The purpose of this paper is to develop a global optimization model, simplification schemes, and a heuristic procedure for the design of a shortcut-enhanced unidirectional loop aisle-network with pick-up and drop-off stations. The objective is to minimize the total loaded and empty trip distances. This objective is the main determinant for the fleet size of the vehicles, which in turn is the driver of the total life-cycle cost of vehicle-based unit-load transport systems. The shortcut considerably reduces the length of the trips while maintaining the simplicity of the system. The global model solves simultaneously for the loop design, stations’ locations and shortcut design. We then develop two simplifications each containing two serial phases. Phase-1 of the first simplification step focuses on both loaded and empty trips, while that of the second simplification focuses only on loaded trips. In phase-2, both designs are enhanced with a shortcut to minimize both loaded and empty trip distances. The quality and efficiency of the three alternative designs are tested for a set of problems with different layout size and product mix. While the solution time of the second simplification procedure is a small percentage of the global formulation, it generates satisfactory solutions. On this foundation, we then develop a heuristic procedure to replace phase-1 of the second simplification. The heuristic procedure is using ant colony system to generate feasible solutions and then we implement a local search algorithm to improve the results. The heuristic algorithm quickly generates close to optimal solutions for phase-1 of the second simplification. By applying phase-2 of the this second simplification on a set of loops generated by the heuristic, close to optimal solutions are also quickly obtained for the global model.

We consider a distribution system composed of a limited-capacity supplier that serves a set of customers exhibiting stochastic demand. The supplier information is limited to the inventory level observed at the currently visited customer and the previously observed inventory levels at all other customers. The supplier follows a fixed routing and sequentially decides on how much stock to allocate to each customer. We formulate the problem as a Markov Decision Process and discuss optimal replenishment policies. We provide managerial insights on the value of dynamic decision making and information availability in the replenishment of such distribution systems. We show that optimal replenishment policies do not necessarily provide all customers with “fair” service levels. We also show that investing in state-of-the-art information systems may not be justified for systems with very tight or ample supplier capacity. We benchmark the performance of myopic solution approaches to that of the optimal solution procedure. Finally, we develop and test the quality of efficient decomposition-based heuristics that can solve for the homogenous customers case. Keyword: Inventory allocation, Vendor Managed Inventory (VMI), Markov Decision Process (MDP).

We consider the problem of resequencing a set of prearranged jobs when there is limited resequencing flexibility and sequence-dependent changeover costs. Resequencing flexibility is limited by how far forward or backward a job can shift in the sequence relative to its original position. We show how the problem can be solved using dynamic programming in polynomial time with respect to the number of jobs. We also show how the same solution approach can be extended to problems where sequencing constraints are job specific and to problems where job features, which determine changeover costs, are jointly determined with the job sequence. We provide an integer programming formulation to the resequencing problem whose linear programming relaxation offers a useful lower bound. We also describe a family of decomposition heuristics that are easy to customize to provide desired levels of solution quality and solution time. We document the quality of the lower bound from the linear programming relaxation and the upper bound from the heuristic using numerical results. We also provide numerical results to support managerial insights regarding the value of flexibility. We show that the value of flexibility is of the diminishing kind with most of the benefit realized with relatively limited flexibility. We also show that a balanced allocation of flexibility among forward and backward position shifting is superior to an unbalanced one. More significantly, we show that forward and backward position shifting flexibility are complements with the value of one increasing in the amount of the other. Finally, we apply our solution approach to a real-world case from the automotive industry.

In this paper, we describe a dynamic network optimization-based solution framework that effectively integrates the management of complex information with traffic strategies and logistics support. This framework is composed of a transportation management function, a logistics support and capacity refinement function, and a validation function. The transportation management function is central to this solution framework and is mainly composed of four modules; a rough-cut capacity plan (RCCP), a detailed capacity plan (DCP), a restricted evacuation plan (REP) and an enforced evacuation plan (EEP). This research has been motivated by the challenges experienced during the hurricane Rita evacuation of the City of Houston. This tool is expected to provide an intelligent systematic methodology that can be used by transportation planners and emergency agencies to improve traffic management during hurricane evacuation, enhance public safety, and mitigate the overall economic impact of evacuation

Abstract Distributed layouts are layouts where multiple copies of the same department type may exist and may be placed in non-adjoining locations. In this paper, we present a procedure for the design of distributed layouts in settings with multiple periods where product demand and product mix may vary from period to period and where a relayout may be undertaken at the beginning of each period. Our objective is to design layouts for each period that balance relayout costs between periods with material flow efficiency in each period. We present a multi-period model for jointly determining layout and flow allocation and offer exact and heuristic solution procedures. We use our solution procedures to examine the value of distributed layouts for varying assumptions about system parameters and to draw several managerial insights. In particular, we show that distributed layouts are most valuable when demand variability is high or product variety is low. We also show that department duplication (e.g., through the disaggregation of existing functional departments) exhibits strong diminishing returns, with most of the benefits of a fully distributed layout realized with relatively few duplicates of each department type.

We consider the problem of resequencing a prearranged set of jobs on a moving assembly line with the objective of minimizing changeover costs. A changeover cost is incurred whenever two consecutive jobs do not share the same feature. Features are assigned from a set of job-specific feasible features. Resequencing is limited by the availability of offline buffers. The problem is motivated by a vehicle resequencing and painting problem at a-major U.S. automotive manufacturer. We develop a model for solving the joint resequencing and feature assignment problem and an efficient solution procedure for simultaneously determining optimal feature assignments and vehicle sequences. We show that our solution approach is amenable to implementation in environments where a solution must be obtained within tight time constraints. We also show that the effect of offline buffers is of the diminishing kind with most of the benefits achieved with very few buffers. This means that limited resequencing flexibility is generally sufficient. Furthermore, we show that the value of resequencing is sensitive to the feature density matrix, With resequencing having a significant impact on cost only when density is in the middle range.

The increased use of flexible manufacturing systems to provide customers with diversified products efficiently has created a significant set of operational challenges for managers. This technology poses a number of decision problems that need to be solved by researchers and practitioners. In the literature, there have been a number of attempts to solve design and operational problems. Special attention has been given to machine loading problems, which involve the assignment of job operations and allocation of tools and resources to optimize specific measures of productivity. Most existing studies focus on modeling the problem and developing heuristics in order to optimize certain performance metrics rather than on understanding the problem and the interaction between the different factors in the system. The objective of this paper is to study the machine loading problem. More specifically, we compare operation aggregation and disaggregation policies in a random flexible manufacturing system (FMS) and analyze its interaction with other factors such as routing flexibility, sequencing flexibility, machine load, buffer capacity, and alternative processing-time ratio. For this purpose, a simulation study is conducted and the results are analyzed by statistical methods. The analysis of results highlights the important factors and their levels that could yield near-optimal system performance.

Distributed layouts are layouts where multiple copies of the same department type may exist and may be placed in non-adjoining locations. In this paper, we present a procedure for the design of distributed layouts in settings with multiple periods where product demand and product mix may vary from period to period and where a relayout may be undertaken at the beginning of each period. Our objective is to design layouts for each period that balance relayout costs between periods with material flow efficiency in each period. We present a multi-period model for jointly determining layout and flow allocation and offer exact and heuristic solution procedures. We use our solution procedures to examine the value of distributed layouts for varying assumptions about system parameters and to draw several managerial insights. In particular, we show that distributed layouts are most valuable when demand variability is high or product variety is low. We also show that department duplication (e.g., through the disaggeagtion of existing functional departments) exhibits strong diminishing returns, with most of the benefits of a fully distributed layout realized with relatively few duplicates of each department type.

Each year the Manufacturing and Service Operations Management (MSOM) Society of INFORMS conducts a student paper competition. In Volume 2, Issue 2 of M&SOM, we published the extended abstracts of the 1999 winners in the hopes that this could become an annual event. Our hopes have become a reality. Sridhar Seshadri and Ravi Anupindi, New York University, cochaired the 2000 competition. The judges for the final round were Garrett van Ryzin, Columbia University; Yehuda Bassok, University of Southern California; and Michael Pinedo, New York University. The first-prize winner received $400, while second prize received $200. Prizes were awarded at the INFORMS meeting in San Antonio, Texas in November 2000. All finalists received a $50.00 discount coupon redeemable at an “e-tailer.” The winners and their faculty mentors were: First Prize Jérémie Gallien, Massachusetts Institute of Technology Faculty Mentor: Lawrence M. Wein, MIT “Design and Analysis of a Smart Market for Industrial Procurement” Second Prize Serguei Netessine, University of Rochester Faculty Mentors: Gregory Dobson and Robert A. Shumsky, University of Rochester “Flexible Service Capacity: Optimal Investment and the Impact of Demand Correlation” M&SOM would like to congratulate the winners as well as all five finalists. The extended abstracts of these papers follow.