Two-warehouse production model for deteriorating inventory items with stock-dependent demand under inflation over a random planning horizon

This paper develops a production-inventory model for a deteriorating item with stock-dependent demand under two storage facilities over a random planning horizon, which is assumed to follow exponential distribution with known parameter. The effects of learning in set-up, production, selling and reduced selling is incorporated. Different inflation rates for various inventory costs and time value of money are also considered. A hybrid genetic algorithm is designed to solve the optimization problem which is hard to solve with existing algorithms due to the complexity of the decision variable. To illustrate the model and to show the effectiveness of the proposed approach a numerical example is provided. A sensitivity analysis of the optimal solution with respect to the parameters of the system is carried out.     

Optimal pricing,production, and inventory for deteriorating items under demand uncertainty: The finite horizon case

This paper aims to investigate the joint dynamic pricing and production decisions of deteriorating items with uncertain demand over a finite selling season, where the demand is price sensitive and the potential demand is characterized by a stochastic process. The stocks deteriorate physically at a constant fraction of the on-hand inventory. A joint dynamic pricing and production problem to maximize the total expected profit is modeled as a stochastic optimal control problem. We derive the closed-form solutions, which are in time-dependent linear feedback form of the inventory level when it is either positive or negative. It is shown that the manufacturer always benefits from a reduction in the volatility of potential market demand. In addition, to highlight the effectiveness of the joint dynamic strategy, we also consider the case of optimal production with a static price. A numerical example is presented to illustrate the validity of the optimal control policy, and sensitivity analysis on major parameters is performed to provide more managerial insights into deteriorating items.     

Exact formulation of stochastic EMQ model for an unreliable production system

The purpose of this paper is to present an exact formulation of stochastic EMQ model for an unreliable production system under a general framework in which the time to machine failure, corrective (emergency) and preventive (regular) repair times are assumed to be random variables. For exact financial implications of the lot-sizing decisions, the EMQ model is formulated based on the net present value (NPV) approach. Then, by taking limitation on the discount rate, the traditional long-run average cost model is obtained. The criteria for the existence and uniqueness of the optimal production time in both the models are derived under general failure and specific repair time distributions. Numerical examples are devoted to find the optimal production policies of the developed models and examine the sensitivity of the parameters involved. Computational results show that the optimal decision based on the NPV approach is superior to that based on the long-run average cost approach, though the performance level strongly depends on the pertinent failure and repair time distributions.     

A hierarchical location model for public facility planning

In this article, we present a discrete hierarchical location model for public facility planning. The main features of the model are: an accessibility maximization objective; several levels of demand and of facilities; a nested hierarchy of facilities (i.e. a facility of a given level can serve demand of equal and lower levels); maximum and minimum capacity constraints; and user-to-facility assignment constraints. The latter include single-assignment and closest-assignment constraints, as well as a new type of constraints called path-assignment constraints. Their purpose is to enforce some desirable properties for the spatial pattern of assignments. If they are not included, model solutions are difficult to interpret and to explain in a public facility planning context, therefore being less likely to be accepted by the users. The usefulness of the model is illustrated through a real-world application to school network planning.     

A fuzzy genetic algorithm with varying population size to solve an inventory model with credit-linked promotional demand in an imprecise planning horizon

A genetic algorithm (GA) with varying population size is developed where crossover probability is a function of parents’ age-type (young, middle-aged, old, etc.) and is obtained using a fuzzy rule base and possibility theory. It is an improved GA where a subset of better children is included with the parent population for next generation and size of this subset is a percentage of the size of its parent set. This GA is used to make managerial decision for an inventory model of a newly launched product. It is assumed that lifetime of the product is finite and imprecise (fuzzy) in nature. Here wholesaler/producer offers a delay period of payment to its retailers to capture the market. Due to this facility retailer also offers a fixed credit-period to its customers for some cycles to boost the demand. During these cycles demand of the item increases with time at a decreasing rate depending upon the duration of customers’ credit-period. Models are formulated for both the crisp and fuzzy inventory parameters to maximize the present value of total possible profit from the whole planning horizon under inflation and time value of money. Fuzzy models are transferred to deterministic ones following possibility/necessity measure on fuzzy goal and necessity measure on imprecise constraints. Finally optimal decision is made using above mentioned GA. Performance of the proposed GA on the model with respect to some other GAs are compared.     

A stochastic control model of investment,production, and consumption on a finite horizon

In this paper, we consider a stochastic control problem on a finite time horizon. The unit price of capital obeys a logarithmic Brownian motion, and the income from production is also subject to the random Brownian fluctuations. The goal is to choose optimal investment and consumption policies to maximize the finite horizon expected discounted hyperbolic absolute risk aversion utility of consumption. A dynamic programming principle is used to derive a time‐dependent Hamilton–Jacobi–Bellman equation. The Leray–Schauder fixed point theorem is used to obtain existence of solution of the HJB equation. At last, we derive the optimal investment and consumption policies by the verification theorem. The main contribution in this paper is the use of PDE technique to the finite time problem for obtaining optimal polices. Copyright © 2014 John Wiley & Sons, Ltd.     

A finite horizon model for repairable systems with repair restrictions

In this paper, we will present a new finite horizon repair/replacement decision model and derive the structure of the optimal policy for components that have a failure intensity that is a non-decreasing function of the number of times the component has been repaired, and independent of the component's age. Furthermore, the component has physical restrictions on the number of times it can be repaired, after which the only feasible decision is to replace the component. The fundamentals of this new decision model are based on the outcomes of several case studies done by the authors. Besides presenting the model and showing the structure of the optimal policy, the model will be applied to a real industry data set, and its results discussed.     

Optimal scheduling of inspection times in a production process with a finite planning horizon

Inspection models applicable to a finite planning horizon are developed for the following lifetime distributions: uniform, exponential, and Weibull distribution. For a given lifetime distribution, maximization of profit is used as the sole optimization criterion for determining an optimal planning horizon over which a system may be operated as well as ideal inspection times. Illustrative examples (focusing on the uniform and Weibull distributions and using Mathematica programs) are given. For some situations, evenly spreading inspections over the entire planning horizon are seen to result in the attainment of desirable profit levels over a shorter planning horizon. Scope for further research is given as well. Copyright © 2016 John Wiley & Sons, Ltd.     

On a finite horizon production lot size inventory model for deteriorating items: An optimal solution

A generalized production lot size inventory model for deteriorating items over a finite planning horizon is considered. The demand, production, and deteriorating rates are assumed to be known and continuous functions of time. Shortages are allowed and completely backlogged. The conditions under which the system total cost attains its (unique) global minimum are derived. An example which illustrate the applicability of theoretical results is also introduced.     

Lot sizing for products with finite demand horizon and periodic review inventory policy

This paper analyzes and develops an efficient algorithm for solving a simple generalization of the classical economic order quantity (EOQ) model where the demand horizon is finite and a periodic review inventory policy is followed. Such problems could arise in production shops where the product under consideration is going to be discontinued and/or replaced by some other product after a certain known number of periods. Such problems could also arise as subproblems in large prodution-inventory problems were demand rate is constant in some periods and varying in others.     

On the single item fill rate for a finite horizon

We investigate expressions for expected item fill rate in a periodic inventory system. The typical treatment of fill rate found in many operations management texts assumes infinite horizon, independent and stationary demand. For the case when the horizon is finite, we show that the expected value of the actual fill rate is greater than the value given by the infinite horizon expression. The implication of our results is that an inventory manager in a finite horizon situation who uses the infinite horizon expression to set stocking levels will achieve a higher than desired expected fill rate at greater than necessary inventory expense.     

Time-dependent and independent control rules for coordinated production and pricing under demand uncertainty and finite planning horizons

We address the effect of uncertainty on a manufacturer’s dynamic production and pricing decisions over a finite planning horizon. The demand for products, which depends on their price, is characterized by two stochastic processes: potential demand and customer price sensitivity. An optimal policy for coordinating production and pricing is a time-dependent feedback rule with respect to the state of the manufacturer’s inventories. We show that when the volatility of customer sensitivity to the product price is negligible, the optimal policy can be obtained analytically. Moreover, our simulations demonstrate that the volatility of stochastic customer price sensitivity does not have a strong effect on the manufacturer’s expected profit. Therefore, the solution derived for the case of customer price sensitivity with zero volatility can serve as a good approximation heuristic for the optimal policy if the true volatility of customer price sensitivity is within 40 % of its mean and the volatility of potential demand is within 25 % of its mean. Moreover, under these conditions, a simplified, time-independent control rule deteriorates expected profits by only 1.5 %.     

A robust decomposition method for the analysis of production lines with unreliable machines and finite buffers

We consider production lines consisting of a series of machines separated by finite buffers. The processing time of each machine is deterministic and all the machines have the same processing time. All machines are subject to failures. As is usually the case for production systems we assume that the failures are operation dependent [3,7]. Moreover, we assume that the times to failure and the repair times are exponentially distributed. To analyze such systems, a decomposition method was proposed by Gershwin [13]. The computational efficiency of this method was later significantly improved by the introduction of the socalled DDX algorithm [5,6]. In general, this method provides fairly accurate results. There are, however, cases for which the accuracy of this decomposition method may not be acceptable. This is the case when the reliability parameters (average failure time and average repair time) of the different machines have different orders of magnitude. Such a situation may be encountered in real production lines. In [8], an improvement of Gershwin's original decomposition method has been proposed that in general provides more accurate results in the above mentioned situation. This other method is referred to as the Generalized Exponential (GE) method. The basic difference between the GEmethod and that of Gershwin is that it uses a twomoment approximation instead of a singlemoment approximation of the repair time distributions of the equivalent machines. There are, however, still cases for which the accuracy of the GEmethod is not as good as expected. This is the case, for example, when the buffer sizes are too small in comparison with the average repair time. We present in this paper a new decomposition method that is based on a better approximation of the repair time distributions. This method uses a threemoment approximation of the repair time distributions of the equivalent machines. Numerical results show that the new method is very robust in the sense that it seems to provide accurate results in all situations.     

A forward algorithm and planning horizon procedure for the production smoothing problem without inventory

We develop a forward algorithm for solving and finding planning horizons for the infinite horizon version of the deterministic production smoothing problem without inventory. This model approximates the real world situation for highly obsolescent or perishable commodities such as newspapers and fresh produce. Computational results show that the algorithm is linear in problem length while linear programming is at least quadratic.     

Decision/forecast horizon for a single facility dynamic location/relocation problem

Procedures to solve finite horizon dynamic location/relocation problems have been reported in the literature by many authors. This paper provides several decision/forecast horizon results for a single facility dynamic location/relocation problem; these results are helpful in finding optimal initial decisions for the infinite horizon problem by using information only for a finite horizon.     

A general model for the undesirable single facility location problem

In this paper, a finite set in which an optimal solution for a general Euclidean problem of locating an undesirable facility in a polygonal region, is determined and can be found in polynomial time. The general problem we propose leads us, among others, to several well-known problems such as the maxisum, maximin, anticentdian or r-anticentrum problem.     

Analysis of a gas field development model with an infinite planning horizon

We consider a nonlinear optimal control problem with an infinite planning horizon, which extends a dynamic gas field development model by taking into account a gas price forecast. (The prices varies in time.) The solution is constructed on the basis of the Pontryagin maximum principle. To prove the optimality of the extremal solution, we use the theorem on sufficient optimality conditions in terms of constructions of the Pontryaginmaximum principle. We discuss the problem of constructing an optimal solution by dynamic programming.     

Optimal internal pricing and capacity planning for service facility with finite buffer

An economic model is developed for exploring optimal internal pricing and capacity planning for service facility with finite buffer capacity. Because of the limited buffer capacity, jobs finding the system full upon their arrival would be rejected. Such rejections create a gap between the value collectively perceived by users and the actual achievement of the organizational value. This gap, called a loss externality, has never been studied before and plays an important role for designing optimal pricing scheme. In general, the underlying economic structure may involve multiple equilibria and it is unclear whether or not the system can be controlled through internal pricing. In this regard, a sufficient condition is given under which the system administrator can find two separate prices for accepted and rejected users at any demand level to be desired so that the desired demand level becomes the unique equilibrium of the system. For a short-run problem, it is shown that the optimal pricing scheme can be expressed as the sum of the congestion and the loss externalities. For a long-run problem, the optimal pricing scheme is expressed in a unified manner so that a structural relationship between the short-run problem and the long-run problem at optimality can be readily observed. A necessary and sufficient condition is also given for the marginal capacity pricing to be optimal, i.e., the optimal long-run pricing consists of the marginal cost for processing capacity and the marginal cost for buffer capacity without involving any externality at all.     

Inventory decisions for a finite horizon problem with product substitution options and time varying demand

The inventory control of substitutable products has been recognized as a problem worthy of study in the operations management literature. Product substitution provides flexibility in supply chain management and enhances response time in production control. This paper proposes a finite horizon inventory control problem for two substitutable products, which are ordered jointly in each replenishment epoch. Demand for the products are assumed to be time–varying. In case of a stock–out for one of the products, its demand is satisfied by using the stock of the other product. The optimal ordering schedule, for both products, that minimizes the total cost over a finite planning horizon is derived. Numerical examples along with sensitivity analyses are also presented.     

Group technology in production management: The short horizon planning level

We consider a set of parts divided into subsets called part types, determined in such a way that the parts belonging to the same part type are manufactured using the same sequence of tasks (i.e. the same working process). We are looking for a partition of the set of part types into subsets called part families, and for a partition of the set of tasks into subsets called production subsystems defined as follows: (1) the number of part families and the number of production subsystems are equal, (2) one (one only one) production subsystem corresponds to each part family, (3) one (and only one) part family corresponds to each production subsystem, (4) the previous partitions minimize the number of tasks performed in a production subsystem different from that which corresponds to the part family containing the part involved. We give a fast algorithm which leads to a good solution depending on the initial set of part families. We also propose an algorithm to find a ‘good’ initial set of part families.