A credibility-based multi-objective temporary logistics hub location-allocation model for relief supply and distribution under uncertainty

Disaster response operations revolve around uncertainties. While uncertainties arising due to randomness can be avoided for post-disaster location problem, those arising because of impreciseness may persist long after the disaster's occurrence. Despite the uncertainties and lack of sufficient information about the extent of the damage, disaster response facilities must be established quickly after the occurrence of the disaster. Moreover, the decisions of whether to open, where to locate, and when to open disaster response facilities are based on the amount and quality of information available during the decision-making period. To address these issues, we develop a multi-objective location-allocation model for relief supply and distribution that accounts for the imprecise and time-varying nature of different parameters and time-varying coverage, while also accommodating the subjective attributes necessary to enable establishment and operation of the temporary logistics hubs (TLHs). A credibility-based fuzzy chance-constrained programming model is employed to account for the impreciseness inherent in predicting parameter values during disaster response. The results show where, when, and how many TLHs to open and how to allocate relief supplies. Meanwhile, the sensitivity analysis provides a broader understanding of the impact of limiting the number of TLHs as well as the confidence level and the spread of the symmetric triangular fuzzy numbers on the attainment of the model objectives.     

Designing an integrated humanitarian logistics network for the preparedness and response phases under uncertainty

In the current paper, a model of possibilistic location-allocation is proposed for designing a multi-period bi-objective humanitarian logistics network that pursues a reduction in the total cost and maximizes the total network coverage. The model considers the inventory management of perishable relief items and the flow of affected and wounded people in pre-disaster and post-disaster phases, simultaneously. Moreover, wounded people are categorized according to the injury severity. The uncertainty associated with key parameters is also addressed. To handle the uncertainty, a fuzzy chance-constrained programming method originated in the Me measure is used. The purpose of this measure is to prevent the extreme attitudes of the decision maker by considering the combination of possibility and necessity measures and using the optimistic-pessimistic parameter. This paper presents a solution procedure derived from a fuzzy interactive programming approach and two meta-heuristic algorithms, imperialist competitive algorithm and invasive weed optimization, so as to solve the study model. As for the validation of the proposed model and solution procedures, a number of test problems have been generated. A real-life case study is also implemented to instantiate whether the proposed model is applicable or not.     

Joint inventory and transshipment decisions with consumer behavioral heterogeneity

Transshipment is an effective method for reducing mismatches between supply and demand among retailers. Consumers attempting to purchase out-of-stock items may wait for transshipment, purchase at another store, or choose not to buy. In this paper, the consumer behavioral heterogeneity is characterized using the transshipment request rate and consumer switching rate. It affects the replenishment and transshipment decisions, as well as the system profits. The inventory replenishment and transshipment decisions are studied in both centralized and decentralized two-location inventory systems with consumer behavioral heterogeneity. We characterize optimal replenishment decisions in a centralized system, prove the existence of a unique Nash equilibrium in a decentralized system with a specific demand distribution, and determine the coordinating transshipment price for some decentralized systems. In numerical studies, the performance of various systems is analyzed for consumers with identical (symmetric) or differing (asymmetric) behavior between retailers. For scenarios with symmetric consumers, a higher transshipment request rate and consumer switching rate resulted in increased total profit in all systems. For scenarios with asymmetric consumers, the retailer with the higher consumer switching rate should reduce ordering in a centralized system but increase ordering in a decentralized system. Moreover, the retailer with the higher transshipment request rate reduces ordering to increase profit, whereas the other retailer increases order quantity yet earns less profit.     

Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories

Demand uncertainty coupled with the short lifespan of itemized platelets has caused a significant shortage at public therapeutic centers. In this regard, this paper presents a multi-objective robust stochastic mixed integer linear programming model for an integrated platelet supply chain considering unidirectional lateral transshipment between hospitals and clinics. In the proposed model, platelets are divided into typical, irradiated and washed platelets with lifespan differentiated for various types of patients. This model evaluates the risk of inappropriate production by concentrating on three different procedures for extracting platelet, including apheresis, platelet rich plasma and buffy coat methods. In this investigation, staff errors, laboratory hygiene and device failure, as well as donors' blood contamination, are considered for obtaining the function of inappropriate production rate. In our model, the logistics costs are considered as the first objective and the maximum unsatisfied demand as the second objective, both also to be minimized. To tackle demand uncertainty, a robust stochastic approach is then developed. Afterwards, to create a more realistic model, the Monte Carlo sampling approach is used for generating required scenarios. Finally, some managerial insights are provided through a real case study to evaluate the applicability of the presented model. The results indicate that the proposed model not only improves the demand satisfaction but also reduces the risk of itemized platelets wastage.     

A general method for addressing forecasting uncertainty in inventory models

In practice, inventory decisions depend heavily on demand forecasts, but the literature typically assumes that demand distributions are known. This means that estimates are substituted directly for the unknown parameters, leading to insufficient safety stocks, stock-outs, low service, and high costs. We propose a framework for addressing this estimation uncertainty that is applicable to any inventory model, demand distribution, and parameter estimator. The estimation errors are modeled and a predictive lead time demand distribution obtained, which is then substituted into the inventory model. We illustrate this framework for several different demand models. When the estimates are based on ten observations, the relative savings are typically between 10% and 30% for mean-stationary demand. However, the savings are larger when the estimates are based on fewer observations, when backorders are costlier, or when the lead time is longer. In the presence of a trend, the savings are between 50% and 80% for several scenarios.     

Dynamic inventory decisions for humanitarian aid materials considering budget limitations

In case of disaster, providing relief supplies to the affected people has vital importance. Governmental or non-governmental organizations (NGOs) prepare for disasters by purchasing and stockpiling these aid materials in appropriate quantities. They operate under a limited budget and this budget can either be used before the disaster for stocking decisions under uncertainty or it can be used after the disaster to satisfy the required demand at a higher cost when the uncertainty is resolved. If all the budget is used before the disaster, and if there is no disaster in a long term, there will be a high holding cost. On the other hand, if all the budget is reserved for use after the disaster, meeting the demand will be more costly or the demand may not be met within a certain period of time. Thus, NGOs need to decide how to allocate the budget for pre and post-disaster usage. In this system, the budget of NGOs may also change over time through donations or other incomes. In this point of view, NGOs need to make dynamic stock and budget allocation decisions, under the available budget at hand. In our study, we analyze the dynamic stocking decisions of NGOs using stochastic dynamic programming formulations under budget constraints. We develop infinite horizon stochastic dynamic programming models with and without budget considerations, and compare the results of these models via numerical analysis. Detailed numerical studies and results of the sensitivity analysis show the significance of budget considerations in inventory decisions and the effects of different parameters on the system results.     

Field vehicle fleet management in humanitarian operations: A case-based approach

Transportation is the second largest overhead cost to humanitarian organizations after personnel. Academic knowledge about fleet management in humanitarian operations is scarce. Using a multiple case research design we study Field Vehicle Fleet Management (Field VFM) in four large International Humanitarian Organizations (IHO): the International Committee of the Red Cross, the International Federation of Red Cross and Red Crescent Societies, the World Food Program and World Vision International. Our field research includes more than 40 interviews at headquarters, regional and national level in Africa, the Middle East and Europe. The paper answers three research questions: (1) How do IHO manage their field vehicle fleets? (2) What are the critical factors affecting IHO Field VFM? (3) How does Field VFM affect in-country program delivery? The contribution of this research is twofold. First, it helps to fill the existing gap in the humanitarian literature regarding Field VFM. Second, it expands the fleet management literature to a new and virtually unexplored area.     

A novel option contract integrated with supplier selection and inventory prepositioning for humanitarian relief supply chains

This paper proposes a novel two-period option contract integrated with supplier selection and inventory prepositioning. A two-stage scenario-based mixed possibilistic-stochastic programming model is developed to cope with various uncertainties. The first stage's decisions include supplier selection and capacity reservation level at each supplier/period and the level of inventory prepositioning. Furthermore, decisions regarding the time and exercised amount are made in the second stage. Applicability of the model is validated through a real case study. Finally, several sensitivity analyses are conducted to examine the effect of important parameters on the solutions to gain useful managerial insights.     

Deteriorating inventory model for ready-to-eat food under fuzzy environment

Decreased costs and enhanced customer satisfaction are two main objectives of SCM, and good inventory policy can achieve both simultaneously. Product perishability is a critical aspect of inventory policy. Ready-to-eat food products are very common consumer goods that are, in fact, perishable. The value the RTE food retains is, however, closely dependent on its quality. From the vendor’s point of view, quantifying quality and remaining value should be a critical business issue. In consequence, we combined the traditional deterioration model and quality prediction model to develop a new deteriorating inventory model for RTE food products. This new model quantifies food quality and remaining value. We also improved the new model by fuzzifying storage temperature to simulate temperature fluctuation. The proposed model uses real deterioration rate data. Numerical analysis is conducted in a case study. Overall, the model demonstrates that high storage temperatures reduce profits and force shorter order cycles.     

Forecasting in humanitarian operations: Literature review and research needs

Forecasting research in the humanitarian context is scarce. In this literature review, our goal is not only to show why forecasting research is important for the humanitarian sector, but also to identify what has been done so far, and where are the needs for further research. We conducted a structured literature search in Scopus, Web of Science, ABI Inform, and Google Scholar resulted in only 38 papers published between 1990 and 2018. Based on our findings we highlight three case studies as exemplary research in forecasting within the humanitarian context and list seven future research streams with specific research needs identified in each stream.     

Optimal growth under model uncertainty

The optimal growth of a wealth process toward a goal is studied under ambiguous markets with first- and second-order moment uncertainties relating to stock returns. Optimal strategies and value functions are solved explicitly. A verification theorem is proved to show that the results solve the original stochastic control problem. Quantitative analyses of the investment strategies indicate that a rational individual with ambiguity aversion reduces market participation when return and volatility are uncorrelated, while there is an exception for synchronous return and volatility. The welfare of shorting a discounted reward is computed, which demonstrates that in an ambiguous pricing economy, investors can generate a positive premium via appropriate asset allocations.     

Balancing provision of relief and recovery with capacity building in humanitarian operations

Several humanitarian organizations today find themselves thinly stretched in multiple protracted relief and recovery operations around the world. At the same time, the need for humanitarian relief and recovery operations is forecasted to increase dramatically in the next decades. Hence, humanitarian organizations will face increased challenges to provide assistance (e.g., assessing needs, moving the displaced, tending the wounded, restoring water and sewage systems) while trying to build and maintain capacity (e.g., hiring and training people, capturing lessons learned, structuring organizational processes). In this paper we develop a formal simulation model that quantifies the tradeoff that exists between providing assistance and building capacity in humanitarian organizations. We explore in our model the performance of two polar resource allocation strategies: one focusing on relief and recovery efforts and another focusing on capacity building. When humanitarian organizations cannot retain the knowledge gained in the field, a strategy that emphasizes relief and recovery is not enduring and leads to a better-before-worse behavior. However, if humanitarian organizations can retain a large fraction of the lessons learned in the field, they can achieve more enduring performance with a relief and recovery strategy. Nevertheless, high stress levels, caused by relief requirements significantly above those which can be made available by the organization, increase personnel turnover and limit the fraction of learning that the organization can retain, imparing a relief and recovery strategy. Our work sheds light on the tradeoff that humanitarian organizations face between providing relief and building capacity in stressful and demanding environments.     

An optimization model for volunteer assignments in humanitarian organizations

One of the challenges facing humanitarian organizations is that there exist limited decision technologies that are tailored specifically to their needs. While employee workforce management models have been the topic of extensive research over the past decades, very little work has yet concentrated on the problem of managing volunteers for humanitarian organizations. This paper develops a multi-criteria optimization model to assist in the assignment of volunteers to tasks, based upon a series of principles from the field of volunteer management. In particular, it offers a new volunteer management approach for incorporating the decision maker's preferences and knowledge into the volunteer assignment process, thus allowing him or her to closely examine the tradeoffs between potentially conflicting objectives. Test results illustrate the model's ability to capture these tradeoffs and represent the imprecision inherent in the work of humanitarian organizations, and thus demonstrate its ability to support efficient and effective volunteer management.     

A flexible inventory model for municipal solid waste recycling

State laws mandate the recycling of municipal solid waste (MSW) across most of the United States. In order to comply, municipalities recycle quotas of materials, generally without regard to fluctuating prices. An inventory system is proposed that allows municipalities to be sensitive to materials prices as they recycle in accordance with state mandates. A dynamic model is developed that uses historical secondary material prices as exogenous inputs to minimize the net present value of MSW recycling system cost. The model provides a cost effective method for municipalities to achieve their MSW recycling targets. The savings is approximately $1.28 per ton of MSW generated as based on total MSW management costs of $13.5 per ton. The model also allows one to investigate the effectiveness of various strategies for increasing the recycling rate, including reducing the transportation cost for recyclables, supporting the market price of selected secondary materials, and landfill bans on selected materials. Our proposed model may also be used to investigate the effect of market price changes on the portfolio of materials held in inventory for recycling.     

A model for irreversible investment with construction and revenue uncertainty

This paper presents a model of investment in projects that are characterized by uncertainty over both the construction costs and revenues. Both processes are modeled as spectrally negative Lévy jump-diffusions. The optimal stopping problem that determines the value of the project is solved under fairly general assumptions. It is found that the current value of the benefit-to-cost ratio (BCR) decreases in the frequency of negative shocks to the construction process. This implies that the cost overruns that can be expected if one ignores such shocks are increasing in their frequency. Based on calibrated data, the model is applied to the proposed construction of high-speed rail in the UK and it is found that its economic case cannot currently be made and is unlikely to be met at any time in the next decade. In addition it is found that ignoring construction uncertainty leads to a substantial probability of an erroneous decision being taken.     

A pragmatic decision model for inventory management with heterogeneous suppliers

For enterprises, it is imperative that the trade-off between the cost of inventory and risk implications is managed in the most efficient manner. To explore this, we use the common example of a wholesaler operating in an environment where suppliers demonstrate heterogeneous reliability. The wholesaler has partial orders with dual suppliers and uses lateral transshipments. While supplier reliability is a key concern in inventory management, reliable suppliers are more expensive and investment in strategic approaches that improve supplier performance carries a high cost. Here we consider the operational strategy of dual sourcing with reliable and unreliable suppliers and model the total inventory cost where the likely scenario lead-time of the unreliable suppliers extends beyond the scheduling period. We then develop a Customized Integer Programming Optimization Model to determine the optimum size of partial orders with multiple suppliers. In addition to the objective of total cost optimization, this study takes into account the volatility of the cost associated with the uncertainty of an inventory system.     

Toward a coordination of inventory and distribution schedules for blood in disasters

The need for efficient blood supply is of more significance in the event of disasters, when there is a lack of coordination between distribution and inventory management. The recent earthquake in Kermanshah province in Iran is among such cases that confirmed the need for coordinating such schedules. In this respect, a two-stage stochastic programming (SP) approach is presented for planning supply of blood after disasters that can assist in inventory decisions under hybrid uncertainty, minimizing the shortage and wastages. The uncertainty stems from imprecise parameters and scenario variability, and a robust-fuzzy-stochastic programming (RFSP) approach is devised to hedge against the uncertainty. The perishability of blood, the substitutability of blood groups, and the age-based characteristic of blood are taken into account to make the model more reliable. The compromise programming is applied to solve the multi-objective model. The results illustrate that the RFSP model can make a reasonable trade-off between mean value, feasibility robustness, and optimality robustness, which results in a robust and reliable solution under disastrous conditions.     

A theory of stochastic choice under uncertainty

In this paper we propose a characterization of stochastic choice under risk and under uncertainty. We presume that decision makers’ actual choices are governed by randomly selected states of mind, and study the representation of decision makers’ perceptions of the stochastic process underlying the selection of their state of mind. The connections of this work to the literatures on random choice, choice behavior when preference are incomplete; choice of menus; and grades of indecisiveness are also discussed.     

Decision-making under uncertainty for capital budgeting and hiring

Most business decisions are not merely risky but are subject to uncertainty. Different individuals estimate different measures of merit. It is shown that the traditional procedure of ranking the alternatives by their estimated measure of merit and then choosing the highest ranked alternative frequently gives the wrong answer even where the estimates are unbiased. The argument will be made using a simple decision tree. The effect will be shown to be especially serious where there is competition, direct or indirect, between the alternatives.