Burcu B. Keskin

A simulation-optimization approach for integrated sourcing and inventory decisions

In this paper, we study a generalized vendor selection problem that integrates vendor selection and inventory replenishment decisions of a firm. In addition to vendor-specific procurement and management costs, we consider inventory replenishment, holding, and backorder costs explicitly to meet stationary stochastic demand faced by the firm. Our goal is to select the best set of vendors along with the optimum inventory decisions at each plant of the firm so that we minimize the system-wide total costs and achieve desired service and reliability levels. Due to uncertainties inherent in the problem related to demand observed by the firm, quality provided by vendors, and disruptions observed by vendors, we utilize a simulation-optimization approach to solve the problem. More specifically, we build a discrete-event simulation model to evaluate the objective function of the problem that works in concert with a scatter search-based metaheuristic optimization approach to search the solution space. Computational results not only provide managerial insights and measure the significance of intangible factors in the vendor selection process but also highlight the importance of computational tools such as simulation-optimization for the vendor selection problem.

Integrated warehouse location and inventory decisions in a three-tier distribution system

A three-tier distribution network which consists of a single supplier at a given location, a single intermediate warehouse whose location is to be determined, and multiple retailers at given locations is examined. The problem is the integration of warehouse location and inventory replenishment decisions with the objective of minimizing the system-wide transportation and inventory-related costs. The case where the inventory replenishment decisions are coordinated using a power-of-two policy is considered and a mathematical model for the simultaneous computation of the warehouse coordinates and coordinated replenishment policy parameters is developed. The analytical properties of the integrated location-inventory model are characterized and efficient solution methods that rely on these properties are developed. Computational results demonstrating the performance of the proposed heuristic methods and the potential practical impact of integrated decision making for location and inventory decisions are reported. These results indicate that the proposed methods are capable of effectively producing high-quality power-of-two solutions within 6% of the lower bound for the instances tested. The presented analysis is also useful for identifying: (i) the level of interaction between these two types of decisions that are treated separately in the traditional literature; and (ii) problem settings where the integrated location–inventory model offers significant cost savings.

Integration of strategic and tactical decisions for vendor selection under capacity constraints

We examine a generalized vendor selection problem of a multi-store firm where the goal is the simultaneous determination of (i) the set of vendors the firm should work with and (ii) how much each store should order from the selected vendors. In addition to the typical costs associated with vendor selection and delivery between the vendors and their assigned stores, we explicitly consider the inventory-related costs and decisions of the stores. We emphasize the relationship between facility location applications and the problem at hand, and we propose an integrated vendor selection and inventory optimization model. Also, arguing that our model creates a venue for precise incorporation of realistic capacity constraints, we model throughput and dispatch capacities, explicitly. The model is a challenging mixed integer nonlinear program for which we propose an efficient solution approach that relies on Generalized Benders Decomposition (GBD).

An Integrated Outbound Logistics Model for Frito-Lay: Coordinating Aggregate-Level Production and Distribution Decisions

In this paper, we describe research to improve Frito-Lays outbound supply chain activities by simultaneously optimizing its inventory and transportation decisions. Motivated by Frito-Lays practice, we first develop a mixed-integer programming formulation from which we develop a large-scale, integrated multiproduct inventory lot-sizing and vehicle-routing model with explicit (1) inventory holding costs, truck loading and dispatch costs, and mileage costs; (2) production, storage, and truck capacity limitations; and (3) direct (plant-to-store) and interplant (plant-to-plant) delivery considerations. Second, we present an iterative solution approach in which we decompose the problem into inventory and routing components. The results demonstrate the impact of direct deliveries on distribution costs and show that direct deliveries and efficient inventory and routing decisions can provide significant savings opportunities over two benchmark models, one of which represents the existing Frito-Lay system. We implemented our models using an application that allows strategy evaluation, analysis of output files, and technology transfer. This application was particularly useful in evaluating potential direct-delivery locations and inventory reductions throughout the supply chain.

A Simulation Optimization-Based Decision Support Tool for Mitigating Traffic Congestion

Traffic congestion has grown considerably in the United States over the past 20 years. In this paper, we develop a robust decision support tool based on simulation optimization to evaluate and recommend congestion mitigation strategies to transportation system decision-makers. A tabu search-based optimizer determines different network design strategies on the road network while a traffic simulator evaluates the goodness of fit. The tool is tested with real-world traffic data.

Managing Resource Allocation and Task Prioritization Decisions in Large Scale Virtual Collaborative Development Projects

Business rules guide information system development and maintenance in the organization. The issue of business rules for enterprise information systems has recently received considerable attention. However, as yet little research has been reported on a systematic approach to business rules management. This paper proposes a business rules management model. In this model, business rules are supported by three types of independent information system components: system setting, database, and procedural module. A business rule can be formalized into one or more elementary rule, and a formalized elementary rule is associated with one and only one information system component. Business rules, system components, and their interconnected relationships can be organized into an XML enabled repository for the system development, customization, and maintenance. An example of artifact of business rules management system can be found in an apartment rental management system. This example is used to illustrate the concept of business rules management. DOI: 10.4018/irmj.2010102604 IGI PUBLISHING This paper appears in the publication, Resources Management Journal, Volume 23, Issue 1 edited by Mehdi Khosrow-Pour

Bi-criteria dynamic location-routing problem for patrol coverage

In this paper, we address the problem of dynamic patrol routing for state troopers for effective coverage of highways. Specifically, a number of state troopers start their routes at temporary stations (TS), patrol critical locations with high crash frequencies, and end their shifts at other (or the same) TS so the starting points for the next period are also optimized. We determine the number of state troopers, their assigned routes, and the locations of the TS where they start and end their routes. The TS are selected from a given set of potential locations. The problem, therefore, is a multi-period dynamic location-routing problem in the context of public service. Our objective is to maximize the critical location coverage benefit while minimizing the costs of TS selections, vehicle utilizations, and routing/travel. The multi-objective nature of the problem is handled using an ɛ-constraint approach. We formulate the problem as a mixed integer linear programming model and solve it using both off-the-shelf optimization software and a custom-built, efficient heuristic algorithm. The heuristic, utilizing the hierarchical structure of the problem, is built on the decomposition of location and routing problems. By allowing routing to start from multiple locations, our model improves the coverage by as much as 12% compared with the single-depot coverage model.

A decomposition-based heuristic for stochastic emergency routing problems

Abstract This paper proposes a decomposition-based heuristic for a network delivery problem in which relief workers acquire valuable emergency supplies from relief warehouses, and transport them to meet the urgent needs of distressed population centres. The problem context dictates that the relief items reach these population centres before critical deadlines. However, co-ordination challenges and random disruptions introduce uncertainty in both network travel times and the destination deadlines. Hence, relief workers have to negotiate the tension between ensuring a high probability of punctual delivery and maximising the combined value of the relief supplies delivered. For an arbitrary routing scheme which guarantees punctual delivery in an uncertainty-free state of nature, the heuristic yields an upper bound on the probability that, under uncertainty, the routing scheme described will lead to tardy delivery. We demonstrate our solution approach on a small numerical example and glean insights from experiments on a realistically sized problem. Overall, our central model and proposed solution approach are useful to managers who need to evaluate routing options and devise effective operational delivery plans in humanitarian crisis situations.

An improved formulation for the maximum coverage patrol routing problem

Abstract We present an improved formulation for the maximum coverage patrol routing problem (MCPRP). The main goal of the patrol routing problem is to maximize the coverage of critical highway stretches while ensuring the feasibility of routes and considering the availability of resources. By investigating the structural properties of the optimal solution, we formulate a new, improved mixed integer program that can solve real life instances to optimality within seconds, where methods proposed in prior literature fail to find a provably optimal solution within an hour. The improved formulation provides enhanced highway coverage for both randomly generated and real life instances. We show an average increase in coverage of nearly 20% for the randomly generated instances provided in the literature, with a best case increase over 46%. Similarly, for the real life instances, we close the optimality gap within seconds and demonstrate an additional coverage of over 13% in the best case. The improved formulation also allows for testing a number of real life scenarios related to multi-start routes, delayed starts at the beginning of the shifts, and taking a planned break during the shift. Being able to solve these scenarios in short durations help decision and policy makers to better evaluate resource allocation options while serving public.

A multi-period dynamic location planning model for emergency response

We investigate a multi-period ambulance location problem for an emergency medical service (EMS) provider that routinely aims for high quality medical response in a short period of time. However, both quality and timing of service are very costly goals to achieve. In this research, our goal is to minimize the total operational cost of an EMS organization while maintaining acceptable response times for a newly defined EMS network that consists of supply centers, hospitals, responder locations, and incidents. We propose a new dispatch model, a deferred service model, to analyze the daily operations of ambulances that involve dispatch as needed and redeploy periodically. The main premise of this model is, given incident priority levels, incident demand patterns, and EMS provider resources, to decide which incidents to serve immediately and which incidents to defer to following periods while incurring deferral penalty costs. Additionally, high-priority incidents that are not addressed within the required response window incur delay penalty costs. Considering network size, fleet size, incident patterns, and time-dependent parameters as different factors that may influence the solution, we compare the results in terms of service quality, response time, and total cost through extensive experimentation. Experimental results indicate that sending the closest ambulance to an incident site is not always the best choice.