Joint pricing and production planning of multiple products

Many industries are beginning to use innovative pricing techniques to improve inventory control, capacity utilisation, and ultimately the profit of the firm. In manufacturing, the coordination of pricing and production decisions offers significant opportunities to improve supply chain performance by better matching supply and demand. This integration of pricing, production and distribution decisions in retail or manufacturing environments is still in its early stages in many companies. Importantly it has the potential to radically improve supply chain efficiencies in much the same way as revenue management has changed the management of the airline, hotel and car rental industries. These developments raise the need and interest of having models that integrate production decisions, inventory control and pricing strategies.In this thesis, we focus on joint pricing and production planning, where prices and production values are determined in coordination over a multiperiod horizon with non-perishable inventory. We specifically look at multiproduct systems with either constant or dynamic pricing. The fundamental problem is: when the capacity limitations and other parameters like production, holding, and backordering costs are given, what the optimal values are for production quantities, and inventory and backorder levels for each item as well as a price at which the firm commits to sell the products over the total planning horizon. Our aim is to develop models and solution strategies that are practical to implement for real sized problems.We initially formulate the problem of time-varying pricing and production planning of multiple products over a multiperiod horizon as a nonlinear programming problem. When backorders are not allowed, we show that if the demand/price function is linear, as a special case of the without backorders model, the problem becomes a Quadratic Programming problem which has only linear constraints. Existing solution methods for Quadratic Programming problem are discussed. We then present the case of allowed backorders. This assumption makes the problem more difficult to handle, because the constraint set changes to a non-convex set. We modify the nonlinear constraints to obtain an alternative formulation with a convex set of constraints. By this modification the problem becomes a Mixed Integer Nonlinear Programming problem over a linear set of constraints. The integer variables are all binary variables. The limitation of obtaining the optimal solution of the developed models is discussed. We describe our strategy to overcome the computational difficulties to solve the models.We tackle the main nonlinear problem with backorders through solving an easier case when prices are constant. This resulting model involves a nonlinear objective function and some nonlinear constraints. Our strategy to reduce the level of difficulty is to utilise a method that solves the relaxed problem which considers only linear constraints. However, our method keeps track of the feasibility with respect to the nonlinear constraints in the original problem. The developed model which is a combination of Linear Programming (LP) and Nonlinear Programming (NLP) is solved iteratively. The solution strategy for the constant pricing case constructs a tree search in breadth-first manner. The detailed algorithm is presented. This algorithm is practical to implement, as we demonstrate through a small but practical size numerical example.The algorithm for the constant pricing case is extended to the more general problem. More specifically, we reformulate the timevariant problem in which there are multi blocks of constant pricing problems. The developed model is a combination of Linear Programming (LP) and linearly constrained Nonlinear Programming (NLP) which is solved iteratively. Iterations consist of two main stages: finding the value of LP’s objective function for a known basis, solving a very smaller size NLP problem. The detailed algorithm is presented and a practical size numerical example is used to implement the algorithm. The significance of this algorithm is that it can be applied to large scale problems which are not easily solved with the existing commercial packages

Production and inventory management under multiple resource constraints

In this paper we present a model and solution methodology for production and inventory management problems that involve multiple resource constraints. The model formulation is quite general, allowing organizations to handle a variety of multi-item decisions such as determining order quantities, production batch sizes, number of production runs, or cycle times. Resource constraints become necessary to handle interaction among the multiple items. Common types of resource constraints include limits on raw materials, machine capacity, workforce capacity, inventory investment, storage space, or the total number of orders placed. For example, in a production environment, there may be limited workforce capacity and limits on machine capacities for manufacturing various product families. In a purchasing environment where a firm has multiple suppliers, there are often constraints for each supplier, such as the total order from each supplier cannot exceed the volume of the truck. We present efficient algorithms for solving both continuous and integer variable versions of the resource constrained production and inventory management model. The algorithms require the solution of a series of two types of subproblems: one is a nonlinear knapsack problem and the other is a nonlinear problem where the only constraints are lower and upper bounds on the variables. Computational testing of the algorithms is reported and indicates that they are effective for solving large-scale problems

Nonlinear Blend Scheduling via Inventory Pinch-based Algorithm using Discrete- and Continuous-time Models

This work uses multi-period, inventory pinch-based algorithm with continuous-time model (MPIP-C algorithm1) for scheduling linear or nonlinear blending processes. MPIP-C decomposes the scheduling problem into (i) approximate scheduling and (ii) detailed scheduling. Approximate scheduling model is further decomposed into two parts: a 1st level model which optimizes nonlinear blend models (with time periods delineated by inventory pinch points), and a 2nd level multi-period mixed-integer linear programming model (which uses fixed blend recipes from the 1st level solution) to determine optimal production plan and swing storage allocation, while minimizing the number of blend instances and product changeovers in the swing tanks. The 3rd level computes schedules using a continuous-time model including constraints based on the short-term plan solution. Nonlinear constraints are used for the Reid vapor pressure in our case studies. Excellent computational performance is illustrated by comparisons with previous approach with discrete-time scheduling model

A two-dimensional non-equilibrium dynamic model

This paper develops a non-equilibrium dynamic model (NEDyM) with Keynesian features (it allows for a disequilibrium between output and demand and it considers a constant marginal propensity to consume), but where production is undertaken under plain neoclassical conditions (a constant returns to scale production function, with the stocks of capital and labor fully employed, is assumed). The model involves only two endogenous / prognostic variables: the stock of physical capital per unit of labor and a goods inventory measure. The two-dimensional system allows for a careful analysis of local and global dynamics. Points of bifurcation and long-term cyclical motion are identified. The main conclusion is that the disequilibrium hypothesis leads to persistent fluctuations generated by intrinsic deterministic factors

A linearization approach to the stochastic dynamic capacitated lotsizing problem with sequence-dependent changeovers

Inspired by the production and planning process of coreboard of our industry partner, a Fortune 1000 player in packaging, we present a mixed-integer linear programming model that can jointly optimize lot sizes, production sequences and safety stocks in the presence of sequence-dependent changeovers. First, we formulate a nonlinear (MINLP) model that can handle both the stochasticity and the sequence-dependency of the stochastic dynamic capacitated lotsizing problem, based on the stochastic sequence-independent (Tempelmeier et al. 2018) and deterministic sequence-dependent (Guimaraes et al. 2014) version of the problem. Then, we develop a piecewise linearization approach for the non-linear inventory on hand and backorder curves that builds on and challenges earlier research published by van Pelt and Fransoo 2018 and Tempelmeier et al. 2018. We use the derivatives of the inventory on hand and backorder functions to develop a tailored breakpoint selection strategy that reduces the maximum approximation error between the linearized and non-linear objective function from 20.3% to 0.5% in comparison to the equidistant linearization strategy recommend by the aforementioned articles. As a third and last contribution, we develop a Relax-and-Fix with Fix-andOptimize heuristic and show in an extensive numerical study that it improved the objective value by 20% on average and realized an average run time reduction of 60% over a state-of-the-art solver

Generating a robust model for production and inventory control

Ankara : Department of Industrial Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1993.Thesis (Master's) -- Bilkent University, 1993.Includes bibliographical references leaves 82-84In tills stud}', we generate a production and inventory control model which gives h'obust‘ solutions against demand estimation errors. This model is applied to a real production and inventory system; howe\’er, it is a general model where the demand rate is stochastic with a known probability distribution and other parameters of the system are constant. The proposed model is a bi-objective chicision making model, with two decision variables. .A ‘compromised* solution is found for the problem using the trade-off curve generated by a constrained sequential optimization technique, applied on a nonlinear programming model parametrically. Robustness against parameter estimation errors is tested by sensitivity analysis. Here a new dimension is added to sensitivity analysis methodology by including a sensitivity measure as a ‘cost of error* of parameter estimation. By so doing, the proposed model is tested against the classical EOQ model and it is shown that the proposed model ])erforms far better.Sencer, AslıM.S

Aggregate constrained inventory systems with independent multi-product demand: control practices and theoretical limitations

In practice, inventory managers are often confronted with a need to consider one or more aggregate constraints. These aggregate constraints result from available workspace, workforce, maximum investment or target service level. We consider independent multi-item inventory problems with aggregate constraints and one of the following characteristics: deterministic leadtime demand, newsvendor, basestock policy, rQ policy and sS policy. We analyze some recent relevant references and investigate the considered versions of the problem, the proposed model formulations and the algorithmic approaches. Finally we highlight the limitations from a practical viewpoint for these models and point out some possible direction for future improvements

The impact of nonlinear dynamics on the resilience of a grocery supply chain

Purpose of this paper: In an effort to improve operational and logistical efficiencies, UK grocery retailers combined primary and secondary distribution increasing the importance of designing resilient replenishment systems in the distribution centre. This paper has the purpose to analyse the resilience performance of the distribution centre stock ordering system within a grocery retailer. Design/methodology/approach: A system dynamics approach is used for framing and building a credible representation of the real system. Mathematical analysis of the nonlinear model based on nonlinear control engineering techniques in combination with system dynamics simulation have been used to understand the behaviour of stock and shipment output responses in the distribution centre given step and periodic demand signals. Findings: Preliminary mathematical analysis through nonlinear control theory techniques has been undertaken in order to gain initial insights in the understanding of the replenishment control model. This practice allowed the researcher to identify specific behaviour change in the DC stock and shipment responses, which are key indicators for assessing supply chain resilience, without going through a time-consuming simulation process. Transfer function analysis and describing function serve as a guideline for undertaking system dynamics simulation. Value: This paper aims to fill the gap in the literature of supply chain resilience by using quantitative system dynamics methods to assess the resilience performance of a grocery retailer. In this way, we also supplement the literature with empirical data. Moreover, we explore different analytical methods since simulation is the predominant method for quantitative analysis of system dynamics. Research limitations/implications (if applicable): This research is limited to the dynamics of single-echelon supply chain systems. Although the EPOS sales data and the store replenishment system have been considered in the validation process, this study has focused on analysing the resilience performance of the DC replenishment system only. Considering the multi-echelon supply chain is intended for further research activities. Practical implications (if applicable): The findings suggest that the distribution centre replenishment system can be re-designed in order to improve the supply chain resilience performance. The ‘As Is’ scenario produces slow response of stock levels and inventory targets are never recovered due to a permanent offset

Establishing a framework for the effective design of resilient supply chains with inherent non-linearities

Purpose of this paper: Previous control theory research on supply chain dynamics has predominantly taken a linear perspective of the real world, whereas nonlinearities have usually been studied via a simulation approach. Nonlinearities can naturally occur in supply chains through the existence of physical and economic constraints, for example, capacity limitations. Since the ability to flex capacity is an important aspect of supply chain resilience, there is a need to rigorously study such nonlinearities. Hence, the purpose of this paper is to propose a framework for the dynamic design of supply chains so that they are resilient to nonlinear system structures