity cost is very low, the optimal capacity is determined by its financial budget; when the capacity cost is very high, the firm keeps its optimal capacity at its safety production level; and when the cost is in between of the two thresholds, the optimal capacity is determined by the capacity cost, the number of market periods and the unit cost of additional production. Further, we explore the endogenous safety production level. We verify the conditions under which the firm has different optimal safety production levels. Finally, we prove that the firm can benefit from the investment only when the designed planning horizon is longer than a threshold. Moreover, we also derive the formulae for the above three thresholds.     

Elliptic, hyperbolic, and condenser capacity; geometric estimates for elliptic capacity

The elliptic capacity of a compact setE in the plane is defined by R. Kühnau by least energy considerations analogous to the definition of logarithmic capacity but using the elliptic distance in place of the euclidean distance. We prove an identity connecting elliptic capacity, extremal length, and the capacity of the condenser with platesE andE ^*, whereE ^* is antipodal toE in the Riemann sphere. An analogous identity for hyperbolic capacity, conjectured by P. Duren and J. Pfaltzgraff, is also established. We show that elliptic capacity is always smaller than hyperbolic capacity, thus answering a question of P. Duren and R. Kühnau. We prove some geometric estimates for elliptic capacity. These estimates involve geometric transformations such as polarization, circular symmetrization, projection, and shoving.     

On the Harmonic Measure and Capacity of Rational Lemniscates

We study the lemniscates of rational maps. We prove a reflection principle for the harmonic measure of rational lemniscates and we give estimates for their capacity and the capacity of their components. Also, we prove a version of Schwarz’s lemma for the capacity of the lemniscates of proper holomorphic functions.     

Models for capacity and electricity market design

The paper employs Operations Research methods for analysis of electricity and capacity markets. We provide two algorithms that determine the optimal capacity structure with account of fixed and variable costs. The first one relates to the case where there are several capacity types, and for each type the capacity constraint is not binding. The second algorithm is applicable when electricity is produced by standard small generators with the same capacity and different costs. Then we study two typical architectures of the market and examine their Nash equilibria. We consider a uniform price supply function auction in the electricity market. For pay-as-bid and uniform price versions of the capacity market design, we compare the equilibrium outcomes with the optimal capacity structure. The paper shows that the market equilibrium corresponds to the optimal capacity structure under conditions of pure competition, full rationality, and completely informed agents in the market. However, under more realistic assumptions, selection of the optimal structure is unlikely. Finally we provide the auction design that realizes such selection of capacities and does not require any additional information of each producer besides his own production costs. We establish sufficient conditions for perfect competition in the market.     

The water capacity of geometrically distributed words

Aequationes mathematicae - We consider the bargraph representation of geometrically distributed words, which we use to define the water capacity of such words. We first find a bivariate capacity...     

Population models with periodic recruitment functions and survival rates

We study the combined effects of periodically varying carrying capacity and survival rate on populations. We show that our populations with constant recruitment functions do not experience either resonance or attenuance when either only the carrying capacity or the survival rate is fluctuating. However, when both carrying capacity and survival rate are fluctuating the populations experience either attenuance or resonance, depending on parameter regimes. In addition, we show that our populations with Beverton–Holt recruitment functions experience attenuance when only the carrying capacity is fluctuating.     

Sourcing under incomplete information and negative capacity-cost correlation

We study a sourcing problem where a buyer reserves capacity from a set of suppliers. The suppliers have finite capacity and their unit production cost is a decreasing function of their capacity, implying scale economies. The capacity of each supplier and therefore the cost is his private information. The buyer and other suppliers only know the probability distribution of the supplier’s capacity. The buyer’s demand is random and she has to decide how much capacity to reserve in advance from a subset of suppliers and how much to source from marketplace. In this study we determine the buyer’s optimum reservation quantity and the size of the supply base. We find the presence of such capacity cost correlation leads to supply base reduction.     

Wireless network capacity management: A real options approach

This paper applies financial option valuation methods to new wireless network capacity investment decision timing. In particular, we consider the case of network capacity for cellular telephone service. Given a cluster of base stations (with a certain traffic capacity per base station), we determine when it is optimal to increase capacity for each of the base stations contained in the cluster. We express this in terms of the fraction of total cluster capacity in use, i.e. we calculate the optimal time to upgrade in terms of the ratio of observed usage to existing capacity. We study the optimal decision problem of adding new capacity in the presence of stochastic wireless demand for services. A four factor algorithm is developed, based on a real options formulation. Numerical examples are provided to illustrate various aspects of the model.     

Joint management of capacity and inventory in make-to-stock production systems with multi-class demand

We evaluate the benefits of coordinating capacity and inventory decisions in a make-to-stock production environment. We consider a firm that faces multi-class demand and has additional capacity options that are temporary and randomly available. We formulate the model as a Markov decision process (MDP) and prove that a solution to the optimal joint control problem exists. For several special cases we characterize the structure of the optimal policy. For the general case, however, we show that the optimal policy is state-dependent, and in many instances non-monotone and difficult to implement. Therefore, we consider three pragmatic heuristic policies and assess their performance. We show that the majority of the savings originate from the ability to dynamically adjust capacity, and that a simple heuristic that can adjust production capacity (based on workload fluctuation) but uses a static production/rationing policy can result in significant savings.     

Stochastic binary problems with simple penalties for capacity constraints violations

This paper studies stochastic programs with first-stage binary variables and capacity constraints, using simple penalties for capacities violations. In particular, we take a closer look at the knapsack problem with weights and capacity following independent random variables and prove that the problem is weakly ${\mathcal{N}\mathcal{P}}$ -hard in general. We provide pseudo-polynomial algorithms for three special cases of the problem: constant weights and capacity uniformly distributed, subset sum with Gaussian weights and strictly positively distributed random capacity, and subset sum with constant weights and arbitrary random capacity. We then turn to a branch-and-cut algorithm based on the outer approximation of the objective function. We provide computational results for the stochastic knapsack problem (i) with Gaussian weights and constant capacity and (ii) with constant weights and capacity uniformly distributed, on randomly generated instances inspired by computational results for the knapsack problem.     

A stochastic service network model with application to hospital facilities

This paper presents a methodology for estimating expected utilization and service level for a class of capacity constrained service network facilities operating in a stochastic environment. A semi-Markov process describes the flows of customers (patients) through a network of service units. We model the case where one of the units has finite capacity and no queues are allowed to form. We show that the expected level of utilization and service can be computed from a simple linear relationship based on (a) the equilibrium arrival rates at each unit which are associated with the case of infinite capacity, (b) mean holding times for each unit, and (c) the probability that the finite capacity unit is at full capacity. We use Erlang's loss formula to calculate the probability of full capacity, show this calculation to be exact for two cases, and recommend its use as an approximation in the general case. We test the accuracy of the approximation on a set of published data. In the discussion, we present a technique for analyzing collected patient flow data using the results of this methodology.     

Optimal allocation of MRI scan capacity among competing hospital departments

We consider an MRI scanning facility run by a Radiology department. Several hospital departments compete for capacity and have private information regarding their demand for scans. The fairness of the capacity allocation by the Radiology department depends on the quality of the information provided by the hospital departments. We employ a generic Bayesian game approach that stimulates the disclosure of true demand (truth-telling), so that capacity can be allocated fairly. We derive conditions under which truth-telling is a Bayesian Nash equilibrium. The usefulness of the approach is illustrated with a numerical example.     

Uniqueness and characterization of capacity constrained Cournot-Nash equilibrium

We add capacity constraints to a multi-market Cournot model in which asymmetric firms have linear demand functions. We show that the problem is equivalent to maximizing a concave objective function over a convex region which ensures the existence of a unique capacity constrained Cournot-Nash equilibrium.     

Inventory control with Markovian capacity and the option of order rejection

We consider a stochastic inventory control problem with Markovian capacity and the option of order rejection. We show its optimal policy to be the combination of a capacity-dependent modified base-stock production policy and a capacity-dependent critical-point order acceptance policy. When capacity is stochastically monotone, we show that the policy parameters change over the current capacity level in an intuitive way. All these results can be extended to the infinite-horizon case, with or without cost discounting. Our computational study substantiates the benefits from both exploiting the Markovian behavior of the capacity and rejecting orders when necessary.     

Competitive capacity expansion under demand uncertainty

We analyze the capacity expansion behavior of firms in a duopoly faced with an uncertain new market. The market demand may be high or low with a given probability mass function. Firms obtain private information about the market size and build their capacity before the market demand is known. Once the demand is revealed, firms enter a capacity constrained price competition phase which determines their revenues. Two scenarios are considered: first, when firms choose their capacities simultaneously in the investment phase, and second, when they do so sequentially. For each case, we determine the unique symmetric Nash equilibrium. Excess capacity can occur in equilibrium in the industry. It is seen that preempting the competitor in the capacity expansion phase offers first mover benefits. We argue that the sequential moves game is more prone to equilibrium excess capacity compared to the simultaneous case. We show that preemption is a good strategy if the investing environment is either highly optimistic or highly pessimistic. If the industry outlook is only moderately optimistic, a capacity planner is still better off preempting his competitor, however, the industry may encounter overcapacity as a consequence.     

Hospital capacity,capability, and emergency preparedness

Disasters often result in shifts in hospital capacity utilization. Emergency preparedness plans must recognize capacity at the service-line level. This information can provide an additional level of detail to better design response activities and develop cost-effective disaster response plans. We model a possible preparedness plan for Florida hospitals in the case of a major disaster. We model a hurricane event because, in addition to its similarity to other disasters, it provides enough warning for substantive preparation activities. Following Johansen, we measure capacity in a frontier setting using data envelopment analysis. We also use a criterion of economic capability to ensure that a Pareto Optimal situation can be maintained. Information on hospital capacity, patient characteristics of inpatient discharges, and financial performance was merged to perform this study. Our findings suggest there is not enough excess capacity for some specialized services in Florida. However, possible evacuation policies can still be derived from our findings satisfying medical and economic capabilities.