Flexibility and complexity in periodic distribution problems

In this paper, we explore trade‐offs between operational flexibility and operational complexity in periodic distribution problems. We consider the gains from operational flexibility in terms of vehicle routing costs and customer service benefits, as well as the costs of operational complexity in terms of modeling, solution methods, and implementation challenges for drivers and customers. The period vehicle routing problem (PVRP) is a variation of the classic vehicle routing problem in which delivery routes are constructed for a period of time; the PVRP with service choice (PVRP‐SC) extends the PVRP to allow service (visit) frequency to become a decision of the model. For the periodic distribution problems represented by PVRP and PVRP‐SC, we introduce operational flexibility levers and a set of quantitative measures to evaluate the trade‐offs between flexibility and complexity. We develop a Tabu Search heuristic to incorporate a range of operational flexibility options. We analyze the potential value and the increased operational complexity of the flexibility levers. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Optimal control of an M/G/1 queue with impatient priority customers

An optimal operating policy is characterized for the infinite‐horizon average‐cost case of a single server queueing control problem. The server may be turned on at arrival epochs or off at departure epochs. Two classes of customers, each of them arriving according to an independent Poisson processes, are considered. An arriving 1‐customer enters the system if the server is turned on upon his arrival, or if the server is on and idle. In the former case, the 1‐customer is selected for service ahead of those customers waiting in the system; otherwise he leaves the system immediately. 2‐Customers remain in the system until they complete their service requirements. Under a linear cost structure, this paper shows that a stationary optimal policy exists such that either (1) leaves the server on at all times, or (2) turns the server off when the system is empty. In the latter case, we show that the stationary optimal policy is a threshold strategy, this feature being commonplace in most of priority queueing systems and inventory models. However, the optimal policy in our model is determined by two thresholds instead of one. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 201–209, 2001     

Scheduling policies for an antiterrorist surveillance system

This article concerns scheduling policies in a surveillance system aimed at detecting a terrorist attack in time. Terrorist suspects arriving at a public area are subject to continuous monitoring, while a surveillance team takes their biometric signatures and compares them with records stored in a terrorist database. Because the surveillance team can screen only one terrorist suspect at a time, the team faces a dynamic scheduling problem among the suspects. We build a model consisting of an M/G/1 queue with two types of customers—red and white—to study this problem. Both types of customers are impatient but the reneging time distributions are different. The server only receives a reward by serving a red customer and can use the time a customer has spent in the queue to deduce its likely type. In a few special cases, a simple service rule—such as first‐come‐first‐serve—is optimal. We explain why the problem is in general difficult and we develop a heuristic policy motivated by the fact that terrorist attacks tend to be rare events. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

Scheduling a production–distribution system to optimize the tradeoff between delivery tardiness and distribution cost

We consider a make‐to‐order production–distribution system with one supplier and one or more customers. A set of orders with due dates needs to be processed by the supplier and delivered to the customers upon completion. The supplier can process one order at a time without preemption. Each customer is at a distinct location and only orders from the same customer can be batched together for delivery. Each delivery shipment has a capacity limit and incurs a distribution cost. The problem is to find a joint schedule of order processing at the supplier and order delivery from the supplier to the customers that optimizes an objective function involving the maximum delivery tardiness and the total distribution cost. We first study the solvability of various cases of the problem by either providing an efficient algorithm or proving the intractability of the problem. We then develop a fast heuristic for the general problem. We show that the heuristic is asymptotically optimal as the number of orders goes to infinity. We also evaluate the performance of the heuristic computationally by using lower bounds obtained by a column generation approach. Our results indicate that the heuristic is capable of generating near optimal solutions quickly. Finally, we study the value of production–distribution integration by comparing our integrated approach with two sequential approaches where scheduling decisions for order processing are made first, followed by order delivery decisions, with no or only partial integration of the two decisions. We show that in many cases, the integrated approach performs significantly better than the sequential approaches. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

The vehicle-routing problem with delivery and back-haul options

In this article we consider a version of the vehicle-routing problem (VRP): A fleet of identical capacitated vehicles serves a system of one warehouse and N customers of two types dispersed in the plane. Customers may require deliveries from the warehouse, back hauls to the warehouse, or both. The objective is to design a set of routes of minimum total length to serve all customers, without violating the capacity restriction of the vehicles along the routes. The capacity restriction here, in contrast to the VRP without back hauls is complicated because amount of capacity used depends on the order the customers are visited along the routes. The problem is NP-hard. We develop a lower bound on the optimal total cost and a heuristic solution for the problem. The routes generated by the heuristic are such that the back-haul customers are served only after terminating service to the delivery customers. However, the heuristic is shown to converge to the optimal solution, under mild probabilistic conditions, as fast as N^−0.5. The complexity of the heuristic, as well as the computation of the lower bound, is O(N³) if all customers have unit demand size and O(N³ log N) otherwise, independently of the demand sizes. © 1996 John Wiley & Sons, Inc.     

Value management of diagnostic equipment with cancelation,no‐show,and emergency patients

Magnetic resonance imaging and other multifunctional diagnostic facilities, which are considered as scarce resources of hospitals, typically provide services to patients with different medical needs. This article examines the admission policies during the appointment management of such facilities. We consider two categories of patients: regular patients who are scheduled in advance through an appointment system and emergency patients with randomly generated demands during the workday that must be served as soon as possible. According to the actual medical needs of patients, regular patients are segmented into multiple classes with different cancelation rates, no‐show probabilities, unit value contributions, and average service times. Management makes admission decisions on whether or not to accept a service request from a regular patient during the booking horizon to improve the overall value that could be generated during the workday. The decisions should be made by considering the cancelation and no‐show behavior of booked patients as well as the emergency patients that would have to be served because any overtime service would lead to higher costs. We studied the optimal admission decision using a continuous‐time discrete‐state dynamic programming model. Identifying an optimal policy for this discrete model is analytically intractable and numerically inefficient because the state is multidimensional and infinite. We propose to study a deterministic counterpart of the problem (i.e., the fluid control problem) and to develop a time‐based fluid policy that is shown to be asymptotically optimal for large‐scale problems. Furthermore, we propose to adopt a mixed fluid policy that is developed based on the information obtained from the fluid control problem. Numerical experiments demonstrate that this improved policy works effectively for small‐scale problems. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 287–304, 2016     

Evasion policies for a vessel being chased by pirate skiffs

Piracy attack is a serious safety problem for maritime transport worldwide. Whilst various strategic actions can be taken, such as rerouting vessels and strengthening navy patrols, this still cannot completely eliminate the possibility of a piracy attack. It is therefore important for a commercial vessel to be equipped with operational solutions in case of piracy attacks. In particular, the choice of a direction for rapidly fleeing is a critical decision for the vessel. In this article, we formulate such a problem as a nonlinear optimal control problem. We consider various policies, such as maintaining a straight direction or making turns, develop algorithms to optimize the policies, and derive conditions under which these policies are effective and safe. Our work can be used as a real‐time decision making tool that enables a vessel master to evaluate different scenarios and quickly make decisions.     

Simultaneous price,location, and capacity decisions on a line of time‐sensitive customers

Consider a monopolist who sells a single product to time‐sensitive customers located on a line segment. Customers send their orders to the nearest distribution facility, where the firm processes (customizes) these orders on a first‐come, first‐served basis before delivering them. We examine how the monopolist would locate its facilities, set their capacities, and price the product offered to maximize profits. We explicitly model customers' waiting costs due to both shipping lead times and queueing congestion delays and allow each customer to self‐select whether she orders or not, based on her reservation price. We first analyze the single‐facility problem and derive a number of interesting insights regarding the optimal solution. We show, for instance, that the optimal capacity relates to the square root of the customer volume and that the optimal price relates additively to the capacity and transportation delay costs. We also compare our solutions to a similar problem without congestion effects. We then utilize our single‐facility results to treat the multi‐facility problem. We characterize the optimal policy for serving a fixed interval of customers from multiple facilities when customers are uniformly distributed on a line. We also show how as the length of the customer interval increases, the optimal policy relates to the single‐facility problem of maximizing expected profit per unit distance. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

A new outranking‐based approach for assigning alternatives to ordered classes

We consider the problem of assigning alternatives evaluated on several criteria into ordered categories C₁,C₂,…,C_p. This problem is known as the multi‐criteria sorting problem and arises in many situations such as classifying countries into different risk levels based on economical and socio‐political criteria, evaluating credit applications of bank customers. We are interested in sorting methods that are grounded on the construction of outranking relations. Among these, the Electre Tri method requires defining multidimensional profiles that represent the “frontier” separating consecutive categories C_h and C_h+1, and assigns an alternative to categories according to how it compares to each of the profiles. The explicit specification of the profiles of consecutive categories can be difficult for decision makers. We develop a new outranking based sorting method that does not require the explicit definition of profiles. We instead require the decision maker to assign a subset of reference alternatives to the categories. To assign the remaining alternatives, each such alternative is compared to reference alternatives, and assigned to categories accordingly. © 2008 Wiley Periodicals, Inc. Naval Research Logistics 2009     

Generating Pareto‐optimal boundary points in multiparty negotiations using constraint proposal method

In this paper a constraint proposal method is developed for computing Pareto‐optimal solutions in multiparty negotiations over continuous issues. Constraint proposal methods have been previously studied in a case where the decision set is unconstrained. Here we extend the method to situations with a constrained decision set. In the method the computation of the Pareto‐optimal solutions is decentralized so that the DMs do not have to know each others' value functions. During the procedure they have to indicate their optimal solutions on different sets of linear constraints. When the optimal solutions coincide, the common optimum is a candidate for a Pareto‐optimal point. The constraint proposal method can be used to generate either one Pareto‐optimal solution dominating the status quo solution or several Pareto‐optimal solutions. In latter case a distributive negotiation among the efficient points can be carried out afterwards. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 210–225, 2001     

Optimal policies for M/M/m queue with two different kinds of (N,T)‐policies

In this paper, two different kinds of (N, T)‐policies for an M/M/m queueing system are studied. The system operates only intermittently and is shut down when no customers are present any more. A fixed setup cost of K > 0 is incurred each time the system is reopened. Also, a holding cost of h > 0 per unit time is incurred for each customer present. The two (N, T)‐policies studied for this queueing system with cost structures are as follows: (1) The system is reactivated as soon as N customers are present or the waiting time of the leading customer reaches a predefined time T, and (2) the system is reactivated as soon as N customers are present or the time units after the end of the last busy period reaches a predefined time T. The equations satisfied by the optimal policy (N*, T*) for minimizing the long‐run average cost per unit time in both cases are obtained. Particularly, we obtain the explicit optimal joint policy (N*, T*) and optimal objective value for the case of a single server, the explicit optimal policy N* and optimal objective value for the case of multiple servers when only predefined customers number N is measured, and the explicit optimal policy T* and optimal objective value for the case of multiple servers when only predefined time units T is measured, respectively. These results partly extend (1) the classic N or T policy to a more practical (N, T)‐policy and (2) the conclusions obtained for single server system to a system consisting of m (m ≥ 1) servers. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 240–258, 2000     

Scheduling of depalletizing and truck loading operations in a food distribution system

This paper studies a scheduling problem arising in a beef distribution system where pallets of various types of beef products in the warehouse are first depalletized and then individual cases are loaded via conveyors to the trucks which deliver beef products to various customers. Given each customer's demand for each type of beef, the problem is to find a depalletizing and truck loading schedule that fills all the demands at a minimum total cost. We first show that the general problem where there are multiple trucks and each truck covers multiple customers is strongly NP‐hard. Then we propose polynomial‐time algorithms for the case where there are multiple trucks, each covering only one customer, and the case where there is only one truck covering multiple customers. We also develop an optimal dynamic programming algorithm and a heuristic for solving the general problem. By comparing to the optimal solutions generated by the dynamic programming algorithm, the heuristic is shown to be capable of generating near optimal solutions quickly. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

The effect of catastrophes on the strategic customer behavior in queueing systems

We consider the single server Markovian queue subject to Poisson generated catastrophes. Whenever a catastrophe occurs, all customers are forced to abandon the system, the server is rendered inoperative and an exponential repair time is set on. During the repair time new arrivals are allowed to join the system. We assume that the arriving customers decide whether to join the system or balk, based on a natural linear reward‐cost structure with two types of rewards: A (usual) service reward for those customers that receive service and a (compensation) failure reward for those customers that are forced to abandon the system due to a catastrophe. We study the strategic behavior of the customers regarding balking and derive the corresponding (Nash) equilibrium strategies for the observable and unobservable cases. We show that both types of strategic behavior may be optimal: to avoid the crowd or to follow it. The crucial factor that determines the type of customer behavior is the relative value of the service reward to the failure compensation. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

Optimal control for entry of many classes of customers to an M/M/1 queue

A method is developed for determining the optimal policy for entry of customers from many independent classes of Poisson arrivals to a first-come, first-serve (for customers admitted to the queue) single-server queue with exponential service times. The solution technique utilizes a semi-Markov formulation or the decision problem.     

Production‐inventory systems with imperfect advance demand information and updating

We consider a supplier with finite production capacity and stochastic production times. Customers provide advance demand information (ADI) to the supplier by announcing orders ahead of their due dates. However, this information is not perfect, and customers may request an order be fulfilled prior to or later than the expected due date. Customers update the status of their orders, but the time between consecutive updates is random. We formulate the production‐control problem as a continuous‐time Markov decision process and prove there is an optimal state‐dependent base‐stock policy, where the base‐stock levels depend upon the numbers of orders at various stages of update. In addition, we derive results on the sensitivity of the state‐dependent base‐stock levels to the number of orders in each stage of update. In a numerical study, we examine the benefit of ADI, and find that it is most valuable to the supplier when the time between updates is moderate. We also consider the impact of holding and backorder costs, numbers of updates, and the fraction of customers that provide ADI. In addition, we find that while ADI is always beneficial to the supplier, this may not be the case for the customers who provide the ADI. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Scheduling customer arrivals to a stochastic service system

An efficient algorithm for determining the optimal arrival schedule for customers in a stochastic service system is developed. All customers arrive exactly when scheduled, and service times are modeled as iid Erlang random variables. Costs are incurred at a fixed rate per unit of time each customer waits for service, and an additional cost is incurred for every unit of time the server operates beyond a scheduled closing time. The objective is to minimize total operating cost. This type of problem arises in many operational contexts including transportation, manufacturing, and appointment‐based services. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 549–559, 1999     

The dynamic and stochastic knapsack Problem with homogeneous‐sized items and postponement options

This article generalizes the dynamic and stochastic knapsack problem by allowing the decision‐maker to postpone the accept/reject decision for an item and maintain a queue of waiting items to be considered later. Postponed decisions are penalized with delay costs, while idle capacity incurs a holding cost. This generalization addresses applications where requests of scarce resources can be delayed, for example, dispatching in logistics and allocation of funding to investments. We model the problem as a Markov decision process and analyze it through dynamic programming. We show that the optimal policy with homogeneous‐sized items possesses a bithreshold structure, despite the high dimensionality of the decision space. Finally, the value (or price) of postponement is illustrated through numerical examples. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 267–292, 2015     

Decomposing inventory routing problems with approximate value functions

We present a time decomposition for inventory routing problems. The methodology is based on valuing inventory with a concave piecewise linear function and then combining solutions to single‐period subproblems using dynamic programming techniques. Computational experiments show that the resulting value function accurately captures the inventory's value, and solving the multiperiod problem as a sequence of single‐period subproblems drastically decreases computational time without sacrificing solution quality. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Some aspects in estimating warranty and post‐warranty repair demands

When customers buy a product, they are often eligible for free repairs for a certain warranty period. In this article, we study some important aspects, which are often overlooked in the literature but are of interest to the manufacturer, in estimating both warranty and post‐warranty repair demands. We consider that the installed base of the product (i.e., the number of units of the product actually in use) varies with time due to both new sales and units being taken out of service. When estimating warranty and post‐warranty repair demands, we explicitly address the fact that customers may not always request repairs for failed units. For the case where the product failure time distribution is exponential, we derive the closed‐form expressions for both types of repair demands of a single unit and of the time‐varying installed base. The insights into some risk‐related quantities are also presented. Furthermore, the proposed model is extended by considering delayed warranty claims that are frequently seen in practice. Numerical examples illustrate that understanding both types of repair demands and the related decision variables is important for managing the obligatory and profitable repair services. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 499–511, 2013     

Scheduling and due‐date quotation in a make‐to‐order supply chain

We consider a manufacturer, served by a single supplier, who has to quote due dates to arriving customers in a make‐to‐order production environment. The manufacturer is penalized for long lead times and for missing due dates. To meet due dates, the manufacturer has to obtain components from a supplier. We model this manufacturer and supplier as a two‐machine flow shop, consider several variations of this problem, and design effective due‐date quotation and scheduling algorithms for centralized and decentralized versions of the model. We perform extensive computational testing to assess the effectiveness of our algorithms and to compare the centralized and decentralized models to quantify the value of centralized control in a make‐to‐order supply chain. Since complete information exchange and centralized control is not always practical or cost‐effective, we explore the value of partial information exchange for this system. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008