Mike Hewitt

Service Network Design with Resource Constraints

We first present a new service network design model for freight consolidation carriers, one that selects services and routes both commodities and resources needed to support the services that transport them, while explicitly recognizing that there are limits on how many resources are available at each terminal. We next present a solution approach that combines column generation, meta-heuristic, and exact optimization techniques to produce high-quality solutions. We demonstrate the efficacy of the approach with an extensive computational study and benchmark its performance against both a leading commercial solver and a column generation-based heuristic.

Scenario grouping in a progressive hedging-based meta-heuristic for stochastic network design

We propose a methodological approach to build strategies for grouping scenarios as defined by the type of scenario decomposition, type of grouping, and the measures specifying scenario similarity. We evaluate these strategies in the context of stochastic network design by analyzing the behavior and performance of a new progressive hedging-based meta-heuristic for stochastic network design that solves subproblems comprising multiple scenarios. We compare the proposed strategies not only among themselves, but also against the strategy of grouping scenarios randomly and the lower bound provided by a state-of-the-art MIP solver. The results show that, by solving multi-scenario subproblems generated by the strategies we propose, the meta-heuristic produces better results in terms of solution quality and computing efficiency than when either single-scenario subproblems or multiple-scenario subproblems that are generated by picking scenarios at random are solved. The results also show that, considering all the strategies tested, the covering strategy with respect to commodity demands leads to the highest quality solutions and the quickest convergence.

Integer programming techniques for solving non-linear workforce planning models with learning

In humans, the relationship between experience and productivity, also known as learning (possibly also including forgetting), is non-linear. As a result, prescriptive planning models that seek to manage workforce development through task assignment are difficult to solve. To overcome this challenge we adapt a reformulation technique from non-convex optimization to model non-linear functions with a discrete domain with sets of binary and continuous variables and linear constraints. Further, whereas the original applications of this technique yielded approximations, we show that in our context the resulting mixed integer program is equivalent to the original non-linear problem. As a second contribution, we introduce a capacity scaling algorithm that exploits the structure of the reformulation model and reduces computation time. We demonstrate the effectiveness of the techniques on task assignment models wherein employee learning is a function of task repetition.

Multi-period technician scheduling with experience-based service times and stochastic customers

Introduce a new multi-period technician scheduling problem.Present a Markov decision process model for the problem.Approximate the value of todays assignments on the ability to serve future demand.Demonstrate approximate Bellman equation can be solved as a mixed integer program.Show proposed approach leads to higher quality solutions than myopic approach. This paper introduces the multi-period technician scheduling problem with experience-based service times and stochastic customers. In the problem, a manager must assign tasks of different types that are revealed at the start of each day to technicians who must complete the tasks that same day. As a technician gains experience with a type of task, the time that it takes to serve future tasks of that type is reduced (often referred to as experiential learning). As such, while the problem could be modeled as a single-period problem (i.e. focusing solely on the current days tasks), we instead choose to model it as a multi-period problem and thus capture that daily decisions should recognize the long-term effects of learning. Specifically, we model the problem as a Markov decision process and introduce an approximate dynamic programming-based solution approach. The model can be adapted to handle cases of worker attrition and new task types. The solution approach relies on an approximation of the cost-to-go that uses forecasts of the next days assignments for each technician and the resulting estimated time it will take to service those assignments given current period decisions. Using an extensive computational study, we demonstrate the value of our approach versus a myopic solution approach that views the problem as a single-period problem.

A matheuristic for workforce planning with employee learning and stochastic demand

Abstract This paper focuses on the opportunity to direct the development of responsive capacity by recognising that individuals learn through experience when designing workforce plans. We focus on the operations of a product manufacturer that seeks to maximise profit by selling multiple products, while recognising that demands for each product is uncertain. As such, we study a stochastic integer program wherein an organisation can hedge against uncertainty in demand both by holding inventory (at a cost) and building a more responsive production process. Solving this stochastic program presents many computational difficulties, including the fact that quantitative models of human learning are non-linear and the explosion of instance size that result from modelling uncertainty with scenarios. As a result, we propose a matheuristic for this problem and with an extensive computational study demonstrate its ability to produce high-quality solutions in little time.

Scheduled Service Network Design with Resource Acquisition and Management

We present a new planning model for freight consolidation carriers, one that links strategic, resource acquisition, and allocation decisions with tactical, service network design-related decisions. Specifically, such as service network design models that recognize resource constraints, the model selects services and routes both commodities and the resources needed to support the services that transport them. In addition, the model recognizes that resources can be grouped into types that differ from one another with respect to capabilities, e.g., speeds, capacities, scheduling rules, etc. Ultimately, along with recognizing resource constraints, the model also makes strategic decisions such as how many resources of each type should be acquired, to what terminal new resources should be assigned, and which existing terminal-based resources should be reassigned. As such, the model can be used from a strategic planning, resource acquisition, mixing, and allocation perspective as it provides an estimate of the impact of such decisions on transportation costs. We extend a matheuristic for a service network design problem with a fixed set (both in number and allocation) of resources of a single type to one that can also make these acquisition and allocation decisions for multiple types of resource. Then, with an extensive computational study, we demonstrate the efficacy of the matheuristic and benchmark its performance against both a leading commercial solver and a column generation-based heuristic. Finally, we perform an extensive computational study to understand how the resource-related and service network design-related components of the model interact, including how freight volumes and cost structures impact how many resources should beacquired.

Mileage bands in freight transportation

Contract carriers in the trucking industry are known to offer shippers a per mile rate that decreases stepwise as the shipper’s route lengthens, with a mileage band designated for each rate. While the use of quantity-based pricing discounts in supply chains has been well studied, there has been no research on how shippers should route under such a pricing scheme or how carriers should set such bands. In this paper, we provide methods for both. Route construction is complicated by the fact that the per mile rate cannot be determined until after the route has been created. With this consideration, we develop a model of this problem and then an algorithm for solving it that assists the shipper in constructing the lowest cost route. It is beneficial for the shipper to extend the length of certain routes to incur a lower per mile cost, and we find that most of these routes can be constructed to equal any mileage required to receive the lower rate. As an extended route generates unnecessary expense and energy use for the carrier, theoretical and analytical insights provide guidelines for a carrier to use in developing better mileage bands. These guidelines assist a carrier in constructing bands that maximize profit and minimize the cost associated with a shipper extending a route.

Workforce grouping and assignment with learning-by-doing and knowledge transfer

We consider a workforce allocation problem in which workers learn both by performing a job and by observing the performance of and interacting with co-located colleagues. As a result, an organisation can benefit from both effectively assigning individuals to jobs and grouping workers into teams. A challenge often faced when solving workforce allocation models that recognise learning is that learning curves are non-linear. To overcome this challenge, we identify properties of an optimal solution to a non-linear programme for grouping workers into teams and assigning the resulting teams to sets of jobs. With these properties identified, we reformulate the non-linear programme to a mixed integer programme that can be solved in much less time. We analyse (near-)optimal solutions to this model to derive managerial insights.

Consolidating home meal delivery with limited operational disruption

Non-profit organizations like the Meals On Wheels (MOW) association of America prepare and deliver meals, typically daily, to approximately one million homebound individuals in the United States alone. However, many MOW agencies are facing a steadily increasing number of clients requesting meal service without an increase in resources (either financial or human). One strategy for accommodating these requests is to deliver multiple (frozen) meals at a time and thus make fewer deliveries. However, many of the stakeholders (funders, volunteers, meal recipients) value the relationships that are developed by having a client receive daily deliveries from the same volunteer. Further, meal recipients may be concerned with the quality of food delivered in a frozen meal. In this paper, we develop a method for introducing consolidation into home meal delivery while minimizing operational disruptions and maintaining client satisfaction. With an extensive computational study, the savings associated with various levels and types of disruptions are detailed. The question of whether delivering frozen meals will enable an agency to both save money and deliver meals to a larger client base is also studied.

Call for Papers ---Special Issue of Transportation Science: Uncertainty in Logistics and Transportation Systems

Researchers have long recognized the practical importance of incorporating uncertainty into planning models for logistics and transportation systems. With recent advances in computational tools and techniques for solving such models, more and more researchers are building and solving them. The rise of “big data” and the resulting opportunity to understand and estimate the degree of uncertainty in many transportation problems will only accelerate this trend. Similarly, the large number of high-quality talks scheduled for the 2014 INFORMS Transportation Science and Logistics Society Workshop (also focused on this topic) indicates that there are very intense and rich research efforts in this area. In conjunction with the TSL workshop, this special issue intends to establish the landscape for the state-of-the-art in handling uncertainty in planning problems and to guide researchers in their future efforts. We anticipate articles covering results from a wide spectrum of research efforts, including models of novel applications, new ways to represent/model uncertainty in well-known applications, innovative and effective solution techniques for new or existing models, and analyses of how uncertainty impacts the plans organizations should execute. Applications of interest include (but are not limited to):