Game-theoretic analysis to examine how government subsidy policies affect a closed-loop supply chain decision

The pros and cons of government subsidy policies in a closed-loop supply chain (CLSC) setting on optimal pricing, investment decisions in improving product quality, and used product collection under social welfare (SW) optimization goal have not been examined comprehensively. This study compares the outcomes of three government policies under manufacturer-Stackelberg (MS) and retailer-Stackelberg (RS), namely (i) direct subsidy to the consumer, (ii) subsidy to the manufacturer to stimulate used product collection, and (iii) subsidy to the manufacturer to improve product quality. Results demonstrate that the greening level, used product collection, and SW are always higher under the RS game, but the rate of a subsidy granted by the government is always higher under the MS game. Profits for the CLSC members and SW are always higher if the government provides a subsidy directly to the consumer, but productivity of investment in the perspective of the manufacturer or government are less. In a second policy, the government organizations grant a subsidy to the manufacturer to stimulate used product collection, but it does not necessarily yield the desired outcome compared to others. In a third policy, the manufacturer receives a subsidy on a research and development (R&D) investment, but it yields a sub-optimal greening level. This study reveals that the outcomes of subsidy policies can bring benefit to consumers and add a degree of complication for CLSC members; government organizations need to inspect carefully among attributes, mainly product type, power of CLSC members, and investment efficiency for the manufacturer, before implementing any subsidy policies so that it can lead to an environmentally and economically viable outcome

Scheduling unmanned aerial vehicle and automated guided vehicle operations in an indoor manufacturing environment using differential evolution-fused particle swarm optimization

Intelligent manufacturing technologies have been pursued by the industries to establish an autonomous indoor manufacturing environment. It means that tasks, which are comprised in the desired manufacturing activities, shall be performed with exceptional human interventions. This entails the employment of automated resources (i.e. machines) and agents (i.e. robots) on the shop floor. Such an implementation requires a planning system which controls the actions of the agents and their interactions with the resources to accomplish a given set of tasks. A scheduling system which plans the task executions by scheduling the available unmanned aerial vehicles and automated guided vehicles is investigated in this study. The primary objective of the study is to optimize the schedule in a cost-efficient manner. This includes the minimization of makespan and total battery consumption; the priority is given to the schedule with the better makespan. A metaheuristic-based methodology called differential evolution-fused particle swarm optimization is proposed, whose performance is benchmarked with several data sets. Each data set possesses different weights upon characteristics such as geographical scale, number of predecessors, and number of tasks. Differential evolution-fused particle swarm optimization is compared against differential evolution and particle swarm optimization throughout the conducted numerical simulations. It is shown that differential evolution-fused particle swarm optimization is effective to tackle the addressed problem, in terms of objective values and computation time. </jats:p

A multi-parent genetic algorithm for solving longitude–latitude-based 4D traveling salesman problems under uncertainty

In this study, we propose a mathematical model of a 4D clustered traveling salesman problem (CTSP) to address the cost-effective security and risk-related difficulties associated with the TSP. We used a multiparent-based memetic genetic algorithm to optimize paths between all clusters and proposed unique heuristic approaches to create clusters and reconnect them. We constructed a 4D CTSP considering multiple routes between two locations and multiple available vehicles on each route. Travel expenses and risks impact every itinerary; however, the behaviors of these costs and risks are always uncertain. We inspected various standard benchmark problems from (TSPLIB) using the proposed calculations. Real-life problems in the tourism industry motivate a longitude–latitude-based CTSP with risk constraints. Thus, we determined the risk of each path based on longitude and latitude. The contributions of this study are twofold: developing a genetic algorithm and heuristics based on mathematical modeling of a real problem.</p

Procurement planning in a multi-period supply chain: An epiphany

Pricing and inventory distribution strategy in a multi-period supply chain environment has not yet been comprehensively explored. We derive closed-form solutions for both two-and three-echelon supply chains under a manufacturer-stackelberg game framework where the supply chain members execute integrated procurement planning by taking account upto four consecutive selling periods. Optimal pricing and inventory distribution policy is identified in the perspective of each member among three pragmatic procurement scenarios. Our results demonstrate that both integrated procurement strategies outperform conventional single-period decision and reduce double-marginalization effect. But, the retailer can prefer bulk procurement to earn maximum profit. The distributor acts as a catalyst, prevents the retailer from executing integrated multi-period procurement planning and creates conflict among supply chain members. Procurement decisions in presence of strategic inventory may lead to suboptimal profits compared to bulk procurement and supply chain members can face some implementation issues. Supply chain members always receive higher profits if the retailer distributes inventory strategically in a multi-period supply chain. Keywords: Multi-period supply chain, Inventory, Procuremen