Time decomposition of multi-period supply chain models

Many supply chain problems involve discrete decisions in a dynamic environment. The inventory routing problem is an example that combines the dynamic control of inventory at various facilities in a supply chain with the discrete routing decisions of a fleet of vehicles that moves product between the facilities. We study these problems modeled as mixed-integer programs and propose a time decomposition based on approximate inventory valuation. We generate the approximate value function with an algorithm that combines data fitting, discrete optimization and dynamic programming methodology. Our framework allows the user to specify a class of piecewise linear, concave functions from which the algorithm chooses the value function. The use of piecewise linear concave functions is motivated by intuition, theory and practice. Intuitively, concavity reflects the notion that inventory is marginally more valuable the closer one is to a stock-out. Theoretically, piecewise linear concave functions have certain structural properties that also hold for finite mixed-integer program value functions. (Whether the same properties hold in the infinite case is an open question, to our knowledge.) Practically, piecewise linear concave functions are easily embedded in the objective function of a maximization mixed-integer or linear program, with only a few additional auxiliary continuous variables. We evaluate the solutions generated by our value functions in a case study using maritime inventory routing instances inspired by the petrochemical industry. The thesis also includes two other contributions. First, we review various data fitting optimization models related to piecewise linear concave functions, and introduce new mixed-integer programming formulations for some cases. The formulations may be of independent interest, with applications in engineering, mixed-integer non-linear programming, and other areas. Second, we study a discounted, infinite-horizon version of the canonical single-item lot-sizing problem and characterize its value function, proving that it inherits all properties of interest from its finite counterpart. We then compare its optimal policies to our algorithm's solutions as a proof of concept.PhDCommittee Chair: George Nemhauser; Committee Member: Ahmet Keha; Committee Member: Martin Savelsbergh; Committee Member: Santanu Dey; Committee Member: Shabbir Ahme

A TSSA algorithm based approach to enhance the performance of warehouse system

In this plethora of increased competitiveness and globalization the effective management of the warehouse system is a challenging task. Realizing that proper scheduling of the warehouses is necessary to outperform the competitors on cost, lead time, and customer service basis (Koster, 1998); the proposed research focuses on optimization of warehouse scheduling problems. This research aims to minimize the total tardiness so that the overall time involved in managing the inventory inside the warehouse could be effectively reduced. This research also deals with the vehicle routing issues in the warehousing scenario and considers various constraints, and decision variables, directly influencing the undertaken objective so as to make the model more realistic to the real world environment. The authors have also proposed a hybrid tabu sample-sort simulated annealing (TSSA) algorithm to reduce the tardiness as well as to enhance the performance of the warehousing system. The proposed TSSA algorithm inherits the merits of the tabu search and sample-sort annealing algorithm. The comparative analysis of the results of the TSSA algorithm with other algorithms such as simulated annealing (SA), tabu search (TS), and hybrid tabu search algorithms indicates its superiority over others, both in terms of computational time as well as total tardiness reduction

Downstream logistics optimization at EWOS Norway

The Norwegian company EWOS AS produces fish feed for the salmon farming industry, supplying approximately 300 customers spread along the coast of Norway. The feed is produced at three factory locations and distributed by a fleet of 10 dedicated vessels. The high seasonality of the demand and the large number of customers make the distribution planning a substantial challenge. EWOS handles it by operating a system of mostly fixed routes with decentralized planning at each factory. The distribution can be described as a multi-depot vehicle routing problem with time windows, multiple vehicle usage, inter-depot routes, heterogeneous fleet and a rolling horizon. The paper presents a mathematical model for this problem, which is solved by heuristics and meta heuristics. Based on detailed historical data collected by EWOS during the autumn of 2010, the model has proposed a dynamic set of routes with a significant reduction of travelled distance - close to 30% - and an increase of average vessel fill-rate - from 60% up to 95%. This implies a substantial fuel saving, with a positive environmental impact, and also a potential for downscaling the fleet, with additional considerable cost savings for the company.publishedVersio

An integrated model for cash transfer system design problem

This paper presents an integrated model that incorporates strategic, tactical, and operational decisions for a cash transfer management system of a bank. The aim of the model is to decide on the location of cash management centers, number and routes of vehicles, and the cash inventory management policies to minimize the cost of owning and operating a cash transfer system while maintaining a pre-defined service level. Owing to the difficulty of finding optimal decisions in such integrated models, an iterative solution approach is proposed in which strategic, tactical, and operational problems are solved separately via a feedback mechanism. Numerical results show that such an approach is quite effective in reaching greatly improved solutions with just a few iterations, making it a promising approach for similar integrated models

Optimization of polling systems with Bernoulli schedules

Optimization;Polling Systems;Queueing Theory;operations research