Cyclical schedules for the joint replenishment problem with dynamic demands

The Joint Replenishment Problem (JRP) involves production planning for a family of items. The items have a coordinated cost structure whereby a major setup cost is incurred whenever any item in the family is produced, and an item-specific minor setup cost is incurred whenever that item is produced. This paper investigates the performance of two types of cyclical production schedules for the JRP with dynamic demands over a finite planning horizon. The cyclical schedules considered are: (1) general cyclical schedules—schedules where the number of periods between successive production runs for any item is constant over the planning horizon—and (2) power-of-two schedules—a subset of cyclical schedules for which the number of periods between successive setups must be a power of 2. The paper evaluates the additional cost incurred by requiring schedules to be cyclical, and identifies problem characteristics that have a significant effect on this additional cost. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 577–589, 1997.     

A heuristic approach to bicriteria scheduling

We consider the problem of sequencing jobs on a single machine while minimizing a nondecreasing function of two criteria. We develop a heuristic procedure that quickly finds a good solution for bicriteria scheduling. The procedure is based on using several arcs in the criterion space that are representative of the possible locations of nondominated solutions. By sampling a small number of points on these arcs, a promising point is identified in the criterion space for each arc. An efficient sequence in the neighborhood of each of the promising points is found and the best of these efficient sequences is selected as the heuristic solution. We implement the procedure for two different bicriteria scheduling problems: (i) minimizing total flowtime and maximum tardiness and (ii) minimizing total flowtime and maximum earliness. The computational experience on a wide variety of problem instances show that the heuristic approach is very robust and yields good solutions. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 777–789, 1999     

Reassessing Military Assistance to the Civil Powers: Are Traditional British Anti-Terrorist Responses Still Effective?

The provision of Military Assistance to the Civil Powers (MACP) encapsulates the traditional approach towards countering terrorism on the British mainland, which emphasises minimum force, proportionality, police primacy and the rule of law, with the military retained as a supporting instrument, being employed only as a measure of last resort. Recent terrorist incidents in major urban centres do, however, question the effectiveness of these established methods. This analysis initially codifies the conceptual issues that underscore the tensions between civil policing and military anti-terrorist approaches in the UK, and proceeds to evaluate and compare a number of operations that indicate the nature of the challenges facing the MACP apparatus. In particular, potential shortcomings in the MACP provisions are identified in the form of threat perception and deployment and enforcement gaps. The assessment finds that there are facets of MACP that are of enduring relevance in the current era, yet a continuing need persists to reconcile police and military imperatives toward serious civil disorder contingencies and conditions of emergency.     

Attrition through a partially coordinated area defense

An area defense consists of several groups that act independently, i.e., do not communicate with each other. Each group has a fixed number of defenders and a controller that allocates these defenders optimally against the individual attackers comprising an attack. We analyze the effectiveness of this partially coordinated defense against a simultaneous attack of known size in which all attackers are considered to be equally lethal. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Stochastic modeling for computational warranty analysis

We examine two key stochastic processes of interest for warranty modeling: (1) remaining total warranty coverage time exposure and (2) warranty load (total items under warranty at time t). Integral equations suitable for numerical computation are developed to yield probability law for these warranty measures. These two warranty measures permit warranty managers to better understand time‐dependent warranty behavior, and thus better manage warranty cash reserves. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Scheduling with centralized and decentralized batching policies in concurrent open shops

This article considers batch scheduling with centralized and decentralized decisions. The context of our study is concurrent open shop scheduling where the jobs are to be processed on a set of independent dedicated machines, which process designated operations of the jobs in batches. The batching policy across the machines can be centralized or decentralized. We study such scheduling problems with the objectives of minimizing the maximum lateness, weighted number of tardy jobs, and total weighted completion time, when the job sequence is determined in advance. We present polynomial time dynamic programming algorithms for some cases of these problems and pseudo‐polynomial time algorithms for some problems that are NP‐hard in the ordinary sense. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 58: 17–27, 2011     

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.