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In this paper, we investigate the properties of the sampled version of the fictitious play algorithm, familiar from game theory, for games with identical payoffs, and propose a heuristic based on fictitious play as a solution procedure for discrete optimization problems of the form max {u(y) y = (y1,…yn ∈ y1 x … x yn} i.e., in which the feasible region is a Cartesian product of finite sets y1 i ∈ N = (1, …, n). The contributions of this paper are twofold. In the first part of the paper, we broaden the existing results on convergence properties of the fictitious play algorithm on games with identical payoffs to include an approximate fictitious play algorithm that allows for errors in players 'best replies. Moreover, we introduce sampling-based approximate fictitious play that possesses the above convergence properties, and at the same time provides a computationally efficient method for implementing fictitious play. In the second part of the paper, we motivate the use of algorithms based on sampled fictitious play to solve optimization problems in the above form with particular focus on the problems in which the objective function u(·) comes from a "black box," such as a simulation model, where significant computational effort is required for each function evaluation. [ABSTRACT FROM AUTHOR]

This paper proposes a simulation-based optimization (SO) method that enables the efficient use of complex stochastic urban traffic simulators to address various transportation problems. It presents a metamodel that integrates information from a simulator with an analytical queueing network model. The proposed metamodel combines a general-purpose component (a quadratic polynomial), which provides a detailed local approximation, with a physical component (the analytical queueing network model), which provides tractable analytical and global information. This combination leads to an SO framework that is computationally efficient and suitable for complex problems with very tight computational budgets. We integrate this metamodel within a derivative-free trust region algorithm. We evaluate the performance of this method considering a traffic signal control problem for the Swiss city of Lausanne, different demand scenarios, and tight computational budgets. The method leads to well-performing signal plans. It leads to reduced, as well as more reliable, average travel times. [ABSTRACT FROM AUTHOR]

Robust optimization is a methodology that has gained a lot of attention in the recent years. This is mainly due to the simplicity of the modeling process and ease of resolution even for large scale models. Unfortunately, the second property is usually lost when the cost function that needs to be "robustified" is not concave (or linear) with respect to the perturbing parameters. In this paper we study robust optimization of sums of piecewise linear functions over polyhedral uncertainty set. Given that these problems are known to be intractable, we propose a new scheme for constructing conservative approximations based on the relaxation of an embedded mixed-integer linear program and relate this scheme to methods that are based on exploiting affine decision rules. Our new scheme gives rise to two tractable models that, respectively, take the shape of a linear program and a semidefinite program, with the latter having the potential to provide solutions of better quality than the former at the price of heavier computations. We present conditions under which our approximation models are exact. In particular, we are able to propose the first exact reformulations for a robust (and distributionally robust) multi-item newsvendor problem with budgeted uncertainty set and a reformulation for robust multiperiod inventory problems that is exact whether the uncertainty region reduces to a L1-norm ball or to a box. An extensive set of empirical results will illustrate the quality of the approximate solutions that are obtained using these two models on randomly generated instances of the latter problem. [ABSTRACT FROM AUTHOR]

In this paper, we propose a methodology for constructing uncertainty sets within the framework of robust optimization for linear optimization problems with uncertain parameters. Our approach relies on decision maker risk preferences. Specifically, we utilize the theory of coherent risk measures initiated by Artzner et al. (1999) [Artzner, P., F. Delbaen, J. Eber, D. Heath. 1999. Coherent measures of risk. Math. Finance 9 203-228.], and show that such risk measures, in conjunction with the support of the uncertain parameters, are equivalent to explicit uncertainty sets for robust optimization. We explore the structure of these sets in detail. In particular, we study a class of coherent risk measures, called distortion risk measures, which give rise to polyhedral uncertainty sets of a special structure that is tractable in the context of robust optimization. In the case of discrete distributions with rational probabilities, which is useful in practical settings when we are sampling from data, we show that the class of all distortion risk measures (and their corresponding polyhedral sets) are generated by a finite number of conditional value-at-risk (CVaR) measures. A subclass of the distortion risk measures corresponds to polyhedral uncertainty sets symmetric through the sample mean. We show that this subclass is also finitely generated and can be used to find inner approximations to arbitrary, polyhedral uncertainty sets. [ABSTRACT FROM AUTHOR]

In this paper, we examine the cross-efficiency concept in data envelopment analysis (DEA). Cross efficiency links one decision-making unit's (DMU) performance with others and has the appeal that scores arise from peer evaluation. However, a number of the current cross-efficiency approaches are flawed because they use scores that are arbitrary in that they depend on a particular set of optimal DEA weights generated by the computer code in use at the time. One set of optimal DEA weights (possibly out of many alternate optima) may improve the cross efficiency of some DMUs, but at the expense of others. While models have been developed that incorporate secondary goals aimed at being more selective in the choice of optimal multipliers, the alternate optima issue remains. In cases where there is competition among DMUs, this situation may be seen as undesirable and unfair. To address this issue, this paper generalizes the original DEA cross-efficiency concept to game cross efficiency. Specifically, each DMU is viewed as a player that seeks to maximize its own efficiency, under the condition that the cross efficiency of each of the other DMUs does not deteriorate. The average game cross efficiency score is obtained when the DMU's own maximized efficiency scores are averaged. To implement the DEA game cross-efficiency model, an algorithm for deriving the best (game cross-efficiency) scores is presented. We show that the optimal game cross-efficiency scores constitute a Nash equilibrium point. [ABSTRACT FROM AUTHOR]

High sensitivity to initial conditions is generally viewed as a drawback of tree search methods because it leads to erratic behavior to be mitigated somehow. In this paper we investigate the opposite viewpoint and consider this behavior as an opportunity to exploit. Our working hypothesis is that erraticism is in fact just a consequence of the exponential nature of tree search that acts as a chaotic amplifier, so it is largely unavoidable. We propose a bet-and-run approach to actually turn erraticism to one's advantage. The idea is to make a number of short sample runs with randomized initial conditions, to bet on the "most promising" run selected according to certain simple criteria, and to bring it to completion. Computational results on a large testbed of mixed integer linear programs from the literature are presented, showing the potential of this approach even when embedded in a proof-of-concept implementation. [ABSTRACT FROM AUTHOR]

In this paper we discuss multistage programming with the data process subject to uncertainty. We consider a situation where the data process can be naturally separated into two components: one can be modeled as a random process, with a specified probability distribution, and the other one can be treated from a robust (worst-case) point of view. We formulate this in a time consistent way and derive the corresponding dynamic programming equations. To solve the obtained multistage problem, we develop a variant of the stochastic dual dynamic programming method. We give a general description of the algorithm and present computational studies related to planning of the Brazilian interconnected power system. [ABSTRACT FROM AUTHOR]

One approach to coping with the apparent difficulty of many schedule-optimization problems, such as occur in machine shops and computer processing, is to devise efficient algorithms that find schedules guaranteed to be "near-optimal." This paper presents an introduction to this approach by describing its application to a well-known multiprocessor scheduling model and illustrating the variety of algorithms and results that are possible. The paper concludes with a brief survey of what has been accomplished to date in the area of scheduling using this approach. [ABSTRACT FROM AUTHOR]

This paper examines the nature and promise of the revolutionary approach to social problems that is represented by systems analysis. It first describes social decisions and how they are usually made. Then it examines systems analysis, considers its worth for social decisions, and describes some prerequisites required for it to be useful. Finally, the paper concludes that systems analysis of social problems can contribute substantially to the intelligent allocation of society's resources. [ABSTRACT FROM AUTHOR]

We consider a revenue-maximizing make-to-order manufacturer that serves a market of price- and delay-sensitive customers and operates in an environment in which the market size varies stochastically over time. A key feature of our analysis is that no model is assumed for the evolution of the market size. We analyze two main settings: (i) the size of the market is observable at any point in time; and (ii) the size of the market is not observable and hence cannot be used for decision making. We focus on high-volume systems that are characterized by large processing capacities and market sizes, and where the latter fluctuate on a slower timescale than that of the underlying production system dynamics. We develop an approach to tackle such problems that is based on an asymptotic analysis and that yields near-optimal policy recommendations for the original system via the solution of a stochastic fluid model. [ABSTRACT FROM AUTHOR]

Model reference adaptive search (MRAS) for solving global optimization problems works with a parameterized probabilistic model on the solution space and generates at each iteration a group of candidate solutions. These candidate solutions are then used to update the parameters associated with the probabilistic model in such a way that the future search will be biased toward the region containing high-quality solutions. The parameter updating procedure in MRAS is guided by a sequence of implicit probabilistic models we call reference models. We provide a particular algorithm instantiation of the MRAS method, where the sequence of reference models can be viewed as the generalized probability distribution models for estimation of distribution algorithms (EDAs) with proportional selection scheme. In addition, we show that the model reference framework can also be used to describe the recently proposed cross-entropy (CE) method for optimization and to study its properties. Hence, this paper can also be seen as a study on the effectiveness of combining CE and EDAs. We prove global convergence of the proposed algorithm in both continuous and combinatorial domains, and we carry out numerical studies to illustrate the performance of the algorithm. [ABSTRACT FROM AUTHOR]

We study the classical multistage lot sizing problem that arises in distribution and inventory systems. A celebrated result in this area is the 94% and 98% approximation guarantee provided by power-of-two policies. In this paper, we propose a simple randomized rounding algorithm to establish these performance bounds. We use this new technique to extend several results for the capacitated lot sizing problems to the case with submodular ordering cost. For the joint replenishment problem under a fixed base period model, we construct a 95.8% approximation algorithm to the (possibly dynamic) optimal lot sizing policy. The policies constructed are stationary but not necessarily of the power-of-two type. This shows that for the fixed based planning model, the class of stationary policies is within 95.8% of the optimum, improving on the previously best known 94% approximation guarantee. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization model of incorporating competence sets of group decision makers to maximize the total benefit of the whole group. Such an incorporation model is formulated as finding a deduction graph linked from the nodes of existing competencies to the nodes of desired competencies. Compared with other methods treating competence set problems (Yu and Zhang 1991, Li and Yu 1994, and Shi and Yu 1996), the proposed model can solve problems involving multiple decision makers; in addition it allows the network to be cyclic and to contain compound nodes. [ABSTRACT FROM AUTHOR]

This is the first of two papers dealing with the establishment of shadow prices for measures of effectiveness in an optimization-based combat model. In this paper we explain how the requirement for the analysis arose, and we show how to build a simple linear model that produces shadow prices for kill requirements. When the model is further specialized, these shadow prices become the classical eigenvalue weights familiar from Lanchester theory. [ABSTRACT FROM AUTHOR]

This paper presents a method for finding optimal flows in a dynamic network with random inputs into the system and congestion limits on flow. This model has been used in deterministic settings to represent dynamic traffic assignment and job shop routing. This paper builds on the deterministic results to show that a globally optimal solution in the stochastic problem may be obtained by a sequence of linear optimizations. A decomposition algorithm for this procedure is presented that efficiently solves problems with large-scale deterministic equivalents of up to 66,000 variables. [ABSTRACT FROM AUTHOR]

This paper treats the problem of locating a given number of interrelated physical facilities in a single- or multi-story building as an optimization model such that the weighted sum of the distances along orthogonal directions is minimized, and describes a constructive heuristic algorithm for the one-dimensional model. This linear placement algorithm is then extended to higher dimensions for placing uniform-size equipment modules at homogeneous fixed locations. The algorithm accommodates lower-bound constraints for the linear case efficiently and produces near-optimal solutions. Its significance, as demonstrated by comparison with some existing heuristic procedures, is that it has the capability of handling efficiently problems with large numbers of equal-size facilities whose solutions are computationally intractable using the present optimum-producing methods. [ABSTRACT FROM AUTHOR]

This paper considers the problem of determining the policy to be followed by each member of a team so as to optimize some over-all criterion function characterizing the objective of the team. The reasoning of dynamic programming is employed to formulate the optimization problem, and the discussion is couched in the terms of a classical network problem. The paper first formulates a simple bipartite network optimization and then presents a number of generalizations. Finally, it gives two slightly different computational solutions to an example bipartite optimization problem. [ABSTRACT FROM AUTHOR]

We study a class of two-echelon serial systems with identical ordering/production capacities or limits for both echelons. Demands are assumed to be integer valued. For the case where the lead time to the upstream echelon is one period, the optimality of state-dependent modified echelon base-stock policies is proved using a decomposition approach. For the case where the upstream lead time is two periods, we introduce a new class of policies called "two-tier base-stock policies," and prove their optimality. Some insight about the inventory control problem in N echelon serial systems with identical capacities at all stages and arbitrary lead times everywhere is also provided. We argue that a generalization of two-tier base-stock policies, which we call "multitier base-stock policies," are optimal for these systems; we also provide a bound on the number of parameters required to specify the optimal policy. [ABSTRACT FROM AUTHOR]

We seek optimal inventory levels and prices of multiple products in a given assortment in a newsvendor model (single period, stochastic demand) under price-based substitution, but not stockout-based substitution. We address a demand model involving multiplicative uncertainty, motivated by market share models often used in marketing. The pricing problem that arises is known not to be well behaved in the sense that, in its deterministic version, the objective function is not jointly quasi-concave in prices. However, we find that the objective function is still reasonably well behaved in the sense that there is a unique solution to the first-order conditions, and this solution is optimal for our problem. [ABSTRACT FROM AUTHOR]

We analyze the problem of debt issuance through the sale of innovative financial products. The problem is broken down to questions of designing the financial products, specifying the debt structure with the amount issued in each product. and determining an optimal level of financial leverage. We formulate a hierarchical optimization model to integrate these three issues and provide constructive answers. Input data for the models are obtained from Monte Carlo simulation procedures that generate scenarios of holding period returns of the designed products.The hierarchical optimization model is specialized for the problem of issuing a portfolio of callable bonds to fund mortgage assets. The upper level optimization program is multimodal, and a tabu search procedure is developed for its solution. Empirical results illustrate the efficacy of the developed models in designing the appropriate structure of the callable bonds and making optimal allocations of equity and debt among the designed products. Computational results with the implementation of tabu search on both serial and parallel computers are also presented. [ABSTRACT FROM AUTHOR]

Linear regression models are traditionally built through trial and error to balance many competing goals such as predictive power, interpretability, significance, robustness to error in data, and sparsity, among others. This problem lends itself naturally to a mixed integer quadratic optimization (MIQO) approach but has not been modeled this way because of the belief in the statistics community that MIQO is intractable for large scale problems. However, in the last 25 years (1991-2015), algorithmic advances in integer optimization combined with hardware improvements have resulted in an astonishing 450 billion factor speedup in solving mixed integer optimization problems. We present an MIQO-based approach for designing high quality linear regression models that explicitly addresses various competing objectives and demonstrate the effectiveness of our approach on both real and synthetic data sets. [ABSTRACT FROM AUTHOR]

This paper deals with a serial production line where each of the stages can be either a processing or an assembly stage. A processing stage processes outputs from an upstream stage and transforms them into inputs for the downstream stage. Each processing stage is subject to yield losses. At an assembly stage, a batch of identical input components is processed and the nondefective components are then combined with the output from the upstream stage. The processing operation for the input component is imperfect. For such a system, we characterize the form of the optimal policy for input quantity at each stage in the production line, when stochastically proportional yield is assumed at each stage, i.e., the distribution of the yield fraction is assumed to be independent of the lot size. The optimal policy exhibits some form of a critical number policy. For production lines consisting of assembly stages only, the critical numbers can be computed easily. When yields are of the "exponential" type, the critical number policy is still optimal, although there are interesting differences in the policies for the different types of yield characterizations. [ABSTRACT FROM AUTHOR]

Economies of scale are fundamental to manufacturing operations. With respect to scheduling, this phenomenon manifests itself in efficiencies gained from grouping similar jobs together. This paper reviews the rapidly growing literature on single-machine scheduling models that incorporate benefits from job grouping. We focus on three basic models known as family scheduling with item availability, family scheduling with batch availability, and batch processing. We present known results and introduce new results, and we pay special attention to key theoretical properties and the use of these properties in optimization procedures. [ABSTRACT FROM AUTHOR]

We consider a production/distribution system represented by a general directed acyclic network. Each node is associated with a specific "product" at a given location and/or production stage. An arc (i, j) indicates that item i is used to "produce" item j. External demands may occur at any of the network's nodes. These demands occur continuously at item-specific constant rates. Components may be assembled in any given proportions. The cost structure consists of inventory carrying, variable, and fixed production/distribution costs. The latter depend, at any given replenishment epoch, on the specific set of items being replenished, according to an arbitrary set function merely assumed to be monotone and submodular. It has been shown that a simply structured, so-called power-of-two policy is guaranteed to come within 2% of a lower bound for the minimum cost. In this paper, we derive efficient algorithms for the computation of an optimal power-of-two policy, possibly in combination with this lower bound. These consist of a limited number of polymatroidal maximum flow calculations in networks closely associated with the original production/distribution network. [ABSTRACT FROM AUTHOR]

Over the past decade, optimization models have been widely used to help select bond portfolios. Several different formulations are popular. The purposes of this paper are to clarify the basic structures of the models, to explain the relationships among them, and to assess their strengths and weaknesses. [ABSTRACT FROM AUTHOR]

Many models used in policy or systems analysis either cannot be validated in any fully adequate sense, such as by comparing them with actual data, or could adequately be validated but have not been For example, in the area of combat analysts, the central models are arguably almost entirely unvalidated and most will never be susceptible to adequate validation Nevertheless, such models are often used and can be used fruitfully, even though we have no theory for how to use them or how to interpret and place value on the results they produce This paper takes a step toward providing such a theory by focusing on the logic that should govern the use of inadequately validated models and the costs and benefits of using them. To this end, it identifies and evaluates six legitimate uses to which such models can be put. [ABSTRACT FROM AUTHOR]

This paper studies how sampling variation in component reliability estimates affects the computation of system reliability that uses these estimates as input. If these components are in parallel they lead to an understatement of system reliability as the number of components k increases. In series, they lead to an overstatement as k increases. For a sample component reliability based on n observations, our results show that lim[sub k arrow right Infinity] (k[sup 2]/n) = 0 is required for the sample system reliability to converge in probability to the true system reliability. [ABSTRACT FROM AUTHOR]

This paper presents a branch-and-bound algorithm for solving fixed charge transportation problems where not all cells exist. The algorithm exploits the absence of full problem density in several ways, thus yielding a procedure which is especially applicable to solving real world problems which are normally quite sparse. Additionally, streamlined new procedures for pruning the decision tree and calculating penalties are presented. We present computational experience with both a set of large test problems and a set of dense test problems from the literature. Comparisons with other codes are uniformly favorable to the new method, which runs more than twice as fast as the best alternative. [ABSTRACT FROM AUTHOR]

This paper discusses the formulation and solution of a mathematical model of the selection and staging of bridge replacements in rural road networks. Both relaxation and decomposition strategies are used. First, a relaxation of the problem into a decomposable shortest path problem allows the identification of the bridge replacement alternatives which are most promising with respect to user cost. Next, the planning horizon is divided into time periods which are then ordered according to their relative importance. Finally, another decomposition in the form of a lexicographic optimization is used to select and schedule the bridge replacements. Within this lexicographic optimization a sequence of computationally feasible integer programming problems is solved. [ABSTRACT FROM AUTHOR]

In this paper, we develop tour heuristic methods to obtain approximate solutions to the multidimensional 0-1 knapsack problem. The four methods are tested on a number of problems of various sizes. The solutions are compared to the rigorous optimum as well as to a heuristic method of Toyoda. They are statistically better than the latter, with average relative errors of the order of less than 1%. [ABSTRACT FROM AUTHOR]

Expressions are derived for the expected number and expected wait in queue for the following generalizations of the GI/G/1 queue: (1) arrivals in batches of random size, (2) service in batches of fixed size, (3) queues with added delay for the first customer in a busy period. These expressions contain the mean and variance of the idle time distribution. Results on the idle period moments, which were derived by the author in a previous paper, are used to obtain bounds for the expected wait and expected number in queue under two different assumptions on A (t), the interarrival time distribution. As an example of (3) we mention the queue where the server, once he becomes idle, does not restart until m customers have arrived (called the GI[sub m]/G/1 queue). Such a queue arises when one considers a class of optimization problems in single server queues. [ABSTRACT FROM AUTHOR]

A multistage decision problem is optimized using a new formulation of stochastic dynamic programming. The problem optimized in this paper concerns a semiconductor production process where the transitions at each work station are stochastic. The mathematical model employs at one stage a Markov decision process with an infinite number of substages and shows how this process may be compressed and handled as one stage in the larger problem. [ABSTRACT FROM AUTHOR]

The research results reported in this paper present a higher level generalization about deterministic procurement-inventory systems embracing multi-item and multisource characteristics. Lower ordered deterministic systems embracing single-item or single-source characteristics are also presented. Dynamic programming is used to find the optimal procurement and inventory policy for each system in the hierarchy. [ABSTRACT FROM AUTHOR]

This paper presents two versions of a method for solving large linear programs through decomposition. In both versions the problem is decomposed into subprograms as in the DANTZIG-WOLFE model, but each version uses a different type of linking program. The subprograms are first solved, and then the infeasibility of the resulting solution for the linking program is used to generate a price-adjustment process (much like a market mechanism), which gradually corrects the objective functions of the subprograms, until either some optimal solutions to the latter yield a feasible solution to the linking program (then the problem is solved), or evidence is obtained that the problem has no feasible solution. [ABSTRACT FROM AUTHOR]

Sequencing problems are among the most prominent problems studied in operations research, with primary application in, e.g., scheduling and routing. We propose a novel approach to solving generic sequencing problems using multivalued decision diagrams (MDDs). Because an MDD representation may grow exponentially large, we apply MDDs of limited size as a discrete relaxation to the problem. We show that MDDs can be used to represent a wide range of sequencing problems with various side constraints and objective functions, and we demonstrate how MDDs can be added to existing constraint-based scheduling systems. Our computational results indicate that the additional inference obtained by our MDDs can speed up a state-of-the art solver by several orders of magnitude, for a range of different problem classes. [ABSTRACT FROM AUTHOR]

Dike height optimization is of major importance to the Netherlands because a large part of the country lies below sea level, and high water levels in rivers can cause floods. Recently impovements have been made on the cost-benefit model introduced by van Dantzig after the devastating flood in the Netherlands in 1953. We consider the extension of this model to nonhomogeneous dike rings, which may also be applicable to other deltas in the world. A nonhomogeneous dike ring consists of different segments with different characteristics with respect to flooding and investment costs. The individual segments can be heightened independently at different moments in time and by different amounts, making the problem considerably more complex than the homogeneous case. We show how the problem can be modeled as a mixed-integer nonlinear programming problem, and we present an iterative algorithm that can be used to solve the problem. Moreover, we consider a robust optimization approach to deal with uncertainty in the model parameters. The method has been implemented and integrated in software, which is used by the government to determine how the safety standards in the Dutch Water Act should be changed. [ABSTRACT FROM AUTHOR]

We consider a system in which an order is placed every T periods to bring the inventory position up to the base stock S. We accept demand until the inventory position reaches a sales rejection threshold M. Our objective is to find the optimal values of S and M that minimize the long-run average cost per period. We establish the stationary distribution of our system and develop structural properties of the optimal solution that facilitate computation. In particular, we show that in an optimal solution, the optimal value of M is nonnegative under some reasonable conditions. Hence, in our model a mixture of backorders and lost sales may occur. Additionally, we compare our system against traditional systems in which demand during stockouts is either fully backordered or lost. [ABSTRACT FROM AUTHOR]

We study the modeling of nonconvex piecewise-linear functions as mixed-integer programming (MIP) problems. We review several new and existing MIP formulations for continuous piecewise-linear functions with special attention paid to multivariate nonseparable functions. We compare these formulations with respect to their theoretical properties and their relative computational performance. In addition, we study the extension of these formulations to lower semicontinuous piecewiselinear functions. [ABSTRACT FROM AUTHOR]

A methodology is given for modeling the dynamics of discrete-event stochastic systems as optimization problems. The intent is to provide a means to utilize the rich mathematical theory and algorithms of optimization in the study of this important class of systems. A procedure for mapping a simulation event relationship graph into a mixed-integer program is presented, along with examples of queueing networks and manufacturing systems that illustrate the approach. Several potential applications are examined, including automatic constraint generation for optimal resource scheduling, representations of max-plus algebra models for queueing system dynamics, response gradient estimation, and an unconventional technique for simulating queueing systems using virtual resources that are identified from the optimization models for these systems. [ABSTRACT FROM AUTHOR]

No good formal arguments exist for a central question in biology: Why, in species that have sexual reproduction, are there usually only "males" and "females"? We present a nonlinear optimization model that supports the conclusion that having only two sexes maximizes long-run viability. [ABSTRACT FROM AUTHOR]

We study echelon-stock (R, nQ) policies in a multistage, serial inventory system with compound Poisson demand. We provide a simple method for determining near-optimal control parameters. This is achieved in two steps. First, we establish lower and upper bounds on the cost function by over- and under-charging a penalty cost to each upstream stage for holding inadequate stock. Second, we minimize the bounds, which are simple, separable functions of the control parameters, to obtain heuristic solutions. We also provide an algorithm that guarantees an optimal solution at the expense of additional computational effort. A numerical study suggests that the heuristic solutions are easy to compute (even for systems with many stages) and are close to optimal. It also suggests that a traditional approach for determining the order quantities can be seriously suboptimal. All the results can be easily extended to the discrete-time case with independent, identically distributed demands. [ABSTRACT FROM AUTHOR]

The petroleum recovery process has two potential phases: primary recovery, during which the natural drive forces the oil to the well bore, and secondary recovery, during which a producer uses artificial means to recover the resource. A producer may choose to switch from primary to secondary recovery if the desired ultimate output is more than can be obtained by exploiting the natural drive, or when the desired current extraction rate is larger than can be obtained by the natural drive. These decisions are affected by the interaction between geological, technical and market conditions. We formulate an investment and production model that incorporates the relationships between extraction rates, investment decisions and cumulative recovery, and investigate a two-phase dynamic optimization problem. We do so by defining and analyzing a more general optimization problem and applying the results to our model. The major findings relate to the timing and size of investment, the rate and duration of secondary recovery, and the optimal switching policies from primary to secondary recovery. We discuss each of these issues in detail, and outline and explain quantitative policy recommendations. [ABSTRACT FROM AUTHOR]

We consider a two-person competitive problem in which one player (BLUE) wishes to move across a rectangular lattice in such a way as to avoid being ambushed by his opponent (RED), who has placed obstacles in BLUE's path. Under some conditions, optimal strategies for BLUE and RED are obtained and the value of the game is computed. [ABSTRACT FROM AUTHOR]

We consider a single-server queuing system with several classes of customers who arrive according to independent Poisson processes. The service time distributions are arbitrary, and we assume a linear cost structure. The problem is to decide, at the completion of each service and given the state of the system, which class (if any) to admit next into service. The objective is to maximize the expected net present value of service rewards received minus holding costs incurred over an infinite planning horizon, the interest rate being positive. One very special type of scheduling rule, called a modified static policy, simply enforces a (nonpreemptive) priority ranking except that certain classes are never served. It is shown that there is a modified static policy that is optimal, and a simple algorithm for its computation is presented. [ABSTRACT FROM AUTHOR]

Mathematical decision rules for the control of inventories in a large supply system are derived. These rules allow the minimization of total system shortages where this minimization is subject to specified system expenditures. Alternatively, the rules permit the minimization of system expenditures for a given level of system shortages. The mathematical model used to derive these rules includes the features random demands, random lead times, set-up and order costs, interest costs, obsolescence costs, physical holding costs, item unit costs, relative essentialities of items, and a measure of shortage costs which takes into account the durations that shortages persist. Derivation of the optimal rules employs an iterative solution of a nonlinear programming problem. Comparisons with presently-used decision rules for a large supply system indicate that significant improvements in system effectiveness can be obtained by the use of the optimal rules. [ABSTRACT FROM AUTHOR]

A complex system is to be placed in the field for a fixed period. During the period only the spares initially provided may be used to replace components that have failed Independence of failures is assumed among the essential components considered. Given the cost of components, the number of like components of each type simultaneously operating, the length of operation scheduled for each component, the failure distributions of components, a general mathematical solution is obtained for the composition of the spare parts kit which maximizes assurance of continued operation during the period, subject to a fixed budget for spares. Explicit formulas are obtained in the case of exponential failure distributions, constructive procedures in the case of monotone likelihood ratio densities. Fortunately, the identical mathematical model is applicable in determining the optimal allocation of redundancy in designing system reliability under a weight or cost restraint. [ABSTRACT FROM AUTHOR]