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Appendix A. Supplementary materials: Acrobat PDF file (378KB) available at: https://www.sciencedirect.com/science/article/pii/S0377221723000668?via%3Dihub#sec0023 (Supplementary Data S1. Supplementary Raw Research Data. This is open data under the CC BY license https://creativecommons.org/licenses/by/4.0/.) Copyright © 2023 The Author(s). The tradition of marketing only aesthetically agreeable produce by retailers contributes to a major source of food loss through “ugly veg”, i.e., the produce that does not look “regular”. In this paper, we examine the relations between different tiers of agri-food supply chains to study the impact of marketing ugly veg on different supply chain members and the food loss in the system. We examine and compare scenarios of a centralized supply chain, a traditional supply chain without ugly veg, an ugly veg supply chain with a single retailer offering both regular produce and ugly veg, and a two-retailer supply chain where an auxiliary retailer sells the ugly veg. We characterize the equilibrium decisions in these systems and also provide analytical results and insights on the effectiveness of different supply chain designs based on a comprehensive numerical study. We demonstrate the conditions under which the supply chain can reduce overall food loss. For sufficiently high cost of effort, selling ugly veg through the single retailer reduces food loss. Nonetheless, the grower is generally better off offering the ugly veg to an auxiliary retailer. We show that the ratio of food loss per cultivated land always decreases in the two-retailer supply chain, while the total food loss might increase for sufficiently high cost of effort.

Human trafficking, the commercial exploitation of individuals, is a gross violation of human rights and harms societies, economies, health and development. The related disciplines of Operations Management (OM), Analytics, and Operations Research (OR) are uniquely positioned to support trafficking prevention and intervention efforts by efficiently evaluating a plethora of decision alternatives, and providing quantitative, actionable insights. As operations and analytical efforts in the counter-trafficking field emerge, it is imperative to grasp subtle yet distinctive nuances associated with human trafficking. This note is intended to inform those practitioners working in the field by highlighting key features of human trafficking activity. We grouped nine themes around three broad categories: (1) representation of human trafficking, (2) consideration of survivors and communities, and (3) analytics related. These insights are derived from our collective experience in working in this area and substantiated by domain expertise.

Public decisions are typically related to large investments leaving long legacies. We should therefore strive for wide societal agreement regarding such decisions, which meet the diversity of preferences between stakeholders and over time. But if, how and why do stakeholder preferences change over time? In decision analysis, these questions received little attention. We explored them using three real-world public decision processes, based on Multi-Criteria Decision Analysis (MCDA). We used repeatedly elicited ranking of objectives over time. These were obtained during three to five moderated workshops we organised several months apart (total N = 200 questionnaires, and 100 stakeholders). We analysed individual and aggregated (group) preferences, their changes and potential drivers including demographic and experience variables. We also analysed the effect of preference evolution on the performance of decision-alternatives with MCDA over time. We found that stakeholder preferences often changed over time, both on an individual and group level. These changes did not systematically diminish over time, but some convergence of preferences was observed for stakeholders who repeatedly participated in workshops. High-ranking objectives were relatively stable and similar between stakeholders. While preference changes could not be explained by demographics and personal experiences, repeated interaction with the decision problem might play a role. Neither the observed disagreement between stakeholders, nor the preference changes over time affected the best and worst performing alternatives in our decision problems. Thus, despite changing stakeholder preferences over time, public decision-makers can contrive robust solutions to complex public decision problems in the present.

Energy efficiency in manufacturing can be improved by controlling energy modes and production dynamically. We examine a production-inventory system that can operate in Working, Idle, and Off energy modes with mode-dependent energy costs. There can be a warm-up delay to switch between one mode to another. With random inter-arrival, production and warm-up times, we formulate the problem of determining in which mode the production resource should operate at a given time depending on the state of the system as a stochastic control problem under the long-run average profit criterion considering the sales revenue together with energy, inventory holding and backlog costs. The optimal solution of the problem for the exponential inter-arrival, production and warm-up times is determined by solving the Markov Decision Process with a linear programming approach. The structure of the optimal policy for the exponential case uses two thresholds to switch between the Working and Idle or Working and Off modes. We use the two-threshold policy as an approximate policy to control a system with correlated inter-event times with general distributions. This system is modelled as a Quasi Birth and Death Process and analyzed by using a matrix-geometric method. Our numerical experiments show that the joint production and energy control policy performs better compared to the pure production and energy control policies depending on the system parameters. In summary, we propose a joint energy and production control policy that improves energy efficiency by controlling the energy modes depending on the state of the system.