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Because increasing product variety in retail conflicts with limited shelf space, managing assortment and shelf quantities is a core decision in this sector. A retailer needs to define the assortment size and then assign shelf space to meet consumer demand. However, the current literature lacks not only information on the comprehensive structure of the decision problem, but also a decision support system that can be directly applied to practice in a straightforward manner. The paper aims to discuss these issues.

The findings were developed and evaluated by means of explorative interviews with grocery retail experts. An optimization model is proposed to solve the problem of assortment planning with limited shelf space for data sets of a size relevant in real retail practice.

The author identifies the underlying planning problems based on a qualitative survey of retailers and relates the problems to each other. This paper develops a pragmatic approach to the capacitated assortment problem with stochastic demand and substitution effects. The numerical examples reveal that substitution demand has a significant impact on total profit and solution structure.

The author shows that the model and solution approach are scalable to problem sizes relevant in practice. Furthermore, the planning architecture structures the related planning questions and forms a foundation for further research on decision support systems.

The planning framework structures the associated decision problems in assortment planning. An efficient solution approach for assortment planning is proposed.

Carbon emission is a significant issue for the current business market and global warming. Nowadays, most countries have focused to reduce the environmental impact of business with durable financial benefits. The purpose of this study is to optimize the entire cost functions with carbon emission and to find the sustainable optimal ordering quantity for retailers.

This paper illustrates a sustainable inventory model having a set of two non-instantaneous substitutable deteriorating items under joint replenishment with carbon emission. In this model demand and deterioration rate are considered as deterministic, constant and triangular fuzzy numbers. The objective is to find the optimal ordering quantity for retailers and to minimize the total cost function per unit time with carbon emission. The model is then solved with the help of Maple software.

This paper presents a solution method and also develop an algorithm to determine the order quantities which optimize the total cost function. A numerical experiment illustrates the improvement in optimal total cost of the inventory model with substitution over without substitution. The graphical results show the convexity of the cost function. Finally, sensitivity analysis is given to get the impact of parameters and validity of the model.

This study considers a set of two non-instantaneous substitutable deteriorating items under joint replenishment with carbon emission. From the literature review, in the authors’ knowledge no researcher has undergone this kind of study.

This chapter examines factors that cause violations of regularity conditions and biases in estimates of substitution. In the context of the Fourier demand system, failing to impose curvature restrictions but correcting for serial correlation results in few violations of the curvature conditions. In contrast, imposing curvature restrictions without correcting for serial correlation biases substitution estimates and can cause violations of monotonicity. For serially correlated data, results suggest that correcting for serial correlation may be more important than imposing curvature. Furthermore, the artificially break-adjusted data that are inconsistent with consumer optimization can severely bias estimates. Results from the Bank of England's (BOE) preferred non-break-adjusted data establish that money and goods are substitutes in demand.

This paper focuses on multi-objective order allocation with product substitution for the vaccine supply chain under uncertainty.

The weighted-sum minimization approach is used to find a compromised solution between three objectives of minimizing inefficiently vaccinated people, postponed vaccinations, and purchasing costs. A mixed-integer formulation with substitution quantities is proposed, subject to capacity and demand constraints. The substitution ratios between vaccines are assumed to be exogenous. Besides, uncertainty in supplier reliability is formulated using optimistic, most likely, and pessimistic scenarios in the proposed optimization model.

Covid-19 vaccine supply chain process is studied for one government and three vaccine suppliers as an illustrative example. The results provide essential insights for the governments to have proper vaccine allocation and support governments to manage the Covid-19 pandemic.

This paper considers the minimization of postponement in vaccination plans and inefficient vaccination and purchasing costs for order allocation among different vaccine types. To the best of the authors’ knowledge, there is no study in the literature on order allocation of vaccine types with substitution. The analytical hierarchy process structure of the Covid-19 pandemic also contributes to the literature.

Baumol has argued that problems experienced by cities in providing metropolitan services might be the product of an uneven growth process. Slow productivity growth sectors, he argues, could experience increased real costs and, unless demand for their service is highly inelastic, their outputs could decline severely. Looks at these issues in a framework which allows explicitly both for demand, for examination of the sources of productivity growth, and for substitution between productive factors. This extended framework allows for verification and extension of Baumol’s results. For instance, it is found that with uneven growth the relative price of the non‐progressive sector’s output will rise if that good is normal.

This chapter proposes a multiobjective model to design a Closed Loop Supply Chain (CLSC) network. The first objective is to minimize the total cost of the network, while the second objective is to minimize the carbon emission resulting from production, transportation, and disposal processes using carbon cap and carbon tax regularity policies. In the third objective, we maximize the service level of retailers by using maximum covering location as a measure of service level. To model the proposed problem, a physical programming approach is developed. This work contributes to the literature in designing an optimum CLSC network considering the service level objective and product substitution.

This study investigates the pricing and inventory control decisions of a company that owns a coffeehouse chain providing drip coffee and a coffee manufacturing factory producing ready-to-drink (RTD) coffee. To determine a way to maximize profit, optimal pricing and inventory control strategies are studied for both the RTD coffee as well as the coffeehouse chain network.

It is assumed that the company sells only drip coffee through its coffeehouse chain and would like to launch RTD coffee via other channels such as convenience stores, supermarkets and so on to maximize its total profit. A mathematical model–based optimization is adopted to address the decision-making for the given problem situation, where the demand for both drip and RTD coffee are dependent on the values of decision variables. To solve the proposed mathematical model, particle swarm optimization (PSO) is applied due to nonlinearity of the developed model.

It is confirmed that a company can earn more profit by launching RTD coffee, even though the profit from drip coffee would reduce. In addition, the scenario analysis shows that by launching RTD coffee under various conditions, the total profits would also improve.

The value of this study is the proposed basic framework for the industry. In addition to the modeling framework and cost structure, realistic cost figures and technical details can be considered when applying the model to a practical setting.

This chapter explains dollarization process in Turkey by an extended portfolio model where dollarization is determined by the relative rates of return of domestic and foreign currencies denominated assets, expected change in the exchange rate, exchange rate risk, and credibility of current economic policies. The econometrics results are in line with the intuitive predictions of the model. We have found that interest rate differential and the expected exchange rates are the dominant variables in determining dollarization. This chapter also provides evidence of inertia in the process of dollarization in Turkey.

This research estimates a multi-product flexible cost function of airport variable costs. Data for the analysis are a panel of 50 airports from 1996 to 2008. Output includes domestic and international departures, non-aeronautical operating revenues, and the number of transport workload units, where a workload unit is a passenger or the equivalent of a 220 pound packet of cargo. The quasi-fixed factor is the equivalent number of 10,000′ × 150′ runways at an airport. After correcting for first-order serial correlation, the analysis finds that airports operate under constant returns to runway utilization and multi-product decreasing returns to scale, production technology is consistent with product specific returns to capacity utilization and anti-complementarity across outputs, and general airport operations have input substitution possibilities with personnel and contractual repair/maintenance inputs. The study also finds 1.05% technology progress over the sample period, due to strong growth prior to 2001, with similar productivity growth rates for large and medium hubs.