In today's competitive market place, all the components of a supply chain must be well coordinated to achieve economic and service goals. This dissertation is devoted to the modeling, analysis, and development of solution approaches for some logistics problems with emphasis on coordination of various supply chain components and decisions. Specifically, we have addressed four problems in this domain that span various decision levels. The first problem deals with integrated production and shipping scheduling for a single manufacturer and multiple customers. We develop an optimum-seeking algorithm and a fast heuristic, both of which exploit structural properties of the problem. The second problem is a joint production and delivery scheduling problem in which a single vendor supplies goods to a single buyer over a finite horizon. We model this multi-period problem by using a dynamic programming framework and develop an effective Lagrange multiplier method for the solution of the single-period problem, which is then used to solve the multi-period problem. We show that the optimal shipments in each period follow a pattern of geometric-then-equal sizes except for the last shipment, which may be of a larger size. We also show that an optimal solution for the infinite horizon problem can be derived as a special case of our finite horizon approach. In addition, we propose two fast heuristic methods, which, as we show, can obtain almost optimal solutions. We also address the design and logistics operation of biomass feedstock supply chain. To that end, we consider two problems. The first of these problems arises in the context of delivering biomass sorghum to a biorefinery. We propose multi-period, mixed integer linear programming models, which prescribe the strategic and tactical logistics decisions. Our aim is to investigate different logistical configurations available in a sorghum biomass feedstock logistics system. The second of these problems further allows sharing of loadout equipment among storage facilities. We develop an efficient Benders decomposition-based algorithm, and also, two heuristic methods that are capable of effectively solving large-scale instances. We also show the advantage of using mobile equipment. / Doctor of Philosophy / Invariably, logistics cost constitutes a significant portion of the total cost incurred in operating a supply chain. In today’s fierce market competition, it is imperative to reduce this cost to a maximum extent. To that end, our work in this dissertation is devoted to the modeling, analysis, and development of solution approaches for some supply chain problems with the aim of reducing logistics cost. Specifically, we address four problems that span strategic-, tactical- and operational-level decisions in supply chain optimization. The first problem that we address deals with integrated production and shipping scheduling for a single manufacturer and multiple customers. Our aim is to integrate the production and shipping functions of a manufacturer for the objective of minimizing the sum of the shipping cost and the penalty incurred for late deliveries. We develop an optimum-seeking algorithm and a fast heuristic both of which exploit structural properties of the problem. The results of our computational investigation reveal efficacy of our approaches and a significant benefit that accrues from integrating the production and distribution functions. In the second problem, we address a joint production and delivery scheduling problem in which a single vendor supplies goods to a single buyer over a finite horizon. The vendor’s production rate and buyer’s demand rate can vary from period to period and are known in advance. The objective is to determine a production/shipment schedule that minimizes the total cost of production setup, shipment of orders, and holding of inventory at both the vendor and the buyer. We model this problem as a dynamic program, each stage of which constitutes a single-period problem with prescribed starting and ending inventory levels. We develop an effective approach for the solution of this single-period problem, which is then embedded within the dynamic programming framework. We show that the optimal shipments in each period follow a pattern of geometric-then-equal sizes except for the last shipment, which may be larger in size. We show that an optimal solution for the infinite horizon problem can be obtained as a special case of our finite horizon approach. In addition, we propose two fast heuristic methods, which, as we show, can obtain almost optimal solutions. For the third problem, we aim to address the design and operation of a biomass feedstock supply chain. We first present a comprehensive taxonomic literature review of the work in this area that exploits the operations research (OR) methodologies. Then, we study sorghum-biomass-to-biofuel logistics supply chain, and call it as a sorghum biomass feedstock logistics system (S-BFLS). We propose a multi-period, mixed integer linear programming model which prescribes the strategic locations and sizes of storage facilities, number of equipments to purchase, and allocation of farms to satellite storage facilities (SSLs), as well as tactical decisions including period-to-period biomass transportation flows and period-to-period biomass inventory plans. We study a wide spectrum of available harvest, preprocessing, transportation, and storage options as a part of the sorghum biomass feedstock logistics system. We have also investigated the option of just-in-time (JIT) delivery in conjunction with regular delivery, and call it as a hybrid delivery system. Our model is applied to a real-life-inspired case. Based on our analysis, the most cost-effective S-BFLS consists of forage-chopping for harvesting, bunkers or bags for ensiling, and hybrid delivery. Ensiling by modules is not found to be as cost-effective as by bags or bunkers due to the occurrence of high equipment ownership cost and operating cost. Compression of biomass is also not found to be cost-effective. It incurs extra equipment ownership and operating costs while not amounting to sufficient reduction in transportation cost because of the requirement of over 50% moisture content for ensiling. Forage-chop harvest and whole-stalk harvest have little difference in economic effectiveness. The hybrid delivery system is found to be effective since it reduces logistics cost for all the configurations. In the fourth and last problem, we permit sharing of mobile equipment among SSLs for loading biomass on tractor-trailers. We develop an efficient Benders decomposition algorithm (BA) to solve this problem. Our model formulation implicitly takes into account transportation of loadout equipment among SSLs. The BA further takes advantage of this feature of our formulation and Benders cuts. We have also proposed two fast approximate methods called Heuristics H1 and H2, both of which exploit the decision hierarchy of the problem. Computational experiments reveal efficacy of the proposed methods. Heuristic H2 generates fast and high quality solutions. However, the BA generates solution of desired accuracy (optimality gap) when not optimal. Our real-life-inspired case study has shown that 1.73–4.13% of cost reduction can be achieved by mobilizing loadout equipment in a BFSC. Also, expensive equipment leads to a greater benefit due to mobilization.