Linear and nonlinear shelf space allocation problemswith vertical and horizontal bands

Product category management (PCM) plays a pivotal role in today’s large stores. PCM manages to answer questions such as assortment planning (AP) and shelf space allocation (SSA). AP problem seeks to determine a list of products and suppliers, while SSA problem tries to design the layout of the selected products in the available shelf space. These problems aim to maximize the retailer sales under different constraints, such as limited purchasing budget, limited space of classes for displaying the products, and having at least a certain number of suppliers. This paper makes an attempt to develop an integrated mathematical model to optimize integrated AP, SSA, and inventory control problem for the perishable products. The objective of the model is to maximize the sales and retail profit, considering the costs of supplier contracting/selecting and ordering, assortment planning, holding, and procurement cost. GAMS BARON solver is hired to solve the proposed model in small and medium scales. However, because the problem is NP-hard, an evolutionary genetic algorithm (GA), and an efficient local search vibration damping optimization (VDO) algorithm are proposed. A real case study is considered to evaluate the effectiveness and capabilities of the model. Besides, some test problems of different sizes are generated and solved by the proposed metaheuristic solvers to confirm the efficient performance of proposed algorithms in solving large-scale instances.

Product category management (PCM) plays a pivotal role in today's large stores. PCM manages to answer questions such as assortment planning (AP) and shelf space allocation (SSA). AP problem seeks to determine a list of products and suppliers, while SSA problem tries to design the layout of the selected products in the available shelf space. These problems aim to maximize the retailer sales under different constraints, such as limited purchasing budget, limited space of classes for displaying the products, and having at least a certain number of suppliers. This paper makes an attempt to develop an integrated mathematical model to optimize integrated AP, SSA, and inventory control problem for the perishable products. The objective of the model is to maximize the sales and retail profit, considering the costs of supplier contracting/selecting and ordering, assortment planning, holding, and procurement cost. GAMS BARON solver is hired to solve the proposed model in small and medium scales. However, because the problem is NP-hard, an evolutionary genetic algorithm (GA), and an efficient local search vibration damping optimization (VDO) algorithm are proposed. A real case study is considered to evaluate the effectiveness and capabilities of the model. Besides, some test problems of different sizes are generated and solved by the proposed metaheuristic solvers to confirm the efficient performance of proposed algorithms in solving large-scale instances.

Efficiently managing retail space is critical as the increase in product variety is in conflict with limited shelf space and instore replenishment constraints. This paper develops a general framework for retail space management and presents a decision support model with the related problems within the framework of optimizing assortment, shelf space assignment and replenishment. An integrative approach to these planning problems becomes particularly relevant for fast-moving consumer goods and groceries, where stores are regularly replenished from distribution centers. The planning problem at hand is a multi-product shelf space allocation problem where demand is a composite function of the shelf space allocated and assortment-related demand substitution, and actual replenishment practices from retail are incorporated. The model developed extends existing models of shelf space management by jointly considering space-elastic demand and assortment-based substitution and integrating restocking constraints. For the latter, we consider real-world replenishment processes of retailers that distinguish between period-based and ad-hoc replenishment from the backroom. We develop three solution approaches that are based on efficient pre-processing and a nonlinear binary integer programming formulation of the problem. The computation tests based on retail data show the efficiency of the solution approaches in terms of computation time and solution quality. We reveal the improvement in profit levels that can be achieved from integrating assortments, shelf space planning and replenishment where challenges arise in obtaining feasible solutions with limited shelf space and replenishment constraints. We also use sensitivity analyses to demonstrate the high impact of replenishment constraints on profits and solution structures.

Comparison of the nonlinear and linear forms of the van't Hoff equation for calculation of adsorption thermodynamic parameters (∆S° and ∆H°)

For the retailing, the shelf space allocation is a key issue which impacts on the retailer's product sales and profits directly. For the small-scale supermarket, due to the small store and the product variety, the traditional "format style" shelf space layout is not appropriate. To solve this problem, a shelf space allocation solution based on data mining techniques is proposed in this paper. With the introduction of association distance, we realize the description of the relevance between two any items purchased together in transaction database. With the concept of promotions coefficient, we get the description of the promotions ability of two items. Based on these concepts, with the cluster analysis techniques and the association rule mining technique, we propose a resolution based on "island style", and get the optimization and automation of shelf space allocation in the small-scale supermarket.

This paper introduces a novel class of generalized assignment problems with location/allocation considerations that arises in several applications including retail shelf space allocation. We consider a set of items where each item may represent a family of products and a set of variable-sized knapsacks that may represent shelves, which comprise contiguous segments having distinct attractiveness. The decision-maker seeks to assign the set of items to these knapsacks, specify their segment assignments within knapsacks, and determine their total allocated space within predetermined lower/upper bounds in a fashion that maximizes a reward-based objective function. We develop an effective optimization-based very large-scale neighborhood search (VLNS), which greatly outperforms the best solution identified by CPLEX within one CPU hour, whereas general-purpose solver heuristics failed to provide feasible solutions to most of the larger instances within a time limit comparable to the VLNS algorithm run times. Our computational study was carried out on randomly generated computationally challenging instances with up to 210 items and 42 knapsacks and on a case study motivated by a shelf space allocation problem. Our results demonstrate that the proposed approach consistently produces high-quality solutions.

Exact Optimization and Decomposition Approaches for 2D Shelf Space Allocation

The increasing product variety trend prevalent in most industries today does not stop at retailers. Unfortunately, this trend is a mounting task due to limited shelf space. Based on a survey of 376 people, this research provides an insightful picture of the connection between product variety and impulse buying decisions. One of the study's main findings is that product variety offered by retailers is one of the main in-store factors driving impulse purchases. Since the survey results show the importance of product variety for retailers, the paper also presents some concepts as to how retailers can deal with the numerous product varieties under the condition of limited shelf spaces, e.g., by allowing manufacturers to compete for shelf space.

The paper purpose is to present some aspects regarding the calculus model and technical solutions for small satellites attitude control. Mathematical model is put in nonlinear and linear form. The linear form is used for attitude control system synthesis. The attitude control system obtained is used in nonlinear form in order to maintain desired attitude. A few numerical simulations are made for standard input and the satellite behavior is obtained. The satellite model presented will be with six DOF and use Cartesian coordinates. At this item, as novelty of the work we will use the rotation angles to describe the kinematical equations. Also this paper proposes a Fourier linearising of Trigger Schmidt element used for applying the command moment. The results analyzed will be the rotation angles of the satellite as well the rotation velocity. The conclusions will focus the comparison between results obtained using optimal attitude control system, and the possibility to use such system for small satellite.

Asignificant amount of work has investigated inventory control problems associated with fresh produce. Much of this work has considered deteriorating inventory control with many models having been proposed for various situations. However, no researchers have specifically studied fresh produce, which has its own special characteristics. Most research categorizes fresh produce into more general deteriorating categories with random lifetimes and nondecaying utilities. However, this classification is not reasonable or practical because the freshness of an item usually plays an important role in influencing the demand for the produce. In this paper, a single-period inventory and shelf-space allocation model is proposed for fresh produce. These items usually have a very short lifetime. The demand rate is assumed to be deterministic and dependent on both the displayed inventory (the number of facings of items on the shelves) and the items' freshness condition (which decreases over time). Several problem instances of different sizes are provided and solved by a modified generalized reduced gradient algorithm.

Chapter 10 - Move Merchandise

PVA/PDADMAC/ZSM-5 zeolite hybrid matrix membranes for dye adsorption: Fabrication, characterization, adsorption, kinetics and antimicrobial properties

Allocation of shelf space: some theoretical considerations

This paper deals with the issue of shelf-space allocation and advertising decisions in marketing channels. We consider a network composed of a unique retailer offering the products of two competing manufacturers. The retailer controls the amount of shelf-space to allocate to both brands, while the manufacturers make advertising decisions in order to build their brand image (i.e. the goodwill stock). The demand for each brand is affected by its own goodwill level and the shelf-space allocated to the brand at retailer's store. The problem is formulated as a Stackelberg differential game played over an infinite horizon, with the manufacturers as leaders and the retailer as the follower. Stationary feedback equilibria are computed. Our main results indicate that the shelf-space allocated to each brand, manufacturers' advertising strategies at the equilibrium and channel members' value functions are affected by the goodwill levels of both products.

A model and solution approach for store-wide shelf space allocation