Hybrid Meta-Heuristics for Robust Scheduling

The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimization problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronization, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel metaheuristic approach for robust scheduling. Our approach integrates mathematical programming, multi-objective evolutionary computation, and problem-specific constructive heuristics. The optimization algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete.Meta-Heuristics;Multi-Objective Genetic Optimization;Robust Scheduling;Supply Networks

Network Flexibility for Recourse Considerations in Bi-Criteria Facility Location

What is the best set of facility location decisions for the establishment of a logistics network when it is uncertain how a company’s distribution strategy will evolve? What is the best configuration of a distribution network that will most likely have to be altered in the future? Today’s business environment is turbulent, and operating conditions for firms can take a turn for the worse at any moment. This fact can and often does influence companies to occasionally expand or contract their distribution networks. For most companies operating in this chaotic business environment, there is a continuous struggle between staying cost efficient and supplying adequate service. Establishing a distribution network which is flexible or easily adaptable is the key to survival under these conditions. This research begins to address the problem of locating facilities in a logistics network in the face of an evolving strategic focus through the implicit consideration of the uncertainty of parameters. The trade-off of cost and customer service is thoroughly examined in a series of multi-criteria location problems. Modeling techniques for incorporating service restrictions for facility location in strategic network design are investigated. A flexibility metric is derived for the purposes of quantifying the similarity of a set of non-dominated solutions in strategic network design. Finally, a multi-objective greedy random adaptive search (MOG) metaheuristic is applied to solve a series of bi-criteria, multi-level facility location problems

A Bi-objective cap-and-trade model for minimising environmental impact in closed-loop supply chains

A Closed-Loop Supply Chain (CLSC) is a complex network with unique environmental features and attributes that requires specific managerial policies and strategies. Quantitative models can provide a solid basis for these policies and strategies. This study expands the work of Shoaeinaeini et al. (2021) on Green Supply Chain Management. We propose a bi-objective facility location, demand allocation, and pricing model for CLSC networks. The proposed model considers two conflicting objective functions: maximising profits and minimising emissions. We show consumer environmental awareness can predict the products’ rate of return and determine a more suitable price for new products and the acquisition price for used products. The cap-and-trade policy has been implemented at its fullest potential, allowing the trading of carbon quotas. Therefore, companies may decide to produce less to sell more quotas or vice-versa, effectively picking the most profitable option. The model is solved and tested with the commercial solver BARON. The model effectively shows the trade-off between generating profits and emission reduction. Companies are able to turn a profit while abiding by the government’s intention of reducing emissions. The comparison with a single-objective version of the model highlights that the concurrent optimisation of economic and environmental objectives yields better results. The acquisition price of used products is a value worthy of monitoring. The government should focus on policies to assist the reverse flow of used products

Bundled Payment vs. Fee-for-Service: Impact of Payment Scheme on Performance

Healthcare reimbursements in the United States have been traditionally based on a fee-for-service (FFS) scheme, providing incentives for high volume of care, rather than efficient care. The new healthcare legislation tests new payment models that remove such incentives, such as the bundled payment (BP) system. We consider a population of patients (beneficiaries). The provider may reject patients based on the patient’s cost profile and selects the treatment intensity based on a risk-averse utility function. Treatment may result in success or failure, where failure means that unforeseen complications require further care. Our interest is in analyzing the effect of different payment schemes on outcomes such as the presence and extent of patient selection, the treatment intensity, the provider’s utility and financial risk, and the total system payoff. Our results confirm that FFS provides incentives for excessive treatment intensity and results in suboptimal system payoff. We show that BP could lead to suboptimal patient selection and treatment levels that may be lower or higher than desirable for the system, with a high level of financial risk for the provider. We also find that the performance of BP is extremely sensitive to the bundled payment value and to the provider’s risk aversion. The performance of both BP and FFS degrades when the provider becomes more risk averse. We design two payment systems, hybrid payment and stop-loss mechanisms, that alleviate the shortcomings of FFS and BP and may induce system optimum decisions in a complementary manner. This paper was accepted by Serguei Netessine, operations management

Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques

Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims

Integrated Water Resources Management Modelling For The Oldman River Basin Using System Dynamics Approach

Limited freshwater supply is the most important challenge in water resources management, particularly in arid and semi-arid basins. However, other variations in a basin, including climate change, population growth, and economic development intensify this threat to water security. The Oldman River Basin (OMRB), located in southern Alberta, Canada, is a semi-arid basin and encompasses several water challenges, including uncertain water supply as well as increasing, uncertain water demands (consumptive irrigation, municipal, and industrial demands, and non-consumptive hydropower generation, and environmental demands). Reservoirs, of which the Oldman River Reservoir is the largest in the basin, are responsible for meeting most of demands, and, protecting the basin’s economy. The OMRB has also faced extreme natural events, floods and droughts, in the past, which reservoir management plays a critical role to adapt to. The complexity of the climate, hydrology, and water resource system and water governance escalates the challenges in the basin. These factors are highly interconnected and establish dynamic, non-linear behavior, which requires an integrated, feedback-based tool to investigate. Integrated water resources (IWRM) modelling using system dynamics (SD) is such an approach to tackle the different water challenges and understand their non-linear, dynamic pattern. In this research study the Sustainability-oriented Water Allocation, Management, and Planning (SWAMPOM) model for the Oldman River Basin is developed. SWAMPOM comprises a water allocation model, dynamic irrigation demand, instream flow needs (IFN), and economic evaluation sub-models. The water allocation model allocates water to all the above-mentioned demands at a weekly time step from 1928 to 2001, and under different water availability scenarios. Meeting irrigation demands relies on the crop water requirement (CWR), which is calculated under different climatic conditions by the dynamic irrigation demand sub-model. This sub-model estimates the weekly irrigation demand for main crops planted in the basin. SWAMPOM also computes environmental demands or instream flow need (IFN) for the Oldman River, and allocates water to rivers to meet IFN under different policy scenarios and uncertain water supply. Finally, the major water-related economic benefit in the basin, earned by agriculture and hydropower generation, is computed by the economic evaluation sub-model. The results show that SWAMPOM could reasonably satisfy the demands at a weekly time step and provide an adequate estimation of the crop water requirement under different hydrometeorological conditions. Based on the SWAMPOM’s results, the average annual irrigation demand is 306 mm over the historical time period from 1928 to 2001 in the main irrigation districts. The average weekly instream flow need of the Oldman River is calculated to be approximately 20.5 m3/s, which can be met in more than 97% of weeks in the historical time period. Average annual water-related economic benefit was computed to be 192.5 M$in the OMRB. It decreased to 82.8 M$ in very dry years, and increased up to 328.6 M$ in very wet years. This research also developed different sets of Oldman Reservoir’s operation zones, resulting in trade-offs between the optimal economic benefit, water allocated to the ecosystem, minimum floodwater and minimum flood frequency. This helps decision makers to decide how much water should be stored in the reservoir to meet a specific objective while not sacrificing others. A multi-objective performance assessment, Pareto curve approach, is applied to identify the optimal trade-offs between the four objective functions (OFs), and 18 different optimal, or close to optimal sets of operating zones are provided. The decision regarding the operating zones depends on decision makers’ preference for higher economic benefit, water allocated to IFN, or flood security. However, the set of operating zones with minimum floodwater causes 11 less flood events; the operating zones with maximum economic benefits result in 4.1% more financial gain; and the zones with maximum water allocated to IFN lead to 10.1% more ecosystem protection in the whole 74 years, compared to current zones

Optimising Supply Chain Performance via Information Sharing and Coordinated Management

Supply chain management has attracted much attention in the last decade. There has been a noticeable shift from a traditional individual organisation-based management to an integrated management across the supply chain network since the end of the last century. The shift contributes to better decision making in the supply chain context, as it is necessary for a company to cooperate with other supply chain members by utilising relevant information such as inventory, demand and resource capacity. In other words, information sharing and coordinated management are essential mechanisms to improve supply chain performance. Supply chains may differ significantly in terms of industry sectors, geographic locations, and firm sizes. This study was based on case studies from small and medium sized manufacturing supply chains in People Republic of China. The study was motivated by the following facts. Firstly, small and medium enterprises have made a big contribution to China’s economic growth. Several studies revealed that most of the Chinese manufacturing enterprises became aware of the importance of supply chain management, but compared to western firms, the supply chain management level of Chinese firms had been lagging behind. Research on supply chain management and performance optimisation in Chinese small and medium sized enterprises (SMEs) was very scarce. Secondly, there had been plenty of studies in the literature that focused on two or three level supply chains whilst considering a number of uncertain factors (e.g. customer demand) or a single supply chain performance indicator (e.g. cost). However, the research on multiple stage supply chain systems with multiple uncertainties and multiple objectives based on real industrial cases had been spared and deserved more attention. One reason was due to the lack of reliable industrial data that required an enormous effort to collect the primary data and there was a serious concern about data confidentiality from the industry aspect. This study employed two SME manufacturing companies as case studies. The first one was in the Aluminium industry and another was in the Chemical industry. The aim was to better understand the characteristics of the supply chains in Chinese SMEs through performing in-depth case studies, and built models and tools to evaluate different strategies for improving their supply chain performance. The main contributions of this study included the following aspects. Firstly, this study generalised a supply chain model including a domestic supply chain part and an international supply chain part based on deep case studies with the emphasis on identifying key characteristics in the case supply chains, such as uncertainties, constraints and cost elements in association with flows and activities in the domestic supply chain and the international supply chain. Secondly, two important SCM issues, i.e. the integrated raw material procurement and finished goods production planning, and the international sales planning, were identified. Thirdly, mathematical models were formulated to represent the supply chain model taking into account multiple uncertainties. Fourthly, several operational strategies utilising the concepts of just-in-time, safety-stock/capacity, Kanban, and vendor managed inventory, were evaluated and compared with the case company's original strategy in various scenarios through simulation methods, which enabled quantification of the impact of information sharing on supply chain performance. Fifthly, a single objective genetic algorithm was developed to optimise the integrated raw material ordering and finished goods production decisions under (s, S) policy (a dynamic inventory control policy), which enabled the impact of coordinated management on supply chain performance to be quantified. Finally, a multiple objectives genetic algorithm considering both total supply chain cost and customer service level was developed to optimise the integrated raw material ordering and finished goods production with the international sales plan decisions under (s, S) policy in various scenarios. This also enabled the quantification of the impact of coordinated management on supply chain performances