Non-permutation flowshop scheduling in a supply chain with sequence-dependent setup times

In this paper, we consider a flowshop scheduling problem with sequence-dependent setup times and a bicriteria objective to minimize the work-in-process inventory for the producer and to maximize the customers' service level. The use of a bicriteria objective is motivated by the fact that successful companies in today's environment not only try to minimize their own cost but also try to fulfill their customers' need. Two main approaches, permutation and non-permutation schedules, are considered in finding the optimal schedule for a flowshop. In permutation schedules the sequence of jobs remains the same on all machines whereas in non-permutation schedule, jobs can have different sequence on different machines. A linear mathematical model for solving the non-permutation flowshop is developed to comply with all of the operational constraints commonly encountered in the industry, including dynamic machine availabilities, dynamic job releases, and the possibility of jobs skipping one or more machines, should their operational requirements deem that it was necessary. As the model is shown to be NP-hard, a metasearch heuristic, employing a newly developed concept known as the Tabu search with embedded progressive perturbation (TSEPP) is developed to solve, in particular, industry-size problems efficiently. The effectiveness and efficiency of the search algorithm are assessed by comparing the search algorithmic solutions with that of the optimal solutions obtained from CPLEX in solvable small problem instances.     

Modeling determinants of agricultural production cooperatives' performance in Iran

The purpose of this article is to identify the fundamental factors that are key determinants in promoting effective operation of Agricultural Production Cooperatives (APCs). A theoretical framework “determinants of APCs' performance” was used to analyze the links between the webs of factors. A survey method was used to collect data from two southern provinces in Iran. Data were collected from 52 managers and 260 member farmers in 52 APCs using a stratified random sampling method. It is concluded that the theoretical model can to a great extent predict the performance of APCs. Cooperative structure and government support factors are the most important factors explaining the performance of APCs. Also, policy strategies need to focus on conditioning variables that affect the performance of APCs.     

An approximation formula for normal implied volatility under general local stochastic volatility models

We approximate normal implied volatilities by means of an asymptotic expansion method. The contribution of this paper is twofold: to our knowledge, this paper is the first to provide a unified approximation method for the normal implied volatility under general local stochastic volatility models. Second, we applied our framework to polynomial local stochastic volatility models with various degrees and could replicate the swaptions market data accurately. In addition we examined the accuracy of the results by comparison with the Monte‐Carlo simulations.     

An Algorithm for Allocating Sponsored Recommendations and Content: Unifying Programmatic Advertising and Recommender Systems

Retailing and media platforms recommend two types of items to their users: sponsored items that generate ad revenue and nonsponsored ones that do not. The platform selects sponsored items to maximize ad revenue, often through programmatic auctions, and nonsponsored items to maximize user utility with a recommender system (RS). We develop a binary integer programming model to allocate sponsored recommendations considering dual objectives of maximizing ad revenue and user utility. We propose an algorithm to solve it in a computationally efficient way. Our method is a form of postfiltering to a traditional RS, making it widely applicable in two-sided markets. We apply the algorithm to data from an online grocery retailer and show that user utility for the recommended items can be improved while reducing ad revenue by a small amount. This multiobjective approach unifies programmatic advertising and RS and opens a new frontier for advertising and RS research. We provide an extended discussion of future research topics.